frontoparietal activation during visual conjunction search...

Bottom-up Search, page 1

Frontoparietal Activation During Visual Conjunction Search: Effects of

Bottom-up Guidance and Adult Age

David J. Madden1,2,*, Emily L. Parks 1,2, Catherine W. Tallman1, Maria A. Boylan1 , David A.

Hoagey1, Sally B. Cocjin1, Micah A. Johnson1, Ying-hui Chou1,2, Guy G. Potter1,2,

Nan-kuei Chen1,3, Michele T. Diaz4

1Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710

2Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham,

NC 27710

3Department of Radiology, Duke University Medical Center, Durham, NC 27710

4Department of Psychology, and Social, Life, and Engineering Sciences Imaging Center,

Pennsylvania State University, University Park, PA 16803

Version: December 7, 2015

*Address correspondence to:

David J. Madden Brain Imaging and Analysis Center Box 3918 Duke University Medical Center Durham, NC 27710 Phone: 919-681-9345 E-mail: [email protected]


Abstract

Visual target identification relies on the combined influences of top-down attentional guidance

from task goals and bottom-up guidance from featural salience. We incorporated event-related

fMRI with a visual search paradigm to test the hypothesis that aging is associated with

increased frontoparietal involvement in both target detection and bottom-up attentional

guidance. Participants were 69 healthy adults, distributed continuously across 19-78 years of

age. The search target was defined by a conjunction of color and orientation. Each display

contained one item that was larger than the others. This size singleton was not informative

regarding target identity and thus provided a bottom-up form of attentional guidance. In the

behavioral data, mean reaction time (RT) increased linearly with age by approximately 3 ms per

year. Target-present RT was reduced by 40 ms when the size singleton corresponded to the

target. This bottom-up guidance effect did not vary significantly with age. The fMRI data

indicated that frontoparietal activation related to target detection was constant as a function of

age, as was the reduction in activation associated with bottom-up guidance. Left frontal eye

field (FEF) activation was correlated significantly with RT measures of both target detection and

bottom-up guidance. The FEF activation-RT correlation for bottom-up guidance varied

significantly with age and was evident only for individuals > 60 years of age. We conclude that

older adults have a different neural signature of bottom-up attentional guidance, relative to

younger and middle-aged adults, but that the age-related differences do not exhibit all the

features of a compensatory mechanism.


As Yantis (1998) noted, the distinction between top-down and bottom-up forms of

attentional control has a long history, reaching back at least to the description by William

James, in his Principles of Psychology (James, 1890), relating to the difference between active

and passive modes of attention. Yantis also emphasized that top-down and bottom-up aspects

of attention rarely operate independently but instead combine their influences within

measures of task performance. More concretely, in the context of visual search, current

theories characterize target identification in terms of the comparison between a template of

the target-defining features (e.g., color, orientation, size) and a salience map of the features

activated by the visual display (Eckstein, 2011; Theeuwes, 2010; Wolfe, 2007; Wolfe &

Horowitz, 2004). Each item in the display contributes to a weighted sum of activated features,

combining the top-down emphasis on target features, bottom-up visual salience, and noise. For

example, a salient display item (e.g., a color singleton) can be ignored if its distinguishing

feature is not relevant for target detection (Leber & Egeth, 2006; Yantis, 1993) (; cf. Theeuwes

& Burger, 1998), but observers may adjust their search strategy to include bottom-up guidance

from an irrelevant but salient feature (Bacon & Egeth, 1997; Proulx, 2007).

Neuroimaging studies using functional magnetic resonance imaging (fMRI) and related

techniques have revealed many clues to the neural mechanisms of visual attention (Freiwald &

Kanwisher, 2004; Kastner & Ungerleider, 2000; LaBerge, 1995; Shipp, 2004; Shulman, Astafiev,

& Corbetta, 2004). Several lines of research converge to suggest that top-down and bottom-up

forms of attentional guidance are associated with dorsal and ventral components, respectively,

of a distributed frontoparietal network (Corbetta & Shulman, 2002; Shulman et al., 2003; Yantis

et al., 2002). The components of the frontoparietal network are highly interactive, and the


cortical pattern of attention-related activation depends on the strength of the bottom-up signal

and task goals (Melloni, van Leeuwen, Alink, & Müller, 2012; Parks & Madden, 2013; Serences

et al., 2005; Vossel, Geng, & Fink, 2014). Neuroimaging research on the frontoparietal

attentional network has most often relied on measures of event-related cortical activation (e.g.,

Corbetta & Shulman, 2002; Shulman et al., 2003; Yantis et al., 2002), but differentiation of the

frontoparietal components is also possible with resting-state fMRI, which examines the

temporal covariation of fMRI data obtained without an assigned behavioral task (Anderson,

Ferguson, Lopez-Larson, & Yurgelun-Todd, 2011; Fox, Corbetta, Snyder, Vincent, & Raichle,

2006; He et al., 2007; Laird et al., 2011; Raichle, 2015).

In this experiment, we used both event-related and resting-state fMRI to investigate

frontoparietal attentional network activation, in relation to bottom-up attentional guidance

and adult age. Behavioral studies have demonstrated that, beyond an overall slowing of

information processing (Madden, 2001; Salthouse, 1996), specific age-related decline occurs in

some aspects of attention, especially those related to executive function and the inhibition of

distracting information (Craik & Bialystok, 2006; Hasher & Zacks, 1988; Kramer, Humphrey,

Larish, Logan, & Strayer, 1994; Kramer & Madden, 2008; McAvinue et al., 2012). In visual search

tasks, age-related decline is consequently more pronounced for conjunction search, with

associated demands on perceptual discriminability and distractor inhibition, relative to feature

search, in which a target can pop-out perceptually as a result of the high level of local contrast

between the target and distractors (Hommel, Li, & Li, 2004; Humphrey & Kramer, 1997;

Madden, 2007; Madden & Whiting, 2004). Other aspects of attention, however, particularly

top-down attentional guidance by target probability or semantic context, are maintained or


even enhanced during healthy aging (Madden, 1987; Madden, Spaniol, Bucur, & Whiting, 2007;

Madden, Whiting, Cabeza, & Huettel, 2004; Madden, Whiting, Spaniol, & Bucur, 2005). Few

studies have examined bottom-up attentional guidance specifically, but related research on

implicit learning and procedural memory (e.g., repetition priming) suggests that many forms of

implicit processing are spared during healthy aging (Howard & Howard, 2013; Howard, Howard,

Dennis, Yankovich, & Vaidya, 2004; Lustig & Buckner, 2004; Schmitter-Edgecombe & Nissley,

2002). Consistent with this, a color singleton that is not informative leads to a small but

significant decrease in search reaction time (RT), when it corresponds to the target, for both

younger and older adults (Madden, Spaniol, Whiting, et al., 2007; Madden et al., 2004) (cf.

Jonides & Yantis, 1988), suggesting a preservation of bottom-up attentional guidance.

Previous neuroimaging studies have found that aging is associated with increased

activation of the frontoparietal network, particularly the dorsal component, during cognitive

tasks (Dennis & Cabeza, 2008; Grady, 2012; Madden, Whiting, & Huettel, 2005; Spreng,

Wojtowicz, & Grady, 2010). This may represent a compensatory allocation of top-down

attention to support task performance (Cabeza & Dennis, 2012; Davis, Dennis, Daselaar, Fleck,

& Cabeza, 2008; Li, Gratton, Fabiani, & Knight, 2013; Park & Reuter-Lorenz, 2009; Reuter-Lorenz

& Cappell, 2008). In an fMRI study of visual letter search, for example, Madden et al. (2007)

found that although older adults exhibited a higher level of frontoparietal activation overall,

both age groups exhibited reduced activation to color singleton targets, relative to

nonsingleton targets, when the color singleton was likely to correspond to the target (guided

condition). Further, the guided condition was associated with an age group difference in the

correlation between event-related activation and the improvement in the identification of


singleton targets (decreased RT) relative to nonsingleton targets. The RT-activation correlations

comprised dorsal frontoparietal activation for older adults but occipital activation for younger

adults, suggesting an age-related increase in the involvement of the dorsal (top-down)

frontoparietal network supporting more efficient processing of singleton targets.

