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Acquisition and Reversal of Tone-Visual Associations 1

Running Head: Acquisition and Reversal of Tone-Visual Associations

Masters Thesis:

Brain Regions Involved in the Acquisition and Reversal of Tone-Visual

Associations in Humans: A PET Studv

M. Natasha Rajah

A thesis submitted in conformity with the requirements

for the degree of Masters of Arts

Graduate Department of Psychology

University of Toronto

@Copyright by M. Natasha Rajah (1998)

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Acquisition and Reversa1 of Tone-Visuai Associations 2

Abstract

Positron emission tomography (PET) was used to identie brain regions that showed

changes in regional cerebral blood flow (rCBF) as subjects participated in voluntary

response differential conditioning study. Two tones (Tl and T2) served as the

conditioned excitor (CS+) and conditioned inhibitor (CS-) and a visual stimulus served as

the unconditioned stimulus (UCS). In phase one of the experiment T l was the CS+ and

T2 the CS-. In phase 2 of the experiment the contingencies were reversed. Sixteen

subjects (9 males and 7 fernales) between the ages of 19 and 35 (mean age = 23.3)

participated in this study. Subjects were told their task was to press a button with their

right index finger each time they were presented with the UCS. Eight PET scans were

obtained from each subject. The scans were obtained while subjects were being

presented with predictive CS+ and UCS trials or while subjects were presented with

nonpredictive CS- and UCS trials. The scan types were altemated across the experiment

and four scans were obtained dunng each of the phases. Half of the subjects learned both

associations. PLS analysis of these subjects' PET data indicated that d u h g the

acquisition of the phase two association subjects engaged a network of brain regions

consisting of: right middle frontal gyrus. nght inferior parietal lobule, right precentrai and

postcentral gyri, and nght precuneus. In addition a network of brain regions involved in

extinction to the first association during phase two was identified. This network

consisted of nght middle frontal gyms, right thalamus, left inferior parietal lobule, and

left cerebellum. Right hippocampal gyrus, right rniddle occipital gyms and left rniddle

temporal gyms were found to involved in both learning the second association and

extinction of the first association. The analysis did not identify any significant patterns of

brain activation related to phase one of the expenment.


Brain Regions Involved in the Acquisition and Reversal of Tone-Visual Associations

in Humans: A PET Studv

Associative learning refers to the ability to make connections between temporally

and/or propositionally contingent events (Furedy & Riley, 1987). Pavlovian conditioning

is one exarnple of associative learning. A common assumption regarding associative

learning is that repeated pairings of two temporally contingent events will gradually lead

to an increase in the associative strength between the two events. Another assumption is

that repeated presentations of one event alone will reduce the associative strength

between two events and Iead to extinction (Wagner, 1971).

These assurnptions may be interpreted according to the "saturation" view of

associative learning which states that the unconditioned stimulus (UCS) is consistently

effective and that it is the conditioned stimulus (CS) that acquires associative strength up

to a certain asymptotic lirnit. Therefore, as the CS approaches its upper and lower lirnit

of associative strength it becomes less possible for it to increase or decrease its

associative value, respectively (Wagner, 197 1).

In contrast to the saturation view of associative learning, Wagner (197 1)

postulates that these same assumptions c m be explained by a variable-reinforcement view

of associative learning. According to this view, what changes across CS-UCS pairings is

the effectiveness of the UCS (or its absences) in the formation of an association between

it (the UCS) and the cue(s) that preceded it (the CS). For exarnple, after repeated CS-

UCS painngs the progressively smailer increments in the associative strength of the CS is

due to the UCS becorning less and less reinforcing as it is announced by the CS which in

tum is gaining more and more associative strength (Wagner, 197 1).

Acquisition and Reversal of Tone-VisuaI Associations 4

In recent years incremental-decremental models of associative strength in human

conditioning have been challenged for several reasons. First, in hurnans conditioned

responses (CRS) can be spontaneously acquired and extinguished by informing subjects

of the changes in CS-UCS relationships @avey, 1987). Second, experiments have found

that subjects falsely informed of CS-UCS contingencies prior to conditioning behave

according to the false information provided rather than to the actually expenenced

contingencies. Therefore associative learning is not always a graduai process as

presented in incremental-decremental models and it is not only dependent on the actual

CS-UCS relationship but is also influenced by the subjects pnor beliefs or expectations.

Incremental-decremental models also fail to explain why some types of associative

learning require conscious awareness to develop whereas others do not (Davey, 1987).

These problems imply that during human associative learning there is much more

happening than can be explained by simple incremental-decremental associative strength

models as proposed by the saturation and variable-reinforcement views (Davey, 1987:

Wagner, 1971).

It seems likely that cognition influences human associative learning since the

subject's ability to verbalize associative contingencies and verbal transmission of

information about the experimental learning situation to the subject via experimental

instructions influence the CR observed during associative learning. Therefore, the

questions of current interest are: (I) how does cognition influence associative learning and

(11) given that cognition influences associative learning, what is the relationship between

simple associative learning and higher foms of learning (such as episodic learning).


Pavlovian classical conditionirig will be used to illustrate the key issues raised by these

two questions.

Behavioral studies on associative learning

In Pavlovian conditioning, initiaily, an UCS produces an unconditioned response

(UR). M e r several pairings of the CS pnor to the UCS it is observed that the

presentation of the CS alone induces the UFt (now referred to as the conditioned response

(CR)) *

Historically, there has been much debate arnongst behaviorists over what was

being learned during conditioning: the stimulus-response (S-R) association between CS

and CR or the stimulus-stimulus (S-S) or expectancy association between CS and UCS

(Furedy & Riley, 1987). The debate between S-R versus S-S theorists is popularly

referred to as the Hull-Tolman dispute, narned after the two leading figures in the S-R and

S-S camps, respectively. Hullians argued that al1 learning was response learning whereas

the Tolmanians argued that the important thing being Ieamed was the cognitive

association between stimuli (Furedy & Riley, 1987). Hence, S-R leaming was

traditionally thought to represent basic conditioning whereas as S-S Iearning was thought

to represent cognitive learning (Kimble, 197 1).

During the 1930s through until the 1950s the S-R learning theorists led by Hull

and Spence appeared to be winning this debate (Furedy & Riley, 1987). During this era

cognition was thought to be a nuisance variable which had to be controlled for. The

dominance of human eyeblink conditioning studies was evident during this time, since

this form of human conditioning was not believed to be influenced by cognitive factors

Acquisition and Reversal of Tone-Visuai Associations 6

(Furedy & Riley, 1987; Kimble, 197 1; Dawson & Schell, 1957; Kimble & Perlmuter,

1970).

Duting this same period of S-R learning theory dominance, the interstimulus

interval (IST) was believed to be the most important determinant of classical conditioning

(Furedy & Riley, 1987). Human eyeblink conditioning studies consistently found that an

ISI of slightly less than 0.5 msec yielded optimal performance while ISIS exceeding 2 sec

produced little if any conditioning. This short ISI duration required for successfül human

eyeblink conditioning, was interpreted as reaffirming that learning occurred implicitly and

involved the formation of S R associations; since it stressed the importance of temporal

versus propositional or cognitive relationships between events during learning (Furedy &

Riley, 1987).

In the late 1960s flaws in S-R learning theory were identified which led to the

cognitive revolution of classical conditioning. The first blow to S-R theory occurred

when Rescorla (1967) showed that the explicitly unpaired CS control traditionally used in

S-R oriented conditioning studies was infenor to the truly random CS control. This

finding discredited the pure S-R perspective because the definition of a truly randorn

control was based on cognitive, S-S learning, view of conditioning in which what is being

learned is the propositional or semantic contingency between CS and UCS and not the

temporal contingency which was stressed by S-R theorists (Furedy & Riley, 1987).

Another study by Rescorla (1 973) lent additional support to S-S learning theory

versus S-R leaming theory. To test between the S-R and S-S interpretations, Rescorla

(1973) conducted an expenment on fear conditioning in rats in which a light (CS)

predicted a loud noise (UCS) which induced fear (UCR) in rats. After several CS-UCS

Acquisition and Reversai of Tone-Visual Associations 7

pairings the CS elicited a conditioned fear response (CR) in the rats. Following fear

conditioning the rats were habituated the to the UCS, thus breaking the link between the

UCS and UCR. According to S-R theory subjects learn the CS-CR association dunng

learning, thus it is the CS that dives the CR. S-S theory suggests that subjects learn the

CS-UCS association and the reason that the CS elicits the CR after training is due to the

CS eliciting a mental representation of the UCS which in tum causes the CR. If S-S

theory is correct, following habituation the noise should no longer produce fear and

Rescorla argued that its representation should not either. Therefore, the rats should not

expenence fear when the CS is presented following habituation. If S-R theory is correct,

that it is the CS that drives the CR and not the mental representation of the UCS, then the

fear response to the CS should still exist after habituation. Rescorla (1973) found that

when the rats were exposed to the CS after habituation they did not show the fear

response. This rzsult supports the S-S interpretation of fear conditioning in rats.

Another finding that harmed the credibility of S-R theory, was that some forms of

involuntary conditioning could occur with ISLs much greater than 2 sec. This observation

de-emphasized the importance of temporal associations during learning which was an

important aspect of S-R learning theory (Furedy & Riley, 1987). A compilation of

studies on the conditioned emotional response (CER) by Kamin (in Furedy & Riley,

1987) showed that increasing the ISI up to intervals of several minutes did not effect the

acquisition of the CER. Garcia (in Furedy & Riley, 1987) found that an association cm

be formed between two events with an ISI as long as several days. These data indicate

that the ISI or the temporal relationship between events was not as citical for associative

Acquisition and Reversa1 of Tone-Visual Associations 8

learning as previously believed by S-R theorists. Therefore during the late 1960s and

early 1970s S-S learning and cognitive influences on conditioning were highlighted.

Several discrimination conditioning studies by Dawson and colleagues, conducted

during the 1970s, investigated the extent to which cognitive factors, such as cognitive

awareness, were involved in human autonomic conditioning of the skin conductance

response (GSR) (in Dawson & Schell, 1987). This research question was of interest to

several investigators because of its direct relevance to the question of how conditioning

was related to highzr mental processes, such as conscious awareness. In a senes of

studies Dawson and colleagues used an auditory perception masking task in which

subjects were presented with a tone followed by five comparison tones, one of which

matched the initial tone. Subjects were told to identify which of the five comparison

tones matched the initial tone, which of the five comparison tones had the highest pitch

(always 1200Hz) and which had the lowest pitch (always 800Hz) for each trial. T'ne

subjects were not aware that the highest and lowest pitched tones served as CS+ (a

conditioned excitor) and CS- (a conditioned inhibitor), respectively, for an electric shock

(the UCS). Subjects were misinformed that the shock was being used to alter their

physiological state to determine whether it affected their auditory perception. Cognitive

awareness was assessed at each trial by measuring subjects' expectancy of the UCS by

asking them to rate their expectancy by pressing a senes of buttons. Dawson and

colleagues (in Dawson & Schell, 1987) found that contrary to earlier studies on

autonomic conditioning, cognitive awareness of the stimulus contingencies was necessary

for human autonornic conditioning to occur.

Acquisition and Reversa1 of Tone-VisuaI Associations 9

The failure of earlier autonornic conditioning studies conducted during the S-R

era to detect the influence of cognitive factors was attributed to the use of post-

experirnentd recall questionnaires, which consisted of open-ended questions, as indices

of the subjects awareness of stimulus contingencies. These recall questionnaires were

found to be insensitive measures of cognitive awareness (Dawson &Schell, 1987).

To further support the idea that human autonomic conditioning of GSRs involves

cognitive processes, Dawson and colleagues used a secondary reaction time technique in

which subjects were required to respond as quickly as possible to a tone by pressing a

switch while sirnultaneously undergoing visual discrimination conditioning using two

colored lights as the CS + and CS - and using a shock as the UCS (Dawson & Schell,

1987). The limited capacity notion of the central processing system of cognitive

processes predicts that if cognitive processes are involved in a particular task, then

performance on a secondary task will be hindered because these two tasks are in

cornpetition for a limited supply of cognitive resources (Dawson & Schell, 1987). It was

found that reaction times to the tone were significantly slower if it was presented during a

CS+ versus a CS- visual stimulus. This finding supports the hypothesis that cognitive

processes are involved in autonomic conditioning since it indicates that greater cognitive

processing capacity was allocated to the CS+ versus CS- thus interfering with the

secondary tone-response task.

Though it seems clear that cognitive factors do play an important role in human

autonomic conditioning, this does not mean that non-cognitive factors are not involved.

Furedy (in Furedy & Riley, 1987) used a subjective contingency (SC) measure

concurrently with the galvanic skin response (GSR) in a human autonornic conditioning


study to discriminate between cognitive and non-cognitive factors in conditioning. It was

found that there was not a one-to-one correspondence between the SC and GSR

rneasures. Furthermore, the cognitive SC variable was found to be sensitive to different

types of experimentd conditions in which either the explicitly unpaired CS control or the

tmly random CS control were used, whereas the GSR was not sensitive to these

differences (Furedy & Riley, 1987). Therefore it appears that both cognitive and non-

cognitive factors play a role in autonomic conditioning.

The above research findings support the role of cognitive processes in human

autonomic conditioning. However it does not appear that performance of previously

leamed autonornic conditioned responses need to involve cognitive factors (Dawson &

Schell, 1987). For exarnple, Corteen and colleagues (in Dawson & Schell, 1987)

conducted a study in which subjects first underwent an autonomic conditioning study of

the GSR. Semantic categories of words were used as conditioned excitors and inhibitors

(CS+ and CS-) and a shock was used as the UCS. Then subjects participated in a dichotic

listening task. During the dichotic listening task subjects were asked to verbally shadow

a passage presented to their right ear while unrelated words were presented in their left

ear. Subjects were not informed that some of the words presented to the left would be the

sarne as those that served as a CS+ or a CS- in the autonomic conditioning task. It was

found that during the dichotic listening task GSRs were elicited more frequently during

CS+ presentations to the left ear than to CS- presentations. Furthermore, these changes in

GSR occurred despite the subjects' inability to report having heard the critical words

presented in the left ear. Therefore, though conscious awareness is required for initial

autonomic conditioning it does not appear to be required for later performance of CRS.

Acquisition and Reversal of Tone-Visual Associations 1 1

Human autonomic conditioning is behaviorally very different to human skeletal

conditioning (for example eyeblink conditioning). Whereas autonomic conditioning can

occur with long ISIs (several minutes) skeletal conditioning occurs only with short ISIs

(500msec to 2 sec). Also autonomic conditioning is acquired much faster than skeletal

conditioning (Dawson & Schell, 1987; Furedy & Riley, 1987). Despite these

dissirnilarities there is evidence that conscious awareness of the CS-UCS contingency is

also required during the acquisition of a skeletal conditioned response. However, these

cognitive factors do not appear to be the primary determinants of this type of associative

learning (Martin & Levey, 1987). Researchers have aiso found that continued

performance of skeletal CRS eventually leads to automaticity of behavior which does not

require attention nor cognitive awareness (Furedy & Riley, 1987).

