Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task

Multiattribute Decision-making in Macaques Relies on Direct Attribute Comparisons

In value-based decisions, there are frequently multiple attributes, such as cost, quality, or quantity, that contribute to the overall goodness of an option. Because one option may not be better in all attributes at once, the decision process should include a means of weighing relevant attributes. Most decision-making models solve this problem by computing an integrated value, or utility, for each option from a weighted combination of attributes. However, behavioral anomalies in decision-making, such as context effects, indicate that other attribute-specific computations might be taking place. Here, we tested whether rhesus macaques show evidence of attribute-specific processing in a value-based decision-making task. Monkeys made a series of decisions involving choice options comprising a sweetness and probability attribute. Each attribute was represented by a separate bar with one of two mappings between bar size and the magnitude of the attribute (i.e., bigger = better or bigger = worse). We found that translating across different mappings produced selective impairments in decision-making. Choices were less accurate and preferences were more variable when like attributes differed in mapping, suggesting that preventing monkeys from easily making direct attribute comparisons resulted in less accurate choice behavior. This was not the case when mappings of unalike attributes within the same option were different. Likewise, gaze patterns favored transitions between like attributes over transitions between unalike attributes of the same option, so that like attributes were sampled sequentially to support within-attribute comparisons. Together, these data demonstrate that value-based decisions rely, at least in part, on directly comparing like attributes of multiattribute options.

Dissociable Representations of Decision Variables within Subdivisions of the Macaque Orbital and Ventrolateral Frontal Cortex

The ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subdivisions of VFC in male macaque monkeys performing a two-choice probabilistic task for different fruit juice outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral Area 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within Area 12o, in contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal Area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subdivisions, while the properties of the reward, such as its flavor, were more strongly represented in Areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Thalamocortical architectures for flexible cognition and efficient learning

The brain exhibits a remarkable ability to learn and execute context-appropriate behaviors. How it achieves such flexibility, without sacrificing learning efficiency, is an important open question. Neuroscience, psychology, and engineering suggest that reusing and repurposing computations are part of the answer. Here, we review evidence that thalamocortical architectures may have evolved to facilitate these objectives of flexibility and efficiency by coordinating distributed computations. Recent work suggests that distributed prefrontal cortical networks compute with flexible codes, and that the mediodorsal thalamus provides regularization to promote efficient reuse. Thalamocortical interactions resemble hierarchical Bayesian computations, and their network implementation can be related to existing gating, synchronization, and hub theories of thalamic function. By reviewing recent findings and providing a novel synthesis, we highlight key research horizons integrating computation, cognition, and systems neuroscience.

Humans forage for reward in reinforcement learning tasks

How do we make good decisions in uncertain environments? In psychology and neuroscience, the classic answer is that we calculate the value of each option and then compare the values to choose the most rewarding, modulo some exploratory noise. An ethologist, conversely, would argue that we commit to one option until its value drops below a threshold, at which point we start exploring other options. In order to determine which view better describes human decision-making, we developed a novel, foraging-inspired sequential decision-making model and used it to ask whether humans compare to threshold ("Forage") or compare alternatives ("Reinforcement-Learn" [RL]). We found that the foraging model was a better fit for participant behavior, better predicted the participants' tendency to repeat choices, and predicted the existence of held-out participants with a pattern of choice that was almost impossible under RL. Together, these results suggest that humans use foraging computations, rather than RL, even in classic reinforcement learning tasks.

Dorsal raphe neurons integrate the values of reward amount, delay, and uncertainty in multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when confronting reward uncertainty. However, it has been unclear whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider these attributes to make a choice. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes, and this population tended to integrate the attributes in a manner that reflected monkeys' preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how the DRN participates in value computations, guiding theories about the role of the DRN in decision-making and psychiatric disease.

Optogenetic Inhibition of the Orbitofrontal Cortex Disrupts Inhibitory Control during Stop-Change Performance in Male Rats

Historically, the orbitofrontal cortex (OFC) has been implicated in a variety of behaviors ranging from reversal learning and inhibitory control to more complex representations of reward value and task space. While modern interpretations of the OFC's function have focused on a role in outcome evaluation, these cognitive processes often require an organism to inhibit a maladaptive response or strategy. Single-unit recordings from the OFC in rats performing a stop-change task show that the OFC responds strongly to STOP trials. To investigate the role that the OFC plays in stop-change performance, we expressed halorhodopsin (eNpHR3.0) in excitatory neurons in the OFC and tested rats on the stop-change task. Previous work suggests that the OFC differentiates between STOP trials based on trial sequence (i.e., gS trials: STOP trials preceded by a GO vs sS trials: STOP trials preceded by a STOP). We found that yellow light activation of the eNpHR3.0-expressing neurons significantly decreased accuracy only on STOP trials that followed GO trials (gS trials). Further, optogenetic inhibition of the OFC speeded reaction times on error trials. This suggests that the OFC plays a role in inhibitory control processes and that this role needs to be accounted for in modern interpretations of OFC function.

Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subregions of VFC in macaque monkeys performing a two-choice probabilistic task for different fruit juices outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral subdivision 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within 12o, by contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subregions, while the properties of the reward, such as its flavor, were more strongly represented in areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Increased brain nucleus accumbens functional connectivity in melancholic depression

BACKGROUND

Melancholic depression, marked by typical symptoms of anhedonia, is regarded as a homogeneous subtype of major depressive disorder (MDD). However, little attention was paid to underlying mechanisms of melancholic depression. This study aims to examine functional connectivity of the reward circuit associated with anhedonia symptoms in melancholic depression.

METHODS

Fifty-nine patients with first-episode drug- naive MDD, including 31 melancholic patients and 28 non-melancholic patients, were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two healthy volunteers were recruited as controls. Bilateral nucleus accumbens (NAc) were selected as seed points to form functional NAc network. Then support vector machine (SVM) was used to distinguish melancholic patients from non-melancholic patients.

