Hemispheric specialization Wada test: injection of sodium amytal (a barbiturate) into carotid artery selectively puts to sleep left or right hemisphere for several minutes and thus is used to differentiate left vs right hemispheric function. It shows that hemispheres have both language & emotional differences. a. Principally used for determining the side of language dominance in pre-surgery epileptic patients. Patient instructed to speak continuously while sodium amytal is injected. If hemisphere dominant for speech is affected, patient stops speaking & does not respond to a command to continue. Language lateralization study (1977): 96% of right handers have left representation 4% of right handers have right representation 70% of left handers have left representation 15% of left handers have right representation 15% of left handers have bilateral representation Studies of children indicate that childhood left hemisphere lesions may cause shifts to right or bilateral representation in either right- or left-handers.

Lesion evidence for left hemispheric lateralization Usually patients don't have a single impairment, but show a complex pattern of symptoms. Most patients show the following symptoms due to left hemisphere lesion: i. reading ii. writing iii. understanding & speaking iv. verbal ideation v. verbal memory

The left hemispheric specialization for language, determined first by lesion evidence, has largely been verified by neuroimaging.

Lesion evidence for right hemisphere lateralization i. manipulospatial skills -- e.g. block arrangement ii. representation of non-verbal form e.g. abstract drawing, patterns of dots, line figures iii. face recognition iv. recognition of musical stimuli

Split-brain experiments a. Anatomy Corpus Callosum -- bidirectional fiber pathway connecting left & right cortex A.C. -- between medial temporal lobe structures, e.g. amygdala As a general rule, fibers from a particular layer of cortex in 1 hemisphere project to & receive from the same region & layer of opposite hemisphere. b. History Before 1960's, role of C.C. not known. Then patients started having C.C & A.C cut to prevent spread of epileptic seizures. In a series of studies in 1960's, Roger Sperry with Michael Gazzaniga tested humans with C.C. sectioned. Sperry had worked with animals with C.C. sections. He cut optic chiasm in addition to C.C. & A.C. Animal trained to make visual discriminations with 1 eye. When tested with untrained eye, they behaved as if completely naive. The training experience was limited to 1 hemisphere.

c. Hemispheric differences in split-brain patients Human patients with split brains normally do well in real-life situations because both hemispheres normally obtain common info. E.g. as eyes scan environment, each hemisphere receives complete representation of surroundings. Sperry & Gazzaniga set up expt so hemispheres received different info. Used brief tachistoscopic visual stimuli projected to rt or left vis. field. Transmitted only to opposite hemisphere (optic chiasm not cut)

Classical expt -- apple presented to right visual field & asked what was seen -- answered "apple". When presented to left visual field, denied seeing anything. If prompted, would guess or make up answer. However, patient could identify the object by pointing to it or picking it out manually from several others -- using tactile cues. Suggests that learning, memory, motor coordination intact in right hemisphere.

Right hemisphere almost totally incapable of language output, but can process simple linguistic input. Does have some primitive understanding of language. Many words projected to right hemisphere can be read & understood. Letters D-O-G projected in left visual field, patient could pick model of dog with left hand. More complicated commands not understood. d. Hemispheric competition Hemispheres sometimes are seen to interfere with each other.

When patients do block task with the left hand, controlled by the right hemisphere, the left hemisphere sometimes tries to interfere with task. It can impede successful completion by interference with right hand.

e. Chimeric face experiments What happens when a split-brain patient is put in a situation where either hemisphere can take control? Levy, Trevarthen & Sperry (1972) showed chimeric faces to split-brain patients.

When shown picture with fixation pt in middle, verbal report is that of right side of face. As expected, left hemisphere responds verbally. But, what happens when non-verbal response is required? Either hemisphere can direct behavior. Presumably either side could respond when asked to point when shown a series of whole faces. However, patients pick the left-sided face (using right hemisphere). Tasks that can be broken into logical elements in an analytic way are best performed by left hemisphere. Tasks that require global processing of whole input are best performed by right hemisphere.