Very few studies have simultaneously looked at autonomic and skeletal factors

dunng a single conditioning study to see how they interact. A study by Putnarn and

colleagues (in Dawson & Schell, 1987) is one of the few studies that have looked at both

autonomic and skeletal conditioning simultaneously. In this experirnent a differential

eyeblink conditioning paradigm was used in which two tones, a CS+ and a CS-, predicted

or did not predict (respectively) an air puff to the eye (the UCS). The ISI in this

experiment was 800 msec. The heart rate (HR) of subjects was also measured. Al1

subjects showed tnphasic HR change to the CS+ and the CS-. In the first phase the HR

would decelerate for 2 sec, then it would accelerate for 2 sec, and finally it would slowly

go back to normal. hterestingly, in good eyeblink conditioners the HR deceleration

occurred only during early trials and then habituated, whereas in poor eyeblink

conditioners the first phase HR deceleration did not habituate over trials. It appears that


the initial HR deceleration is an onenting response that highlights the importance of the

CS dunng early learning, then this response habituates as the CRS are learned (Dawson &

Schell, 1987).

These findings, in addition to several others (Davey, 1987; Dawson & Schell,

1987; Furedy & Riley, 1987; Martin & Levey, 1987), have been taken to support the

following theory of classical conditioning. During classical conditioning first one must

attend to the CS and UCS; this is believed to involve central processing accompanied by

autonornic responses. Numerous studies have shown that what is initially processed

during associative learning is the positive or negative qualities of the UCS (Martin &

Levey, 1987). This focus of attention on the CS-UCS relationship is believed to lead to

the cognitive awareness of the CS-UCS contingency which in tum leads to pre-attentive

elicitation of the oriented response on subsequent CS presentations. This pattern of

events quickly leads to autonornic conditioning of the orienting response (such as HR or

GSR) which is later followed by a skeletal conditioned response (such as eyebiink).

During extinction it has been found that the skeletal CR extinguishes quite rapidly

however the autonornic orienting response CR persists (Dawson & Schell, 1987). This

difference in the pattern of extinction implies that autonomic conditioning is more

resistant to extinction due to the greater involvement of cognitive factors in its

acquisition.

It is clear that associative leming consists of both autonornic and skeletal

components both of which are influenced by cognitive and non-cognitive factors. The

role of cognition in autonornic conditioning is better understood whereas skeletal

conditioning appears to be more dependent on non-cognitive learning factors. Therefore,

Acquisition and Reversal of Tone-Visuai Associations 13

the traditional definition of S-S learning better defines autonornic conditioning and S-R

learning better defines skeletal conditioning (Dawson & Schell, 1987; Furedy & Riley,

1987).

The two-process theory of associative learning appears to test represent the

empirical data to date. The two-process theory in its strong form States that associative

processes involving responses wiîh poor skeletal feedback follow S-S leaming whereas

those involving strong skeletal feedback follow S-R iearning (RescorIa & Solomon,

1967). A more modern interpretation of the two-process theory would be that associative

learning fdls dong a continuum in which some types of learning require little or no

cognitive influence and are thus pure example of S-R learning whereas other types of

learning require some cognitive influence and are examples of S-S learning (Furedy &

Riley, 1987; Rescorla & Solornon, 1967).

It is clear that cognition influences simple associative leaming. How cognition

influences simple associative processes is complicated. From the above discussion on

autonornic conditioning it appears that cognitive processes allow subjects to focus their

attention on stimuli, evaluate their importance and Iearn their contingent relationships.

However the question of how simple associative learning, such as classical conditioning,

which involves cognitive factors but is still not an instance of pure cognitive learning, is

related to higher order cognitive Ieaming has not yet been addressed.

Learning theorists have traditionally viewed higher order cognitive leaming as

simply being mediated by a hierarchy of associations. Associations at each level are

formed in the sarne way as the simple associations found in classical conditioning. As we

climb the hierarchy it is assumed that the associations become more and more cognitively


/semanticaily based. There is also an inhibitory role played by higher order associations

on lowerfsimpler associations (Kimble, 197 1). Neuroanatomically this hierarchy of

associations was believed to be mirrored by the central nervous system (CNS) as the

higher order inhibitory level of associations, progressively down towards the simple

reflex circuit which was believed to explain pure S-R learning (Kimble, 197 1).

The importance of understanding the relationship between simple associative

processes, such as classicai conditioning, and higher order learning is critical for Our

greater understanding of the role cognitive and non-cognitive factors contribute to

learning in general. Furthermore, human associative learning studies have indicated that

older subjects condition worse than younger subjects and that Alzheimer's disease also

leads to decrements in associative processes (Solomon & Pendlebury, 1994). Age-related

deficits have also been observed in higher order cognitive learning paradigrns involving

face encoding and semantic learning of words and pictures (Craik & Byrd, 1982; Craik &

Rabinowitz, 1985). Therefore, it would be interesting to determine whether these higher

order deficits may be due to problems in simple associative learning.

Behaviorally it would be difficult to study the relationship between simple

associative and higher order cognitive learning because they are two different forms of

learning that yield different behavioral data that are hard to analyze comparatively.

However, with the advent of new functional neuroimaging techniques, which allow us to

"see" what brain regions are involved while subjects participate in various behaviord

paradigms, we can attempt to make the link between these two types of learning by

determining whether similar brain regions are involved in both types of learning.

Furthermore we could detennine whether the age-related deficits observed in


conditioning paradigms are related to age differences in brain activation patterns. If age-

related differences of brain activation were observed we couId then compare these

patterns to those obtained from higher order cognitive studies of age-related deficits and

leaming. Therefore, further investigation of the functional neuroanatomy underlying

human associative learning is required to better understand human learning and the

process of aging.

Neuroanatomical correlates of associative learning

Animal S tudies

Chaiupa et al (1975) recorded the single unit activity of cells in the Iaterai

geniculate nucleus (LGN) in cats during continuous associative pairings of auditory and

visual stimuli. Neurons of the LGN are usually only responsive to visual stimuli;

however, after successive tone-light pairings it was found that activity in LGN cells could

be elicited by the tone alone. In a more recent study Cahill and Scheich (1991) used 2-

deoxyglucose (2-DG) metabolic mapping to examine changes in brain activity in gerbils

as they leamed a visual-auditory association. They found that animals that learned the

association between the light and tone showed activation of the primary auditory cortex

d u h g later presentations of the light alone. Therefore, the two studies cited above

indicate that brain areas involved in stimulus reception are aIso involved in forrning

stimulus associations.

In an autonornic differential conditioning study in which a tone served as either

the conditioned excitor or inhibitor across conditions (T+ and T-) and a mild foot-shock

served as the UCS, McIntosh and Gonzalez-Lima (1994) found several cortical and

subcortical regions that elicited differentiai levels of FDG uptake during tone excitor


versus the tone inhibitor conditions. The regions identified included basal forebrain and

thalamocorticai areas. In particular, frontal cortex, retrosplenid cortex, anteroventral

thalamic nucleus, and the cerebelium were amongst the regions identified. Given that the

associative paradigm employed in this study was far more complex than the two

previously mentioned studies it is not surprising that additional cortical regions were

engaged.

Several animal siudies have dso shown that both premotor and motor regions are

involved in fearning conditional motor associations (Aou, Woody & Birt, 1992;

Germaine & Larnarre, 1993; Mitz, Godschalk & Wise, 199 1; Seitz & Roland, 1992).

Aou et al (1992) conducted an electromyopgraphic (EMG) conditioned eyeblink study in

cats in which an auditory click was the CS and an airpuff was the UCS. The results

showed altered motor cortex activity dunng learninp. In addition the results indicated

that the CS caused the observed increase in motor cortex activity during acquisition of the

CR.

An EMG study on monkeys by Mitz et al (1991) found that premotor neurons

showed a learning dependent change in their firing rate during the acquisition of

visuornotor associations. The increases in motor and prernotor activity during associative

learning rnay be related to a practice effect: as subjects continue producing a CR the

behavior becomes practiced and automatic. Previous studies on procedural motor

learning have shown premotor, motor and cerebellar regions show a change in activity as

the task becomes automatic (Seitz & Roland, 1992).


As in animais, studies on humans have also identified a variety of brain regions

that are involved in associative learning. Electroencephalographic (EEG) studies on

human auditory-visual associative Iearning have noted that a particular event-related

potential (ERP) occurs during the associative learning between paired tone/warning-

visuaUtarget stimuli (Andreassi & Greco, 1975; Proulx & Picton, 1978; Rohrbaugh,

Syndulko & Lindsley, 1976). During the paired association trials a contingent-negative

variation (CNV) waveform appears that has pnmarily a frontal and central distribution.

The CNV is though to develop as a result of learning a significant relationship between

two stimuli (Proulx & Picton, 1978). Furthemore, it is not apparent dunng non-paired

triais. An improved reaction time to the target stimulus after the presentation of the

warning stimulus during paired trial develops concurrently with the CNV (Proulx &

Picton, 1978; Rohrbaugh et al, 1976). Therefore human studies indicate that associative

Iearning involves both frontal and central brain regions.

Frontal cortical involvement in associative learning was also evidenced in a PET

study of human eye-blink conditioning (Molchan, SunderIand, Mchtosh, Herscovitch &

Schreurs, 1994). In this study a tone served as the CS and an airpuff served as the UCS.

It was found that during acquisition of the CS-LTCS association there were decreases in

the regional cerebral blood flow (rCBF) of inferior prefrontal, inferior parietal, insular

and cerebellar cortices in the right hernisphere. Increased cortical activation was observed

in bilateral primary auditory, and left postenor cingulate cortices. During extinction of

the CS-UCS association ( CS presented done) there was a bilateral increase in rCBF in

the inferior prefrontal cortex. Bilateral supenor temporal cortex, left pons, and left


posterior cingulate showed significant decreases in rCBF dunng extinction. The changes

in rCBF observed dunng extinction were thought to refiect the changes in associative

significance of the CS (Molchan et aI, 1994).

Sirnilar regions of activation have been found in other experiments on hurnan

eyeblink conditioning (Blaxton, Zeffiro, Gabrielli, Bookheimer, Carrillo, Theodore &

Disterhoft, 1996; Schreurs, McIntosh, Bahro, Herscovitch, Sunderland & Molchan,

1996). In the Blaxton et al (1996) study on human eyeblink conditioning, the

hippocampus was aiso found to be involved in learning. However, hippocarnpal lesions

have not been found to prevent simple delay conditioning, in humans, in previous studies

(Blaxton et al., 1996). Therefore, Blaxton and colleagues (1996) concluded that though

the hippocampus is not necessary for conditioning it was activated as a precaution to

facilitate future complex learning that rnay some how be related to the conditioning task.

McIntosh and colleagues (in press) investigated cross-modal human associative

learning using PET. In this study subjects were told that their task was to respond to a

target visual stimulus by pressing a button with their right hand; and to not respond to a

distracter visual stimulus. Subjects were also informed that they would on occasion hear

an auditory tone. Subjects were not aware that about 80% of the time that a tone was

heard it was followed by a visual event. The behavioral results indicate that the subjects

learned the tone-visuai association since their reaction times became faster for paired

tone-target triais versus target alone trials across time. The scanning results show that

initially the tone alone presentations ciid not elicit visual cortex activity; however after

successive pairings of the tone with visual stimuli there was an increase in lefi

hernisphere dorsal visual area activity to the tone alone. Several prefrontal, cingulate,


limbic, temporal, occipital, and parietal regions were also found to play an important part

in the behavioral acquisition of associative learning. Therefore, even in simple

associative learning paradigrns a variety of brain regions are activated during the

acquisition of associations.

Neuroanatornical correlates of episodic learning

PET studies investigating higher, more cognitive, forms of leaming have

identified a number of brain regions that are similar to those found in studies of

associative learning (Grady et al, 1998; Haxby et al, 1996; Nyberg et al, 1996). Episodic

encoding of words has been shown to activate left hippocarnpal, left prefrontal, left

fusiform and right parietal regions (Nyberg et al., 1996). The activation of left

hippocampus during encoding was found to be specific to item whereas left fusiform and

right parietal activations were related to the encoding of time and location, respectively.

Interestingly the left prefrontal activation was found to generally be involved in encoding

and was not specific to learning a particular aspect of an episodic event (Nyberg et al.,

1996).

In order to deterrnine whether learning of different visual stimuli activated

different brain regions, Grady and colleagues (1998) conducted a PET study in which

subjects were scanned while encoding either words or pictures. Kncreased rCBF was

observed in left prefrontal cortex during encoding of both pictures and words. Encoding

of pictures versus words showed increased rCBF in bilateral extrastriate and medial

temporal regions during picture encoding; whereas increased left temporal and

ventrolateral prefrontal activity was greater during word encoding. These findings

support the notion that left prefrontal cortex plays a general role in learning new events


and is most likely responsible for semantic processing (Cabeza, Grady, Nyberg,

Mchtosh, Tulving, Kapur, Jennings, Houle, & Craik, 1997). It is also evident from these

results that different visual events activate different brain regions.

Haxby et al (1996) measured the rCBF of subjects during face encoding, face

recognition, face perception and sensorimotor tasks. In a pairwise cornparison of brain

regions differentially activated during face encoding versus face perception they found

that face encoding was associated with increased activation of right hippocampus, right

media1 temporal cortex, left inferior temporal gyrus, left anterior cingulate and left

prefrontal cortex ( H ~ b y et al, 1996).

The preceding studies indicate that during higher semantic and episodic learning

of visual stimuli there seems to be a core network of regions consistently activated across

tasks: prefrontal, hippocampal, temporal and extrastriate regions . The laterality and

specificity of these particular activations differ between tasks and additionai brain regions

appear to be engaged depending on the particularities of the leaming task employed.

Interestingly in simple associative leaming involving visual stimuli this sarne core

network of regions are activated across studies. The roles of parietal cortex and cingulate

gyrus in leaming are less clear. In some studies (including studies of both simple

associative learning and "higher" cognitive learning) they show changes in activation

during l e m i n g while in others they do not.

The discussion thus far has highlighted the similarities between simple associative

learning and "higher" cognitive learning in regards to the brain regions involved in

mediating these two types of learning. This finding is not surprising considering the

behavioral studies on autonomie conditioning which indicate that even simple

Acquisition and Reversal of Tone-Yisual Associations 2 1

conditioning tasks involve cognitive factors. The task-related differences in brain

activations observed between associative and cognitive learning are likely to reflect the

differences in the extent to which the two types of learning engage cognitive factors or the

different level of complexity in the cognitive processing required to perform the two

types of leaming. It is likely that as complexities are added to an associative learning

paradigrn and the subject is required to engage more cognitive strategies to acquire the

association there will be fewer differences in brain areas involved in these two types of

learning.

It is clear that more human associative learning studies, involving increasingly

complex paradigms, are required to bridge the gap between associative and cognitive

learning. Furthemore, very few studies involving cross-modal visual-auditory

associative Iearning have been conducted. If the differences between cognitive and

associative learning are not as profound as previously believed, it is likely that results

obtained from cross-modal associative learning rnay have considerable bearing on how

we l e m more complex cognitive cross-modal events (for example, the fact that the

image of a bunny also triggers our knowledge that it is soft to touch).

Another reason for conducting more associative learning studies is based on the

observation that most experiments conducted to date have studied only the acquisition

and extinction of associations. In addition no neuroimaging studies and only a few

behavioral studies on humans, since the earfy 1970s, have been conducted on

reacquisition following extinction (Perlmuter, 1966). The only studies to date that have

looked at neural correlates of extinction of an association have involved invoiuntary

human eyeblink conditioning (Molchan et al, 1994; Schreurs, 1997). It would be


interesting to determine whether extinction in a voluntary and a more complex associative

paradigm would correlate with rCBF changes in different brain regions than those found

in previous eyeblink studies.