RESULTS

Relative to non-melancholic patients, melancholic patients displayed increased functional connectivity (FC) between bilateral NAc and right middle frontal gyrus (MFG) and between right NAc and left cerebellum lobule VIII. Compared to healthy controls, melancholic patients showed increased FC between right NAc and right lingual gyrus and between left NAc and left postcentral gyrus; non-melancholic patients had increased FC between bilateral NAc and right lingual gyrus. No significant correlations were observed between altered FC and clinical variables in melancholic patients. SVM results showed that FC between left NAc and right MFG could accurately distinguish melancholic patients from non-melancholic patients.

CONCLUSION

Melancholic depression exhibited different patterns of functional connectivity of the reward circuit relative to non-melancholic patients. This study highlights the significance of the reward circuit in the neuropathology of melancholic depression.

The neurocognitive role of working memory load when Pavlovian motivational control affects instrumental learning

Research suggests that a fast, capacity-limited working memory (WM) system and a slow, incremental reinforcement learning (RL) system jointly contribute to instrumental learning. Thus, situations that strain WM resources alter instrumental learning: under WM loads, learning becomes slow and incremental, the reliance on computationally efficient learning increases, and action selection becomes more random. It is also suggested that Pavlovian learning influences people's behavior during instrumental learning by providing hard-wired instinctive responses including approach to reward predictors and avoidance of punishment predictors. However, it remains unknown how constraints on WM resources affect instrumental learning under Pavlovian influence. Thus, we conducted a functional magnetic resonance imaging (fMRI) study (N = 49) in which participants completed an instrumental learning task with Pavlovian-instrumental conflict (the orthogonalized go/no-go task) both with and without extra WM load. Behavioral and computational modeling analyses revealed that WM load reduced the learning rate and increased random choice, without affecting Pavlovian bias. Model-based fMRI analysis revealed that WM load strengthened RPE signaling in the striatum. Moreover, under WM load, the striatum showed weakened connectivity with the ventromedial and dorsolateral prefrontal cortex when computing reward expectations. These results suggest that the limitation of cognitive resources by WM load promotes slow and incremental learning through the weakened cooperation between WM and RL; such limitation also makes action selection more random, but it does not directly affect the balance between instrumental and Pavlovian systems.

Multi-attribute decision-making in macaques relies on direct attribute comparisons

In value-based decisions, there are frequently multiple attributes, such as cost, quality, or quantity, that contribute to the overall goodness of an option. Since one option may not be better in all attributes at once, the decision process should include a means of weighing relevant attributes. Most decision-making models solve this problem by computing an integrated value, or utility, for each option from a weighted combination of attributes. However, behavioral anomalies in decision-making, such as context effects, indicate that other attribute-specific computations might be taking place. Here, we tested whether rhesus macaques show evidence of attribute-specific processing in a value-based decision-making task. Monkeys made a series of decisions involving choice options comprising a sweetness and probability attribute. Each attribute was represented by a separate bar with one of two mappings between bar size and the magnitude of the attribute (i.e., bigger=better or bigger=worse). We found that translating across different mappings produced selective impairments in decision-making. When like attributes differed, monkeys were prevented from easily making direct attribute comparisons, and choices were less accurate and preferences were more variable. This was not the case when mappings of unalike attributes within the same option were different. Likewise, gaze patterns favored transitions between like attributes over transitions between unalike attributes of the same option, so that like attributes were sampled sequentially to support within-attribute comparisons. Together, these data demonstrate that value-based decisions rely, at least in part, on directly comparing like attributes of multi-attribute options.

Significance Statement

Value-based decision-making is a cognitive function impacted by a number of clinical conditions, including substance use disorder and mood disorders. Understanding the neural mechanisms, including online processing steps involved in decision formation, will provide critical insights into decision-making deficits characteristic of human psychiatric disorders. Using rhesus monkeys as a model species capable of complex decision-making, this study shows that decisions involve a process of comparing like features, or attributes, of multi-attribute options. This is contrary to popular models of decision-making in which attributes are first combined into an overall value, or utility, to make a choice. Therefore, these results serve as an important foundation for establishing a more complete understanding of the neural mechanisms involved in forming complex decisions.

Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons

Dopamine neurons respond to reward-predicting cues but also modulate information processing in the prefrontal cortex essential for cognitive control. Whether dopamine controls reward expectation signals in prefrontal cortex that motivate cognitive control is unknown. We trained two male macaques on a working memory task while varying the reward size earned for successful task completion. We recorded neurons in lateral prefrontal cortex while simultaneously stimulating dopamine D1 receptor (D1R) or D2 receptor (D2R) families using micro-iontophoresis. We show that many neurons predict reward size throughout the trial. D1R stimulation showed mixed effects following reward cues but decreased reward expectancy coding during the memory delay. By contrast, D2R stimulation increased reward expectancy coding in multiple task periods, including cueing and memory periods. Stimulation of either dopamine receptors increased the neurons' selective responses to reward size upon reward delivery. The differential modulation of reward expectancy by dopamine receptors suggests that dopamine regulates reward expectancy necessary for successful cognitive control.

The orbitofrontal cortex: A goal-directed cognitive map framework for social and non-social behaviors

The orbitofrontal cortex (OFC) is regarded as one of the core brain areas in a variety of value-based behaviors. Over the past two decades, tremendous knowledge about the OFC function was gained from studying the behaviors of single subjects. As a result, our previous understanding of the OFC's function of encoding decision variables, such as the value and identity of choices, has evolved to the idea that the OFC encodes a more complex representation of the task space as a cognitive map. Accumulating evidence also indicates that the OFC importantly contributes to behaviors in social contexts, especially those involved in cooperative interactions. However, it remains elusive how exactly OFC neurons contribute to social functions and how non-social and social behaviors are related to one another in the computations performed by OFC neurons. In this review, we aim to provide an integrated view of the OFC function across both social and non-social behavioral contexts. We propose that seemingly complex functions of the OFC may be explained by its role in providing a goal-directed cognitive map to guide a wide array of adaptive reward-based behaviors in both social and non-social domains.

