Reward activates stimulus-specific and task-dependent representations in visual association cortices

The Role of the N170 in Linking Stimuli to Feedback-Effects of Stimulus Modality and Feedback Delay

With increasing feedback delay, feedback processing appears to shift from the striatum to the hippocampus. In addition, higher-order sensory areas might be involved in bridging a temporal gap between stimulus and feedback by reactivating the representation of the feedback-predicting stimulus during feedback processing. We hypothesized that the feedback-locked N170, an occipito-temporal event-related potential (ERP) component linked to higher-order visual processing, is more pronounced when delayed feedback is provided for choices between visual compared to auditory stimuli. 35 subjects completed a probabilistic feedback learning task with immediate (1 s) and delayed (7 s) monetary feedback for choices between visual or auditory stimuli. Participants successfully learned to choose the more rewarding stimuli irrespective of stimulus modality. For the N170 amplitude over the right hemisphere, we found an interaction between feedback timing and the modality of the chosen stimulus. Only for delayed feedback, the N170 was more pronounced for choices between visual than auditory stimuli. Moreover, in this condition, the N170 amplitude particularly reflected the reward prediction error (PE), with larger amplitudes for positive PEs and lower amplitudes for negative PEs. This suggests that the N170 reflects feedback-locked reactivations in higher-order visual areas mediated by the reward PE. While these effects need to be studied further, we discuss the N170 as a counterpart to the feedback-related negativity (FRN) regarding interacting influences of feedback valence, feedback timing, and PE.

Working memory as a representational template for reinforcement learning

Working memory (WM) and reinforcement learning (RL) both influence decision-making, but how they interact to affect behaviour remains unclear. We assessed whether RL is influenced by the format of visual stimuli held in WM, either feature-based or unified, object-based representations. In a pre-registered paradigm, participants learned stimulus-action combinations that provided reward through 80% probabilistic feedback. In parallel, participants retained the RL stimulus in WM and were asked to recall this stimulus after each RL choice. Crucially, the format of representation probed in WM was manipulated, with blocks encouraging either separate features or bound objects to be remembered. Incentivising a feature-based WM representation facilitated feature-based learning, shown by an improved choice strategy. This reveals a role of WM in providing sustained internal representations that are harnessed by RL, providing a framework by which these two cognitive processes cooperate.

Unraveling individual differences in learning potential: A dynamic framework for the case of reading development

Children show an enormous capacity to learn during development, but with large individual differences in the time course and trajectory of learning and the achieved skill level. Recent progress in developmental sciences has shown the contribution of a multitude of factors including genetic variation, brain plasticity, socio-cultural context and learning experiences to individual development. These factors interact in a complex manner, producing children's idiosyncratic and heterogeneous learning paths. Despite an increasing recognition of these intricate dynamics, current research on the development of culturally acquired skills such as reading still has a typical focus on snapshots of children's performance at discrete points in time. Here we argue that this 'static' approach is often insufficient and limits advancements in the prediction and mechanistic understanding of individual differences in learning capacity. We present a dynamic framework which highlights the importance of capturing short-term trajectories during learning across multiple stages and processes as a proxy for long-term development on the example of reading. This framework will help explain relevant variability in children's learning paths and outcomes and fosters new perspectives and approaches to study how children develop and learn.

Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex

Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.

Dynamic modulation of neural feedback processing and attention during spatial probabilistic learning

Learned stimulus-reward associations can modulate behavior and the underlying neural processing of information. We investigated the cascade of these neurocognitive mechanisms involved in the learning of spatial stimulus-reward associations. Using electroencephalogram recordings while participants performed a probabilistic spatial reward learning task, we observed that the feedback-related negativity component was more negative in response to loss feedback compared to gain feedback but showed no modulation by learning. The late positive component became larger in response to losses as the learning set progressed but smaller in response to gains. In addition, feedback-locked alpha frequency oscillations measured over occipital sites were predictive of N2pc amplitudes—a marker of spatial attention orienting—observed on the next trial. This relationship was found to become stronger with learning set progression. Taken together, we elucidated neurocognitive dynamics underlying feedback processing during spatial reward learning, and the subsequent effects of these learned spatial stimulus-reward associations on spatial attention.