Madden et al. (2007) also found that when the color singleton was not likely to

correspond to the target (neutral condition), and thus bottom-up attentional guidance would

presumably be operative, frontoparietal activation was generally higher for color singleton

targets than for nonsingleton targets for both age groups. In theory, bottom-up guidance

should lead to a reduction in both target-related activation and RT, for older as well as younger

adults, if bottom-up guidance can be considered a form of priming that would reduce the

neural resources needed for target identification (Henson, 2003; Lustig & Buckner, 2004;

Schacter & Buckner, 1998). However, the Madden et al. search task was a two-choice

discrimination version of visual search, with letter targets and distractors, in which a target was

present on each trial, and thus the singleton-related effects could not be specifically associated

with target detection rather than response selection. In addition, though letter search is

typically considered to be a form of conjunction search due to the featural overlap of target and

distractor letters, the specific features relevant for the target template were not defined in the

Madden et al. study.

Here we incorporated event-related fMRI with a conjunction search paradigm reported

by Proulx (2007), to distinguish target detection and bottom-up attentional effects. Participants

made a target present/absent decision regarding a target (a conjunction of color and

orientation) that was present on half of the trials. One of the five items in each display (left- and


right-tilted bars) was a size singleton, and on the target-present trials, the size singleton

corresponded to the target on one-fifth of the trials. Thus, the feature of size enhanced the

salience of one of the display items but was not informative regarding target presence. Proulx

found that under these conditions the size singleton did provide bottom-up guidance to the

target, in the form of more efficient search for target-present displays with singleton targets

relative to those with nonsingleton targets. In the Proulx experiment, RTs were also higher for

displays with nonsingleton targets, relative to target-absent displays (at most display sizes).

Thus, unlike standard conjunction search displays without a featural singleton (Wolfe, 1998),

target-present responses were slower than target-absent responses, when the size singleton

did not correspond to the target. Proulx proposed that observers adopted a strategy in which

they attended to the most salient display item first, then shifted attention if this item was not a

target.

We used resting-state fMRI to define participants’ regions of interest (ROIs) relevant for

bottom-up attentional guidance in conjunction search and applied event-related fMRI to

identify regional activation across the task conditions. The participants’ age was sampled

continuously between 19-78 years so that linear age-related effects could be assessed. With

regard to the behavioral data, we predicted an increase in target- present RT for nonsingleton

targets, relative to target-absent displays (i.e., target detection effect), consistent with Proulx

(Proulx, 2007), reflecting participants’ attending to the size singleton first, then shifting

attention to determine whether one of the nonsingleton display items was a target. Because

conjunction search is a relatively inefficient form of search that requires a specific combination

of features in the target template (Eckstein, 2011; Wolfe & Horowitz, 2004), we hypothesized


that the target detection RT effect would increase with age (Hommel et al., 2004; Madden,

2007; Madden & Whiting, 2004), beyond the age-related increase in overall RT. We also

predicted that bottom-up attentional guidance would be evident in search performance, in the

form of decreased RT for targets that were also size singletons, relative to nonsingleton targets,

consistent with Proulx (2007). In view of the age constancy that is often observed for RT

priming effects and implicit memory (Howard & Howard, 2013; Howard et al., 2004; Lustig &

Buckner, 2004; Schmitter-Edgecombe & Nissley, 2002), we did not expect significant age-

related differences in the behavioral measure of bottom-up attentional guidance.

In the event-related fMRI data, we hypothesized that higher activation for nonsingleton

target displays relative to target-absent displays (i.e., target detection effect) would be evident

throughout the frontoparietal network (Corbetta & Shulman, 2002; Shulman et al., 2003; Yantis

et al., 2002). We predicted that bottom-up guidance from the salient display item would be

expressed as decreased frontoparietal activation for size singleton targets, relative to

nonsingleton targets, similar to the effects that have been observed for repetition priming

(Henson, 2003; Lustig & Buckner, 2004; Schacter & Buckner, 1998). We also predicted that

increasing age would be associated with increased frontoparietal involvement in both the

target detection and size singleton effects (Dennis & Cabeza, 2008; Grady, 2012; Madden,

Whiting, & Huettel, 2005; Spreng et al., 2010).

If older adults’ event-related activation is compensatory, in the sense of being a

response to a decline in the efficiency of other aspects of brain structure or function needed for

task performance (Cabeza & Dennis, 2012; Davis et al., 2008; Li et al., 2013; Park & Reuter-

Lorenz, 2009; Reuter-Lorenz & Cappell, 2008), then the relation between activation and the RT


effects (target detection, bottom-up guidance) should increase with age. Note that each of the

RT effects represent a difference between a reference condition (target-present displays with

nonsingleton targets), and a presumably less demanding condition: either target-present

displays with size singleton targets (the size singleton effect) or target-absent displays (the

target detection effect). An increase in the magnitude of the RT effect may be the result of

either an increase in RT for the reference condition, a decrease in RT for the less demanding

condition, or both. Age-related increases in activation associated with the RT effects may

represent either attempted or successful compensation (Cabeza & Dennis, 2012). Thus, in the

case of bottom-up guidance, successful compensation based on target salience should be

reflected in a correlation between increasing activation and decreasing RT for displays with size

singleton targets. In contrast, if the activation is the result of task difficulty or target

discriminability (i.e., attempted compensation), then increasing activation should be correlated

with increasing RT in the more difficult (reference) task condition, target-present displays with

nonsingleton targets.

Method

Participants

The participants were 69 healthy, community-dwelling adults (41 women) between 19

and 78 years of age, who were part of a multi-modal imaging study (Madden et al., 2014, April).

Here we report the event-related fMRI data from that larger study, which have not been

reported previously. Twenty-three individuals were between 19 and 39 years of age, 24 were

between 40 and 59 years of age, and 22 were between 60 and 78 years of age. Participants

gave written informed consent for a protocol approved by the Duke University Institutional


Review Board. All participants reported that they were right-handed, had completed at least 12

years of education, were free of significant health problems (including atherosclerosis,

neurological and psychiatric disorders), and were not taking medications known to affect

cognitive function or cerebral blood flow (except antihypertensive agents).

During a testing session conducted on average of one month before the MRI testing,

participants completed several screening and psychometric tests (Table 1) and an abbreviated

version of the visual search task reported here. Participants were excluded for any one of the

following: corrected visual acuity less than 20/40 (Bach, 1996); raw score less than 27 on the

Mini-Mental State Exam (Folstein, Folstein, & McHugh, 1975); score greater than 10 on the

Beck Depression Inventory (Beck, 1978); scaled score less than the 50th percentile on the

Vocabulary subtest of the Wechsler Adult Intelligence Scale III (Wechsler, 1997); score less than

12 on the Dvorine color vision test (Dvorine, 1963); or less than 75% accuracy on the practice

version of the visual search task. Twenty-five individuals were excluded due to either screening

test criteria (16 individuals), withdrawal from the study (two individuals), or technical

difficulties with the MRI data (seven individuals). Three individuals (21, 66, and 67 years of age)

were excluded due to extremely slow responses (Cook’s D > 0.055) during the scanner version

of the visual search task.