Dorsal raphe neurons signal integrated value during multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when grappling with reward uncertainty. However, whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider all these attributes to make a choice, is unclear. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes. Remarkably, these neurons commonly integrated offer attributes in a manner that reflected monkeys' overall preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how DRN participates in integrated value computations, guiding theories of DRN in decision-making and psychiatric disease.

Dopamine D1 receptor in orbitofrontal cortex to dorsal striatum pathway modulates methamphetamine addiction

The orbitofrontal cortex (OFC)-dorsal striatum (DS) is an important neural circuit that contributes to addictive behavior, including compulsive reinforcement, yet the specific types of neurons that play a major role still need to be further elucidated. Here, we used a place conditioning paradigm to measure the conditioned responses to methamphetamine (MA). The results demonstrated that MA increases the expression of c-Fos, synaptic plasticity in OFC and DS. Patch-clamp recording showed that MA activated projection neurons from the OFC to the DS, and chemogenetic manipulation of neuronal activity in OFC-DS projection neurons affects conditioned place preference (CPP) scores. And the combined patch-electrochemical technique was used to detect the DA release in OFC, the data indicated that the DA release was increased in MA group. Additionally, SCH23390, a D1R antagonist, was used to verify the function of D1R projection neurons, showing that SCH23390 reversed MA addiction-like behavior. Collectively, these findings provide evidence for the D1R neuron is sufficient to regulate MA addiction in the OFC-DS pathway, and the study provides new insight into the underlying mechanism of pathological changes in MA addiction.

Visualizing the IKEA effect: experiential consumption assessed with fNIRS-based neuroimaging

Introduction

In recent years, experiential consumption, which refers to purchases involving hedonic experiences, has been gathering attention in marketing research. Experiential consumption is closely related to cognitive biases, and among them, we focus on the IKEA effect, which is a cognitive bias in which the maximum willingness to pay (WTP) for a product is high because the experience of assembling the product is highly valued. Since no studies have examined the neural mechanism behind the IKEA effect, here we present the first study exploring the neural substrates of the IKEA effect using functional near-infrared spectroscopy (fNIRS). During the WTP evaluation, we expect the attachment to and memory retrieval of DIY products to be the cognitive mechanism for the IKEA effect.

Methods

Thirty healthy students, of which 24 were confirmed to have undergone the IKEA effect, were asked to perform a WTP evaluation task after assembling three types of do-it-yourself (DIY) products and handling three types of Non-DIY products. Their cerebral hemodynamic responses during the evaluation were measured using fNIRS. In order to adjust for temporal variability of cortical responses among participants, a personalized adaptive general linear model (GLM) analysis was adopted. Then, one-sample t-tests were performed for each DIY and Non-DIY condition for the obtained β values, and a paired t-test was performed between DIY and Non-DIY conditions.

Results

We identified brain regions, including the left-inferior frontal gyrus (L-IFG) and left-middle frontal gyrus (L-MFG), which were probably related to cognitive processing related to the IKEA effect. Among them, the L-MFG exhibited more activation during the DIY condition than during the Non-DIY condition.

Conclusion

To our knowledge, the current study is the first to reveal the neural basis of the IKEA effect. The cortical activation during evaluation of WTP for DIY and Non-DIY products exhibited marked differences. In addition to the R-IFG activation often reported for WTP evaluations, we revealed that other regions, in particular the L-IFG and L-MFG, were activated during the DIY condition. These areas are considered to be related to memory and attachment, which would serve as reasonable cognitive constituents for the IKEA effect. In conclusion, this study suggests that the value of experiential consumption can be assessed using fNIRS-based neuroimaging and provides a novel approach to consumer neuroergonomics. It is predicted that visualization the value of experiential consumption will create marketing opportunities for more and more companies and the visualization will become an indispensable method in the future.

Strong evidence for ideomotor theory: Unwilled manifestation of the conceptual attribute in movement control

Scientific understanding of how the mind generates bodily actions remains opaque. In the early 19th century, the ideomotor theory proposed that humans generate voluntary actions by imagining the sensory consequence of those actions, implying that the idea of an action's consequence mediates between the intention to act and motor control. Despite its long history and theoretical importance, existing empirical evidence for the ideomotor theory is not strong enough to rule out alternative hypotheses. In this study, we devised a categorization-action task to evaluate ideomotor theory by testing whether an idea, distinguished from a stimulus, can modulate task-irrelevant movements. In Experiment 1, participants categorized a stimulus duration as long or short by pressing an assigned key. The results show that participants pressed the key longer when categorizing the stimulus as long than they did when characterizing it as short. In Experiment 2, we showed that the keypressing durations were not modulated by the decision category when the property of the decision category, the brightness of a stimulus, was not easily transferable to the action. In summary, our results suggest that while the perceived stimulus features have a marginal effect on response duration linearly, the decision category is the main factor affecting the response duration. Our results indicate that an abstract category attribute can strongly modulate action execution, constraining theoretical conjectures about the ideomotor account of how people voluntarily generate action.

The orbitofrontal cortex, food reward, body weight and obesity

In primates including humans, the orbitofrontal cortex is the key brain region representing the reward value and subjective pleasantness of the sight, smell, taste and texture of food. At stages of processing before this, in the insular taste cortex and inferior temporal visual cortex, the identity of the food is represented, but not its affective value. In rodents, the whole organisation of reward systems appears to be different, with reward value reflected earlier in processing systems. In primates and humans, the amygdala is overshadowed by the great development of the orbitofrontal cortex. Social and cognitive factors exert a top-down influence on the orbitofrontal cortex, to modulate the reward value of food that is represented in the orbitofrontal cortex. Recent evidence shows that even in the resting state, with no food present as a stimulus, the liking for food, and probably as a consequence of that body mass index, is correlated with the functional connectivity of the orbitofrontal cortex and ventromedial prefrontal cortex. This suggests that individual differences in these orbitofrontal cortex reward systems contribute to individual differences in food pleasantness and obesity. Implications of how these reward systems in the brain operate for understanding, preventing and treating obesity are described.