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We can learn which spatial location relates to the highest probability of reward

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Neural processing of feedback valence was not influenced by learning

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LPC amplitude was dynamically modulated by learning, reflecting context updating

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Feedback-locked alpha power was predictive of ensuing orientation of attention

Neural Responses of Benefiting From the Prosocial Exchange: The Effect of Helping Behavior

Prosocial behavior is critical for the natural development of an individual as well as for promoting social relationships. Although this complex behavior results from gratuitous acts occurring between an agent and a recipient and a wealth of literature on prosocial behavior has investigated these actions, little is known about the effects on the recipient and the neurobiology underlying them. In this study, we used functional magnetic resonance imaging to identify neural correlates of receiving prosocial behavior in the context of real-world experiences, with different types of action provided by the agent, including practical help and effort appreciation. Practical help was associated with increased activation in a network of regions spanning across bilateral superior temporal sulcus, temporoparietal junction, temporal pole, and medial prefrontal cortex. Effort appreciation was associated with activation and increased task-modulated connectivity of the occipital cortex. Prosocial-dependent brain responses were associated with positive affect. Our results support the role of the theory of mind network and the visual cortices in mediating the positive effects of receiving gratuitous help. Moreover, they indicate that specific types of prosocial behavior are mediated by distinct brain networks, which further demonstrates the uniqueness of the psychological processes underlying prosocial actions.

Signed Reward Prediction Errors in the Ventral Striatum Drive Episodic Memory

Recent behavioral evidence implicates reward prediction errors (RPEs) as a key factor in the acquisition of episodic memory. Yet, important neural predictions related to the role of RPEs in episodic memory acquisition remain to be tested. Humans (both sexes) performed a novel variable-choice task where we experimentally manipulated RPEs and found support for key neural predictions with fMRI. Our results show that in line with previous behavioral observations, episodic memory accuracy increases with the magnitude of signed (i.e., better/worse-than-expected) RPEs (SRPEs). Neurally, we observe that SRPEs are encoded in the ventral striatum (VS). Crucially, we demonstrate through mediation analysis that activation in the VS mediates the experimental manipulation of SRPEs on episodic memory accuracy. In particular, SRPE-based responses in the VS (during learning) predict the strength of subsequent episodic memory (during recollection). Furthermore, functional connectivity between task-relevant processing areas (i.e., face-selective areas) and hippocampus and ventral striatum increased as a function of RPE value (during learning), suggesting a central role of these areas in episodic memory formation. Our results consolidate reinforcement learning theory and striatal RPEs as key factors subtending the formation of episodic memory.SIGNIFICANCE STATEMENT Recent behavioral research has shown that reward prediction errors (RPEs), a key concept of reinforcement learning theory, are crucial to the formation of episodic memories. In this study, we reveal the neural underpinnings of this process. Using fMRI, we show that signed RPEs (SRPEs) are encoded in the ventral striatum (VS), and crucially, that SRPE VS activity is responsible for the subsequent recollection accuracy of one-shot learned episodic memory associations.

Dopamine is associated with prioritization of reward-associated memories in Parkinson's disease