Previous neuroimaging studies on associative learning have not studied what brain

regions are involved during the reacquisition of an association following extinction. In a

behaviorai study investigating human eyeblink conditioning, Perlmuter (1966) found that

during reacquisition of the conditioned eyeblink response, following overextinction (in

which the CS was presented alone), fewer CRS were produced in comparison to the initial

acquisition triais. This result indicates that subjects do not condition as well following

overextinction. This may be due to adaptation to the CS which in turn reduced its

associative strength during relearning. It would interesting to determine the functional

neuroanatomical correlates of reacquisition. Therefore, the above theoretical issues

emphasize the need for more studies investigating more complex types of associative

learning using functional neuroimaging techniques.

The current PET study is aimed at addressing some of those theoretical issues

raised in the preceding paragraphs. In this study subjects will be required to initially learn

the differential association between auditory-visual stimuli in which one auditory

stimulus predicts a visual event and another does not- After the initial association is

leamed, the associative contingencies will be reversed (the auditory stimuli that used to

predict a visual event no longer does and vice versa) and subjects will be required to

"unleam" the old associations and leam the new associations.

This reversal paradigm has not previously been investigated in humans.

Therefore, it would be interesting to see what functional neuroanatomical activations


occur while subjects unlearn old associations and f o m new associations. In addition it

would be intriguing to determine whether the brain activations observed after reversai

bear any resemblance to those previously observed during initial acquisition. Another

interesting aspect of the current study is that it would allow us to see whether learning the

new association will be hindered by the stimuli's previous associative significance or

whether the new association is learned in the same way, behaviorally and îunctionally, as

the initiai association. Given the poorer behavioral performance observed during

reacquisition noted by Perlmuter (1966) it would not be surprising if leaming the new

associations followed a different behavioral and hnctional pattern. However, one should

note that in Perlmuter's study (1966) the subjects were required to relearn the sarne

association, whereas in the proposed study subjects will be required to learn a new

association. Therefore, there is reason to believe that the learning of the initial and

second associations in the proposed study rnay be more similar than that observed by

Perlmuter ( 1966).

Materials & Methods

Subjects

Sixteen healthy right-handed subjects (9 males and 7 fernales) between the ages of

19 and 35 (mean age = 23.3; excluding one subject whose dernographic data were

unavailable) participated in this study. Al1 subjects were screened for any history of

major medicai, neurological and psychiatnc disorders. Those subjects who agreed to

participate provided inforrned consent and the experiment was conducted with approval

from the Ethics Review Board of Baycrest Geriatric Centre, University of Toronto.


Behavioral Methods

When the subjects arrived on the test date they were given the following

information regarding what the study was investigating: they were told the study was

exarnining the speed of their reaction time (RT) to a visuai stimulus. They were also told

that they would be presented with two auditory tones through headphones and that a tone

may sometimes precede the visual stimulus. The subjects were then placed in the PET

scanner and given example presentations of the visual and the two auditory stimuli.

The subjects' task was to use their nght hand to press a response key as quickly as

they possibly couid each time they were presented with the visual stimuius. They were

told to pay attention to the auditory tones while performing the task. The subjects were

also toId to stare at a fixation cross ("+") in between presentations of the visuai stimulus

and to refrain from talking and moving (other than to make the required response) while

performing the behavioral task. The following clarification statement was used if

subjects asked for additional information concerning the behavioral task: "Your task is to

respond to the circle, by pressing the Ieft mouse button, as quickly as you possibly cm. A

tone may sometimes precede a visual stimulus". Then, the actual experiment began in

which a differential conditioning paradi-gn with reversai was used. The two tones served

as a CS+ and a CS- and the visual stimulus served as the UCS (which required a

voluntary response/ UR). Reaction times (RTs) to the visual stimulus were obtained. A

significant decrease in RT to the UCS was considered indicative of a CR.

In the experiment eight scans were obtained from each subject. Prior to each scan

two prescan blocks of stimuli were presented. After each scan one postscan block was

presented. Therefore one scan block included the two prescan blocks, the scan interval,


and the postscan block. The details regarding what stimuli were used, how the stimuli

were presented, and how the behavioral data were collected are presented below.

The stimuli & apparatus

The visual stimulus used was a pattern of white concentric circles presented on a

grey background (see Appendix A). The outer diarneter of the circle was 12.5 cm. The

fixation cross was a white 40 pnt font "+" presented on a grey background. The visual

stimulus was created using CorelDRAW7 (Corel Corporation, 1996). The visual

stimulus and the fixation cross were presented in the center of a I7" PC colour monitor

which was positioned perpendicular to the subjects' line of sight. The distance from the

subject to the monitor differed between subjects and depended on how far the monitor

needed to be positioned in order for the subject to clearly view the stimuli without

straining his or her eyes. The visual stimulus was presented for a duration of 500 msec.

The subjects were presented binauraily with two pure tones through earphones.

One tone had a frequency of 1200 Hz (Ki tone or Tl ) and the other tone had a frequency

of 600 Hz (Lo tone or T2). The amplitudes of the two tones were adjusted so that they

were perceived as being equally loud by the experimenter. Superlab for Windows version

1.03 (The Experimental Laboratory Software, Cedrus Corporation, 1996) run on a PC

plarform was used to program the method of stimulus presentation and the collection of

behavioral RT data.

Stimulus Presentation during; prescans and scans

Initial Acquisition

In the first four scan blocks each scan block consisted of serni-randornized

presentations of: 18 trials in which Tl predicted the visual stimulus (TI+ visual trials), 18


trials in which T2 was presented alone (T2- trials), 6 trials in which T2 predicted the

visual stimulus (T2+visual trials) and 6 trials in which the visual stimulus was presented

alone (V only trials). Therefore during initiai acquisition T l was the CS + ; 100 % of the

time Tl was presented it was followed by the visual stimulus. T2 did not predict the

onset of a visual stimulus at a high probability and was the CS- during the initial

acquisition phase. Only 33% of the time T2 was followed by the visual stimulus; 66% of

the time T2 was presented alone. Circles were presented alone 25% and they were

preceded by T2 25% of the time. 50% of the time Circles were predicted by T l . The

reason for presenting circle alone and T2 + visual stimulus tr ials was to obtain RT data

for these trials so they may be compared to the RT data obtained from T l +visual stimulus

trials and be used as a comparative measure of learning across trial types.

During paired trials (T 1 + visual stimulus and T2 + visual stimulus) the tone was

presented first for 500msec followed by a 300 msec interstimulus interval (ISI), then the

visual stimulus was presented for 500 msec. During tone alone or circle alone tnds a 300

msec prestimulus interval was presented to keep the timing of unpaired trials sirnila. to

paired trials. It was immediately followed by the stimulus event (500 msec in length).

Therefore paired trial events were 1300 msec in Iength and unpaired trial events were 800

msec in length. The mean intertrial interval (RI) across scan blocks for the entire

expenment (including the scan interval) was approximately 8 sec (ranged from 4 sec to

12 sec).

Scans 1 and 3 during initial acquisition were obtained while subjects were

presented with five consecutive Tl+visual stimulus trials. The average KI during the

scan interval was 10.8 sec (Tl paired scans). The total scan length for was 60.5 sec for

Acquisition and Reversal of Tone-Visud Associations 27

scans 1 and 3. Scans 2 and 4 were obtained while subjects were presented with five T2

alone and five visual alone trials (T2 unpaired scans). The average ITI for scans 2 and 4

was 5.2 sec and the total scan interval was 60 sec. Therefore scans 1 and 3 were taken

during paired CS + trials and scan 2 and 4 were taken during unpaired trials. The total

number of visual and auditory events were equivalent across al1 scans (5 visual and 5

auditory events).

Reversa1 & Reacquisitiun

In the last four scan blocks the contingencies were switched. T2 served as the

CS+ and Tl served as the CS -. The number of trials and the probabilities for each trial

type are the same as mentioned above except now the T 1 and T2 probabilities were

reversed. There were 18 T2 + visual stimulus trials, 6 T 1 +visual stimulus trials, 6 T 1

done trials, and 6 circle alone trials in each scan block. The trial lengths, the mean KI

and the ISI were the same as mentioned above.

Scans 5 and 7 were obtained while subjects were presented with five consecutive

T2+visual stimulus trials (T2 paired scans). The mean KI during these two scans was

10.8 sec and the total scan intervd was 60.5 sec. Scans 6 and 8 were obtained while

subjects were presented with five T 1 alone trials and five circle alone trials (Tl unpaired

scans). The mean ITI for these two scans was 5.2 sec and the total scan interval was 60

sec.

It is important to note that the trials used during the scan interval in both the initial

acquisition and the reversa1 and reacquisition phases were a subset of the 48 trials in each

scan block and were not additional trials. Therefore across the entire experiment there

Acquisition and Reversai of Tone-Visuai Associations 28

were 48 trials (including the scan trials) in each scan block. Refer to Appendix B for a

more detailed description of the order of stimulus presentations.

At the end of the expenment subjects were debriefed and post-experimentai

questions were used to determine whether the subjects were cognitively aware of the

experimental associations dunng initial acquisition and reversal. Subjects were first

asked a general question about what they thought the experiment was about. The subjects

were also asked a more specific question about whether they noticed any association

between the auditory and visual stimuli.

PET Methods

Eight emission scans were obtained from each subject during the experiment.

Scans were taken in-between the two prescan blocks and the postscan block. As

mentioned above scans 1,3,5,7 were taken during the presentation of CS+ paired trials

and scans 2,4, 6,8 were taken during CS- and visual stimulus alone trials. In the first

half of the experiment Tl was the CS+ and T2 the CS- and in the second half of the

experiment T2 was the CS+ and T l was the CS-. There was an 11 min break between

each scan. Four minutes pior to each scan the prescan blocks were presented to the

subjects and following each scan the postscan block was presented (approximately 3 min

in length). Between each scan block subjects had a 4 min break. The entire PET

procedure took approximately 2 hrs per subject.

Ail scans were obtained using a GEMS Scanditronix PC-2048 head scanner (15

slices, 6.5 mm apart, transverse resolution of 6.9 mm FWHM, axial resolution of 5-6 mm

FWHM). Each subject was placed in the scanner in a supine position and head

movement was minirnized with a custom fitted thennoplastic mask. A 10 min


transmission scan was perfonned with a 6 8 ~ e rotating pin for attenuation correction, prior

to the first ernission scan. During the transmission scan subjects were given the

instructions pertaining to the task. Before each ernission scan the subject was given a

bolus intravenous injection of 30 mCi of [015] water into the left forearm, and the

emission scan was 60 sec in duration.

Image Processing

The automated image regisuaüon program (AIR2.0; Woods, Mazziotta, & Cherry,

1993) was used to correct for movement during the scanning duration by aligning al1

scans to the first. The realigned images were spatially transformed by matching each

subjects' image to a rCBF template that conformed to Talairach and Toumoux stereotaxic

space ( 1988) using SPM95 (Statistical Pararnetnc Mapping; wellcome Department of

Cognitive Neurology, London, UK; Friston, Ashburner, Frith, Pline, Heather, &

Frackowiak, 1996). Images were then smoothed, using a 10 mm isotropic Guassian filter,

to minimize individual anatomic variability. To control for individuai differences in

whole brain rCBF, each subject's transformed images were adjusted to their own global

blood flow using a ratio adjustment in which each pixel value was divided by the average

whoIe brain flow value within a scan.

Data Analysis

Behavior analysis

Subjects were designated as either learners and nonleamers at the end of the

experirnent. Each subject's designation was determined by: (i) hislher response to the

debriefing question " Did you notice any particular relationship/association between the

auditory stimuli and the visuai stimulus?" and (ii) changes in hisher reaction times (RT)


to Tl+visual, TS+visual and visuai alone trial types. If subjects learned to associate Tl

and the visual stimulus in the first half of the experiment, their RTs to Tl+visual trials

should be faster than their RTs to other trial types. Similarly in the second half of the

expenment if subjects learned to associate T2 and the visual stimulus their RTs to

T2+visual triais should be faster than their RTs to other trial types.

Subjects were designated as "leamers" if they either: (i) explicitly stated the two

tone-visuaI associations in their debriefing or (ii) their reaction time (RT) data indicated

they learned to associate auditory and visual stimuli. Subjects were designated as

"nonIearners" if they did not state an awareness of any tone-visual association during

debriefing and/or if their RT data did not show Iearning related changes across the

experirnent.

SAS version 6 (SAS Institute Inc., Cary, NC) was used to conduct a 2X2X4

repeated measures analysis of variance (ANOVA) on the subjects' mean RT per scan

block for the following three trial types: Tl+visual, T2+visual, and visual done. The

between group independent variable had two levels: whether the subject was designated

as (i) a learner or (ii) a nonleamer. The two completely crossed within group independent

variables were phase and scan. There were two levels of phase: phase 1 referred to the

first half of the experiment (T 1 predicted a visual event) and phase 2 referred to the

second half of the experiment (T2 predicted a visual event). The scan variable had 4

levels representing the four PET scan blocks in phase 1 and 2, The dependent variables

were subjects' rnean RT per scan block for Tl+visual, T2+visual and visual alone trial

types.

Acquisition and Reversal of Tone-Visuai Associations 3 1

Trend analyses were conducted using SPSS version 7.5.1 for Windows (SPSS

Inc., Chicago, IL) to determine whether there were significant linear, quadratic and cubic

trends in the subjects' RTs to the three trial types across scans. Group by trend

interactions were also tested using the GLM (general linear model) repeated measures

option of SPSS.

Within Group Image analysis: Partial LRast Squares (PU)

PLS, a muItivariate statisticd method, was used to examine the relation between

the PET data and the experimental design. Individual PLS analyses were conducted for

subjects that leamed the associations (learners) and for subjects that did not learn the

associations (nonleamers). The learners' PLS analysis was conducted to identify patterns

of brain activity related to learning the associations. The nonlearners' P U analysis was

conducted to understand why some subjects The steps involved in conducting the PLS

analysis are explained briefly in the following paragraphs. For an in-depth explanation of

this statistical method please refer to the article by McIntosh and colleagues (1996).

F i s t a matrix of orthogonal contrat vectors that defined the experimental design

(design matrix) was cross-correlated with the PET data for al1 subjects in al1 scans

conditions (data matrix). Helmert contrasts comparing each scan to the average of al1

subsequent scans were used to define the design matrix. The resultant cross correlation

matrix S was then decomposed, using singuiar value decomposition (SVD), into a series

of mutually orthogonal paired latent variables (LVs) and into a series of singular values,

d. These singular values (d) represent the covariance between the design and data

matrices.


A LV pair consisted of (i) design saliences and (ii) brain saliences. The design

saliences were weights for the design contrasts that coded the experimental effect

represented by the brain activity in each LV. The brain saliences were a matrix of

weights which were applied to the PET images and they gave an index of each brain

voxels' relation to the experimental effect. Brain saliences can be either negative or

positive. Positive brain saliences identified areas positively correlated to the

experimental effect and negative saliences identified regions that were negatively

correlated with the experimental effect. Each LV pair was displayed as a singular image

to show the spatial pattern of image covariance with each experimental effect (McIntosh

et al., 1996).

To determint how a particular subject's level of brain activity related to a given

LV subject brain scores were calculated by multiplying each subject's image, within a

condition, with the corresponding brain saliences and summing d l the cross-products (dot

product). A plot of the brain scores for d l subjects across scans was used to identify the

how rCBF in brain areas associated with a particular LV were related to the overall

experimental design.

In addition a variable s was calculated for each LV that represents the "proportion

of the sum of squared cross-block correlations" explained by a particular LV (McIntosh et

al., 1996, 144). Therefore, s was a measure of the amount of covariance within the cross

correlation matrix S that was accounted for by each LV. The calculation of s involved

squaring the value of d for each LV and dividing it by the sum of squared correlations in

matrix S (McIntosh et al., 1996).