Reward salience but not spatial attention dominates the value representation in the orbitofrontal cortex

The orbitofrontal cortex (OFC) encodes value and plays a key role in value-based decision-making. However, the attentional modulation of the OFC's value encoding is poorly understood. We trained two monkeys to detect a luminance change at a cued location between a pair of visual stimuli, which were over-trained pictures associated with different amounts of juice reward and, thus, different reward salience. Both the monkeys' behavior and the dorsolateral prefrontal cortex neuronal activities indicated that the monkeys actively directed their spatial attention toward the cued stimulus during the task. However, the OFC's neuronal responses were dominated by the stimulus with higher reward salience and encoded its value. The value of the less salient stimulus was only weakly represented regardless of spatial attention. The results demonstrate that reward and spatial attention are distinctly represented in the prefrontal cortex and the OFC maintains a stable representation of reward salience minimally affected by attention.

Global connectivity fingerprints predict the domain generality of multiple-demand regions

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.

Value representations in the rodent orbitofrontal cortex drive learning, not choice

Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here, we employ a recently developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.

Evidence accumulation occurs locally in the parietal cortex

Decision making often entails evidence accumulation, a process that is represented by neural activities in a network of multiple brain areas. Yet, it has not been identified where exactly the accumulation originates. We reason that a candidate brain area should both represent evidence accumulation and information that is used to compute evidence. Therefore, we designed a two-stage probabilistic reasoning task in which the evidence for accumulation had to be first determined from sensory signals orthogonal to decisions. With a linear encoding model, we decomposed the responses of posterior parietal neurons to each stimulus into an early and a late component that represented two dissociable stages of decision making. The former reflected the transformation from sensory inputs to accumulable evidence, and the latter reflected the accumulation of evidence and the formation of decisions. The presence of both computational stages indicates that evidence accumulation signal in the parietal cortex is computed locally.

Structural and functional variations in the prefrontal cortex are associated with learning in pre-adolescent common marmosets (Callithrix jacchus)

There is substantial evidence linking the prefrontal cortex (PFC) to a variety of cognitive abilities, with adolescence being a critical period in its development. In the current study, we investigated the neural basis of differences in learning in pre-adolescent common marmosets. At 8 months old, marmosets were given anatomical and resting state MRI scans (n=24). At 9 months old, association learning and inhibitory control was tested using a 'go/no go' visual discrimination (VD) task. Marmosets were grouped into 'learners' (n=12) and 'non-learners' (n=12), and associations between cognitive performance and sub-regional PFC volumes, as well as PFC connectivity patterns, were investigated. 'Learners' had significantly (p<0.05) larger volumes of areas 11, 25, 47 and 32 than 'non-learners', although 'non-learners' had significantly larger volumes of areas 24a and 8v than 'learners'. There was also a significant correlation between average % correct responses to the 'punished' stimulus and volume of area 47. Further, 'non-learners' had significantly greater global PFC connections, as well as significantly greater numbers of connections between the PFC and basal ganglia, cerebellum and hippocampus, compared to 'non-learners'. These results suggest that larger sub-regions of the orbitofrontal cortex and ventromedial PFC, as well more refined PFC connectivity patterns to other brain regions associated with learning, may be important in successful response inhibition. This study therefore offers new information on the neurodevelopment of individual differences in cognition during pre-adolescence in non-human primates.

Priming Engineers to Think About Sustainability: Cognitive and Neuro-Cognitive Evidence to Support the Adoption of Green Stormwater Design

Green infrastructure is the application of nature-based solutions like bioswales, rain gardens, and permeable pavements to reduce flooding in urban areas. These systems are underutilized in the design of the built environment. A barrier to their implementation is that design engineers tend to discount the tangential benefits of these greener systems and overweigh the associated risks. This study tested whether priming engineers to think about the environmental and social sustainability benefits of green infrastructure can influence what attributes engineers consider and how they weigh these attributes during the design decision-making process. Forty engineering students trained in stormwater design were asked to evaluate the implementation of a conventional stormwater design option and a green stormwater design option. Their preferred design option was recorded and the changes in their neuro-cognition were measured using functional near infrared-spectroscopy. Half of the engineers were asked to first consider the potential outcomes of these options on the environment and the surrounding community. Priming engineers to first consider environmental and social sustainability before considering the cost and risk of each option, significantly increased the perceived benefits the engineers believed green infrastructure could provide. The priming intervention also increased the likelihood that engineers would recommend the green infrastructure option. The engineers primed to think about environmental and social sustainability exhibited significantly lower oxy-hemoglobin in their ventrolateral, dorsolateral, and medial prefrontal cortex through multiple phases of the judgment and decision-making process. The intervention appears to increase cognitive representativeness or salience of the benefits for green infrastructure when engineers evaluate design alternatives. This relatively low-cost intervention, asking engineers to consider environmental and social sustainability for each design alternative, can shift engineering decision-making and change neuro-cognition.

Altered dynamic interactions within frontostriatal circuits reflect disturbed craving processing in internet gaming disorder

BACKGROUND

Individuals with internet gaming disorder (IGD) are generally characterized by impaired executive control, persistent game-craving, and excessive reward-seeking behaviors. However, the causal interactions within the frontostriatal circuits underlying these problematic behaviors remain unclear. Here, spectral dynamic causal modeling (spDCM) was implemented to explore this issue.

METHODS

Resting-state functional magnetic resonance imaging data from 317 online game players (148 IGD subjects and 169 recreational game users (RGUs)) were collected. Using independent component analysis, we determined six region of interests within frontostriatal circuits for further spDCM analysis, and further statistical analyses based on the parametric empirical Bayes framework were performed.

RESULTS

Compared with RGUs, IGD subjects showed inhibitory effective connectivity from the right orbitofrontal cortex (OFC) to the right caudate and from the right dorsolateral prefrontal cortex to the left OFC; at the same time, excitatory effective connectivity was observed from the thalamus to the left OFC. Correlation analyses results showed that the directional connection from the right OFC to the right caudate was negatively associated with addiction severity.