Patients with Parkinson's disease have reduced reward sensitivity related to dopaminergic neuron loss, which is associated with impairments in reinforcement learning. Increasingly, however, dopamine-dependent reward signals are recognized to play an important role beyond reinforcement learning. In particular, it has been shown that reward signals mediated by dopamine help guide the prioritization of events for long-term memory consolidation. Meanwhile, studies of memory in patients with Parkinson's disease have focused on overall memory capacity rather than what is versus what isn't remembered, leaving open questions about the effect of dopamine replacement on the prioritization of memories by reward and the time-dependence of this effect. The current study sought to fill this gap by testing the effect of reward and dopamine on memory in patients with Parkinson's disease. We tested the effect of dopamine modulation and reward on two forms of long-term memory: episodic memory for neutral objects and memory for stimulus-value associations. We measured both forms of memory in a single task, adapting a standard task of reinforcement learning with incidental episodic encoding events of trial-unique objects. Objects were presented on each trial at the time of feedback, which was either rewarding or not. Memory for the trial-unique images and for the stimulus-value associations, and the influence of reward on both, was tested immediately after learning and 2 days later. We measured performance in Parkinson's disease patients tested either ON or OFF their dopaminergic medications and in healthy older control subjects. We found that dopamine was associated with a selective enhancement of memory for reward-associated images, but that it did not influence overall memory capacity. Contrary to predictions, this effect did not differ between the immediate and delayed memory tests. We also found that while dopamine had an effect on reward-modulated episodic memory, there was no effect of dopamine on memory for stimulus-value associations. Our results suggest that impaired prioritization of cognitive resource allocation may contribute to the early cognitive deficits of Parkinson's disease.

Cognitive Effort Modulates Connectivity between Dorsal Anterior Cingulate Cortex and Task-Relevant Cortical Areas

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand. For that purpose, a perceptual detection task was administered that required male and female human participants to detect either a face or a house in a noisy image. Effort demand was manipulated by adding little (low effort) or much (high effort) noise to the images. Results showed a network of dACC, anterior insula (AI), and intraparietal sulcus (IPS) to be more active when effort demand was high, independent of the performed task (face or house detection). Importantly, effort demand modulated functional connectivity between dACC and face-responsive or house-responsive perceptual areas, depending on the task at hand. This shows that dACC, AI, and IPS constitute a general effort-responsive network and suggests that the neural implementation of cognitive effort involves dACC-initiated sensitization of task-relevant areas.SIGNIFICANCE STATEMENT Although cognitive effort is generally perceived as aversive, its investment is inevitable when navigating an increasingly complex society. In this study, we demonstrate how the human brain tailors the implementation of effort to the requirements of the task at hand. We show increased effort-related activity in a network of brain areas consisting of dorsal anterior cingulate cortex (dACC), anterior insula, and intraparietal sulcus, independent of task specifics. Crucially, we also show that effort-induced functional connectivity between dACC and task-relevant areas tracks specific task demands. These results demonstrate how brain regions specialized to solve a task may be energized by dACC when effort demand is high.

Task-irrelevant financial losses inhibit the removal of information from working memory

The receipt of financial rewards or penalties - though task-irrelevant - may exert an obligatory effect on manipulating items in working memory (WM) by constraining a forthcoming shift in attention or reinforcing attentional shifts that have previously occurred. Here, we adjudicate between these two hypotheses by varying – after encoding- the order in which task-irrelevant financial outcomes and cues indicating which items need to be retained in memory are presented (so called retrocues). We employed a “what-is-where” design that allowed for the fractionation of WM recall into separate components: identification, precision and binding (between location and identity). Principally, valence-dependent effects were observed only for precision and binding, but only when outcomes were presented before, rather than after, the retrocue. Specifically, task-irrelevant financial losses presented before the retrocue caused a systematic breakdown in binding (misbinding), whereby the features of cued and non-cued memoranda became confused, i.e., the features that made up relevant memoranda were displaced by those of non-cued (irrelevant) items. A control experiment, in which outcomes but no cues were presented, failed to produce the same effects, indicating that the inclusion of retrocues were necessary for generating this effect. These results show that the receipt of financial penalties – even when uncoupled to performance – can prevent irrelevant information from being effectively pruned from WM. These results illustrate the importance of reward-related processing to controlling the contents of WM.

Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making

Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision-making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision-making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost-benefit decision-making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real-life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders.