Table 1

Participant Characteristics

Variable M SD r with age

Education (years) 16.493 2.084 0.315**

Color Vision 13.855 0.394 -0.195

Visual Acuity -0.051 0.131 0.341**

BDI 2.275 2.727 0.138

MMSE 29.138 0.919 -0.192

Vocabulary 56.826 5.901 0.042

Digit Symbol RT 1537.0 367.535 0.730***

Response Failures 0.010 0.026 -0.099

Note. n = 69. Color Vision = score on Dvorine color plates (Dvorine, 1963); Visual Acuity = logarithm of the

minimum angle of resolution (MAR), for the Freiburg Visual Acuity Test (Bach, 1996); Log MAR of 0 corresponds to

Snellen 20/20, with negative values corresponding to better resolution. BDI = score on Beck Depression Inventory

(Beck, 1978); MMSE = raw score on Mini-Mental State Exam (Folstein et al., 1975); Vocabulary = raw score on the

Wechsler Adult Intelligence Scale III (Wechsler, 1997); Digit Symbol RT = percentage reaction time (ms) on a

computer test of digit-symbol coding (Salthouse, 1992); Response Failures = percentage of trials in the visual

search task on which the participant failed to respond.

** p < .01

*** p < .001

Visual Search Task

While in the scanner, participants performed a visual search task in which they made a

yes/no decision regarding whether a target bar was present among nontarget (distractor) bars

(Figure 1). This task, modified from Proulx (2007), was a conjunction search task in which

bottom-up guidance was also available. The search target was defined as a conjunction of color

(blue or green) and orientation (45o left- or 45o right-tilted). Each distractor shared only one

feature with the target; for example, a blue, right-tilted target would be accompanied by two


blue, left-tilted bars and two green, right-tilted bars. Thus, for the two target feature values of

color and orientation defining each target, each display contained either two or three instances

of each feature value. Across a total of 350 trials, display size was constant at five items, either

one target and four distractors (target-present, 175 trials) or five distractors (target-absent, 175

trials). For each participant, the search target (e.g., a blue, right-tilted bar) was constant, and

the values of color and orientation defining the target were counterbalanced across

participants. The color bars in the display were isoluminant and presented on a black

background.

Figure 1. Visual search task. Participants performed a conjunction search task in which the target,

displayed here as a right-tilted, black bar, shared one feature (either color or orientation) with each of the

nontarget (distractor) items. Display size was constant at five items. Participants made a yes/no response on each

trial regarding the presence of the target. In each display, one of the distractors was 50% larger than the other

items (i.e., a size singleton). On one-fifth of the target-present trials, the size singleton coincided with the target.

Though illustrated here in black in white, the actual task used isoluminant blue and green bars against a black

background.

Each display contained four bars of equal size (0.8° x 3.2°), and one bar that was 50%

larger (i.e., a size singleton; 1.2° x 4.8°). The size singleton corresponded to the target on 1/5 of

the target-present trials (i.e., 35 trials). Thus, the size singleton was always one of the display

items and provided a bottom-up form of visual salience, but was not informative regarding the


target location. This design yields three trial types of interest: those on which the target is

present and the size singleton is a distractor (Targ Pres/SS Dist), those on which the target is

present and is also the size singleton (Targ Pres/SS Targ), and those on which the target is

absent and the size singleton is a distractor (Targ Abs/SS Dist). When the size singleton was a

distractor, within both the target-present and target-absent trials, it contained each target-

relevant value of color and orientation an approximately equal number of times.

The locations of the five display items were distributed within an approximately 15°

diameter circular area. Items were distributed in an irregular pattern wherein the center-to-

center distance between all pairs of items ranged from 5.4° to 10.6°, and the edges between

adjacent items were no closer than 1°. The distance between the center of the display and the

center of each display item ranged from 3.3° to 7.5°. For each condition, the size

singleton/target location was approximately equally distributed between the top versus

bottom, and left versus right halves of the screen. Participants first completed one practice run

of 18 trials (during structural imaging), followed by five functional imaging runs of 70 trials

each, for a total of 350 trials. The 70 trials per run contained a randomly ordered sequence of

35 target-present trials (including seven size-singleton target trials) and 35 target-absent trials.

Display presentation and response recording were controlled by E-Prime 2.0

(Psychology Software Tools, Sharpsburg, PA, USA). At the presentation of each display,

participants indicated their present/absent decision regarding the target via a button-press

response, using their right index and middle fingers and two buttons on a hand-held, fiber optic

response box (Current Designs, Philadelphia, PA, USA). Participants were instructed to respond

as quickly as possible without sacrificing accuracy, and the assignment of the target-present


response to the response buttons was balanced across participants. Each trial began with a

white fixation cross with variable duration (jitter), followed by the five-item display for a

duration of 350 ms, then a 2650 ms response period, during which the display was black. We

measured RT from display onset. Following the response period, the fixation cross returned to

begin the next trial. The jitter duration was varied among the values of 1500, 3000, 4500, and

6000 ms defined by multiples of the fMRI repetition time (TR) value (1500 ms). The jitter values

and trial order across conditions were randomized and optimized using the Optseq2 program

(Dale, 1999; http://surfer.mnr.mgh.harvard.edu/optseq). No feedback regarding response

accuracy was provided.

MRI Methods

MRI Data Acquisition

We conducted MRI scanning on a 3.0 T GE MR750 whole-body 60 cm bore MRI scanner

(GE Healthcare, Waukesha, WI) equipped with 50 mT/m gradients and a 200 T/m/s slew rate.

An eight-channel head coil was used for radio frequency (RF) reception. Participants wore

earplugs to reduce scanner noise, along with foam pads and a headband to reduce head

motion. Imaging began with 3-plane (straight axial/coronal/sagittal) localizer FSE images that

defined a volume for data collection. A semi-automated high-order shimming program ensured

global field homogeneity. We then acquired two runs of resting-state (eyes open), T2*-

weighted (functional) imaging sensitive to the blood oxygen-level dependent (BOLD) signal,

followed by five runs of event-related, T2*-weighted imaging, and one run of T1-weighted

anatomical images. The protocol also included two runs of diffusion tensor imaging (DTI) and

one run of T2-weighted fluid attenuated inversion recovery (FLAIR) imaging, not reported here.


Both the resting-state and event-related functional imaging included 29 contiguous

slices acquired at an axial oblique orientation, parallel to the AC-PC plane; TR = 1500 ms, TE =

27 ms, FOV = 240 mm, flip angle = 77, voxel size = 3.75 x 3.75 x 4 mm, 64 x 64 matrix, and a

SENSE factor of 1. For each event-related run, 252 brain volumes were collected, whereas each

of the two resting-state runs comprised a time series of 162 brain volumes.

The T1-weighted anatomical images were 166 straight axial slices acquired with a 3D

fast inverse-recovery-prepared spoiled gradient recalled (SPGR) sequence, with TR = 8.10 ms,

echo time (TE) = 3.18 ms, inversion recovery time (TI) = 450 ms, field of view (FOV) = 256 mm,

flip angle = 12, voxel size = 1 x 1 x 1 mm, 256 x 256 matrix, and a sensitivity encoding (SENSE)

factor of 2, using the array spatial sensitivity encoding technique and extended dynamic range.

fMRI Data Analyses

Data quality was assessed using a quality assurance tool that quantifies several metrics

including Signal-to-Noise (SNR), Signal-Fluctuation-to-Noise (SFNR), motion, and voxel-wise

standard deviation measurements (Friedman & Glover, 2006; Glover et al., 2012). We also

visually inspected the data for artifacts and blurring. No participant moved more than 2.5 mm

in any direction, either within or across runs. We used FSL 5.0.5 (Smith et al., 2004;

http://www.fmrib.ox.ac.uk/fsl) and FEAT version 6.0 for preprocessing and for analyses of the

functional data. The structural brain images were skull-stripped using the FSL brain extraction

tool (Smith, 2002). For the functional brain images, we removed the first four volumes of each

run. Using FSL MCFLIRT, the images were then corrected for slice-timing and head motion using

6 rigid-body transformations (Jenkinson, Bannister, Brady, & Smith, 2002), which were included

as nuisance covariates in the overall FSL model. Within each run, each functional image was


spatially normalized to the individual’s FSPGR and subsequently to a study-specific mean brain

co-registered to the MNI152 T1 template (Montreal Neurological Institute, Montreal, Canada)

using a combination of affine and non-linear registrations (Greve & Fischl, 2009; Jenkinson et

al., 2002; Jenkinson & Smith, 2001). Images were spatially smoothed with a 5 mm Gaussian

kernel, and a high-pass filter (cut off = 90.0 sec) was incorporated in every model to correct for

scanner drift.