CONCLUSIONS

These results suggest that the disrupted causal interactions between specific regions might contribute to dysfunctions within frontostriatal circuits in IGD, and the pathway from the right OFC to the right caudate could serve as a target for brain modulation in future IGD interventions.

Planning in the brain

Recent breakthroughs in artificial intelligence (AI) have enabled machines to plan in tasks previously thought to be uniquely human. Meanwhile, the planning algorithms implemented by the brain itself remain largely unknown. Here, we review neural and behavioral data in sequential decision-making tasks that elucidate the ways in which the brain does-and does not-plan. To systematically review available biological data, we create a taxonomy of planning algorithms by summarizing the relevant design choices for such algorithms in AI. Across species, recording techniques, and task paradigms, we find converging evidence that the brain represents future states consistent with a class of planning algorithms within our taxonomy-focused, depth-limited, and serial. However, we argue that current data are insufficient for addressing more detailed algorithmic questions. We propose a new approach leveraging AI advances to drive experiments that can adjudicate between competing candidate algorithms.

Behavioral Prioritization Enhances Working Memory Precision and Neural Population Gain

Humans allocate visual working memory (WM) resource according to behavioral relevance, resulting in more precise memories for more important items. Theoretically, items may be maintained by feature-tuned neural populations, where the relative gain of the populations encoding each item determines precision. To test this hypothesis, we compared the amplitudes of delay period activity in the different parts of retinotopic maps representing each of several WM items, predicting the amplitudes would track behavioral priority. Using fMRI, we scanned participants while they remembered the location of multiple items over a WM delay and then reported the location of one probed item using a memory-guided saccade. Importantly, items were not equally probable to be probed (0.6, 0.3, 0.1, 0.0), which was indicated with a precue. We analyzed fMRI activity in 10 visual field maps in occipital, parietal, and frontal cortex known to be important for visual WM. In early visual cortex, but not association cortex, the amplitude of BOLD activation within voxels corresponding to the retinotopic location of visual WM items increased with the priority of the item. Interestingly, these results were contrasted with a common finding that higher-level brain regions had greater delay period activity, demonstrating a dissociation between the absolute amount of activity in a brain area and the activity of different spatially selective populations within it. These results suggest that the distribution of WM resources according to priority sculpts the relative gains of neural populations that encode items, offering a neural mechanism for how prioritization impacts memory precision.

Differential coding of goals and actions in ventral and dorsal corticostriatal circuits during goal-directed behavior

Goal-directed behavior requires identifying objects in the environment that can satisfy internal needs and executing actions to obtain those objects. The current study examines ventral and dorsal corticostriatal circuits that support complementary aspects of goal-directed behavior. We analyze activity from the amygdala, ventral striatum, orbitofrontal cortex, and lateral prefrontal cortex (LPFC) while monkeys perform a three-armed bandit task. Information about chosen stimuli and their value is primarily encoded in the amygdala, ventral striatum, and orbitofrontal cortex, while the spatial information is primarily encoded in the LPFC. Before the options are presented, information about the to-be-chosen stimulus is represented in the amygdala, ventral striatum, and orbitofrontal cortex; at the time of choice, the information is passed to the LPFC to direct a saccade. Thus, learned value information specifying behavioral goals is maintained throughout the ventral corticostriatal circuit, and it is routed through the dorsal circuit at the time actions are selected.

The neural substrate and underlying mechanisms of executive control fluctuations in primates

Trial-by-trial alterations in response time have been linked to fluctuations of executive control and transient lapses of attention. Here, we report remarkable homologies in performance-dependent fluctuations of response time between humans and monkeys. We examined the effects of selective bilateral lesions in four frontal regions on control fluctuations in the context of a rule-shifting task. Lesions within orbitofrontal cortex (OFC), but not within superior-lateral prefrontal cortex, significantly exaggerated the performance-dependent fluctuations of control and prevented its restoration following feedback. Lesions within dorsolateral prefrontal cortex (DLPFC) or within anterior-cingulate cortex (ACC) led to instability of control and disruption of its link with monkeys' upcoming decisions. Examining the activity of DLPFC and OFC cells shed more lights on the underlying neuronal mechanisms by showing that before the start of each trial, OFC cell activity conveyed detailed information regarding the current state of executive control and the likelihood of success or failure in the future decisions. This further emphasizes the crucial role of OFC in the trial-by-trial allocation (setting) of control to the ongoing task. These findings bring insights to the neural architecture of executive control in primates and suggest that DLPFC and ACC support sustained executive control, but OFC is more involved in setting and restoring the control.

Reinforcement learning and its connections with neuroscience and psychology

Reinforcement learning methods have recently been very successful at performing complex sequential tasks like playing Atari games, Go and Poker. These algorithms have outperformed humans in several tasks by learning from scratch, using only scalar rewards obtained through interaction with their environment. While there certainly has been considerable independent innovation to produce such results, many core ideas in reinforcement learning are inspired by phenomena in animal learning, psychology and neuroscience. In this paper, we comprehensively review a large number of findings in both neuroscience and psychology that evidence reinforcement learning as a promising candidate for modeling learning and decision making in the brain. In doing so, we construct a mapping between various classes of modern RL algorithms and specific findings in both neurophysiological and behavioral literature. We then discuss the implications of this observed relationship between RL, neuroscience and psychology and its role in advancing research in both AI and brain science.

Subpopulations of neurons in lOFC encode previous and current rewards at time of choice

Studies of neural dynamics in lateral orbitofrontal cortex (lOFC) have shown that subsets of neurons that encode distinct aspects of behavior, such as value, may project to common downstream targets. However, it is unclear whether reward history, which may subserve lOFC's well-documented role in learning, is represented by functional subpopulations in lOFC. Previously, we analyzed neural recordings from rats performing a value-based decision-making task, and we documented trial-by-trial learning that required lOFC (Constantinople et al., 2019). Here, we characterize functional subpopulations of lOFC neurons during behavior, including their encoding of task variables. We found five distinct clusters of lOFC neurons, either based on clustering of their trial-averaged peristimulus time histograms (PSTHs), or a feature space defined by their average conditional firing rates aligned to different task variables. We observed weak encoding of reward attributes, but stronger encoding of reward history, the animal's left or right choice, and reward receipt across all clusters. Only one cluster, however, encoded the animal's reward history at the time shortly preceding the choice, suggesting a possible role in integrating previous and current trial outcomes at the time of choice. This cluster also exhibits qualitatively similar responses to identified corticostriatal projection neurons in a recent study (Hirokawa et al., 2019), and suggests a possible role for subpopulations of lOFC neurons in mediating trial-by-trial learning.