Dissociating Reward- and Attention-driven Biasing of Global Feature-based Selection in Human Visual Cortex

Objects that promise rewards are prioritized for visual selection. The way this prioritization shapes sensory processing in visual cortex, however, is debated. It has been suggested that rewards motivate stronger attentional focusing, resulting in a modulation of sensory selection in early visual cortex. An open question is whether those reward-driven modulations would be independent of similar modulations indexing the selection of attended features that are not associated with reward. Here, we use magnetoencephalography in human observers to investigate whether the modulations indexing global color-based selection in visual cortex are separable for target- and (monetary) reward-defining colors. To assess the underlying global color-based activity modulation, we compare the event-related magnetic field response elicited by a color probe in the unattended hemifield drawn either in the target color, the reward color, both colors, or a neutral task-irrelevant color. To test whether target and reward relevance trigger separable modulations, we manipulate attention demands on target selection while keeping reward-defining experimental parameters constant. Replicating previous observations, we find that reward and target relevance produce almost indistinguishable gain modulations in ventral extratriate cortex contralateral to the unattended color probe. Importantly, increasing attention demands on target discrimination increases the response to the target-defining color, whereas the response to the rewarded color remains largely unchanged. These observations indicate that, although task relevance and reward influence the very same feature-selective area in extrastriate visual cortex, the associated modulations are largely independent.

Neurobiology of value-driven attention

What we pay attention to is influenced by reward learning. Converging evidence points to the idea that associative reward learning changes how visual stimuli are processed in the brain, rendering learned reward cues difficult to ignore. Behavioral evidence distinguishes value-driven attention from other established control mechanisms, suggesting a distinct underlying neurobiological process. Recently, studies have begun to explore the neural substrates of this value-driven attention mechanism. Here, I review the progress that has been made in this area, and synthesize the findings to provide an integrative account of the neurobiology of value-driven attention. The proposed account can explain both attentional capture by previously rewarded targets and the modulatory effect of reward on priming, as well as the decoupling of reward history and prior task relevance in value-driven attention.

Getting a grip on cognitive flexibility

Cognitive flexibility refers to the ability to quickly reconfigure our mind, like when we switch between different tasks. This review highlights recent evidence showing that cognitive flexibility can be conditioned by simple incentives typically known to drive lower-level learning, such as stimulus-response associations. Cognitive flexibility can also become associated with, and triggered by, bottom-up contextual cues in our environment, including subliminal cues. Therefore, we suggest that the control functions that mediate cognitive flexibility are grounded in, and guided by, basic associative learning mechanisms, and abide by the same learning principles as more low-level forms of behavior. Such a learning perspective on cognitive flexibility offers new directions and important implications for further research, theory, and applications.

Interactions between frontal and posterior oscillatory dynamics support adjustment of stimulus processing during reinforcement learning

Reinforcement learning (RL) in humans is subserved by a network of striatal and frontal brain areas. The electrophysiological signatures of feedback evaluation are increasingly well understood, but how those signatures relate to the use of feedback to guide subsequent behavioral adjustment remains unclear. One mechanism for post-feedback behavioral optimization is the modulation of sensory processing. We used source-reconstructed MEG to test whether feedback affects the interactions between sources of oscillatory activity in the learning network and task-relevant stimulus-processing areas. Participants performed a probabilistic RL task in which they learned associations between colored faces and response buttons using trial-and-error feedback. Delta-band (2-4 Hz) and theta-band (4-8 Hz) power in multiple frontal regions were sensitive to feedback valence. Low and high beta-band power (12-20 and 20-30 Hz) in occipital, parietal, and temporal regions differentiated between color and face information. Consistent with our hypothesis, single-trial power-power correlations between frontal and posterior-sensory areas were modulated by the interaction between feedback valence and the relevant stimulus characteristic (color versus identity). These results suggest that long-range oscillatory coupling supports post-feedback updating of stimulus processing.