Region of interest construction. We used a region of interest (ROI) approach to analyze

the task-related fMRI activation, focusing our analyses on the frontoparietal network associated

with visual search and attention (Corbetta & Shulman, 2002; Shulman et al., 2003; Yantis et al.,

2002). We also included occipital cortical regions, because in this task (Proulx, 2007) bottom-up

guidance is provided by visual salience. To maintain independence between activation

magnitude and region selection (Kriegeskorte, Simmons, Bellgowan, & Baker, 2009), ROIs were

defined on the basis of task-relevant functional connectivity networks, obtained from the

resting-state functional scans included in the imaging protocol for these participants. These

scans were always conducted before the task-related, functional scans.

To define resting-state networks, we performed a group independent component

analysis (ICA) of the resting-state data using FSL MELODIC (Beckmann, DeLuca, Devlin, & Smith,

2005), described in more detail in Supplemental Material. From a template-matching

procedure, we identified two relevant networks, in our resting-state data, that matched

network exemplars in a meta-analysis of functional connectivity data reported by Laird et al.

(2011). The first was a spatially distributed network that encompassed frontal, parietal, and

occipital regions (labeled ICN 10 in the Laird et al. data), and the second was a network


primarily comprising visual sensory cortical regions (labeled ICN 12 in the Laird et al. data). Laird

et al. described ICN 10 as being relevant for complex visual perception and ICN 12 as being

relevant for visual sensory processing (Figure 2). The average connectivity of all non-zero voxels

within the network declined significantly with age for the visual sensory network, r = -.372, p <

.01, but not for the frontoparietal network, r = -.176, p < .15.

Figure 2. Resting-state networks. ICN = independent component network. Voxels represent components from an

independent component analysis (ICA) of participants’ fMRI resting-state data obtained immediately prior to task

presentation. From a template-matching procedure, two networks were identified that matched exemplars in a

meta-analysis of functional connectivity data reported by Laird et al. (2011). Laird et al. described ICN 10 as being

relevant for complex visual perception and ICN 12 as being relevant for visual sensory processing. The regions of

interest (ROIs) for the participants’ task-related activation analyses were defined as being the highest bilateral

local maxima within these two resting-state networks.

For each resting-state network map, we used the FSL Cluster command at a threshold of

z = 4 to obtain the local maxima representing homologous peaks in the left and right

hemispheres (i.e., bilateral ROIs). From the more extensive, frontoparietal network (ICN 10), we

selected the six bilateral ROIs with the highest local maxima; and from the visual sensory


network (ICN 12), we selected the bilateral ROI with the highest local maxima, for a total of

seven pairs of local maxima (Table 2). Thus, the frontoparietal network comprised six ROIs,

each with a local maximum in both the left and right hemisphere: the frontal eye field (FEF),

middle frontal gyrus (MFG), intraparietal sulcus (IPS), superior parietal lobule (SPL), lateral

occipital cortex (LOC), and fusiform gyrus (FUSI). To obtain both left and right hemisphere peaks

in the visual sensory cortex (VIS), we selected local maxima that were slightly posterior to the

striate cortex at the midline and contained a mixture of primary and association cortical

regions. Each local maximum was separated from any other local maximum by a Euclidean

distance of at least 16 mm, except for the IPS and SPL, which were separated by a Euclidean

distance of at least 11 mm. The local maxima were then expanded to 8 mm diameter spherical

ROIs (Figure 3; ROIs are depicted with a 16 mm diameter for ease of visualization). The ROIs did

not overlap each other, and thus, no voxel contributed to more than one ROI.1

1 The temporoparietal junction (TPJ) and ventral frontal cortex, particularly in the right hemisphere, are

often reported as hubs of the ventral component of the frontoparietal attentional network (Corbetta &

Shulman, 2002; Shulman et al., 2003). These regions, however, were not associated with significant local

maxima in the resting-state data that we analyzed as a basis for selecting the ROIs. Vossel et al. (2014)

noted that, in contrast to dorsal frontoparietal areas, there is no current agreement on the anatomical

definitions of the TPJ and ventral prefrontal regions, and that these areas do not have distinct

anatomical homologues in non-human primates. Thus, reliable identification of these particular ventral

frontoparietal regions from resting-state data may be difficult. However, the resting-state data for these

participants did yield local maxima for ventral regions including lateral occipital cortex (LOC), fusiform

cortex (FUSI), and visual sensory cortex (VIS), which were included in the task-related analyses and

should be sensitive to salience-related effects.


Figure 3. Regions of interest (ROIs) for task-related activation analyses,

derived from local maxima of participants’ resting-state networks (Figure

2). ROIs are displayed as 16-mm spheres for clarity, though analyses were

based on 8-mm spherical ROIs. ICN = independent component network;

FEF = frontal eye field; MFG = middle frontal gyrus; IPS = intraparietal

sulcus; SPL = superior parietal lobule; LOC = lateral occipital cortex; FUSI =

fusiform gyrus; VIS = visual sensory cortex.

Table 2

Regions of Interest

ROI Hem x y z BA

FEF L -26 -2 46 6

FEF R 28 -2 48 6

MFG L -48 6 26 9

MFG R 50 10 26 9

IPS L -26 -56 46 7

IPS R 28 -54 46 7

SPL L -20 -68 44 7

SPL R 24 -64 48 7

LOC L -28 -74 22 18/19

LOC R 32 -74 22 19/39

FUSI L -42 -64 -10 37

FUSI R 46 -60 -14 37

VIS L -8 -88 18 18/19

VIS R 10 -84 6 17/18

Note. ROI = region of interest; Hem = hemisphere; L =

left; R = right; FEF = frontal eye field; MFG = middle

frontal gyrus; IPS = intraparietal sulcus; SPL = superior

parietal lobule; LOC = lateral occipital cortex; FUSI =

fusiform gyrus; VIS = visual sensory cortex. x, y, z = center

of mass coordinates in Montreal Neurological Institute

(MNI) standard space; BA = Brodmann area.


Region of interest analyses. We used FSL 5.0.5 (Smith et al., 2004;

http://www.fmrib.ox.ac.uk/fsl), FEAT version 6.0, and FSL Featquery to estimate the magnitude

of activation within the ROIs. We conducted event-related voxelwise analyses at the first level,

using a double γ function to model the hemodynamic response of each trial. For each

participant, we modeled the correct trials for each condition (Targ Pres/SS Dist; Targ Abs/SS

Dist; Targ Pres/SS Targ); errors (incorrect or omitted responses) were modeled as a separate

regressor. Data for each participant were combined across the five experimental runs, and

subsequent participant-specific maps were masked by a group-level contrast reflecting all task

conditions versus the implicit baseline, z > 1.96, p < .05, cluster corrected according to Gaussian

random field theory (GRF), to ensure that only differences based on significant positive

activations were included in the analyses.

From the participant-level models, we used FSL Featquery to extract parameter

estimates for each condition, across all voxels within each of the pre-selected ROIs. The

parameter estimates were converted to percent signal change and averaged within each ROI.

The analyses of task-related activation were conducted within the general linear model using

ROI and trial type as independent variables. Statistical methods are described further in Results.

To confirm that the 14 ROIs selected from the resting-state networks were actually

relevant for the task-related activation, we also constructed a whole-brain, voxelwise map of

activation, across all participants, using FMRIB Local Analysis of Mixed Effects (FLAME 1 & 2;

Beckmann, Jenkinson, & Smith, 2003; Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004)

and a group-level contrast of all three trial types versus the implicit baseline. The resulting

clusters (Supplemental Figure S1) reflected widespread activations in the frontal, parietal, and


occipital regions. For each of the ROIs defined by our resting-state analysis, at least 97% of the

ROI voxels were located within this map.

