Leslie Ungerleider had a tremendous impact across many different areas of cognitive neuroscience. Her ideas and her approach, as well as her findings, will continue to impact the field for generations to come. One of the most impactful aspects of her approach was her focus on the ways that anatomical connections constrain functional communications among brain regions. Furthermore, she emphasized that changes in these functional communications, whether from lesions to the anatomical connections or temporary modulations of the efficacy of information transmission resulting from selective attention, have consequences for cognition and behavior. By necessity, this short review cannot cover the vast amount of research that contributed to or benefited from Leslie's work. Rather, we focus on one line of research that grew directly from some of Leslie's early work and her mentoring on these important concepts. This research and the many other lines of research that arose from these same origins has helped develop our understanding of the visual system, and cognitive systems more generally, as collections of highly organized, specialized, dynamic, and interacting subsystems.

Traditional models of attention state that target enhancement and distractor suppression utilize the same underlying neural mechanisms, which would predict that the strength of enhancement should covary with the strength of suppression. Recent evidence at the group-level, however, suggests that enhancement and suppression mechanisms are separable. These findings would be further supported if the strength of enhancement and suppression varied independently across individuals. To examine this, however, requires reliable measures of individual differences of both effects. Here we investigate within-task reliability of individual-difference measures of target enhancement and distractor suppression using adaptations of Posner Cuing and Additional Singleton visuo-spatial search tasks. Exp.1 examined enhancement using endogenous cues and suppression using statistical learning. Exp.2 examined enhancement and suppression using statistical learning only. In these experiments we found reliable individual differences in enhancement using both spatial cues (validity effect) and statistical learning (frequency of target or distractor location). Individual differences in suppression using statistical learning, however, were not reliable. In Exp.3 colors and shapes changed randomly across trials, encouraging the use of singleton detection mode. Here, statistical learning yielded reliable individual differences for both target enhancement and distractor suppression. Furthermore, we found that the magnitude of target enhancement did not covary with the magnitude of distractor suppression across individuals. Together, these results demonstrate the utility of an individual-differences approach. We find that individual differences in spatial enhancement are consistently reliable across tasks. In contrast, spatial suppression is less reliable and highly contextual, and thus may fluctuate more within individuals due to additional factors (i.e., shifting strategy, recent experience). When experimental design constrains search strategy, however, individual differences in distractor suppression may be reliably measured. With reliable measures, we show a lack of covariance between enhancement and suppression, contrary to the traditional unitary models of attention.

Cognitive decline with age is associated with brain atrophy and reduced brain activations, but the underlying neurophysiological mechanisms are unclear, especially in deeper brain structures primarily affected by healthy aging or neurodegenerative processes. Here, we characterize time-resolved, resting-state magnetoencephalography activity of the hippocampus and subcortical brain regions in a large cohort of healthy young and older volunteers from the Cam-CAN open repository. The data show age-related changes in both rhythmic and arrhythmic signal strength and temporal variability in multiple deeper brain regions, including the hippocampus, striatum, and thalamus. We observe a slowing of neural activity in deeper brain regions, which echoes previous reports of cortical slowing. We also report reduced occipito-parietal alpha peak associated with increased theta-band activity and signal variability in the hippocampus, an effect that may reflect compensatory processes as theta activity and variability were more strongly expressed when cognitive performances are preserved. Overall, this study advances the understanding of the biological nature of inter-individual variability in aging. The data provide new insight of how hippocampus and subcortical neurophysiological activity evolve with biological age, and highlight frequency-specific effects associated with cognitive decline vs. cognitive maintenance.

The interaction of spatial and temporal structures across perceptual modalities impacts representation in working memory. Shared temporal structure between a stream of visual targets and a stream of sounds promotes representation of the spatial structure of those sounds. This integration of perceptual information occurs whether helpful or harmful, differentially impacting performance.

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.

Fluctuations in strategy, attention, or motivation can cause large variability in performance across task trials. Typically, this variability is treated as noise, and assumed to cancel out, leaving supposedly stable relationships among behavior, neural activity, and experimental task conditions. Those relationships, however, could change with a participant’s internal cognitive states, and variability in performance may carry important information regarding those states, which cannot be directly measured. Therefore, we used a mathematical, state-space modeling framework to fit internal cognitive states to measured behavioral data, quantifying each participant’s sensitivity to factors such as past errors or distractions, to characterize their underlying fluctuations in reaction time. We show how integrating the states into the modeling framework could help explain trial-by-trial variability in behavior. Further, we identify EEG functional connectivity features that modulate with each state. These results illustrate the potential of this approach and how it could enable quantification of intra- and inter-individual differences and provide insight into their neural bases.