The statistical strength of each LV was assessed by conducting a permutation test

to determine the probability that the d value for the LV could be obtained due to chance.

The permutation test first involved expressing the data matrix ce11 values as deviation

values fkom each subject's own grand rnean. This was done to ensure that the average

activity level of each scan across subjects would be expressed as a deviation kom the

grand mean. The second step involved randomly reordering the scan conditions of the

deviation data rnatrix and recalculating a PLS of the reordered dataset. 500 permutations

were conducted. The probability that a reordered d value exceeded the original d value

for a particular LV was determined. Therefore the permutation test allowed us to asses

the strength of the experimental effects identified by the PLS analysis.

To identifi dominant and stable voxels within a LV, a bootstrap analysis was

conducted (Efron & Tibshirani, 1986). The procedure involved: (i) creating a new data

matrix by resampling with replacement individual subject's PET data (fiom the original

data matrix) while maintaining the original order of scan conditions and (ii) conducting a

PLS analysis using the resarnpled data matrix. This procedure was repeated 100 times,

each time new design saliences, brain saliences and d values were calculated for each LV.

A standard error (SE) was calculated for each LV's brain salience. This was done by

taking the square root of the sum of squared deviations for the brain salience, using the

resampled values. The onginal brain saliences were divided by their respective SE.

Ratios greater than two were identified as significant since they corresponded to brain

regions that were two standard deviation (SD) values greater than the mean and had an

approximate p<0.05. Local maxima were selected f?om the bootstrap results. The

Talairach and Toumoux atlas (1988) was used to localize these maxima. Therefore the


bootstrap method allowed us to identify voxels that consistently contributed to the

experimental effect within each LV. For voxels of interest graphs were plotted depicting

the standardized mean activity level of the voxel across scans.

Between- Group Image Analysis: PLS

A group PLS analysis was conducted to compare the distributed pattern of

activations across the brains of learners versus nonleamers. The statistical method was

the sarne as that used in the single group PLS results. The design rnaîrix consisted of

orthogonal Helmert contrasts that coded for experimental main effects and group

interactions. The group data matrix and design matrix were cross-correlated and

decomposed using SVD. Mutually orthogonal paired LVs identiQing main effects and

interactions were obtained. As in the single group analyses, a permutation test was

conducted to determine the statistical strength of the LV patterns and a bootstrap analysis

was conducted to identiQ dominant and stable voxels within each LV.

Results

Behavioral Resul ts

Table 1 contains demographic information about the subjects and each subject's

designation as either a "leamer" or "nonlearner". The last column contains each subject's

response to the debnefing question "did you notice any particular relationship/association

between the auditory stimuli and the visual stimulus?" which was used to determine their

designation.

Appendix C contains individual subject graphs depicting changes in the mean

RT, in msec, for the three trial types of interest, across the entire experiment. A subject's

designation as a learner or nonlearner was influenced by hisher mean RT graph. For


most leamers their mean RT was: (i) faster for Tl+visual trials versus the other two tria1

types in the first four scan blocks and was (ii) faster for T2+visual trials versus the other

two trial types in the last four scan blocks. Most nonlearners' mean RT graphs did not

show any consistent pattern across the experirnent.

The mean RT (msec) graphs for learners and nonlearners for each trial type by

scan block are presented in figure 1. Part (a) presents the average group RT data for the

eight leamers. In the first half of the experiment learners' RTs for Tl+visual trials were

faster than their RTs for T2+visual and visual aione trials. In the second haif of the

experiment, after the contingencies had been reversed, the Ieamers' RTs for TS-tvisual

trials were faster than their RTs for T ltvisual and visual alone trials. The learners' RTs

to visual alone trials were always slower than tone+visual trials across the entire

experiment. Part (b) presents the average group RT data for the eight nonleamers. The

nonlearners' RTs to T 1 +visual and TZ+visual triais were similar throughout the

experiment, regardless of the changes in tone-visuai association contingencies between

the first and second phases of the experiment. The nonlearners' RTs to visual alone trials

were always slower than tone-visual trials.

The 2X2X4 repeated measures ANOVA results are presented in table 2. Group

(leamers vs. nonlearners), scan and phase were the main effects tested for each trial type.

Scan-by-group and phase-by-group interactions were also tested for each trial type. The

results show that there were no group main effects. This indicates that overall the mean

RTs of leamers and nonlearners were not different for the three trial types.

There was no significant scan main effect nor was there a significant scan-by-

group interaction for any of the trial types (see table 2). There were significant phase


main effects for al1 three trial types. In addition there was a significant phase-by-group

interaction for the Tl+visual trial type. The phase-by-group interaction was approaching

significance for the T2+visual triai type (F(1,14)=3.64, p=û.077). To clariQ the meaning

of the phase-by-group interactions for T l+visual and T2+visud trial types, graphs were

plotted comparing the mean RT of learners versus nonleamers across scan blocks for

these two trial types (see figure 2). The graphs indicate that the Tl+visual phase-by-

group interaction was due to the learners' RT to this trial type becorning much slower in

the second phase of the expenment while the nonlearners' RT to this trial type remained

approxirnately the same for the entire experiment. The near significant T2+visual phase-

by-group interactions was due the learners' RT to this trial type becorning much faster in

the second versus the first half of the experiment while the nonlearners' RT to this triai

type rernained approximately the same across the experiment. There was not a significant

three-way group by phase by scan interaction for any of the trial types.

The trend analysis indicates that there was a significant linear trend, F(1,14 ) =

1 1.58 at p ~ 0 . 0 5 , in the subjects' RT to visual done trials. Figure 1 indicates that

significant linear trend was due to an increase in subjects' RT to visud alone trials across

scan blocks. There were no significant group by trend interactions (p0.05) for the

subjects' RT to visual alone triais.

There was a significant cubic trend in the subjects' RT to Tl+visual trials (F(1,14)

= 9-54, pcO.05). In addition there was a significant group by cubic trend interaction to

Tl+visual trials (F(1,14) = 8.39, pc0.05). The trend analyses, 2XSX4 repeated measures

ANOVA and figures 1 and 2 corroborate the conclusion that the learners' RT showed a


significant reversal effect between the two expenmental phases whereas the nonleamers'

RT to Tl+visual trials was approximately the same across the experiment.

The trend analysis of subjects' RT to T2+visual trials yielded a significant linear

trend, F(1,14)=8.98, pe0.05. There were significant group by fourth order and group by

fifth order polynornial interactions for subjects' T2+visual RT (F( 1, L4)=6.09 p cO.05 for

the group by fourth order polynornial interaction, and F(l, 14) = 7.77 p c 0.05 for group

by fifth order poIynomial interaction). Figures 1 and 2 indicate that the significant linear

trend in T2+visual RT was due to a general decrease in subjects' RT to T2tvisual trials in

both groups. It c m be inferred from the 2X2X4 ANOVA and figures 1 and 2 that the

significant group by higher-order polynornial interactions were due to the combination of:

(i) learners' becorning faster to T2+visual trials in phase two of the experiment and (ii) a

general decrease in RT to T2+visual trials in both groups.

PET Results

Within Group PLS Analyses

Seven LVs were identified for each within group PLS analysis. Table 3 contains

the results from the permutation tests and the s value for each LV for learners and

nonleamers. Since the permutation test yields an actud distribution of obtained d values,

and does not rely on the assumption of normality, there was no need to keep with the

traditional p <O.OS level of significance. LVs that: (i) had a permutation probability less

that 0.15 and (ii) had a s value greater that 0.10 will be discussed in detail in the

subsequent paragraphs. Only these LVs were selected because they had a low probability

of appeaxing due to chance done and they accounted for at least ten percent of the

variance in cross correlation matrix S.


The first LV (LV1) for both within group PLS analyses (learners and nonlearners)

identified brain regions that showed a general time-related change in rCBF across scan

conditions. Figures 3 and 4 show the scatter plot of brain scores across scans for LV1

and the corresponding singular image (si.) for learners and nonlearners, respectively.

The scatterplot for LV1 for leamers and nonlearners indicate that the brain activation

pattern identified by this LV corresponds to areas that show a task independent increase

or decrease in rCBF across scans.

The local maxima from LV1 (p < 0.05, identified from the bootstrap anaiysis), are

presented in Tables 4 and 5 for learners and nonlearners respectively. These voxels

showed the strongest change in rCBF across time. For learners, across scans there was a

general decrease in rCBF bilaterally in cuneus regions (Brodmann are2 (BA) 19). In

addition there were decreases in activity in left parahippocampal gyrus (BA 28 or 36),

thalamus and precuneus (BA 7 or 3 1) regions. Right posterior cingulate (BA 3 l), middle

frontal (BA 8), inferior temporal (BA 20)- and post/precentral (BA 4 or6) gyri and right

lateral cerebellum also showed general decreases in rCBF across scans. Areas that

showed a general increase in rCBF across cans were: bilaterai medial cerebellum, left

transverse temporal (BA 4 1 ), left inferior occipital (BA 18), left inferior frontal (BA 47),

nght superior frontal (BA 10) and right middle cingulate (BA 24) gyri.

In nonlearners regions that showed a general decrease in rCBF across scans

included: medial cerebellum, bilateral putarnen, right superior frontal gyms (BA IO), left

inferior frontal gyms (BA 47), left middle frontal gyms (BA 46), left middle temporal

gyrus (BA 39) and left cingulate gyms (BA 23) (refer to Table 5). There was a bilateral

increase in activation in cuneus (BA 18) and in rniddle temporal gyrus (BA 2 1) across


scans. In the right hemisphere there was increased activation across scans in cerebellum,

uncus ( BA 28) and inferior temporal gyms @A 37). Lefi hemisphere regions that had a

general increase in rCBF across scans included: inferior temporal gyms (BA 20), inferior

frontal g p s (BA 47)' cingulate gyrus (BA 3 l ) and middle frontal gyms (BA 8).

In learners LV2 and LV3 had a permutation probability less that 0.15 and a s

value greater that 0.10. Figure 5 shows the leamers' scatter plot of brain scores across

scans for LV2 and the corresponding s.i. . The scatter plot shows that this LV identifies

brain regions that showed a change in rCBF between scans 4,5 and 7. Scan 4

corresponds to the last unpaired scan in the first experimental phase and was obtained

while subjects were presented with T2 alone and visual alone trials. Scans 5 and 7

correspond to the two paired scans in the second expenment phase and were obtained

while subjects were presented with T2tvisud trials. Therefore LV2 in leamers identified

brain regions that showed a change in activity as subjects leamed the T2 + visual

associatior.. Table 6 contains the local maxima ( ~ ~ 0 . 0 5 ) obtained from the learners'

LV2. Decreased activity was observed in the following brain regions as learners leamed

the T2 + visual association: nght hippocarnpal gyms (BA 35), right BA 38, nght BA 25,

right BA 4, right BA 2 1, right medid BA 45, right BA 1 1, and bilateral cerebellum.

There was increased in activation of nght BAS 44,8,40, and 19 and of Iefe BA 6 as

subjects learned the T2 and visual association.

The learners' scatter plot of brain scores across scans for LV3 and the

corresponding s.i. are presented in Figure 6. The scatter plot indicates that this LV

identifies brain regions that were differentially active during scan 8 versus scans 6 and 7.

Scans 6 and 8 corresponded to the first and last unpaired scans in the second experimental


phase, respectively. They were obtained while subjects were presented with T l alone and

visual alone trials. Scan 7 was the last paired scan in the second expenmental phase and

was obtained during the presentation of TZ+visual trials. This implies that in the second

phase of the experiment, initiaily T 1 alone presentations activated regions that were also

active after subjects learned the T2 and visual association; however, Iater (after the T2

and visual association was learned) Tl unpaired trials were associated with the activation

of different brain regions. Therefore brain regions that changed activity as subjects

Ieamed that T 1 no longer predicted the visual stimulus (extinction to the first association)

were identified by this LV.

Table 7 contains the local maxima (p<0.05) obtained from LV3. Brain regions

that were more active during scan 8 versus scans 6 and 7 included: right BA 46, right BA

4 or 6, right thalamus, left BA 1 1 and left BA 40. These regions showed increased rCBF

in the last T 1 alone and visual alone scan and may reflect regions that were involved in

leaming that negative prediciive value of Tl or altematively were involved in

extinguishing to the first Tl+visual association. Brain regions that were more activated

during scans 6 and 7 versus scan 8 included: bilateral cerebellum, right BA 19, right BA

18 and left BA 2 1. These regions were more active to Tl alone and visual alone

presentations before subjects completely extinguished to the first association since these

regions were also more active during the last T2+visual scan.

Other than LVI, that was discussed previously, in nonleamers the only LV that

had a permutation probability less that 0.15 and a s value greater that 0.10 was LV2.

Figure 7 shows the nonleamers' scatter plot of brain scores across scans and s.i. for LV2.

The scatterplot indicates that this LV mainly identifies brain regions that were


differentially active during scan 2,3, and 5 versus scans 1 and 7. Scans 1,3,5, and 7

were taken during tone-tvisual paired trials and scan 2 was the first unpaired Tl alone and

visual alone scan. It is unclear what expenmental effect this LV defines. However, it

appears that this LV might identi@ regions that: (i) showed a gradua1 increase or decreaçe

in activation across unpaired (tone alone and visual alone) scans and (ii) were

differentially active during the middle two paired scans (one Tl+visual scan and one

T2+visual scan) versus the first and last paired scans.

The local maxima @ c0.05) obtained from the nonleamers' LV2 are presented in

table 8. Brain regions that were more active during the middle paired scans and showed a

decrease in activity across unpaired scans included: bilateral cerebellum, right BA 47 or

lateral sulcus, nght BA 1 or 3, lei? BA 24 and left BA 40. In the right hemisphere there

was more activity during the first and last paired scans and an increase in activity across

unpaired scans in BA 37, BA 10 and BA 8. Lefi hemisphere regions that showed this

pattern of activation were: BA 47, BA 9, BA 18, BA 19 and caudate.

Behveen Group PLS Analysis

In the between group PLS analysis only the first LV &VI) had a permutation

probability less that 0.15 and a s value greater than 0.10. LV 1 identified brain regions

that showed a general increase or decrease in rCBF across scans in both learners and

nonleamers. This LV \vas sirnilar to the first LVs fiom the within group analyses for

learners and for nonleamers. Brain regions that showed a general increase across scans in

leamers and nonleamers included: bilateral BA 20, bilateral BA 19, right BA 28, right

BA 6, left BA 40, left BA 10, Ieft BA 3 1. There was decreased rCBF across scans in

bilateral BA 17, right BA 18, right BA 10, right BA 24, and lefi BA 1.


Voxels of Interest: Chosen from the Leamers ' Wilhin Group PLS analyses

In learners LV2 and LV3 identified regions that were involved in leamhg the

second tone-visual association and in extinguishg to the first tone-visual association,

respectively. Some of the brain regions involved in these learning processes included:

right precentral gyms, right hippocarnpal gyms, right middle fiontal gynis, right

precuneus, right middle occipital gyrus, nght thalamus, left middle temporal gyms,

bilateral inferior parietal lobule and bilateral premotor cortex (refer to Tables 6 and7).

The role of these brain regions in associative leaming are especially interesting because

these regions have been found to be important for leaming in previous studies (Blaxton et

al., 1996; Deiber, Wise, Honda, Catalan, Grafinan & Hallett, 1997; Grafion, Fagg &

Arbib, 1998; Iacoboni, Woods & Mauiotta, in press; Mchtosh et al., in press; Mchtosh

& Gonzalez-Lima, 1994; Molchan et al., 1994; Petrides, 1996; Schreurs et al., 1997).