Results

Visual search task

Trials on which the participant either failed to respond, or responded in less than 250

ms, were deleted. This comprised an average of 1.03% of the trials per participant and did not

vary significantly across age (Table 1).

Each display was one of three types, which as noted previously, occurred in a random

sequence within each imaging run: those on which a target was present and the size singleton

was a distractor (Targ Pres/SS Dist), those on which a target was present and it was also the size

singleton (Targ Pres/SS Targ), and those on which the target was absent and the size singleton

was a distractor (Targ Abs/SS Dist). For each participant, we obtained the median RT for correct

responses to each type of display. We then calculated a scaled RT measure for each

combination of participant and trial type, defined as the participant’s median correct RT divided

by his or her accuracy for that trial type (Horowitz & Wolfe, 2003; Townsend & Ashby, 1983).

This scaled RT measure reflects the overall efficiency of the decision and is sensitive to both RT

and accuracy, while retaining the original metric of time. Increases in either RT or error rate will

lead to increased scaled RT. Mean scaled RT, and the means of the original median RTs and

accuracy values, for the 69 participants, are presented in Table 3.


Table 3

Visual Search Performance by Task Condition

Trial Type Scaled RT (ms) RT (ms) Accuracy

Nonsingleton target (Targ Pres/SS Dist) 890 (160) 850 (142) 0.958 (0.044)

Target absent (Targ Abs/SS Dist) 853 (154) 818 (127) 0.962 (0.046)

Singleton target (Target Pres/SS Targ) 850 (156) 822 (143) 0.969 (0.040)

Note. n = 69. Values are means across participants, with standard deviations in parentheses. Targ Pres/SS Dist =

Target Present/Size Singleton Distractor; Targ Abs/SS Dist = Target Absent/Size Singleton Distractor; Targ Pres/SS

Targ = Target Present/Size Singleton Target. RT = reaction time in ms. Scaled RT = RT/accuracy (Horowitz & Wolfe,

2003; Townsend & Ashby, 1983). Scaled RT is calculated at the individual participant level, so the mean of the

individually scaled RTs, across all participants, will not necessarily be equal to the mean RT across participants

divided by the mean accuracy across participants.

As illustrated in Figure 4, scaled RT increased significantly with years of age for each trial

type, with r > 0.332, p < .01, in each case. The age-related increase in RT was primarily linear for

each trial type, and adding a quadratic age term to the regression models (i.e., age2) did not

significantly increase the variance accounted for by age. Paired comparisons of the age-RT

correlations with Steiger’s Z (Steiger, 1980) indicated that the age-RT correlations did not differ

significantly across the task conditions. The regression coefficients for the age-RT functions

ranged from 2.91-3.22 across the task conditions, reflecting an increase in scaled RT of

approximately 3 ms per year of age.

Scaled RT was higher on the Targ Pres/SS Dist trials than on both of the other trial types,

yielding two effects of interest. First, scaled RT on Targ Pres/SS Dist trials was 37 ms higher than

on Targ Abs/SS Dist trials, t = 3.49, p < .001, defining the target detection effect: the additional


processing time for deciding that a nonsingleton target was present, relative to target-absent

displays, and selecting the target-present response (Figure 5, Panel A). Second, scaled RT on

Targ Pres/SS Dist trials was 40 ms higher than on the Targ Pres/SS Targ trials, t = 3.72, p < .001,

defining the size singleton effect: a reduction in time required for the target-present response

when the target corresponded to the size singleton (Figure 5, Panel B). Neither the target

detection effect nor the size singleton effect varied significantly as a function of years of age,

consistent with the similarity of the age trends for the individual trial types (Figure 4). Best

corrected visual acuity (Bach, 1996) was not correlated significantly with either the target

detection RT effect, r = -0.006, p =.96, or the size singleton RT effect, r = -0.099, p =.42.

Figure 4. Scaled reaction time (RT) as a function of age group and trial type. Scaled RT is RT divided by accuracy, for

each participant. Targ Pres/SS Dist = Target Present, Size Singleton Distractor; Targ Abs/SS Dist = Target Absent,

Size Singleton Distractor; Targ Pres/SS Targ = Target Present, Size Singleton Target.


Figure 5. Target detection (Panel A) and size singleton (Panel B) effects for scaled reaction time (RT). The target

detection effect is the increase in RT for target present, size singleton distractor trials relative to target absent, size

singleton distractor trials. The size singleton effect is the decrease in RT for target present, size singleton target

trials relative to target present, size singleton distractor trials (i.e., positive values represent a larger reduction in

RT). Both effects were significantly greater than zero but did not vary significantly with age.

We converted each of the RT effects to the percentage decrease in scaled RT, relative to

the nonsingleton target trials (Targ Pres/SS Dist), for each participant. The target detection

effect represents a 3.63% decrease in scaled RT for target-absent responses relative to target-

present responses, t = 3.20, p < .01, and the size singleton effect represents a 4.19% decrease in

scaled RT for target-present responses when the target coincided with the size singleton,

relative to nonsingleton targets, t = 3.98, p < .001. The pattern of statistical effects for the

target detection and size singleton effects, in relation to both age and the activation data, was

essentially the same whether the RT effects were expressed as differences in scaled RT or as

percentage change values. Because scaled RT retains the metric of time, we find the RT

difference scores to be more easily interpretable than the percentage change scores, and thus

we report only the results for the scaled RT difference scores.


fMRI activation

Mean fMRI activation, expressed as percent signal change for each combination of ROI

and trial type, is presented in Table 4. For each of the three trial types, we tested for linear age-

related effects in a regression model with years of age as a predictor of activation, correcting

for multiple comparisons across the 14 ROIs (i.e., seven ROIs x left/right hemisphere), setting

alpha p = .00357 (i.e., .05/14). None of the ROIs exhibited a significant age-related linear trend

in activation, and the quadratic age effect (i.e., age2) was not significant for any ROI.

Table 4

Task-Related Activation by Region of Interest, Hemisphere, and Trial Type

ROI Hem Trial Type

Targ Pres/SS Dist Targ Abs/SS Dist Targ Pres/SS Targ

FEF L 0.223 (0.110) 0.164 (0.110) 0.183 (0.130)

FEF R 0.238 (0.109) 0.184 (0.121) 0.202 (0.130)

MFG L 0.296 (0.188) 0.233 (0.179) 0.270 (0.213)

MFG R 0.316 (0.193) 0.264 (0.183) 0.261 (0.188)

IPS L 0.271 (0.122) 0.205 (0.119) 0.249 (0.135)

IPS R 0.230 (0.133) 0.184 (0.109) 0.207 (0.121)

SPL L 0.381 (0.172) 0.284 (0.163) 0.338 (0.195)

SPL R 0.318 (0.160) 0.256 (0.154) 0.273 (0.154)

LOC L 0.348 (0.158) 0.263 (0.139) 0.308 (0.173)

LOC R 0.412 (0.168) 0.356 (0.157) 0.359 (0.180)

FUSI L 0.290 (0.140) 0.235 (0.134) 0.272 (0.155)

FUSI R 0.309 (0.149) 0.272 (0.136) 0.278 (0.147)

VIS L 0.096 (0.112) 0.112 (0.117) 0.102 (0.124)

VIS R 0.267 (0.191) 0.242 (0.187) 0.235 (0.206)

Note. n = 69. Values are mean percent signal change, relative to the implicit baseline (jitter), with standard deviations in parentheses. ROI = region of interest; Hem = hemisphere; L = left; R = right; FEF = frontal eye field; MFG = middle frontal gyrus; IPS = intraparietal sulcus; SPL = superior parietal lobule; LOC = lateral occipital cortex; FUSI = fusiform gyrus; VIS = visual sensory cortex. Targ Pres/SS Dist = Target Present, Size Singleton Distractor; Targ Abs/SS Dist = Target Absent, Size Singleton Distractor; Targ Pres/SS Targ = Target Present, Size Singleton Target.