Adult-born neurons boost odor-reward association

Odor–reward association during appetitive learning is a fundamental process that requires multiple forms of plasticity. In the adult olfactory bulb, the continual production of newborn interneurons contributes to the functional plasticity of the system, placing the newcomers in a key position to participate in olfactory associative learning. Here, we demonstrate that adult-born neurons, but not preexisting ones, contain information about learned positive value. Moreover, specific heightening of this signal improves associative learning and odor value update and is enough in some cases to trigger behavior even without odor stimulus. Collectively, our findings show an important role of this adult-born interneuron population in odor–reward association and unveil the relevance of odor value encoding at early stages of sensory processing.

Olfaction is an important sensory modality driving fundamental behaviors. During odor-dependent learning, a positive value is commonly assigned to an odorant, and multiple forms of plasticity are involved when such odor–reward associations are formed. In rodents, one of the mechanisms underlying plasticity in the olfactory bulb consists in recruiting new neurons daily throughout life. However, it is still unknown whether adult-born neurons might participate in encoding odor value. Here, we demonstrate that exposure to reward-associated odors specifically increases activity of adult-born neurons but not preexisting neurons. Remarkably, adult-born neuron activation during rewarded odor presentation heightens discrimination learning and enhances the ability to update the odor value during reversal association. Moreover, in some cases, activation of this interneuron population can trigger olfactory learning without sensory stimulation. Taken together, our results show a specific involvement of adult-born neurons in facilitating odor–reward association during adaptive learning.

Neurodevelopmental Changes in Social Reinforcement Processing: A Functional Magnetic Resonance Imaging Study

Objective

In the current study we investigated neurodevelopmental changes in response to social and non-social reinforcement.

Methods

Fifty-three healthy participants including 16 early adolescents (age, 10-15 years), 16 late adolescents (age, 15-18 years), and 21 young adults (age, 21-25 years) completed a social/non-social reward learning task while undergoing functional magnetic resonance imaging. Participants responded to fractal image stimuli and received social or non-social reward/non-rewards according to their accuracy. ANOVAs were conducted on both the blood oxygen level dependent response data and the product of a context-dependent psychophysiological interaction (gPPI) analysis involving ventromedial prefrontal cortex (vmPFC) and bilateral insula cortices as seed regions.

Results

Early adolescents showed significantly increased activation in the amygdala and anterior insula cortex in response to non-social monetary rewards relative to both social reward/non-reward and monetary non-rewards compared to late adolescents and young adults. In addition, early adolescents showed significantly more positive connectivity between the vmPFC/bilateral insula cortices seeds and other regions implicated in reinforcement processing (the amygdala, posterior cingulate cortex, insula cortex, and lentiform nucleus) in response to non-reward and especially social non-reward, compared to late adolescents and young adults.

Conclusion

It appears that early adolescence may be marked by: (i) a selective increase in responsiveness to non-social, relative to social, rewards; and (ii) enhanced, integrated functioning of reinforcement circuitry for non-reward, and in particular, with respect to posterior cingulate and insula cortices, for social non-reward.

Reward Selectively Modulates the Lingering Neural Representation of Recently Attended Objects in Natural Scenes