To better understand the role of these brain regions during learning and extinction

graphs depicting standardized mean activity level by scan were plotted for some of the

local maxima extracted from these regions. Table 9 lists the voxels graphed fiom LV2

and LV3 for leamers. By graphing the standardized mean activity level of these voxels,

three different patterns of standardized mean activity across scans were identified in

learners. Some voxels showed increased activity in both T2 paired or both T l unpaired

scans relative to other scans in phase 2 (Pattern A). Figure 8 depicts the standardized

mean activity of brain regions that showed this pattern of activity in learners. Voxels

that showed this pattern were those extracted fiom nght middle fiontal gyrus (BA 8),

right precentral gyrus (BA 4), right postcentral gyms (BA 6) , nght infenor parietal cortex

(BA 40), and right precuneus (BA 19). Figure 9 depicts the standardized mean activity


of these regions across scans in nonleamers. In nonlearners these brain regions do not

show sirnilar patterns of activity in phase 2.

The second pattem of activity (Pattern B) represented voxels that showed

decreased activity across T2 paired scans and increased activity across T l unpaired scans

in phase 2. Figure 10 depicts the standardized mean activity of voxels that showed this

pattern of activity in learners. Voxels extracted from right middle prefiontal cortex (BA

9), lefi inferior parietal cortex (BA 40), left cerebellurn and right thalamus showed this

pattem of activity. Figure 11 shows the standardized mean activity of these regions in

nonleamers. Ln nonlemers these regions do not show any pattern of activity.

The third pattem of activity (Pattern C) represented voxels that showed increased

activity across T2 paired scans and decreased activity across T l unpaired scans in phase

2. Figure 12 shows voxels that showed Pattern C activity in learners. Voxels that

showed this pattern of activity included those extracted fkom right hippocampal g p s

(BA 3 3 , right middle occipital gyms (BA 19 and BA 18), and nght middle temporal

3 ~ s (BA 21). The activity of these regions in nonlearners did not show any

interpretable pattem in phase 2 (see figure 13).

It is important to note that the activity patterns graphed for nonlearners, of voxels

chosen from the leamers' PLS results, do not depict significant expenmental effects. The

reason for plotting these graphs was to gain M e r insight as to what brain regions are

involved in learning and extinction process and to understand how nonieamers' differed

from learners.

Discussion

Cornitive Awareness and Associative Leaming


The behavioral data fiom the eight subjects designated as cclearners" indicate that

they success£Ûlly leamed the two associations since they were faster on Tl +visual trials

in phase 1 compared to the other trial types and became faster on T2+visual trials and

slower on Tlivisual trials in phase 2. The significant group by phase interaction for

subjects' RT to Tl+visual trials indicate that learners' RT in this trial type becarne

significantly slower in phase 2 whereas nonleamers' RT to this trial did not change across

the experiment. The group by phase interaction for subjects' RT to TS+visual trials was

not significant. However, the trend analysis indicates there was a significant group by

higher-order polynomial interaction that may be due to the combination of: (i) learners'

becoming faster to TZ+visual trials in phase two of the expenment and (ii) a general

decrease in RT to T2+visual trials in both groups.

These eight learners also verbally reported noticing these associations in

debriefing (some subjects' reports were more precise than others) indicating that they

were cognitively aware of these associations. Nonleamers did not verbally report

knowing the associations. As mentioned in the introduction several studies on human

autonornic classical conditioning studies have found subjects that leamed the association

were aware of the task contingencies (Dawson & Schell, 1987). It has been debated that

though verbal reports regarding the associative contingencies require cognitive awareness

of the task this does not mean that learning these associative contingencies requires

cognitive awareness (Arcediano, Ortega & Matute, 1996). This has been shown to be

true in studies of skeletal conditioning. Frcka and colleagues (in Martin & Levey, 1987)

found that subjects level of demand awareness, contingency awareness and response

awareness was not related to performance on an eyeblink conditioning task. Furedy (in


Martin & Levey, 1987, p. 75) has found that also in autonornic conditioning there is a

dissociation between awareness and autonornic performance, when the data are anaiyzed

on a trial-by-trial basis. This implies that cognitive awareness is not strictly necessary for

skeletal or autonornic conditioning performance.

It is important to note that the current study was not concerned with skeletal or

autonornic conditioning but instead was a study involving conditioning of a voluntary

response. As in other fonns of classical conditioning, the percentage of CRS produced in

conditioned voluntary response tasks are inversely related to the ISI (interstimulus

interval) duration (Perlmuter, Fink & Taylor, 1969). This led Perlmuter and colleagues

(1 969) to conclude that conditioning of a voluntary response is a subclass of classical

conditioning. However, voluntary behavior is different fiom involuntary behavior in that

it requires intention or consciousness (Kimble & Perlmuter, 1970). Therefore, cognitive

awareness may play a more important roIe in conditioning of a voluntary response.

In the previously mentioned EEG study by Proulx and Picton (1978) exarnining

auditory associative learning, subjects were required to make a voluntary response (press

a button) whenever they were presented by a target auditory stimulus. The subjects were

also presented with two additional tones: a high tone and a low tone. The subjects were

not informed that in the middle of the experiment the target auditory stimulus was always

preceded by a high-high sequence. Twelve of the 18 subjects became aware of the

auditory association and were designated "leamers". Behaviorally these twelve subjects

responded faster to target stimuli during the middle experimental phase than the six

unaware subjects (nonleamers) and they showed a significant decrease in RT during the

middle phase. The nonlearners' decrease in RT barely met significance. These results


indicate that awareness improves performance in studies requiring conditioning of a

voluntary responses. The results fiom the current study imply that conditioning of a

voluntary response always requires awareness of the associative contingencies. Since in

the current study and in the previous study by Proulx and Picton (1978) determination of

contingency awareness occurred after the leaming process it is unclear whether cognitive

awareness precedes conditioning of a voluntary response or follows it (Arcediano, Ortega

& Matute, 1996 ). Therefore, cognitive awareness may play a more important role in

conditioning voluntary response than in skeletal and autonornic conditioning since

voluntary behavior requires intent.

Cognitive awareness of learning has been related to activation of specific brain

regions (Grafion, Hazeltine & Ivry, 1995; Proulx & Picton, 2978). For example, in the

EEG study by Proulx and Picton (1978) subjects that were aware of the stimulus

associations exhibited a frontally and centraily distributed CNV waveform during paired

association trials. In a PET study exarnining motor sequence Learning Graiton and

colleagues (1 995) found that at the end of the experiment seven out of the 12 subjects

reported becoming aware of the sequence. Awareness of the sequence was related to

increased activation of bilateral infkrior parietal, bilateral temporal, right premotor, and

anterior cingulate cortices. Awareness was related to decreased activation of bilateral

superior temporal and insular cortices.

Ln the cwrent study a between group analysis was not conducted on the PET data

to examine rCBF changes related to cognitive awareness. However, in the Ieamers

bilateral ùiferior parietal, bilateral occipitotemporal and right premotor regions were

activated during learning (see Tables 6 and 7). In nonleamers these brain regions were


not related to any significant experimental effect (See Table 8). These observations are

sornewhat congruent with the results obtained by Grafton et al. (1995). However, it is

unclear whether the brain regions activated in leamers were solely related to the leaming

processes andlor were also related to group differences in awareness.

General increases and decreases in rCBF across scans: the time effect

In leamers and nonleamers PLS analyses the e s t LV identified b r i n

regions that showed a generaI increase or decrease in rCBF across scans. Brain regions

that showed a general increase across scans in leamers and nonleamers (fiom between

group PLS) included: bilateral inferior temporal gyms ( BA 20), bilateral cuneus (BA 19

), right uncus (BA 28), nght precentral gynis (BA 6), left inferior parietal lobule (BA 40),

Ieft middle &ontal gynis (BA IO), left cingulate gynis (BA 3 1). There was decreased

rCBF across scans in bilateral lingual gyms (lefi BA 17 and nght BA 18), right supenor

frontal gynis (BA IO), nght cingulate gyrus (BA 24), and left postcentral gynis (BA 1).

These changes were task independent because they did not show a change in activity

following the reversal of contingencies.

Nonspecific and task-independent changes in rCBF due to a prolonged period of

time in the PET scanner have been found in previous studies (Rajah, Hussey, Houle,

Kapur & McIntosh, 1998). The changes in rCBF have been referred to as a "time effect".

The general pattern of brain regions showing time-related changes in rCBF observed in

the current study are consistent with those found by Rajah et al. (1998). These tirne-

related changes in rCBF have been interpreted as reflecting habituation and simple motor

learning processes (Rajah et al, 1998). It is likely that the sarne explanations may account

for the results from the current study. There were also differences between the two


studies that may be due to different experïmental dernands. For example, in the current

study there was increased activation of bilateral temporal cortex across scans whereas in

the previous analysis decreased activation of this region was observed across scans. In

the current study there was decreased activation in posterior cingulate gyrus (BA 3 1) and

increased activation in anterior/middle cingulate gyrus (BA 24) across scans. In the

previous analysis decreased activity in BA 3 1 across scans was not observed. The

associative learning studies examined in the previous study by Rajah et al. (1998) did not

include the reversal of contingencies and a reacquisition phase whereas the cment study

did. Perhaps, the differences between the current study and the previous study by Rajah

et al. (1998) are due to these methodological differences.

Chan-ges in rCBF related to reacquisition and extinction processes: PET data from

Learners

In the within group PLS analysis of learners two distinct LVs representing

patterns of rCBF related to learning the second tone-visual association and related to

extinction to the first tone-visual association were identified. In general the brain regions

tbat were involved in these processes included: right precentral gyms, rïght hippocampal

gyrus, right rniddle fiontal gyrus, right precuneus, right middle occipital gyrus, right

thalamus, left middle temporal ,YS, bilateral inferior parietal lobule and bilateral

premotor cortex. Though there were several additional brain regions impiicated in these

processes the current discussion will focus on the previously mentioned regions.

Graphs representing the standardized rnean activity level across scans for voxels

extracted £rom LV 2 and LV 3 of learners indicated that there were three different

patterns of brain activity across scans: Pattern A, Pattern B, and Pattern C. It is possible


that brain regions that show the same pattern of activity represent a neural network

dedicated to a specific leaming function. Therefore, each of these three patterns may

represent distinct networks engaged in acquisition of the second association and

extinction of the f i s t association during phase 2.

Pattern A: Regions involved in Reacquisition

The brain regions that showed Pattern A activity were interpreted as being

involved in learning the second association (reacquisition). This interpretation is in

keeping with the observation that learners behaviorally distinguished TZ+visual and

Tltvisual trials in phase 2; responding faster to T2+visual trials. Figure 1 indicates that

subjects learned the second set of associations (TZ+visual and Tl alone) in the fi& scan

block. To leam this second association subjects must leam to positively associate T2 to

seeing the visual stimulus and to negatively associate T l to seeing the visual stimulus.

One c m infer Erom this that T2 paired scans had a positive associative value whereas T l

unpaired scans had a negative associative value for learners. Therefore brain regions that

differentiated between T2 paired and T l unpaired scans (Pattern A) would be related to

learning.

The interpretation that right middle fiontal gyrus (BA 8), right precentral gyrus

(BA 4), right postcentral aynis (BA 6), nght inferior parietal cortex (BA 40), and right

precuneus (BA 19) are involved in leaming the current task is supported by previous

experimental results (Blaxton et al., 1996; Deiber, Wise, Honda, Catalan, Grahan &

Hallett, 1997; Grafton, Fagg & Arbib, 1998; Iacoboni, Woods & Mazziotta, in press;

Mchtosh et al., in press; Mchtosh & Gonzalez-Lima, 1994; Molchan et al., 1994;

Petrides, 1996; Schreurç et al., 1997). Frontal involvernent in associative learning has


been found in PET studies examining skeletal conditioning of the human eyeblink

response in which a tone fûnctioned as the CS (Ellaxton et al., 1996; Molchan et al., 1994;

Schreurs et al., 1997). In these studies frontal activity was found to be less dunng paired

tone-airpuff presentations versus unpaired tone and airpuff presentations. This finding is

keeping with the current study's results in which right BA 8 was found to be more active

during T l unpaired scans versus T2 paired scans in phase 2.

The prefrontal regions identified in these eyeblink conditioning s ~ d i e s do not

overlap with the right frontal region found in the current study (BA 8). However, in the

PET study by Mchtosh and colleagues (in press) examinhg auditory-visual associative

leaming in which a voluntary response was required, right BA 8 activity was related to

Ieaming the association. Right BA 8 activity was found to decrease as subjects Iearned

the association. Decrease in kontal activation has also been found in a PET study on

conditional rnotor learning (Deiber et al., 1997). In addition to these imaging studies

Petrides (2996) found that patients with frontal cortical excisions were impaired in

leming to associate different color stimuli with different hand postures. Therefore these

findings indicate that: (i) fiontal cortex is important for associative learning and (ii)

&ontal regions become less active across presentations of associative trials and are more

active during unpaired/nonassociative trials. There are several hypotheses conceming the

role fiontal cortex plays in associative Ieaming (Deiber, 1997; Petrides, 1996). Deiber et

al. (1 997) favored the interpretation that frontal (and parietal) involvernent in associative

learning reflected its role in rejecting routine d e s and adopting new d e s . The

decreased fiontal activity as subjects leam associations was interpreted as reflecting the

relaxation of the cortical network subserving the learned behavior as the behavior became

Acquisition and Reversa1 of Tone-Visual Associations 5 1

practiced (Deiber et al., 1997). However this explanation by Deiber et al. (1997) does not

account for the increased rCBF observed in frontal cortex in associative tasks during the

presentation of unpaired stimulus trials.

Petrides (1 996) hypothesized that fiontal cortex involvement in associative tasks

reflected its role in learning to select correct responses to various stimuli fiom a set of

competing responses. However, in the current study and in the human eyeblink

conditioning study subjects were not required to select from several competing responses,

therefore Petrides' (1996) explanation for frontal involvement in associative tasks does

not explain the results fiom hurnan conditioning studies.

Previous studies indicate that the frontal cortex rnay be involved in the neural

inhibition of posterior cortical regions, including primary sensory cortices (Shimamura,

1995). For example, in a PET study Frith and colleagues (in Shimamura, 1995) found

increased dorsolateral prefiontal cortex activity was corrdated to decreased activity in

posterior cortical regions. Furthemore, in an evoked potential (EP) study patients with

frontal lobe damage exhibited potentiated auditory EP relative to controls, which rnay

reflect release of inhibition of primary auditory cortex due to frontal deficits (Shimamura,

1995). Frontal cortex involvement in problem solving has also been observed (Kolb &

Whishaw, 1990; Shimamura, 1995). Taken together these observations imply that in the

current study and previous hurnan conditioning studies, the fiontal cortex rnay be

involved in assigning associative value to conditioned stimuli and in altering itsy

inhibitory control accordingly. In the current study subjects were required to respond to a

visual stimulus as quickly as they possibly could and in phase 2 T2 predicted the visual

stimulus and Tl did not. It is possible that in the current study the frontal cortex was


involved in: assigning T2 a positive associative value and disinhibiting posterior regions

that improve the subjects performance of the task and a s s i m g T l a negative associative

value and inhibiting posterior regions involved in performance of the task. This increased

inhibition of T l may explain the increased right BA 8 activity found during T l unpaired

scans in the current study. The disinhibition of a CS+ rnay explain the decreased activity

of frontal cortex observed in other associative tasks.