Hippocampal neurons construct a map of an abstract value space

The hippocampus is thought to encode a "cognitive map," a structural organization of knowledge about relationships in the world. Place cells, spatially selective hippocampal neurons that have been extensively studied in rodents, are one component of this map, describing the relative position of environmental features. However, whether this map extends to abstract, cognitive information remains unknown. Using the relative reward value of cues to define continuous "paths" through an abstract value space, we show that single neurons in primate hippocampus encode this space through value place fields, much like a rodent's place neurons encode paths through physical space. Value place fields remapped when cues changed but also became increasingly correlated across contexts, allowing maps to become generalized. Our findings help explain the critical contribution of the hippocampus to value-based decision-making, providing a mechanism by which knowledge of relationships in the world can be incorporated into reward predictions for guiding decisions.

Over-representation of fundamental decision variables in the prefrontal cortex underlies decision bias

The brain is organized into anatomically distinct structures consisting of a variety of projection neurons. While such evolutionarily conserved neural circuit organization underlies the innate ability of animals to swiftly adapt to environments, they can cause biased cognition and behavior. Although recent studies have begun to address the causal importance of projection-neuron types as distinct computational units, it remains unclear how projection types are functionally organized in encoding variables during cognitive tasks. This review focuses on the neural computation of decision making in the prefrontal cortex and discusses what decision variables are encoded by single neurons, neuronal populations, and projection type, alongside how specific projection types constrain decision making. We focus particularly on "over-representations" of distinct decision variables in the prefrontal cortex that reflect the biological and subjective significance of the variables for the decision makers. We suggest that task-specific over-representation in the prefrontal cortex involves the refinement of the given decision making, while generalized over-representation of fundamental decision variables is associated with suboptimal decision biases, including pathological ones such as those in patients with psychiatric disorders. Such over-representation of the fundamental decision variables in the prefrontal cortex appear to be tightly constrained by afferent and efferent connections that can be optogenetically intervened on. These ideas may provide critical insights into potential therapeutic targets for psychiatric disorders, including addiction and depression.

A bidirectional corticoamygdala circuit for the encoding and retrieval of detailed reward memories

Adaptive reward-related decision making often requires accurate and detailed representation of potential available rewards. Environmental reward-predictive stimuli can facilitate these representations, allowing one to infer which specific rewards might be available and choose accordingly. This process relies on encoded relationships between the cues and the sensory-specific details of the rewards they predict. Here, we interrogated the function of the basolateral amygdala (BLA) and its interaction with the lateral orbitofrontal cortex (lOFC) in the ability to learn such stimulus-outcome associations and use these memories to guide decision making. Using optical recording and inhibition approaches, Pavlovian cue-reward conditioning, and the outcome-selective Pavlovian-to-instrumental transfer (PIT) test in male rats, we found that the BLA is robustly activated at the time of stimulus-outcome learning and that this activity is necessary for sensory-specific stimulus-outcome memories to be encoded, so they can subsequently influence reward choices. Direct input from the lOFC was found to support the BLA in this function. Based on prior work, activity in BLA projections back to the lOFC was known to support the use of stimulus-outcome memories to influence decision making. By multiplexing optogenetic and chemogenetic inhibition we performed a serial circuit disconnection and found that the lOFC→BLA and BLA→lOFC pathways form a functional circuit regulating the encoding (lOFC→BLA) and subsequent use (BLA→lOFC) of the stimulus-dependent, sensory-specific reward memories that are critical for adaptive, appetitive decision making.

A Preferential Role for Ventromedial Prefrontal Cortex in Assessing "the Value of the Whole" in Multiattribute Object Evaluation

Everyday decision-making commonly involves assigning values to complex objects with multiple value-relevant attributes. Drawing on object recognition theories, we hypothesized two routes to multiattribute evaluation: assessing the value of the whole object based on holistic attribute configuration or summing individual attribute values. In two samples of healthy human male and female participants undergoing eye tracking and functional magnetic resonance imaging (fMRI) while evaluating novel pseudo objects, we found evidence for both forms of evaluation. Fixations to and transitions between attributes differed systematically when the value of pseudo objects was associated with individual attributes or attribute configurations. Ventromedial prefrontal cortex (vmPFC) and perirhinal cortex were engaged when configural processing was required. These results converge with our recent findings that individuals with vmPFC lesions were impaired in decisions requiring configural evaluation but not when evaluating the sum of the parts. This suggests that multiattribute decision-making engages distinct evaluation mechanisms relying on partially dissociable neural substrates, depending on the relationship between attributes and value.SIGNIFICANCE STATEMENT Decision neuroscience has only recently begun to address how multiple choice-relevant attributes are brought together during evaluation and choice among complex options. Object recognition research makes a crucial distinction between individual attribute and holistic/configural object processing, but how the brain evaluates attributes and whole objects remains unclear. Using fMRI and eye tracking, we found that the vmPFC and the perirhinal cortex contribute to value estimation specifically when value was related to whole objects, that is, predicted by the unique configuration of attributes and not when value was predicted by the sum of individual attribute values. This perspective on the interactions between subjective value and object processing mechanisms provides a novel bridge between the study of object recognition and reward-guided decision-making.