Target detection activation effect. As noted previously for the behavioral data, the

target detection effect is defined by the difference between the Targ Pres/SS Dist trials and

Targ Abs/SS Dist trials, and the size singleton effect is defined by the difference between the

Targ Pres/SS Dist trials and the Targ Pres/SS Targ trials. For each of these effects, we conducted

a repeated measures analysis of variance (ANOVA) on the fMRI activation data using ROI (seven

levels), hemisphere (two levels), and trial type (two levels). In the analysis of target detection,

activation was higher on nonsingleton target trials (Targ Pres/SS Dist) than on target-absent

trials (Targ Abs/SS Dist), F(1, 68) = 38.19, p < .0001, higher for the left hemisphere than for the

right hemisphere, F(1, 68) = 19.35, p < .0001, and varied across the ROIs, F(1, 68) = 26.71, p <

.0001. All of the interaction terms were also significant: Hemisphere x Condition, F(6, 408) =

8.05, p < .01, Hemisphere x ROI, F(6, 408) = 17.40, p < .0001, ROI x Condition, F(6, 408) = 14.0, p

< .0001, and Hemisphere x ROI x Condition F(6, 408) = 9.99, p < .0001.

The mean values of the target detection activation effect (i.e., the increase in activation

for nonsingleton target-present trials relative to target-absent trials) are presented in Figure 6

(Panel A). The interaction terms from the initial ANOVA represent a relatively higher target

detection effect for left hemisphere ROIs than for right hemisphere ROIs, combined with

variation in this pattern across ROI. The target detection activation effect was significantly

greater than zero (p < .05, Bonferroni corrected) for all ROIs except the two visual sensory ROIs.

For all six ROI-hemisphere pairs exhibiting a significant target detection activation effect, the

magnitude of the effect was greater for the left hemisphere than for the right hemisphere, with

the smallest difference between hemispheres for the FEF and the largest difference for the SPL.


Across the 14 ROI-hemisphere values, none of the ROIs exhibited a significant correlation

between age and the magnitude of the target detection activation effect.

Figure 6. Mean activation for the target detection (Panel A) and size singleton (Panel B) effects, for each region of

interest (ROI). The target detection activation effect is the increase in activation (percent signal change) for target

present, size singleton distractor trials relative to target absent, size singleton distractor trials. The size singleton

activation effect is the decrease in activation for target present, size singleton target trials relative to target

present, size singleton distractor trials (i.e., positive values represent a larger reduction in activation). L = left; R =

right; Hem = hemisphere; FEF = frontal eye field; MFG = middle frontal gyrus; IPS = intraparietal sulcus; SPL =

superior parietal lobule; LOC = lateral occipital cortex; FUSI = fusiform gyrus; VIS = visual sensory cortex. Activation

effects that are significantly different from zero (p < .05, Bonferroni corrected) are indicated by an asterisk.

Size singleton activation effect. Analyses of the activation on the target-present trials

indicated that activation was for nonsingleton targets (Targ Pres/SS Dist), relative to singleton

targets (Targ Pres/SS Targ), F(1, 68) = 9.53, p < .01, was higher for the right hemisphere than for

the left, F(1, 68) = 6.93, p < .01, and varied across the ROIs, F(1, 68) = 28.08, p < .0001. As with

the target detection activation effect, all of the interaction terms were significant for the size

singleton activation effect: Hemisphere x Condition, F(6, 408) = 15.18, p < .001, Hemisphere x


ROI, F(6, 408) = 18.14, p < .0001, ROI x Condition, F(6, 408) = 3.84, p < .001, and Hemisphere x

ROI x Condition F(6, 408) = 2.91, p < .01. The right-hemisphere advantage for the size singleton

activation effect was present numerically for all ROIs except the FEF, with relatively small

differences between the left and right hemispheres for the SPL and IPS, and relatively larger

differences for the visual sensory regions, LOC, FUSI and MFG (Figure 6, Panel B). Across the

ROIs, the size singleton activation effect was significantly greater than zero (p < .05, Bonferroni

corrected) bilaterally for the FEF, SPL, and LOC, and unilaterally for the right MFG. None of the

ROIs exhibited a significant correlation between age and the magnitude of the size singleton

activation effect.

Relation between fMRI activation and search performance. To examine the relation

between target detection activation and performance, we conducted a stepwise regression

analysis in which the target detection RT effect was the outcome variable and the predictor

variables were the 12 target detection activation effects that were significantly greater than

zero (Figure 6, Panel A). This analysis yielded a single activation variable, the left FEF target

detection effect, as a significant predictor of the target detection RT effect, F(1, 67) = 18.23, p <

.0001, reflecting an increased magnitude of the target detection RT effect as a function of

increasing activation in the left FEF (Figure 7 Panel A). A similar regression analysis of the size

singleton RT effect, with the predictor variables comprising the seven size singleton activation

effects that were significantly greater than zero (Figure 6, Panel B), indicated that the left FEF

size singleton activation effect was a significant predictor of the corresponding RT effect, F(1,

67) = 5.76, p < .05. The activation-RT relation in this case was also positive (Figure 7, Panel B).


Figure 7. The relation between left frontal eye field (FEF) activation and search performance (scaled RT) for the

target detection effect (Panel A) and the size singleton effect (Panel B), for all 69 participants. The target detection

activation effect is the increase in activation for target present, size singleton distractor trials relative to target

absent, size singleton distractor trials. The size singleton activation effect is the decrease in activation for target

present, size singleton target trials relative to target present, size singleton distractor trials (i.e., positive values

represent a larger reduction in activation). The target detection reaction time (RT) effect is the increase in RT for

target present, size singleton distractor trials relative to target absent, size singleton distractor trials. The size

singleton RT effect is the decrease in RT for target present, size singleton target trials relative to target present,

size singleton distractor trials (i.e., positive values represent a larger reduction in RT).

In a regression model with the target detection RT effect as the outcome variable, and

age and the left FEF target detection activation effect as predictors, adding an Age x Activation

interaction term (with years of age as a continuous variable), representing the linear age-

related change in the left FEF target detection activation, did not lead to a significant parameter

estimate for the interaction term. However, in a similar regression model with the size

singleton RT effect as the outcome variable, and age and left FEF size singleton activation as

predictors, adding an Age x Activation interaction term did yield a significant parameter

estimate for the interaction term, t(65) = 2.85, p < .01. To examine this interaction, we divided


the sample into 23 younger adults (19-39 years of age), 24 middle-aged adults (40-59 years of

age), and 22 older adults (60-78 years of age). Separate activation-RT regression models for the

size singleton effects, within each age group, indicated that the left FEF size singleton activation

effect was a significant predictor of the corresponding RT effect for the older adults (Figure 8,

Panel C), t(20) = 3.80, p < .001, but not for either of the other age groups (Figure 8, Panels A and

B).

Figure 8. Relation between the left frontal eye field (FEF) activation and the size singleton reaction time (RT)

effects, for younger (19-39 years), middle-aged (40-59 years), and older (60-78 years) adults. The size singleton

activation effect is the increase in activation for target present, size singleton distractor trials relative to target

present, size singleton target trials. The size singleton RT effect is the decrease in RT for target present, size

singleton target trials relative to target present, size singleton distractor trials (i.e., positive values represent a

larger reduction in RT).