The above interpretation of frontal cortex involvement in associative learning is

consistent with the increased activity of right BA 4 (prirnary motor cortex) observed

during T2 paired scans relative to T l unpaired scans in phase 2 of the current study.

Leaming related activation of right motor cortex was also observed in Mchtosh et al.3

(in press) PET study on auditory-visual associative learning. The laterality of the motor

cortex activity is the sarne as that found in the current and previous motor Ieaming studies

(Grafion et al., 1995; Seitz et al., 1990). As mentioned in the introduction there is ample

evidence for motor cortex involvernent in associative learning. For exarnple, Aou et al

(1992) found altered motor cortex activity in cats during an EMG conditioned eyeblink

study. Furthemore, the results indicated that the CS caused the observed increase in

motor cortex activity during acquisition of the CR.

In the current study the increased right BA 4 activity during T2 paired scans could

not be due to making a rnotor response since unpaired and paired scans required the sarne

number of motor responses (there were 5 visual stimuli in both types of scans). It is

possible that the increased nght BA 4 activity during T2 paired scans was due to paired

scans containing the CS+ which in turn caused more nght BA 4 activity. This

interpretation is consistent with Aou et al. (1992) and implies that right BA 4 activity is


directly related to CS+ presentations. An alternative explanation of increased right BA 4

during T2 paired versus T l unpaired scans is that it was the result of right BA 8

disinhibition of posterior regions involved in task performance and is thus indirectly

related to the CS+. It is unclear at this point which hypothesis best explains the increased

right BA 4 observed in the current study. Since learners responded fastest during

T2+visual trials compared to other trials in phase 2, it is likely that the increased nght BA

4 activity observed during T2 paired scans was related to faster RTs during these scans

versus Tl unpaired scans.

Premotor and inferior parietal cortices have previously been found to be involved

in human eyeblink conditioning, auditory-visual associative learning and visuomotor

associative learning (Deiber et al., 1997; Grafton et al., 1998; Molchan et al., 1994;

McIntosh et al., in press). In the PET study by Molchan and colleagues (1 994)

examining human eyeblink conditioning increased activity of nght inferior parietal cortex

(BA 40) was observed during unpaired tone and airpuff scans versus paired tone-airpuff

scans. This finding is consistent with the pattern of right BA 40 activity observed in the

current shdy.

In a PET study on visuomotor associative learning in which subjects wers

required to associate different visual stimuli with different joystick movements Deiber et

al. (1997) found increased bilateral BA 6 activity as subjects leamed. Grafton et al.

(1 998) found that as subjects learned to associate different colored lights with different

types of grasps (precision and power) there was increased activity of left BA 6. In the

current study bilateral prernotor involvement in learning was found; however only nght

BA 6 showed Pattern A activity. Left BA 6 showed a general increase in activity across


phase 2 scans. Furthemore, x-ight BA 6 showed more activity during T l unpaired scans

compounded by an increase in activity across T l unpaired scans in phase 2. These results

are in keeping with previous hd ings of increased BA 6 activity during learning.

Iacoboni and colleagues (in press) conducted a PET study investigating

sensorimotor integration and sensorimotor leaniing of auditory and of visual stimuli

using a spatial compatibility task. The goal of this study was to determine whether

premotor and parietal regions could be sirnilady activated during auditory sensorimotor

learning and visuaI sensorimotor learning. Iacoboni et al. (in press) found rCBF increases

in left premotor and parietal regions in both rnodalities. It was concluded that premotor

and parietal regions subserve sensorimotor integration in the auditory and visual domains.

The results ftom the Iacoboni et ai.'s (in press) study and Grafton et a1.(1998) and Deiber

et al. (1 997)'s results supporting premotor and parietal involvement in movement

selection indicate: in the current study the roles of nght BA 6 and BA 40 in iearning may

include auditory and visual sensory and motor response integration. Evidence that Iateral

intraparietal neurons in primates show similar response latencies to auditory and visual

stimuli and that strong reciprocal connections exist between premotor and parietal

cortices in the monkey, strongly support this interpretaticin (Iacoboni et al, in press).

Therefore, in the curent study these regions receive auditory and visual irdormation and

are involved in deciding the appropnate motor response.

Right BA 19 also showed Pattern A activity. Many studies have found sensory

cortical involvement in associative learning (Cahill & Sheich, 1996; McIntosh et al., in

press). A more detailed discussion on this topic will be presented in the subsequent

section discussing Pattern C. The existence of corticocortical connections between


motor, parietal, premotor and fiontal and occipitotemporal cortices and the fact that these

regions a11 show Pattern A activity, irnpiy that these brain areas constitute a neural

network involved in leamhg the second tone-visual association in the current study

(lacoboni et al., in press; Molchan et al., 1994; McIntosh et al., 1994; Pandya and

Yeterian, 1 990).

Pattern B: Regions primarily NzvoZved in the extinction process

In the current study the extinction process is the inverse of the reacquisition

process. During extinction subjects had to learn that Tl no longer predicted the presence

of the visuaI stimulus (a positive association). They also had to l e m that T l predicted

the absence of the visual stimulus (a negative association). Therefore subjects had to first

"disconnect" the association between T l and the visual stimulus and switch the

associative value (fiom positive to negative) of T l . This is the opposite of what was

required of subjects to learn the second association. During reacquisition subjects had to

change the associative value of T2 (f?orn negative to positive) and "connect" T2 with the

visual stimulus. One difference between reacquisition and extinction was that subjects

reported learning the new positive (T2+visual) association but they did not report

leaming the new negative (Tl does not predict a visual stimulus) association. Therefore

it is possible that subjects were only cognitively aware of the reacquisition process and

were unaware of the extinction process that was occurring concurrently. One c m argue

that subjects were aware of both processes and that by reporting the one association

subjects believed it would be inferred that they were also aware of the opposite

association.


Since the learners' RT to Tl+visual trials did not become slower than their RT to

visual alone trials in phase 2, it is difficult to argue that subjects extinguished to Tl. This

rnay be due to fact that during phase 2 Tl was still paired with the visual stimulus 33% of

the tirne and still had some positive predictive value. It would be more accurate to

classi@ the learners' increased RT to Tl+visual trials in phase 2 as reflecting acquisition

of T 1 's negative predictive value rather than reflecting pure extinction to the Tl+visual

association. However, learning Tl's negative predictive value is part of the extinction

process. Therefore, brain regions that were involved in leaming Tl 's negative associative

value are also involved in the extinction process.

The brain regions that showed Pattern B activity were interpreted as being

predorninantly involved in leaming Tl's negative predictive value. This interpretation

was based on the observation that activation of these regions increased across Tl

unpaired scans. Therefore regions that were more active in Tl unpaired scans could be

interpreted as supporting the inhibition of the initial positive association and the assertion

of the new negative association. The observation that these regions showed a decreased

activity across T2 paired scans (with the exception of right BA 9 which remained the

same) implies that they may not have been as important in learning the new associative

value of T2. However, the reduced activity across T2 paired scans observed in these

regions may actually play an important role in learning of the second association; perhaps

it reflects reduced inhibition.

Brain regions that showed increased activity during the extinction process in the

current study included: right middle fiontal gyrus (BA 9)' left inferior parietal lobule

(BA 40), left cerebellum and right dorsornedial thalamus. There is some overlap in the


general brain regions involved in the extinction and reacquisition processes. This is not

surprising since in the current study the extinction process was the inverse of

reacquisition process (as mentioned previously). Both processes involved middle fiontal

and inferior parietal regions. During the extinction process lefi iderior parietal lobule

was involved whereas nght iderior parietal lobule was involved in reacquisition.

Reacquisition involved right BA 8 whereas extinction involved nght BA 9. Though there

is some overlap in the two processes they involve distinct neural regions.

As mentioned in the discussion on reacquisition there is evidence for the role of

frontal and parietal regions in associative leaming. The nght parietal region was

interpreted as being important for the sensorimotor integration component of learning the

second association. In the reacquisition process the frontal cortex was thought to be

involved in assigning associative value to conditioned stimuli and altering its' inhibitory

control of posterior regions important for leaming accordingly. As in reacquisition,

extinction involves learning new associations. It is likely that the role of left BA 40 and

right BA 9 in the extinction process paraIleIs the role of right BA 40 and right BA 8 in

reacquisition.

The sensonmotor integration role of left BA 40 in extinction would be slightly

different than that of right BA 40 in reacquisition. In reacquisition nght BA 40

involvement was thought to consist of receiving T2 and visual sensory information and

integrating this information with eliciting a motor output. In extinction left BA 40 may

be involved in receiving Tl sensory information and integrating this with inhibiting a

motor output. This may account for the hemispheric difference in parietal involvement in

the two processes.


Unlike the other regions that exhibited Pattern B activity, right BA 9 did not show

a change in rCBF across T2 paired scans. This region only showed an increase across T l

unpaired scans implying that it is specificaIly involved in the extinction process. It is

likely that the role of right BA 9 was to assign a negative associative value on Tl. This

would be consistent with the role assigned to right BA 8 in reacquisition.

Lefi cerebellum was found to be involved in the extinction process. Previous

studies on hurnan eyeblink conditioning have found extinction-related increased activity

in cerebellum (Schreurs et al., 1997). Ln the Schreurs et al. (1997) PET study on human

eyeblink conditioning increased right cerebellar activity was found during unpaired

extinction scans versus paired scans. This is consistent with the current study in which

left cerebellar activity was found to increase across T l unpaired scans. Associative

learning studies and the current study support the involvement of frontal and cerebellar

regions in extinction. The cerebellum has also been found to be important for the

extinction of the conditioned nictitating membrane response (MAR) in rabbits

(Hardimann, Rarnnani, & Yeo, 1996). Inactivation of the cerebellum with muscimol

prevented extinction of the NMR (Hardimann et al., 1996). This finding implies that in

the current study the role of the cerebellum was to prevent subjects fiom making a CR to

Tl trials.

Thalamic involvement in the extinction process has not previously been found in

hurnan conditioning studies. The dorsomedial nucleus of the thalamus receives

projections fiom temporal cortex and projects to fi-ontal cortex. It is possible that in the

current study right thalamus was involved in transfemng information about the T l


unpaired scans, that was processed by posterior brain regions, (such as temporal gyrus) to

the right BA 9.

Brain regions that exhibited Pattern B activity may constitute a network invoived

in the extinction process. Each region plays a different roie in this process. One

hypothetical way in which this network may fùnction is: (i) nght thalamus sends

information fYom temporal (possibly visual association) regions to the right BA 9 (ii) left

parietal cortex integrates hearing T 1 and inhibiting a motor output (iii) right BA 9

receives information fkom left parietal cortex and right thalamus and assigns a negative

associative value to Tl (iv) this information is received by lefi cerebellum which inhibits

subjects frorn rnaking a CR to Tl. Obviously there are regions omitted fiom this

network. Additional regions that function to comunicate information frorn left BA 40

to right BA 9 and for communicating information fkom nght BA 9 to left cerebellum

must also be incorporated into this hypotheticd rnodel.

Pattern C: Regions involved in bath leaming and extinction

The brain regions that showed Pattern C activity were interpreted as being

involved in learning the second association and in the extinction process. This

interpretation was based on the observation that these regions were most active during the

middle two scans in phase 2 and least active during the last scan in phase 2. This implies

that these regions were more active as subjects Iearned the second association and less

active as subjects becarne extinguished to the first association. Therefore these regions

are both involved in leaniing and extinction.

Voxels that were found to be involved in both leamhg and extinction in the

current study include: nght hippocampal gyms (BA 3 3 , riglit middle occipital gyms


(BA 19 and BA 18), and nght middle temporal gyms (BA 21). Schreun et al. (1997)

found decreased activity in left lateral occipitotemporal @A 18) and nght superior

temporal (BA 22) regions during unpaired extinction scans relative to paired scans. In

the current study decreased activity of middle occipital (BA 19 and BA 18) and rniddle

temporal (BA 2 1) regions was observed during T l unpaired scans relative to T2 paired

scans in phase 2. Though these results do not exactly correspond to those of Schreurs et

al. (1 997) the similarity corroborates the interpretation that these regions show extinction-

related activation.

These occipitotemporal regions also show increased activity from first to second

T2 paired scans. Schreurs et al.3 (1 997) found that left BA 18 and right BA 22 regions

showed increased activity during paired scans relative to unpaired extinction scans. The

involvement of sensory cortices in associative learning tasks has been observed in animal

and human studies (Cahill & Scheich, 1996; McIntosh et al., in press). Cahill and Shiite

(1 996) found once gerbils learned that a light predicted an auditory stimulus,

presentations of the light alone elicited activity in primary auditory cortex. In the

previously mentioned PET study by McIntosh and colleagues (in press) exarnining

auditory-visual associative learning it was found that as subjects learned the association

there was increased activity in visual cortical regions (including BA 19) d h g

presentations of the auditory stimulus alone. These findings in conjunction with the

changes in standardized mean activity observed strongly support a role for nght BA 19,

BA 18 and left BA 21 in learning the second association in the current study.

These brain regions showed increased activity during T2 paired scans. The

number of visual stimuli presented duncg paired and unpaired scans was equivalent

Acquisition and Reversa1 of Tone-Visual Associations 6 1

therefore the increased activity during T2 paired scans cannot be related to more visual

stimulation. Instead the difference must fie related to the T2tvisual association. It is

possible that this pattern of activity is related to learned expectancy (Mchtosh et al., in

press). The positive associative value of T2 in phase 2 induces expectancy of a visual

stimulus when T2 is heard which in tum increases the subjects' attention to the visual

domain. Therefore this pattern is both learning and attention related (McIntosh et al., in

press).

As mentioned in the previous section concerning Pattern A activity there are

reciprocal connections behveen frontal and posterior sensory regions (Pandya and

Yeterian, 1990; Shimamura, 1995). Frontal cortex receives visual information fkom

dorsal and ventral visual pathways and receives visual and auditory sensorimotor

intepration information from premotor and parietal cortices (Grafton et al., 1998;

Iacoboni et al, in press; Kolb & Whishaw, 1990). Therefore in the current study it is

possible that premotor-parietal-frontal interactions led to the formation of the TZ+visual

association which resulted in the increased activity of occipitotemporal regions due to

frontal disinhibition. This might explain why leaming related increased activity of

occipitotemporal regions changed across T2 paired scans whereas activity of the anterior

regions within this network remained equally more active during both T2 paired or Tl

unpaired scans compared to the other scans in phase 2 (with the exception of nght BA 6

which showed an increase fkorn scan 6 to scan 8). A future consideration is to conduct a

network analysis of these data using structural equation modeling to determine whether

the preceding network explmation of the data is accurate. Furthexmore, obtaining ERP


measures as subjects l e m the current task would also shed light on the time course of the

activations related to learning and to extinction.

Traditionally associative learning has been categorized as an implicit procedural

mernory task that does not require hippocampal involvement to be learned (Squire,

Knowlton & Musen, 1993). However it has also been postulated that the hippocampal

region is involved in forming relational representations (Squire et al., 1993). The current

study found increased right hippocampaf gyral activity (BA 35) across T2 paired scans.

This observation supports the latter interpretation regarding hippocampal involvement in

learning. PET studies on human eyeblink conditioning have also found hippocampal

activity (Blaxton et al., 1997; Schreurs et al., 1997). Deadwyler and colleagues (1979)

conducted an EP study investigating auditory differential conditioning in rats. The rats

were taught to bar-press for water when they heard one tone (CS+) and not to when they

heard another tone (CS-). An increased number of EP firings was observed in the dentate

&gyrus only during CS+ presentations. Hippocarnpal lesions in humans have been found

to be related to poorer performance on more complex or demanding conditioning tasks

such as: trace conditioning, conditioned inhibition, latent inhibition and blocking

(Blaxton et al., 1997). These data, in addition to the data obtained from the current study,

support the hypothesis that the hippocampal region is involved in forming relational

representations in associative learning tasks.