I propose working memory be considered, not as a process for static maintenance in a particular set of brain regions, but rather as a dynamic process unfolding to serve future needs. Brain regions such as the hippocampus, or sensory and motor regions, may be necessarily recruited during this process, depending on task demands. Information stored in working memory is thus a distributed representation reflected in the structural and functional state of multiple brain areas and the trajectory of that state over time. Recent research is discussed in support of this view.

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we investigate the cognitive variability in older adults by linking the influence of white matter microstructure on the task-related organization of fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual differences in cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal (fMRI and biological markers) brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.

Peak alpha frequency is known to vary not just between individuals, but also within an individual over time. While variance in this metric between individuals has been tied to working memory performance, less understood are how short timescale modulations of peak alpha frequency during task performance may facilitate behavior. This gap in understanding may be bridged by consideration of a key difference between individuals: sex. Inconsistent findings in the literature regarding the relationship between peak alpha frequency and cognitive performance, as well as known sex-related-differences in peak alpha frequency and its modulation motivated our hypothesis that cognitive and neural processes underlying working memory-modulation of peak alpha frequency in particular-may differ based upon sex. Targeting sex as a predictive factor, we analyzed the EEG data of participants recorded while they performed four versions of a visual spatial working memory task. A significant difference between groups was present: females modulated peak alpha frequency more than males. Task performance did not differ by sex, yet a relationship between accuracy and peak alpha frequency was present in males, but not in females. These findings highlight the importance of considering sex as a factor in the study of oscillatory activity, particularly to further understanding of the neural mechanisms that underlie working memory.

ASSOCIATE EDITORS DEANNA L. BENSON Icahn School of Medicine at Mount Sinai EDWARD M. CALLAWAY Salk Ins tute THOMAS E. FINGER University of Colorado School of Medicine JEFFREY H. KORDOWER Rush University Medical Center ANDREW D. HUBERMAN Stanford University School of Medicine, Stanford, CA, USA KATHLEEN S. ROCKLAND Boston University School of Medicine JOHN L.R. RUBENSTEIN University of California, San Francisco UWE HOMBERG University of Marburg

Brain activity and communications are complex phenomena that dynamically unfold over time. However, in contrast with the large number of studies reporting neuroanatomical differences in activation relative to young adults, changes of temporal dynamics of neural activity during normal and pathological aging have been grossly understudied and are still poorly known. Here, we synthesize the current state of knowledge from MEG and EEG studies that aimed at specifying the effects of healthy and pathological aging on local and network dynamics, and discuss the clinical and theoretical implications of these findings. We argue that considering the temporal dynamics of brain activations and networks could provide a better understanding of changes associated with healthy aging, and the progression of neurodegenerative disease. Recent research has also begun to shed light on the association of these dynamics with other imaging modalities and with individual differences in cognitive performance. These insights hold great potential for driving new theoretical frameworks and development of biomarkers to aid in identifying and treating age-related cognitive changes.

Brain activity and communications are complex phenomena that dynamically unfold over time. However, in contrast with the large number of studies reporting neuroanatomical differences in activation relative to young adults, changes of temporal dynamics of neural activity during normal and pathological aging have been grossly understudied and are still poorly known. Here, we synthesize the current state of knowledge from MEG and EEG studies that aimed at specifying the effects of healthy and pathological aging on local and network dynamics, and discuss the clinical and theoretical implications of these findings. We argue that considering the temporal dynamics of brain activations and networks could provide a better understanding of changes associated with healthy aging, and the progression of neurodegenerative disease. Recent research has also begun to shed light on the association of these dynamics with other imaging modalities and with individual differences in cognitive performance. These insights hold great potential for driving new theoretical frameworks and development of biomarkers to aid in identifying and treating age-related cognitive changes.

Our main goal was to determine the influence of white matter integrity on the dynamic coupling between brain regions and the individual variability of cognitive performance in older adults. Electroencephalography was recorded while participants performed a task specifically designed to engage working memory and inhibitory processes, and the associations among functional activity, structural integrity, and cognitive performance were assessed. We found that the association between white matter microstructural integrity and cognitive functioning with aging is mediated by time-varying alpha and gamma phase-locking value. Specifically, better preservation of the inferior fronto-occipital fasciculus in older individuals drives faster task-related modulations of alpha and gamma long-range phase-locking value between the inferior frontal gyrus and occipital lobe and lower local phase-amplitude coupling in occipital lobes, which in turn drives better cognitive control performance. Our results help delineate the role of individual variability of white matter microstructure in dynamic synchrony and cognitive performance during normal aging.