Cortical hierarchy of perceptual networks 1) Primary cortical areas in each sensory modality send fiber projections (by pyramidal cell axons) to higher-order areas that are specialized for processing more complex information in the same modality. 2) Higher-order areas comprise unimodal association cortex within each modality. 3) Transmodal association cortex integrates input from multiple modalities.

Cortical Organization

Primary Unimodal Association

Transmodal Association

Hierarchical level low middle high

Anatomical organization

single areas

multiple distributed

areas

convergence regions

Module size small intermediate large Receptive field size small large none(?)

Perceptual representation

elementary sensory features

complex sensory features

word meaning

Categorical example edges faces names

Lesion effect anopsia

cortical achromatopsia;

visual object agnosia

semantic aphasia

Connectionist models of cognition The basic problem of object recognition is to determine the nature of the isomorphic relation between the connectional hierarchy in the sensory brain and the hierarchical representation of perceptual processing that leads to object recognition. To better understand the problem, computational neural models of cognition have been proposed. Artificial neural networks (ANNs) have been trained to perform object recognition. In psychology, ANNs are called connectionist models. Connectionist models of cognition are ANNs that are useful for studying object recognition. Common features of connectionist models:

1) they assume the distribution of knowledge in assemblies of units, neurons, or nodes that represent the component elements of knowledge.

2) the nodes are interconnected in networks by synapses.

3) the networks are layered. 4) some connections between layers are reciprocal,

supporting reentrant processing. 5) layers are connected by parallel, convergent, and

divergent connections. 6) networks learn by modification of synaptic weights. 7) they learn by unsupervised learning: synapses are

strengthened (weights increase) by temporal coincidence of pre- and post-synaptic activity.

Object recognition implies that learning of an object has previously occurred. Therefore, the sensory analysis used in perception is thought to be guided by perceptual knowledge stored as perceptual network memories.

The argument for passive visual perception Hypothesis:

The visual stimulus operates like a stamp or imprint. Reflected light patterns impinging on the retina determine retinal activity; these light patterns are faithfully reproduced in retina, LGN, & V1.

Implication for perception: All the information needed for visual perception is presented to the retina from the external world. Visual perception only requires progressive processing of visual information, and so depends only on feedforward processes.

Proposed neural mechanism: Low-level visual features are detected in V1, and progressively more elaborate features are detected in higher visual areas. Visual recognition occurs when high-level symbolic features are compared with features stored in memory.

Supporting evidence: Anatomical: the bottom-up projection from retina to V1 and higher visual areas is retinotopic, suggesting faithful reproduction of light patterns. Electrophysiological: single-neuron recording in visual cortex shows that V1 neurons are sensitive to low-level

visual features & higher-level visual neurons are sensitive to more complex visual features. Lesion analysis: lesions along the path from retina to V1 produce blindness for the part of the visual field corresponding to the lesioned cells; lesions in visual areas outside V1 produce "mind-blindness" -- lack of visual comprehension.

The argument for active visual perception Hypothesis:

The visual sensorium is constantly changing, and real-world visual stimuli are ambiguous and indeterminate. Not all the information needed for visual perception is presented to the retina from the external world, and retinal activity does not specify the light pattern to be perceived.

Implication for perception: Visual perception requires information supplied by the brain, and so depends on feed-back (top-down) as well as feed-forward (bottom-up) processes.

Proposed neural mechanism: Low-level activity patterns in V1 undergo progressive elaboration through an iterative feedforward-feedback cycle involving higher visual areas. Visual recognition occurs when high-level visual areas produce patterns from memory representing hypotheses that are consistent with low-level activity patterns.

Supporting evidence: Physical: the flux of photons at the retina is highly variable in time. Anatomical: top-down inputs to V1 are more prevalent than bottom-up inputs. Perceptual: visual perception can occur without visual stimulation, as in imagery.