In the current snidy right BA 35 decreased in activity across Tl unpaired scans. It

is unclear whether this pattem of activity is related to hippocampal , v s involvement in

extinction. In human eyeblink conditioning PET study by Schreurs et al. (1997) lefi B A

3 5 activity was found to show an inverted U shaped pattern of activity across baseline,


leaming, and extinction scans. This pattern of left BA 35 activity is sirnilar to that

observed in right BA 35 in phase 2 of the current study. Therefore as in the Schreurs et

al. (1997) study BA 35 was found to increase with leaming and decrease with extinction

in the current study. The decreased right BA 35 activity during Tl unpaired scans rnay

reflect the extinction of the Tl+visual relational relationship. However, fiom the current

data the functional role of hippocampaVparahippocampa1 g p s in the extinction process

is unclear. Possibly, the differential pattern of activity observed in BA 35 in phase 2 is

involved in a single leaming process that entails assigning a positive associative value to

T2 and a negative associative value to Tl . This interpretation may also hold for the other

brain regions that exhibited Pattern C activity.

Nonlearners: Failure to l e m tone-visual associations.

It is unclear why half of the subjects in the current study did not leam the tone-

visual associations. Demographically these two groups were the sarne. One possible

reason may be that learners and nonlearners adopted different task strategies or attended

to the stimuli in a different way (Proulx and Picton, 1978). Proulx and Picton (1 978)

noted that subjects designated as "learners" attended to al1 sensory stimuli in the task

whereas "nonlearners" focussed primarily on the target stimulus (the UCS). It is possible

that this was also bue of learners and nonleamers in the current study.

The between group SPM contrasts coding for group interaction effects found

group differences in activity in numerous brain regions. The standardized mean activity

voxel plots of nonlearners (for the voxels that were important for leaming in learners)

indicated the majority of these regions did not show any interpretable pattern of activity.

There were two exceptions. In nonleamers the standardized mean activity plot for right


hippocampal g p s indicated that this region was more active during scans that included

T2. In learners however this region showed a learning related increase in phase 2.

Learners had increased activity in this region across T2 paired scans and decreased

activity in this region across Tl unpaired scans. Perhaps, the nonlearners did not engage

this region appropnately during leaming and that was why they failed to l e m the

associations.

In phase 2 right BA 19 (fiom leamers' LV 3) showed more activity during Tl

unpaired scans versus T2 paired scans in nonlearners. In learners this region showed an

increase in activity across T2 paired scans and a decrease in activity during TI unpaired

scans. Perhaps nonleamers failed to appropriately direct their visual attention to T2

paired scans and failed to notice the T2+visual association. It is important to note that

these standardized mean activity patterns in nonlearners were not statistically significant.

It is clear that the nonlearners' brain activation patterns differed from lemers.

This indicates that nonleamers failed to engage the networks necessary for leaming the

task. However, the answer as to why these subjects failed to engage these networks

cannot be answered from the current data. Nonleamers could tell the two tones were

different; however; it is possible that they could not discriminate them as effectively as

learners because they were less alert or more fatigued than Iearners. It is also possible

that nonlearners were considerably more or less anxious tfian learners. Level of anxiety

has been found to influence learning (Corr ; Pickering & Gray, 1997). A future

consideration is to obtain arousaVanxiety measures from subjects pnor to and during their

participation in associative learning studies. This could help us understand why some

subjects learn particular tasks better than others.


Conclusions

The PET data analysis did not yield a pattem of brain activation that was related

to the initial acquisition phase (phase 1). Perhaps in learners the initial acquisition was

acquired too rapidly (figure 1 indicates that learners had Ieamed the first association by

scan 1) or was confounded by the time effect. This rnight have caused a failure to

identiQ a unique pattem of brain activity relating to phase 1. Since the PET data did not

yield a pattern of brain activity related to phase 1, the current data cannot address the

issue of whether initiai acquisition and reacquisition are rnediated by comrnon brain

regions. The behavioral data indicate that the first association may have been easier to

acquire than the second because there was a larger difference in the Iearners' mean RT

during Tltvisual and T2+visual trials in phase 1 versus phase 2.

The standardized mean activity level graphs indicate that in phase 2 leamers

expressed distinct patterns of brain activity related to learning the TZ+visual association

(Pattern A), leaming the nonpredictive value of T l (Pattern B), and learning both the

T2+visual association and the nonpredictive value of T 1 (Pattern C). Therefore, leaming

the positive associative (predictive) value of T2 and the negative associative

(nonpredictive) value of TL appear to be mediated by fùnctionally distinct brain regions.

Since learning the negative associative value of T l is part of the extinction process, it

seems fair to state that (re)acquisition and extinction processes are fuuctionally different.

The current study involved conditioning of a voluntary response. Conditioning

of a voluntary response is more complex than autonornic or skeletal conditioning because

autonomic and skeletal conditioning involve conditioning of reflex whereas as


conditioning of a voluntary response involves conditioning of intendedAearned behavior.

This additional compiexity of the current study may explah why cognitive awareness of

the associative contingencies was necessary for subjects to learn the task. In the current

study there were more numerous brain regions involved in leaming the task compared to

human eyeblink conditioning studies (Schreurs et al., 1997; Blaxton et al., 1996). This

implies that as associative learning task becorne more complex additional brain regions

are involved in the leaniing process. In addition. it is possible that the additional brain

regions involved in learning the current task are involved in mediating cognitive

awareness during learning. A future research consideration rnight be to determine

whether the additional brain regions employed in learning the current task are also

involved in episodic learning which also requires cognitive awareness. Furthemore, it

would interesting to see if the functional role of the additional brain regions in the current

study (compared to human eyeblink conditioning studies) are similar in episodic learning

studies .

Despite the behavioral differences between the current study ad previous human

eyeblink conditioning studies, there was a lot of overlap in the brain regions involved in

learning these two tasks. This indicates that much of the brain regions involved in

skeletal conditioning are also invoIved in conditioning of a voluntary response. This

implies that there may be a core set of brain regions involved across al1 learning tasks.

Possibly there is a continuum of brain regions involved in learning and as the cornplexity

of the learning task increases additional brain regions along this continuum are necessary

for learning. Future studies exarnining similarities and differences across simple and

more complex learnïng paradigms would help determine whether this possibility is m e .


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Wagner, A. R. (1971). Elementary associations. In H. H. Kendler & J. T. Spence

(Eds.). Essavs in Neobehaviorism: A Memorial Volume to Kenneth W. S~ence . New

York: Appleton Century Crofts.

Winocur, G. (1992). Conditional leaming in aged rats: evidence of hippocampal

and prefiontal cortex impairment. Neurobiolow of Aeine. l3(l), 13 1-5.


Woods, R. P., Cherry, S. R., & Mazziotta, J. C. (1 992). Rapid automated

algonthm for aligning and reslicing PET images. Journal of Cornputer Assisted

Torno.uaphv. 16,620-633.


A~pendix A

The visual stimulus that was used in the current experiment is presented below.

Acquisilion and Rcvcrsal of Totie-Visual Associaiioiis 76

Bclow is li lisiirig, by scaii block, of ~Iic ordcr of stiriiiilus prcscniaiioiis.

1 Scan Block 1 Prcscan Prescaii Scari Postscan Block 1 Block 2 Block 3 Block 4 V T2- Tl+ V Tl + Tl+ Tl + Tl+ T2- 1'2- TI+ T2- T2- V TI + TI+ TI+ T2+ TI+ Tl+ T2 - Tl+ V Tl+ 7'2- T2- T2 - V Tl+ V Tl 4- T2- T2- T2 - T2- TI + TI+ T2- T2+ T2- Tl+ T2- T2- T2+ T2t T2+ T2 -

T2t

Scaii Block 2 - - -

>rcscan Prcscan Scaii ~ostscati Block I Block 2 Block 3 Block 4

Scan ~ I o c k 3 I Scaii Block 4 Prcscan Prcscan Scan Poslscliii l~rcscan Prescaii Scari Postscan

-- Block 1 Block 2 Block 3 Block 4 l ~ l o c k 1 Block 2 aock 3 Block 4

&&, V = visual alotic trial, TI+ = Tl+visual trial, T2+ = T2+visual trial, T2- = T2 aloiic trial

Acquisition and Rcvcrsal of Tonc-Visual Associations 7 7

2B: Rcv-c of of- . . .

Scati Block 5 Prcscaii Prcscan Scan Posiscan 3lock I Block 2 Block 3 Block 4

T2+ TI- TI - TI - V T2+ v TI- TI + Tl - T2+ TI- T2+

T2+ V T2+ T2+ T2+ Tl - T2+ TI -

T2+ v Tl + Tl- TI - Tl - T2+ TI- TI+

Scaii Block 6 Prescan Prcscan Scaii Postscan Block I Block 2 Block 3 Block 4 T2+ T2+ TI - TI - T2+ TI + V T2-t. r I - TI - TI - TI - r2+ TI - V T2-1- r i - T2+ V T2+ rl- Ti - TI- TI+ Tl + Tl - V T2+ T2+ T2+ Tl - Tl - TI - Tl- V T2-t- T2+ T2+ TI - T2+ T2+ V TI + T I + T2+ TI-

T2-k TI+

Scati Block 7 Prcscaii Prcscan Scan Postscati 3lock I Block 2 Block 3 Block 4 r 1 + TI + T2+ T2+ r2+ T2+ T2-t V SI- TI - T2+ T2+ T l - TI- T2+ Tl- r l + TI - T 2 t Tl - V V T2t Tl- T2+ V r i - v T I + I'2+ TI - Tl - TI- Tl + 'SI- T2+ Tl - TI- T2+ T2+ 'PZ+ V TI - Tl - r l- T2t T l+

T2+

Scari Block 8 'rcscan Prcscan Scati Posiscan 3lock I Block 2 Block 3 Block 4 r2+ T2+ Tl - Tl - r2+ T I + v TZ+ r l - T I - T I - T I - r2+ TI- V T2+ r l - T2+ V T 2 t r l - T I - TI - T I + r i+ T I - v ~ 2 + r2+ ~ 2 + TI - TI - ït - Tl - V T2+ r2+ T2+ Tt - T2+ r2+ v TI + r l+ T2+ Tl -

T2+ Tl+

V = visual alone trial, TI+ = Tl+vistial trial, T2+ = TL+visiial trial, Ti- = TI alone trial


Amendix C

The individual subject's rnean RT (msec) by scan block gaphs are enclosed for learners

and nonleamers. The first eight graphs are those of leamers and the last eight graphs are those of

nonlearners. Below are the figure captions for the graphs included in this appendix.

Fiwre Captions C

Figures 1C to 8C. Graph depicting rnean RT (msec) by scan block for each learner. In phase 1

(scans 1 to 4) the subject is faster on Tl+visud trials versus other trial types. In phase 2 (scans 5

to 8) the subjects' RT to Tl+visual trials increase and RT to T2+visual trials decrease. The

visual alone trials are always the slowest. The error bars represent 95% confidence intervals.

Fimires 9C to 16C. Graph depicting mean RT (msec) b y scan block for each nonlearner. The

subject's RT to Tl+visual and T2+visual trials are relatively the sarne across the experiment and

are not influence by expenmental phase. The visual alone trials are always the slowest. The

error bars represent 95% confidence intervals

Mean RT (msec)

> C E S E - S 6

Mean RT (msec)

Mean RT (msec)

Subject ID Mean RT (msec): Trial Type by Scan Block Graph

1 2 3 4 5 6 7 8

Scan Block

Mean RT (msec)

Subject ND Mean RT (msec): Trial Type by Scan Block Graph

1 2 3 4 5 6 7 8

Scan Block

Mean RT (msec)


Table 1: Subiect Information and Desimation as a "Learner" or "Nonlearner".

Learner

Nonlearner

Nonlearner

Learner

Learner

i ~ e a r n e r

Nonlearner r

HJ

HK

ID

Learner m

1 AGE SEX EDU Debriefing Statements*

L

23

I 1 1

35 1 F 1 16 l~ t a t ed in the first half T I predicted a visual event & in

26

3 O

20

13 M

M

Knew it was a Iearning task. Noticed there 1 were Tl+visual, T2+ visual and visuai alone triais. Did not notice any tone-visuai association.

M

F

24

20

19

13

M

24

Did not notice any tone-visuai association. Thought stimuli were randomly presented.

Did not notice any tone-visual association.

M

19

17

M

2 1

the second hdf T2 did. Noticed that there was a Tl+visuai and a Tî+visual

15

F

M

association in the experiment. Noticed that there was a T2+visuai association.

17

F thought this occurred randornly across the experiment. Did not notice any association between tones and

20

Did not say whether there was a Tl+visual association. Stated in first 4 blocks T l predicted the visuai stimulus

15

20

and in the last 4 bIocks T2 did. Stated in the first half Tl predicted a visual event & in

15

F

2 1

Note. * The debriefing answers stated above were given in response CO the question: " Did you notice any particular relationship/association between the auditory stimuli and the visuai stimulus?"

the second half T2 did. Stated either T lo r T2 came before a visuai event; but

F

20

15

27 1 F

M

the visual stimulus Did not notice any tone-visual association.

14

M

Stated in the first half T l predicted a visual event & in the second half T2 did.

17

15

Did not initiaily notice any tone-visual association. But tater said T l sornetimes predicted a visuai event. Did not notice any tone-visuai association.

15 Stated in the first half TI predicted a visuai evenc & in the second half T2 did.

Acquisition alid Rcvcrsal of Tone-Visual Associations 96

Table2: 1x2 Repeated Measures Analysis of Variance for RT Data

Source - df RT to Tl+visual Trials RT to T2+visual Trials RT to Visual Alone Trials

Between Group Variables

Group 1 3.42 3.2 0.38

Subjcct(Group) 14 (27973.48) (24925.59) ( 19560.95)

Within Group Variablcs

Scan 3 2.17 2.45 0.6

Phase 1 4.77* 7.54* 14.89**

GroupXScan 3 0.73 1.77 1.72

GroupXPhase 1 5.17" 3.64 1.2

GroupXPhaseXScan 6 1 ,41 1 .O1 1 .1 1

Scan*Subject(Group) 42 (6 1 3.02) (592.34) (1553.17)

Phase*Subject(Group) 14 (49 83.09) (582 1.44) ( 1 628.24)

Phase*Scan*Subject(Group) 42 (962.42) (6 1 1.62) (963.85)

Note. Parenthetical values represcnt mcan square errors. *p < 0.05 **p < 0.01. -


Table 3: Statistical Strength of Within Group PLS LVs

Group ~aterit Variables

LV1 LV2 LV3 LV4 LVS LV6 LV7 - -- - - - pp

Learners Pemutntion Probability 0.00 0.1 1 0.09 0.77 0.9 1 0.7 1 0.73

s value of LV 0.42 0.15 O . 12 0.09 0.08 0.08 0.07 Nonlearners

Permutation Probnbiliîy 0.00 0.12 0.49 0.80 0.86 O. 53 0.69

Note. Permutation Probnbility refers to permutation test results and represents the probability that a reordered dl value exceeded the original dl value for a particular LV. The s valire ofLV represents the amount of variance within the cross correlation matrix S that was accounted for by each LV.