Decreased Functional Connectivity in the Reward Network and Its Relationship With Negative Emotional Experience After Total Sleep Deprivation

Sleep deprivation (SD) induces a negative emotional experience due to a prolonged time spent awake. However, few studies have focused on the mechanism underlying communication within brain networks or alterations during this emotional deterioration. We propose that negative reward judgment is important in poor emotional processing after SD, which will be reflected in functional connectivity in the reward network. We sought to analyze alterations in functional connectivity within the reward network and cerebral cortex. Furthermore, we analyzed changes in functional connectivity correlation with negative emotional experience after SD. Twenty-six healthy volunteers participated in this study. Two resting-state fMRI scans were obtained from the participants, once during resting wakefulness, and once after 36 h of total SD. The bilateral nucleus accumbens (NAc) was selected as a seed region for region of interest (ROI)-to-ROI functional connectivity analysis. Correlation analyses between functional connectivity alterations within the reward network and negative emotional experience were also performed. We found that SD decreased functional connectivity between the left NAc and anterior cingulate cortex (ACC) compared with resting wakefulness. There was a decreased functional connectivity with the ACC and right inferior frontal gyrus (IFG) after SD in the right NAc. Furthermore, decreased functional connectivity between the right NAc and right IFG, and NAc and ACC was negatively correlated with emotional experience scores. Sleep deprivation decreased functional connectivity within the reward network. This may be associated with the enhanced negative emotional experience that was found after total sleep deprivation.

Is value-based choice repetition susceptible to medial frontal transcranial direct current stimulation (tDCS)? A preregistered study

In value-based decision making, people have to weigh different options based on their subjective value. This process, however, also is influenced by choice biases, such as choice repetition: in a series of choices, people are more likely to repeat their decision than to switch to a different choice. Previously, it was shown that transcranial direct current stimulation (tDCS) can affect such choice biases. We applied tDCS over the medial prefrontal cortex to investigate whether tDCS can alter choice repetition in value-based decision making. In a preregistered study, we applied anodal, cathodal, and sham tDCS stimulation to 52 participants. While we found robust choice repetition effects, we did not find support for an effect of tDCS stimulation. We discuss these findings within the larger scope of the tDCS literature and highlight the potential roles of interindividual variability and current density strength.

Heterogeneous value coding in orbitofrontal populations

Value signals in the brain are important for learning, decision-making, and orienting behavior toward relevant goals. Although they can play different roles in behavior and cognition, value representations are often considered to be uniform and static signals. Nonetheless, contextual and mixed representations of value have been widely reported. Here, we review the evidence for heterogeneity in value coding and dynamics in the orbitofrontal cortex. We argue that this diversity plays a key role in the representation of value itself and allows neurons to integrate value with other behaviorally relevant information. We also discuss modeling approaches that can dissociate potential functions of heterogeneous value codes and provide further insight into its importance in behavior and cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The orbitofrontal cartographer

Theories of orbitofrontal cortex (OFC) function have evolved substantially over the last few decades. There is now a general consensus that the OFC is important for predicting aspects of future events and for using these predictions to guide behavior. Yet the precise content of these predictions and the degree to which OFC contributes to agency contingent upon them has become contentious, with several plausible theories advocating different answers to these questions. In this review we will focus on three of these ideas-the economic value, credit assignment, and cognitive map hypotheses-describing both their successes and failures. We will propose that these failures hint at a more nuanced and perhaps unique role for the OFC, particularly the lateral subdivision, in supporting the proposed functions when an underlying model or map of the causal structures in the environment must be constructed or updated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Modulation of cue-guided choices by transcranial direct current stimulation

Environmental cues may anticipate the availability of rewards, thus acting as a guide towards a specific choice (i.e., cue-guided choices). Despite the lateral prefrontal cortex having a critical role in using past learning and flexibly selecting relevant information to guide behavior, the literature on the neural basis of human cue-guided choice mainly focused on the subcortical brain structures implicated, while the specific role of cortical areas remained unclear. The present study aimed to provide causal evidence for the involvement of the lateral prefrontal cortex in two forms of human cue-guided choice, namely outcome-specific and general. To do this, 2 mA cathodal, anodal or sham transcranial direct current stimulation was applied over the lateral prefrontal cortex (with the posterior parietal cortex serving as control region) in three separate groups performing a Pavlovian-to-Instrumental Transfer task. Results showed, for the first time, a dissociation in the cortical structures involved in human cue-guided choice. Cathodal stimulation of the lateral prefrontal cortex reduced the outcome-specific transfer. In striking contrast, there was no influence on the general transfer. These results argue in favor of the presence of at least two possible neural pathways underlying cue-guided choices.

Reward modulates cortical representations of action

People are capable of rapid improvements in performance when they are offered a reward. The neural mechanism by which this performance enhancement occurs remains unclear. We investigated this phenomenon by offering people monetary reward for successful performance in a sequence production task. We found that people performed actions more quickly and accurately when they were offered large reward. Increasing reward magnitude was associated with elevated activity throughout the brain prior to movement. Multivariate patterns of activity in these reward-responsive regions encoded information about the upcoming action. Follow-up analyses provided evidence that action decoding in pre-SMA and other motor planning areas was improved for large reward trials and successful action decoding in SMA was associated with improved performance. These results suggest that reward may enhance performance by enhancing neural representations of action used in motor planning.

Frontotemporal Regulation of Subjective Value to Suppress Impulsivity in Intertemporal Choices

Impulsive decisions arise from preferring smaller but sooner rewards compared with larger but later rewards. How neural activity and attention to choice alternatives contribute to reward decisions during temporal discounting is not clear. Here we probed (1) attention to and (2) neural representation of delay and reward information in humans (both sexes) engaged in choices. We studied behavioral and frequency-specific dynamics supporting impulsive decisions on a fine-grained temporal scale using eye tracking and MEG recordings. In one condition, participants had to decide for themselves but pretended to decide for their best friend in a second prosocial condition, which required perspective taking. Hence, conditions varied in the value for themselves versus that pretending to choose for another person. Stronger impulsivity was reliably found across three independent groups for prosocial decisions. Eye tracking revealed a systematic shift of attention from the delay to the reward information and differences in eye tracking between conditions predicted differences in discounting. High-frequency activity (175-250 Hz) distributed over right frontotemporal sensors correlated with delay and reward information in consecutive temporal intervals for high value decisions for oneself but not the friend. Collectively, the results imply that the high-frequency activity recorded over frontotemporal MEG sensors plays a critical role in choice option integration.SIGNIFICANCE STATEMENT Humans face decisions between sooner smaller rewards and larger later rewards daily. An objective benefit of losing weight over a longer time might be devalued in face of ice cream because they prefer currently available options because of insufficiently considering long-term alternatives. The degree of contribution of neural representation and attention to choice alternatives is not clear. We investigated correlates of such decisions in participants deciding for themselves or pretending to choose for a friend. Behaviorally participants discounted less in self-choices compared with the prosocial condition. Eye movement and MEG recordings revealed how participants represent choice options most evident for options with high subjective value. These results advance our understanding of neural mechanisms underlying decision-making in humans.