ART The Adaptive Resonance Theory (ART) is a view of how active perception may be accomplished. Grossberg & Carpenter developed ART beginning in the 1980s. ART has spawned a class of computer model that captures some of the cardinal features of active perception. ART models are trained to perform object recognition, i.e. after learning, they recognize a category of sensory patterns as coming from a common object. Active perception in the cortex 1. Perception by the brain involves an iterative matching between a set of sensory impressions and pre-established memory networks:

a) If an adequate match occurs, the matching network becomes the percept

b) If no adequate match occurs, a new network is created and becomes the percept

2. Perceptual categorization of sensory information does not require consciousness, and we are not normally aware of the different processes, executed in parallel, that underlie perception. 3. Perceptual processing is usually guided by selective attention. Attentional perception is conscious and is executed sequentially. However, this does not mean that

we are conscious of all the steps involved in perceptual processing. Perhaps, we are only aware of the results.

The Gestalt Perception is seen to consist in the classification of sensory items by the binding of features according to Gestalt grouping principles. Examples of Gestalt grouping principles:

a) common motion Motion example: http://www.biomotionlab.ca/Demos/BMLwalker.html The term “gestalt” has come to mean a pattern of elements unified as a whole with properties that cannot simply be derived from the parts. Figure-Ground Perception Visual perception involves the recognition of objects (figure) as distinct from their backgrounds (ground). Objects appear to “stand out” from the background. Figure-ground perception in vision usually depends on edge assignment and how that effects shape perception. It may be bistable, meaning that either of two (stable) figures may be perceived. This may occur when a visual pattern is too ambiguous for the visual system to recognize it with a single unique interpretation.

Perceptual binding Perceptual binding refers to the joining together of the associated sensory features of a perceptual object into a gestalt. A perceptual network for the Gestalt represents the perceptual object. The object is categorized, and perception of the object occurs, when a perceptual network is activated. A neural binding mechanism is needed to join together the neural activity in different parts of the perceptual network.

What is the neural mechanism of neural binding? A. Evidence from neuroelectric activity Binding has been proposed to operate by the phase synchronization of oscillatory activity from columnar modular assemblies. Assembly oscillations are best observed in the LFP, EEG, and MEG. Oscillations are classified by frequency: delta: 0-3 Hz theta: 4-7 Hz alpha: 8-12 Hz beta: 13-30 Hz gamma: 31-100 Hz When the waves of oscillation in different assemblies are aligned in time, they are called phase-synchronized. Assemblies do not communicate by LFP waves. They send pulse activity back and forth along axonal pathways. However, LFP phase-synchronization may be a sign of functional binding of assemblies. B. Evidence from functional neuroimaging PET & fMRI (neuroimaging) studies are useful for detecting the activation of perceptual networks. Because of their slow time resolution, they do not directly image active networks.

Rather, they show the “ghosts” of heavily activated nodes (epicenters) of excitatory neuronal activity in the networks. These are the most heavily activated nodes of distributed memory networks. In neuroimaging studies of visual perception, the epicenters of perceptual networks for object categories appear in unimodal visual and multimodal association areas. The evidence from meta-analysis of neuroimaging studies is compatible with the idea that objects are represented at several hierarchical levels, from sensory to symbolic. For example, the meta-analysis of neuroimaging studies of color perception shows activation maxima in inferior occipital cortex. Meta-analysis of neuroimaging studies of color word presentation and color imagery have maxima further anterior in inferior temporal cortex.

Attention A cooperative duality of excitation and inhibition, also seen in sensory and motor systems, is utilized in attention:

1) enhancement of processing in selected networks (i.e. foci of attention or targets)

2) suppression of processing in competing networks (i.e. distractors)

In hierarchical systems, with both divergence and convergence from one level to the next, there may be critical nodes that could become bottlenecks to the flow of activity without there being control of their inputs. Attentional selection may be critical to prevent such bottleneck nodes from becoming overwhelmed by an excess of inputs. Attentional processing can be explained within the cortical system of cognitive networks without need for specialized structures dedicated to attention as a specialized function.