Our interactions with the world are guided by our understanding of objects’ physical properties. When packing groceries, we place fragile items on top of more durable ones and position sharp corners so they will not puncture the bags. However, physical properties are not always readily observable, and we often must rely on our knowledge of attributes such as weight, hardness, and slipperiness to guide our actions on familiar objects. Here, we asked whether our knowledge of physical properties not only shapes our actions but also guides our attention to the visual world. In a series of four visual search experiments, participants viewed arrays of everyday objects and were tasked with locating a specified object. The target was sometimes differentiated from the distractors based on its hardness, while a host of other visual and semantic attributes were controlled. We found that observers implicitly used the hardness distinction to locate the target more quickly, even though none reported being aware that hardness was relevant. This benefit arose from fixating fewer distractors overall and spending less time interrogating each distractor when the target was distinguished by hardness. Progressively more stringent stimulus controls showed that surface properties and curvature cues to hardness were not necessary for the benefit. Our findings show that observers implicitly recruit their knowledge of objects’ physical properties to guide how they attend to and engage with visual scenes. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Cognitive changes with aging are highly variable across individuals. This study investigated whether cognitive control performance might depend on preservation of structural and effective connectivity in older individuals. Specifically, we tested inhibition following working memory (WM) updating and maintenance. We analyzed diffusion tensor imaging and functional magnetic resonance imaging data in thirty-four young adults and thirty-four older adults, who performed an arithmetic verification task during functional magnetic resonance imaging. Results revealed larger arithmetic interference in older adults relative to young adults after WM updating, whereas both groups showed similar interference after WM maintenance. In both groups, arithmetic interference was associated with larger activations and stronger effective connectivity among bilateral anterior cingulate, bilateral inferior frontal gyrus, and left angular gyrus, with larger activations of frontal regions in older adults than in younger adults. In older adults, preservation of frontoparietal structural microstructure, especially involving the inferior frontaloccipital fasciculus, was associated with reduced interference, and stronger task-related effective connectivity. These results highlight how both structural and functional changes in the cognitive control network contribute to individual variability in performance during aging.

Attention and working memory (WM) have previously been shown to interact closely when sensory information is being maintained. However, when non-sensory information is maintained in WM, the relationship between WM and sensory attention may be less strong. In the current study, we used electroencephalography to evaluate whether value-driven attentional capture (i.e., allocation of attention to a task-irrelevant feature previously associated with a reward) and its effects on either sensory or non-sensory WM performance might be greater than the effects of salient, non-reward-associated stimuli. In a training phase, 19 participants learned to associate a color with reward. Then, participants were presented with squares and encoded their locations into WM. Participants were instructed to convert the spatial locations either to another type of sensory representation or to an abstract, relational type of representation. During the WM delay period, task-irrelevant distractors, either previously-rewarded or non-rewarded, were presented, with a novel color distractor in the other hemifield. The results revealed lower alpha power and larger N2pc amplitude over posterior electrode sides contralateral to the previously rewarded color, compared to ipsilateral. These effects were mainly found during relational WM, compared to sensory WM, and only for the previously rewarded distractor color, compared to a previous non-rewarded target color or novel color. These effects were associated with modulations of WM performance. These results appear to reflect less capture of attention during maintenance of specific location information, and suggest that value-driven attentional capture can be mitigated as a function of the type of information maintained in WM.

The ability to inhibit actions inappropriate for the context is essential for meeting the shifting demands of complex environments. The stop signal task (SST) has been used in many previous studies to examine the interactions between go and stop responses in a cognitively demanding task involving attention, conflict resolution, and motor plan selection. The current study uses a variant of the SST, in which the continue signal instructs participants to proceed with the go response they were preparing. Reaction times (RTs) on continue trials were bimodally distributed, suggesting that an aspect of inhibition was involved in at least some of the trials. We investigated whether the cognitive processes delaying the generation of a behavioural response on continue trials are the same as for stop trials. We found improvement of stop signal reaction times (SSRTs) following stop trials, but the decrease in continue signal reaction times (CSRTs) was not significant. No improvement in either SSRT or CSRT was found following continue trials, suggesting that activation of the processes delaying the response on continue trials is insufficient to drive subsequent adjustments in SSRT or CSRT. In addition, go RTs only slowed following stop trials. These effects may suggest the presence of a selective learning process, which requires that the initial inhibition captured by SSRT and CSRT be combined with recognition of the stop signal specifically to affect subsequent performance.