Table 4: Local Maxima from LV1 for Leamers

Stereotaxic Coordinutes X Y Z Gyral Location BA

Brain Regions that show a 48 -28 -12 Middle Temporal Gyms 20 decrease in rCBF across scms 48 -46 -28 Cerebellum

Rig fit Henrisp frere 48 -10 44 Precenûai Gyms 4 o r 6 6 -28 36 Cingulate Gyms 31 6 46 44 Superior Frontal Gyms 8 2 -90 28 Cuneus 19

kfr Het~tispltere -10 -46 36 Precuneus 31 or 7 -14 -80 32 Cuneus 19 - 16 -30 4 Thalmus* -20 -6 -20 Parahippocampai Gyms 28 or 36

Brain Reglons thst sliriw an 22 -1 O 40 Cingulate Gyrus 24 increase in rCBF across scans 20 66 8 Superior Frontal Gyrus 10

Righ t Henlispliere 4 -60 -28 Cerebellum Left Hetrrispltere -6 -52 -8 Cerebellum

-28 22 -1 2 Inferior Frontal Gyrus 47 -34 -70 O Inferior Occipiral Gyrus 18 -36 -22 16 Triverse Teni~oriil G m s 41

Note. The stereotaxic coordiriates are measured in mm. Only local maxima witli a p<0.001 are presented. Gyral locations and Brodmann Areas (BA) were determined by reference to Ehirach & Tournoux (1988), * Posterior nucleus of oie Thdamus


Table 5: Lociil, Maxima from LVl for Nonleiuners

Stereotaxic Coordinates X Y Z Gyrsl Location BA

Brtiin Regions thut show a 18 O -4 Putmen decrease in rCBF across scrtns 14 62 20 Superior Froriîiil Cyrus 10

Riglit Herriispiiere O -46 -8 Cerebellum Left Hen~ispheril -1 8 -2 8 24 CinguIate G p s 23

-18 2 -8 Putamen -28 -60 16 Middle Tempord G G ~ s 39 -42 22 -12 Inferior Frontal Gyms 47 -42 52 8 Middle Frontal G m s 46

Brain Regions that show un 62 -3 8 O Middle Temporal Gyms 21 incrwse in rCBF across sclins 56 -64 O In ferior Tempord G p s 37

Riglit Herrr ispltare 44 G -28 InferiorIMiddle Temporal Cyrus 2 1 36 -70 -20 Cerebellum 20 8 -28 Uncus 28 2 -98 16 Cuneus 18

Lef, Hetttisphere -4 98 20 Cuneus 18 - 14 40 40 Cirigulate Gyrus 3 1 -24 40 -8 Inferîor Frontai Gyms 47 -28 28 48 Superior Frontal Gyms 8 -3 2 -44 4 Middle Temporal Gyrus 2 1 -34 -14 -28 Inferior Temporai Gyrus 20 -48 -82 -4 Inferior Occipitül Gyms 18

Note. The stereotaxic coordinates x e rneasured in mm. Only local maxinia with a p<0.001 iire presented. Gyral locations and Brodimn Areas (BA) were determined by reference to Talairach & Toumoux (1988).


Tablç 6: Local M'wim ïrom LV 2 for Lemers

Stereotaxic Coordinutes X Y Z Gyral Locution BA

Brain Regions that show u decretise in rCBF as 60 -4 16 Precentral Gyrus 4 subjects learn the T2 + visual ussociution 28 18 -28 Superior Temporal Gyrus 38

Right Hemispliere 26 -26 - 12 Hippocampal Gyrus 3 5 20 20 16 Middle Frontül Gytus 45 16 8 -12 Subcollosal Gyms 25 8 -70 -20 Cerebellum 6 54 -16 Dorsal Frontal Gyrus 11

Lefi Heniispfiere -14 -54 -24 Cerebellum Brain Regions that show a increrise in rCBF as 3 2 2 24 Inferior Frontal Gyrus 44 subjects leurn the T2 + visual rissociiition 2 8 -62 36 Precuneus 19

Rigitt Heniispitere 32 -60 32 Inferior Parielal Lobule 40

- 36 12 36 Middle Frontai Gyms 8 L.t$? Henr isphere -44 4 36 Precentrd Gyms 6

Note. The stereotaxic coordinates üre measured in mm. Only local m i m a wiîh a pe0.001 are presented. Gyrül locations and Brodmmn Areas (BA) were determined by referencc to Talairach & Tounioux (1988).


Table 7: Local Maxima froni LV 3 for Lemers

Stcreotaxic Coordinates X Y Z Cyral Location

Brain Regions that were more active during the last 50 10 36 Middle Frontai Gyrus 9 phase 2 unpaired scan 48 2 44 Precenîrai Gyms 6

Right Hettiispltere 44 32 8 Iiiferior Frontal Gynis 46 2 -16 12 ?rialümus*

Lefi Heniisphere -18 34 - 16 Middle/ Dorsai Frontal Gyms 11 -54 -46 28 Inferior Pau-ietül Lobule 40

Brliin Regions thnt were more active during the lut 30 -66 4 Middle Occipital Gyms 19 phase 2 puired and the first phase 2 unpaired scsns 28 -40 -1 6 Cercbellum

Rigitt Heniispliere 20 -98 IG Middle Occipital Gyms 18 Lefi Herriispltere -30 -52 -1 6 Cerebellum

-42 -60 4 Middle Temoral Gvrus 21

Note. The stereoîmic coordinates are measured in mm. Only locd maxima with a p<0.001 are presented. - Gyral locatioris and Brodmann Areas (BA) were determined by rcference 10 Talairach & Tournoux (1988). *Dorsornediai nucleus of Lhe thalmus

Acquisition and Rcversal of Tone-Visual Associations 102

Table 8: Local Maximi Srom LV 2 for Norileimers

Stereotuxic Coordinates X Y Z Gyral Location BA

Regions that showed a decrezise in rCBF ucross unpaired 46 -1 8 32 Postcen~ral Gyms 1 or3 scans blr were more active during the middle two paired scans 32 4 0 Laterai Sulcus

Rigltt Henrisplrere 8 -70 -20 Cerebellum LeJi Heniisplrere -4 6 20 Cingulater Gyrus 24

-24 -66 -24 Cerebellum -32 34 20 Supramginai Gyrus 40

Regions that showed an increuse in rCBF across unpaired scons & were less active during the middle two psired scans 40 48 -1 6 Fusiform Gyrus 37

Riglit Heniispliere 6 50 40 Dorsal Frontai Gyrus 8 Lelt Hetriispltere -16 54 36 Superior Frontal Gyrus Y

-16 -94 O Lingual Gyms 18 -18 12 12 Caudale Nucleus -1 8 -56 O Lingual Gynis 1 Si -22 20 - 12 Inferior Frontal Gyrus 47

Note. Tlie stereohxic coordinates are measured in mm. Only local maxima with a pe0.001 are presented. Gyrd locations atid Brodmann Areas (BA) were determined by reference to Talairacti & Tounioux (1988).

Acquisitiuri and Reversal of Tone-Visual Associations 103

Table 9: Voxels of Interest Extracted from Lemers' LV2 and LV3

Stereotaxic Coordinates X Y Z Gyril Location RA

Voxels Extracted from LV 2 Lefi Herriisplren -44 4 36 Left PrecentnI Gryus 6

-14 -54 -24 Left Cerebellum cereb Rigit t Hentisptlere 26 -26 - 12 Right Hippocmpal Gyrus 35

28 -62 36 Riglit Precuneus 19 3 2 -60 32 Right Inferior Parietai Lobule 4 O

36 12 36 Right Middle Frontai Gyms 8

60 -4 16 Right Postcentrül G yms 4

Voxels Extracted from LV 3 -54 -46 28 Left Inferior Parietal Lobule 40

&fi Herrlispliere -4 2 -60 4 Lefl Middle Tempord Gyms 2 1

Righi Heniisp/ier.e 2 -16 12 Riglit Thiflamus* 20 -9 8 16 Wght Middle Ocippital Gyrus 18

3 0 -66 4 Right MiddIe Ocippitai Cyrus 19

4 8 2 44 Right Preccritral Gyrus 6

50 -10 36 Right Middle Fronkd Gyms 9

Note. The stereotaxic coordhatcs ire measured in mm. OnIy local maima with a p<0.001 are presented. - Gyral locations and Brodmii Areaq (BA) were detennined by referencc to Tdüirach & Tournoux (1988). *Dorsornedial nucleus of the thalmus


Figure Captions

Fimire 1. Mean RT (msec) to the three trial types requiring a response across scan blocks. Part

(a) presents the mean RT for the eight learners. Part (b) presents the mean RT for the eight

nonlearners. The learners had faster RTs for Tl+visuai trials versus the other two trial types in

phase 1 of the experiment and had faster RTs for T2+visual trials versus the other two trial types

in phase 2 of the experirnent. The nonlearners' RT to Tltvisual and T2+visuaI trials were

sirnilar across the experirnent and was not effected by the experimental phase. The error bars

represent the 95% confidence interval for each data point. The 95 % confidence intervals for this

graph and al1 subsequent graphs were calculated using the following formulae: - X + I.B6{SD/sqrt(n)]. and where standard deviation = SD s q r t [Z (X -!?)2/n-1]

andn=8.

Figure 2. Mean RT (msec) of learners versus nonlearners for T l+visual and T2+visud trials.

Part (a) presents the leamers' and nonlearners' mean RT to Tl+visual trials across scan blocks.

Part (b) presents the learners' and nonlearners' mean RT to T2+visual trials across scan blocks.

These graphs help clarify the behavioral phase-by-group effects for these trial types. The

Tltvisual phase-by-group effect was due to an increase in the learners' RT to this trial type in

phase 2. The near significant T2+visual phase-by-group effect was to a decrease in the learners'

RT to this trial type in phase 2. The error bars represent the 95% confidence interval for each

data point.

F i a r e 3. Latent variable 1 (LV 1) for the learners. Part (a) shows the singular image for regions

differentially active dunng baseline versus task scans. The white regions represent areas of

positive brain salience and black regions represent areas of negative brain salience. Threshold =

2; brain regions identified were two SE values greater than the mean and had an approximate

Acquisition and Reversal of Tone-Visual Associations 10s

pe0.05. Part @) shows the scatterplot of brain scores across scans. The data for paired scans are

represented by white squares on the graph and the data for unpaired scans are represented by

black squares. This scatterplot indicates that regions of positive brain salience represent areas

that showed a decrease in rCBF across scans. Regions of negative b r in salience showed an

increase in rCBF across scans. T 1- scan condition refers to scans in which T 1 alone and visual

alone trials were presented. T l+ scan condition refers to scans in which T l+visuai trials were

presented. T2- scan condition refers to scans in which T2 alone and visual alone trials were

presented. T2+ scan condition refers to scans in which T2+visual trials were presented. The

same terrninology will be used to refer to scan conditions in subsequent graphs.

Fimire 4. Latent variable 1 (LV 1) for the nonleamers. Part (a) shows the singular image for

regions differentially active during baseline versus task scans. The white regions represent areas

of positive brain salience and black regions represent areas of negative brain salience. Threshold

= 2; brain regions identified were two SE values greater than the mean and had an approximate

pe0.05. Part (b) shows the scatterplot of brain scores across scans. The data for paired scans are


black squares. This scatterplot indicates that regions of positive brain salience represent areas

that showed an increase in rCBF across scans. Regions of negative brain salience showed a

decrease in rCBF across scans.

Fimire 5. Latent variable 2 (LV 2) for the leamers. Part (a) shows the singular image for regions

differentially active during baseline versus task scans. The white regions represent areas of

positive brain salience and black regions represent areas of negative brain salience. Threshold =


pc0.05. Part @) shows the scatterplot of brain scores across scans. The data for paired scans are



black squares. The scatter plot shows that this LV identifies brain regions that showed a change

in rCBF between scans 4,5 and 7. Regions of positive brain salience showed a decrease in rCBF

as subjects leamed the second, T2+visual, association. Regions of negative brain salience

showed an increase in rCBF as subjects acquired the T2+visual association.

F i a r e 6. Latent variable 3 (LV 3) for the leamers. Part (a) shows the singular image for regions

differentially active dunng baseline versus task scans. The white regions represent areas of

positive brain sdience and black regions represent areas of negative brain salience. ThreshoId =


pc0.05. Part (b) shows the scatterplot of brain scores across scans. The data for paired scans are


black squares. The scatter plot indicates that this LV identifies brain regions that were

differentially active during scan 8 versus scans 6 and 7. Regions of positive brain salience were

more active dunng the last unpaired scan. Regions of negative brain salience were more active

during the first unpaired scan in phase 2 and the last paired scan in phase 2.

Fimire 7. Latent variable 2 (LV 2) for the nonleamers. Part (a) shows the singular image for

regions differentially active during baseline versus task scans. The white regions represent areas

of positive brain salience and bIack regions represent areas of negative brain salience. Threshold

= 2; brain regions identified were two SE values greater than the rnean and had an approximate

p<0.05. Part (b) shows the scatterplot of brain scores across scans. The data for paired scans are


black squares. The scatterplot indicates that this LV mainly identifies brain regions that were

differentially active d u h g scan 2, 3, and 5 versus scans 1 and 7. Regions of positive brain


salience were more active during scans 2 ,3 , and 5. Regions of negative brain salience were

more active during scans 1 and 7.

Fimire 8. Standardized mean activity level for brain regions showing Pattem A activity in

leamers. These brain regions showed more activity dunng either both T2 paired or both Tl

unpaired scans compared to the other scans in phase 2. The standardized values of activity were - calculated using the following formula: Z = X - X / SD where SD=standard deviation and was

calculated as mentioned in Figure 1. The standardized mean activity level of voxels in Figures 9

through 13 were calculated in the same manner.

F i a re 9. Nonlearners' mean activity leveI for brain regions that showed Pattem A activity in

leamers. These regions do not show an interpretabIe pattern of activity in nonleamers.

Fiwre 10. Mean activity level of brain regions showing Pattern B activity in learners. These

brain regions showed decreased activity across T2 paired scans and increased activity across Tl

unpaired scans in phase 2.

Fipure 1 1. Nonlearners' mean activity level for brain regions that showed Pattem B activity in

learners. These regions do not show a consistent pattern of activity in nonleamers.

Figure 12. Mean activity IeveI of brain regions showing Pattern C activity in learners. These

brain regions showed increased activity across T2 paired scans and decreased activity across TI

unpaired scans in phase 2.

Figure 13. Nonleamers' mean activity level for brain regions that showed Pattem C activity in

leamers. These regions do not show a consistent pattern of activity in nonleamers.

A, Group Mean RT (msec) Data for Learnan: Trial Type by Scan Block

Scan Block

I t

Group Mean RT (msec) Data for Nonlearners: Trial Type by Block

Scan

Scan Block

A. Mean RT(rnsec) to Tl+visual Trials: Learners Versus Nonlearners

Learners Nonlearners

1 2 3 4 5 6 7 8

Scan Block

Mean RT (msec) to TZ+visual Trials: Learners Versus Nonlearners

Scan Block

Scan Condition

T l + T2- T l + T2- T2+ T l - T2+ T l -

Scan Condition

Standardized activity level O W b L i. I C j O O N h i o t i o i n o u i o v i o t r o i n O O O O O O O O O O O A

Nonlearners' graph of voxels that showed Pattern A activity in learners

H Right O Right Ilil Right @J Right -

Scan Condition

Standardized activity level

Standardized mean activity level

--

'El

Nonlearners' graph of voxels that showed Pattern C activity in learners

Right BA O Right BA

Left BA 2 PI Right BA

Scan Condition

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