As noted in the Introduction, if the age-related differences in activation associated with

the size singleton effect are driven primarily by the difficulty of target detection, then the

magnitude of the older adults’ activation should be correlated positively with the time required

for the detection of nonsingleton targets (i.e., attempted compensation). Alternatively, if the

age-related effects represent that additional bottom-up guidance provided by a size singleton


target (i.e., successful compensation), then the older adults’ activation should be correlated

negatively with RT for trials with size singleton targets. However, the older adults’ data did not

clearly support either of these alternatives. The older adults’ left FEF size singleton activation

effect was not correlated significantly with either scaled RT on Targ Pres/SS Dist trials, r = 0.15,

p = 0.50, or scaled RT on Targ Pres/SS targ trials, r = -0.23, p = 0.30.

Age-related activation. In the initial, combined analysis of the three trial types, the

absence of age-related differences in mean activation, across the 14 predefined ROIs, was

surprising in light of previous findings of age-related activation differences, particularly reports

of increased activation for older adults that indicate compensatory cortical recruitment (Cabeza

& Dennis, 2012; Davis et al., 2008; Li et al., 2013; Park & Reuter-Lorenz, 2009; Reuter-Lorenz &

Cappell, 2008). We therefore conducted a whole-brain, voxelwise analysis of task-related

activation, using age as a covariate of interest. This analysis revealed the presence of age-

related effects outside of our predefined ROIs. Using the whole-brain, voxelwise map of

activation for all participants combined, described previously (Methods), we performed a

mixed-effects (FLAME 1 & 2; Beckmann et al., 2003; Woolrich et al., 2004) group-level analysis

of this map, with age (demeaned and coded as a continuous variable) considered as a covariate

of interest. Separate contrasts were defined representing activation that increased as a

function of increasing age (i.e., positive) and activation that decreased as a function of

increasing age (i.e., negative), yielding clusters thresholded at z > 1.96 and GRF-corrected at p <

.05.

Age-related clusters are presented in Figure 9. The positive age-related contrast yielded

two clusters: a bilateral cluster encompassing the basal ganglia (centered in the putamen) and a


widely distributed, predominately right hemispheric cluster, centered in the premotor cortex

(posterior and superior to the predefined FEF) and extending into parietal lobe. The negative

age-related contrast yielded one bilateral cluster at the occipital pole, posterior to the

predefined occipital ROIs. Using the FSL Cluster tool, we defined the center of gravity of these

age-related activations within each hemisphere and applied 8 mm spheres around the center

coordinates. Resulting age-related ROIs for the bilateral putamen, bilateral occipital pole, and

right premotor cortex (Table 5), each had at least 80% voxel overlap with the age-related

activation map. Activation within these ROIs exhibited significant linear age-related effects

(Table 5), without a detectable non-linear component. Further analyses indicated that the age-

related trends within each ROI did not vary significantly across the three trial types.

If the age-related increase in activation for a particular ROI is compensatory, then the

activation in this ROI should be correlated negatively with the regional activation exhibiting

age-related decreases (e.g., Davis et al., 2008). This pattern, however, was not evident in the

present data. We averaged the activation at the two ROIs at the occipital pole, which had

exhibited decreasing activation as a function of increasing age (Table 5 and Figure 9). With this

activation as an outcome variable, a stepwise regression analysis using the three ROIs with

positive age-related activation (left and right putamen and right premotor cortex) as predictors

did not yield any significant effects, either for all 69 participants combined or the 22 older

adults considered separately.


Table 5

Regions of Age-Related Activation

ROI Hem x y z BA r with age

Positive activations

Putamen L -24 -14 2 0.362**

Putamen R 28 -12 0 0.512***

Premotor R 34 -20 54 3/4 0.404***

Negative activations

Occipital L -14 -90 6 17/18 -0.357**

Occipital R 20 -92 12 17/18 -0.281*

Note. ROI = region of interest; Hem = hemisphere; L = left; R = right;

positive activations = regions with a positive correlation between age and

fMRI signal in whole-brain analyses; negative activations = regions with a

negative correlation between age and fMRI signal in whole-brain analyses;

x, y, z = ROI center of mass coordinates in Montreal Neurological Institute

(MNI) standard space; BA = Brodmann area.

*p < .05

**p < .01

***p < .001

Figure 9. Voxelwise contrasts of task-related activation increasing (red

scale) and decreasing (blue scale) as a function of increasing age.


Discussion

Bottom-up Attentional Guidance

In this experiment, we sought to determine whether behavioral and neuroimaging

measures of bottom-up attentional guidance varied with adult age, in a color/orientation

conjunction search task with a non-informative size singleton (Proulx, 2007). In the behavioral

data (Figure 5), we replicated Proulx’s observation that RT for target-present displays with

nonsingleton targets was higher than for target-absent displays (i.e., the target detection

effect), reflecting the reallocation of attention when the salient display item was not the target.

Also, consistent with Proulx, RT decreased when the target was a size singleton, relative to

nonsingleton targets (i.e., the size singleton effect). Because size was not informative regarding

target identity, this suggests a contribution of bottom-up attentional guidance to this form of

conjunction search. The presence of the size singleton RT effect, however, does not necessarily

imply that guidance was entirely automatic or involuntary. Proulx proposed that participants

were adopting a search strategy in which they were relying on bottom-up guidance from the

size singleton to prioritize display items for further processing. Thus, search performance

represented the combined influences of top-down attentional control settings and bottom-up

guidance (Yantis, 1993, 1998).

Both the target detection and size singleton RT effects were constant as a function of

adult age (Figure 5). In contrast, we had predicted that the target detection effect would likely

increase in magnitude with increasing age, in view of previously reported age-related decline in

the efficiency of conjunction search (Hommel et al., 2004; Humphrey & Kramer, 1997; Madden,

2007; Madden & Whiting, 2004). Thus, the target detection effect obtained here reflected


some aspect of search, presumably the shift of attention away from the salient display item,

that remains relatively stable with adult age. Though the present task was conjunction search,

age-related decline in shifting attention between display items tends to emerge when

perceptual discriminability is demanding (Madden, Connelly, & Pierce, 1994). The current task

did not appear to place substantial demands on perceptual discriminability, however, in that

the age-related increase in search RT was only 3 ms per year of age for each of the three trial

types (Figure 4). This value was significant statistically but is just below the estimates of 4-10 ms

per year obtained from other estimates of age-related slowing in visual discrimination tasks

(e.g., lexical decision; Madden, 1992).

The behavioral data supported our initial expectation that the size singleton effect

would be relatively stable with age, in line with previous findings of age-related constancy of

repetition priming and implicit learning (Howard & Howard, 2013; Howard et al., 2004; Lustig &

Buckner, 2004; Schmitter-Edgecombe & Nissley, 2002). Similarly, in a letter search task with

non-informative color singletons, RT for singleton targets was approximately 5-6% lower than

for nonsingleton targets, for both younger and older adults (Madden, Spaniol, Whiting, et al.,

2007). In this experiment we extended the previous finding, obtained with a two-choice

discrimination version of letter search, to a detection version of Color x Orientation conjunction

search with target present/absent response. We could thus associate the size singleton effect

more specifically with target detection and define the features (color and orientation) that

were relevant for the target template. We found an overall decrease in target-present RT of

approximately 4% for size singleton targets, relative to nonsingleton targets, which is

comparable to the 5-6% decrease observed with two-choice letter discrimination. Thus,


although size was not a relevant feature within the target template, participants apparently

used the salience from size as a form of bottom-up guidance (Proulx, 2007), and this guidance

was available to individuals from 19 to 78 years of age.