Theories of reinforcement learning and approach behavior suggest that reward can increase the perceptual salience of environmental stimuli, ensuring that potential predictors of outcome are noticed in the future. However, outcome commonly follows visual processing of the environment, occurring even when potential reward cues have long disappeared. How can reward feedback retroactively cause now-absent stimuli to become attention-drawing in the future? One possibility is that reward and attention interact to prime lingering visual representations of attended stimuli that sustain through the interval separating stimulus and outcome. Here, we test this idea using multivariate pattern analysis of fMRI data collected from male and female humans. While in the scanner, participants searched for examples of target categories in briefly presented pictures of cityscapes and landscapes. Correct task performance was followed by reward feedback that could randomly have either high or low magnitude. Analysis showed that high-magnitude reward feedback boosted the lingering representation of target categories while reducing the representation of nontarget categories. The magnitude of this effect in each participant predicted the behavioral impact of reward on search performance in subsequent trials. Other analyses show that sensitivity to reward-as expressed in a personality questionnaire and in reactivity to reward feedback in the dopaminergic midbrain-predicted reward-elicited variance in lingering target and nontarget representations. Credit for rewarding outcome thus appears to be assigned to the target representation, causing the visual system to become sensitized for similar objects in the future.SIGNIFICANCE STATEMENT How do reward-predictive visual stimuli become salient and attention-drawing? In the real world, reward cues precede outcome and reward is commonly received long after potential predictors have disappeared. How can the representation of environmental stimuli be affected by outcome that occurs later in time? Here, we show that reward acts on lingering representations of environmental stimuli that sustain through the interval between stimulus and outcome. Using naturalistic scene stimuli and multivariate pattern analysis of fMRI data, we show that reward boosts the representation of attended objects and reduces the representation of unattended objects. This interaction of attention and reward processing acts to prime vision for stimuli that may serve to predict outcome.

Sensitivity to value-driven attention is predicted by how we learn from value

Reward learning is known to influence the automatic capture of attention. This study examined how the rate of learning, after high- or low-value reward outcomes, can influence future transfers into value-driven attentional capture. Participants performed an instrumental learning task that was directly followed by an attentional capture task. A hierarchical Bayesian reinforcement model was used to infer individual differences in learning from high or low reward. Results showed a strong relationship between high-reward learning rates (or the weight that is put on learning after a high reward) and the magnitude of attentional capture with high-reward colors. Individual differences in learning from high or low rewards were further related to performance differences when high- or low-value distractors were present. These findings provide novel insight into the development of value-driven attentional capture by showing how information updating after desired or undesired outcomes can influence future deployments of automatic attention.

Causes and consequences of limitations in visual working memory

Recent methodological and conceptual advances have led to a fundamental reappraisal of the nature of visual working memory (WM). A large corpus of evidence now suggests that there might not be a hard limit on the number of items that can be stored. Instead, WM may be better captured by a highly limited––but flexible––resource model. More resource can be allocated to prioritized items but, crucially, at a cost of reduced recall precision for other stored items. Expectations may modulate resource distribution, for example, through neural oscillations in the alpha band increasing inhibition of irrelevant cortical regions. Our understanding of the neural architecture of WM is also undergoing radical revision. Whereas the prefrontal cortex has previously dominated research endeavors, other cortical regions, such as early visual areas, are now considered to make an essential contribution, for example holding one or more items in a privileged state or “focus of attention” within WM. By contrast, the striatum is increasingly viewed as crucial in determining why and how items are gated into memory, while the hippocampus, it has controversially been argued, might be critical in the formation of temporally resilient conjunctions across features of stored items in WM.

Value-based attentional capture influences context-dependent decision-making

Normative theories posit that value-based decision-making is context independent. However, decisions between two high-value options can be suboptimally biased by the introduction of a third low-value option. This context-dependent modulation is consistent with the divisive normalization of the value of each stimulus by the total value of all stimuli. In addition, an independent line of research demonstrates that pairing a stimulus with a high-value outcome can lead to attentional capture that can mediate the efficiency of visual information processing. Here we tested the hypothesis that value-based attentional capture interacts with value-based normalization to influence the optimality of decision-making. We used a binary-choice paradigm in which observers selected between two targets and the color of each target indicated the magnitude of their reward potential. Observers also had to simultaneously ignore a task-irrelevant distractor rendered in a color that was previously associated with a specific reward magnitude. When the color of the task-irrelevant distractor was previously associated with a high reward, observers responded more slowly and less optimally. Moreover, as the learned value of the distractor increased, electrophysiological data revealed an attenuation of the lateralized N1 and N2Pc responses evoked by the relevant choice stimuli and an attenuation of the late positive deflection (LPD). Collectively, these behavioral and electrophysiological data suggest that value-based attentional capture and value-based normalization jointly mediate the influence of context on free-choice decision-making.