Perceptual attention Proper functioning of the cerebral cortex requires arousal by the “continuous inflow of nonspecific activating influences from several structures of the brain stem.” Attention is not arousal, but requires a minimal level of arousal. The mesencephalic reticular formation is an important source of diffuse excitatory cholinergic input to the cortex. It increases its activity in arousal from sleep to wakefulness, and is tonically active in the maintenance of vigilance and general alertness. The “control” of perceptual attention is a balance between bottom-up and top-down influences.

Bottom-up attention Bottom-up attention originates at the lowest levels of sensory processing and is determined by properties of sensory stimuli, such as saliency and novelty. That is, stimuli that are salient or novel will be processed with greater bottom-up attention. The classical example of bottom-up attentional processing based on saliency in vision is figure-ground separation, or pop-out. Bottom-up attentional control was studied extensively by Julesz, who used the name preattentive processing. According to Julesz, preattentive processing:

1) is fast 2) operates in parallel 3) is automatic (i.e. does not require consciousness)

Bottom-up processing of salient stimuli may be an elaboration in the cortex of the simple center-surround antagonism for contrast enhancement in the LGN discussed above.

Top-down attention Top-down influences occur during the processing of perceptual information as the selective modulation of cortical networks in perceptual hierarchies by those in the executive hierarchy or those higher in the perceptual hierarchy. Top-down influences in perceptual attention promote perceptual matching. Remember the ART model:

a) Sensory inputs gain access to sets of higher-level networks representing different perceptual categories.

b) Each categorical network performs a matching process with the sensory input signal.

c) The categorical networks compete, and the one with the closest match generates a top-down expectation signal that is sent to lower-level networks.

This process is a form of top-down attention because it selects certain categories for further processing, and blocks others. The executive hierarchy may also be a source of top-down attention. Executive networks may selectively “prime” specific perceptual networks (at different levels) that are consistent with ongoing behavioral performance.

The frontal lobe action hierarchy a) primary motor cortex (Brodmann area 4) is at lowest

level b) premotor cortex (Brodmann area 6) is one level up c) prefrontal cortex (Brodmann areas 8,9,10,46) is at the

next level up d) hierarchical arrangement is based on ascending order

of generality of action and temporal dimension e) frontal areas receive projections from and send them to

posterior sensory areas – long connections link corresponding levels in action and perceptual hierarchies

f) prefrontal cortex also has reciprocal connections with limbic structures

Functions of the action hierarchy a) The primary motor cortex (M1) networks represent

distinct movements involving specific muscle groups. Muscles are grouped by intended action as well as by somatic location.

b) The premotor cortex contains networks representing motor trajectories and goals. Movement representations are more distributed than in M1. i) lateral premotor cortex (area 6b): cells tuned to

kinematic properties of movement; also contains mirror units that are activated by the observation of movements made by others.

ii) medial premotor cortex (area 6a or supplementary motor area – SMA): neurons are active before & during the execution of movement sequences: representations are thus defined by temporal order as well as spatial position. SMA is also the source of the readiness potential, a slow ERP wave that precedes self-initiated movements.

c) The prefrontal cortex contains networks representing plans of action. Prefrontal lesions impair functions that depend on temporal integration of sensory and motor information, including spoken language.

Subcortical motor structures Motor pathways from the brain terminate in the spinal cord. These pathways are pyramidal tracts (from neocortex) and extrapyramidal tracts (from other motor structures). Brainstem motor nuclei include the vestibular nuclei, the nuclei of the reticular formation, and nuclei of the substantia nigra. The cerebellum is a large structure of the motor system located behind the brainstem at the level of the hindbrain. It is a major subcortical motor structure, and is also involved in cognition (attention, language, planning). The basal ganglia is another major subcortical motor structure. It is composed of the striatum (putamen + caudate nucleus), subthalamic nucleus, globus pallidus, and substantia nigra.