Frontoparietal Activation

As hypothesized, consistent with previous neuroimaging studies (Corbetta & Shulman,

2002; Shulman et al., 2003; Yantis et al., 2002), a target detection effect was evident in terms of

a higher level of activation for displays with nonsingleton targets, relative to target-absent

displays, throughout all the ROIs, with the exception of the visual sensory regions (Figure 6,

Panel A). The displays were similar in featural composition on each trial, which contained a

display of five tilted bars with one size singleton (Figure 1), and thus the absence of effects in

visual sensory cortex is expected. Similarly, a size singleton activation effect was significant,

expressed as reduced frontoparietal activation for size singleton targets, relative to

nonsingleton-target displays (Figure 6, Panel B), suggesting bottom-up guidance from the

salient target, consistent with priming effects (Henson, 2003; Lustig & Buckner, 2004; Schacter

& Buckner, 1998). The size singleton activation effect was less prominent across ROIs than the

target detection activation, yielding bilateral effects only for the FEF, SPL, and LOC, and a

unilateral MFG effect. Interestingly, the target detection effect was stronger in the left

hemisphere than in the right hemisphere, whereas the converse was true of the size singleton

effect. While we did not have specific predictions regarding the laterality of these effects, the

results are broadly consistent with the proposal, based on the global/local letter identification

task, that the right hemisphere is biased towards the detection of salient stimuli, whereas the


left hemisphere is biased towards the selection of target items that are lower in salience

(Mevorach, Humphreys, & Shalev, 2006 ; 2009).

In contrast to our initial hypotheses predicting compensatory, age-related increases in

activation, the mean level of activation for the target detection and size singleton activation

effects did not vary significantly with age. However, an age-related effect supporting our

hypotheses did emerge in analyses of the relation between the activation and search

performance (Figure 7). The relation between the size singleton activation effect and the

corresponding RT effect varied significantly in the left FEF (Figure 8). The positive relation

between the activation and RT, for the size singleton effect, was significant only for older

adults. For those individuals 60 years of age and older, the degree to which left FEF activation

was lower for singleton targets, relative to nonsingleton targets, was associated with an

increased magnitude of bottom-up guidance in search performance. Thus, older adults’

comparable magnitude of attentional guidance at the behavioral level was associated with an

increased relation to activation at the neural level, suggesting an age-related compensatory

mechanism. However, we could not clearly characterize the activation-performance relation as

being either successful or attempted compensation (Cabeza & Dennis, 2012), because the older

adults’ activation was not related specifically to RT for either the easier (singleton targets) or

more difficult (nonsingleton targets) task condition. Although we observed age-related

increases and decreases in activation outside of our pre-defined ROIs (Table 5 and Figure 9),

these age-related effects did not covary in a manner suggesting compensation at the individual

participant level (Davis et al., 2008). Allen and Payne (2012) have also proposed that the neural


signature of age-related compensation may not necessarily include increases in the mean level

of activation.

The present pattern of age-related effects resembles that of Lustig and Buckner (2004),

who reported that RT reductions associated with verbal repetition priming were also associated

with reductions in left inferior prefrontal activation for both younger and older adults, without

a significant age-related increase in overall activation. Our findings here, with a two-choice

version of target detection, are consistent with the previously reported results of a letter

discrimination version of visual search (Madden, Spaniol, Whiting, et al., 2007), in that both

studies found that older adults exhibited greater involvement of the dorsal frontoparietal

network in relation to the behavioral measure of attentional guidance. The present finding,

indicating reduced activation for singleton targets relative to nonsingleton targets (Figure 6,

Panel B), differs from the Madden et al. (2007) report of increased activation for non-

informative singleton targets (their neutral condition). We believe that the previous results

were influenced by the attentional demands of the discrimination response. The present

findings, in which the bottom-up guidance was associated specifically with the target-present

response, agree with the wider literature on reduced activation for stimulus repetition and

priming effects (Henson, 2003; Schacter & Buckner, 1998).

The target detection and size singleton activation effects were significant throughout

the frontoparietal ROIs but not in the visual sensory ROIs (Figure 6), confirming that the ROIs

defined from these participants’ resting-state data (Figures 2 and 3) were converging on a visual

attention network common to resting-state and event-related measures (Anderson et al., 2011;

Fox et al., 2006; He et al., 2007; Laird et al., 2011; Raichle, 2015). The covariation between the


activation and search performance measures was limited to the left FEF (Figure 7), which is

surprising in view of previous research, from studies of patients (Bays, Singh-Curry, Gorgoraptis,

Driver, & Husain, 2010; Friedman-Hill, Robertson, & Treisman, 1995), non-human primates

(Bisley, 2011; Bisley & Goldberg, 2010), and imaging of healthy individuals (Corbetta, Shulman,

Miezin, & Petersen, 1995; Egner et al., 2008; Wojciulik & Kanwisher, 1999) suggesting that

occipital and parietal regions are critical for the salience map involved in feature-based

attention. As Proulx (2007) emphasized, the design of the present search task identifies one

form of bottom-up guidance associated with a salient display item, but a contribution from top-

down attention cannot be ruled out. Several findings have pointed to the contribution of the

FEF as a source of the top-down signals that modulate the representation of salient features

(Ekstrom, Roelfsema, Arsenault, Bonmassar, & Vanduffel, 2008; Moore & Armstrong, 2003;

Zhou & Desimone, 2011). These latter studies suggest that the salience map may be distributed

across multiple representations in frontal and visual cortex, and that perceptual selection is

embodied in the regional activation best matching the current task goals (Melloni et al., 2012;

Treue, 2003).

Limitations

The participants were screened for physical and cognitive health and thus provide a

conservative estimate of age-related effects (those occurring in the absence of significant

disease), rather than a population-representative estimate of age-related effects. The

behavioral data revealed significant age-related slowing consistent with previous research

(Figure 4), though we did not observe age-related differences in the mean level of activation.

Tasks with higher levels of demand on executive functioning and working memory may be


needed to elicit significant age-related differences in the level of frontoparietal activation.

Similarly, the present assessment of age-related effects was cross-sectional and dependent on

individual differences at one point in time, and confirmation of these findings with longitudinal

measures would be valuable (Hofer & Sliwinski, 2001; Salthouse, 2010).

To maximize the number of trials per task condition, within the scanning session, we

maintained display size constant at five items, rather than varying display size. As a result, we

cannot attribute the bottom-up guidance from the size singleton to the comparison of the

display items, rather than to encoding and response processes. Proulx (2007), however, did

vary display size in this task and found that RT-display size slopes were lower for size singleton

targets, relative to nonsingleton targets, implying increased search efficiency from the bottom-

up guidance. Similarly, although the size singleton was not informative, as noted previously we

cannot completely rule out a top-down contribution, particularly in the form of a search

strategy in which attention is directed to the most salient item first. Top-down effects could be

more clearly isolated by varying the predictability of the target (Madden, Whiting, Spaniol, et

al., 2005; Melloni et al., 2012; Whiting, Madden, Pierce, & Allen, 2005; Wolfe, Butcher, Lee, &

Hyle, 2003).

Conclusions

These findings demonstrate both age-related differences and constancy in the

behavioral and neural signatures of bottom-up attentional guidance during conjunction search.

Although significant age-related slowing was evident in mean RT, the improvement in target

detection provided by bottom-up attentional guidance was constant as a function of adult age.

Similarly, bottom-up guidance was associated with reduced target-related, frontoparietal


activation, throughout the age range. Thus, bottom-up attentional guidance, like priming

effects, leads to a reduction in the activation of task-relevant regions, in a manner that is

relatively age-invariant. Dorsal frontoparietal activation, specifically in the left FEF, covaried

significantly with the RT differences defining the target detection and bottom-up guidance

effects. However, the correlation between left FEF activation and bottom-up guidance emerged

only for individuals 60 years of age and older. This age-related difference in the activation-

performance relation suggests that older adults maintain a comparable level of bottom-up

guidance by means of enhanced tuning of the relation between neural activation and

behavioral response. This age-related pattern, however, did not exhibit all the features

expected of a compensatory mechanism. Overall, these results underscore the Yantis (2005)

view that “The dynamic interplay of stimulus-driven and goal-directed factors determines the

content of perceptual experience as it evolves over time (p. 976).”

Acknowledgments

This research was supported by research grant R01 AG039684 from the National

Institutes of Health. We are grateful to Lauren Packard, Rachel Siciliano, Max Horowitz, Kristin

Sundy, and Jesse Honig for their assistance.


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