Disruption of Nrxn1α within excitatory forebrain circuits drives value-based dysfunction

Goal-directed behaviors are essential for normal function and significantly impaired in neuropsychiatric disorders. Despite extensive associations between genetic mutations and these disorders, the molecular contributions to goal-directed dysfunction remain unclear. We examined mice with constitutive and brain region-specific mutations in Neurexin1α, a neuropsychiatric disease-associated synaptic molecule, in value-based choice paradigms. We found Neurexin1α knockouts exhibited reduced selection of beneficial outcomes and impaired avoidance of costlier options. Reinforcement modeling suggested that this was driven by deficits in updating and representation of value. Disruption of Neurexin1α within telencephalic excitatory projection neurons, but not thalamic neurons, recapitulated choice abnormalities of global Neurexin1α knockouts. Furthermore, this selective forebrain excitatory knockout of Neurexin1α perturbed value-modulated neural signals within striatum, a central node in feedback-based reinforcement learning. By relating deficits in value-based decision-making to region-specific Nrxn1α disruption and changes in value-modulated neural activity, we reveal potential neural substrates for the pathophysiology of neuropsychiatric disease-associated cognitive dysfunction.

A Frontal Neuropsychological Profile in Fitness to Drive

Traffic accidents are a global concern due to the elevated mortality rates of both drivers and pedestrians. The World Health Organization declared 2011-2020 as the Decade of Action for Road Safety, endorsing initiatives to reduce traffic-related deaths. Yet, despite these incentives, fatal accidents still occur. Different studies have linked deficits in executive functions to risky driving attitudes and crashes. The present study focuses on demographic, cognitive and personality factors, related to the prefrontal cortex, that are characteristic of drivers prone to risky behavior behind the wheel. The penalty Points System was used to classify drivers as "safe", with no point loss over a two-year period, or "risky", with full point loss during the same interval. A neuropsychological assessment of prefrontal cognitive functions was carried out on each group to identify variables associated with safe and risky behavior. Neuropsychological indexes were obtained from a continuous performance task without cue (Simple Attention), a continuous performance task with cue (Conditioned Attention), the Tower of Hanoi test and the Neurologically-related Changes in Personality Inventory (NECHAPI). A Discriminant Analysis (DA) found that education level, reaction times in Simple and Conditioned Attention, learning errors in the Tower of Hanoi and vulnerability in the personality test, best predicted whether drivers were likely to be in the safe or risky group. Finally, a cross-validation analysis performed on the same sample correctly classified 87.5% of the drivers. These data suggest that prefrontal dysfunction contributes to risky behavior behind the wheel. The inclusion of cognitive programs to identify and train drivers with this propensity could reduce risky driving, and consequently, save lives on the road.

Rapid Rule-Based Reward Reversal and the Lateral Orbitofrontal Cortex

Humans and other primates can reverse their choice of stimuli in one trial when the rewards delivered by the stimuli change or reverse. Rapidly changing our behavior when the rewards change is important for many types of behavior, including emotional and social behavior. It is shown in a one-trial rule-based Go-NoGo deterministic visual discrimination reversal task to obtain points, that the human right lateral orbitofrontal cortex and adjoining inferior frontal gyrus is activated on reversal trials, when an expected reward is not obtained, and the non-reward allows the human to switch choices based on a rule. This reward reversal goes beyond model-free reinforcement learning. This functionality of the right lateral orbitofrontal cortex shown here in very rapid, one-trial, rule-based changes in human behavior when a reward is not received is related to the emotional and social changes that follow orbitofrontal cortex damage, and to depression in which this non-reward system is oversensitive and over-connected.

Evidence accumulation for value computation in the prefrontal cortex during decision making

A key step of decision making is to determine the value associated with each option. The evaluation process often depends on the accumulation of evidence from multiple sources, which may arrive at different times. How evidence is accumulated for value computation in the brain during decision making has not been well studied. To address this problem, we trained rhesus monkeys to perform a decision-making task in which they had to make eye movement choices between two targets, whose reward probabilities had to be determined with the combined evidence from four sequentially presented visual stimuli. We studied the encoding of the reward probabilities associated with the stimuli and the eye movements in the orbitofrontal (OFC) and the dorsolateral prefrontal (DLPFC) cortices during the decision process. We found that the OFC neurons encoded the reward probability associated with individual pieces of evidence in the stimulus domain. Importantly, the representation of the reward probability in the OFC was transient, and the OFC did not encode the reward probability associated with the combined evidence from multiple stimuli. The computation of the combined reward probabilities was observed only in the DLPFC and only in the action domain. Furthermore, the reward probability encoding in the DLPFC exhibited an asymmetric pattern of mixed selectivity that supported the computation of the stimulus-to-action transition of reward information. Our results reveal that the OFC and the DLPFC play distinct roles in the value computation during evidence accumulation.

The ever-changing OFC landscape: What neural signals in OFC can tell us about inhibitory control

Despite decades of research on OFC function, the exact function(s) of OFC remain elusive. In recent years, 1 of the earliest hypotheses about OFC function, namely its involvement in inhibitory control, has drifted to the periphery of the functional OFC landscape in favor of theories suggesting a role for OFC in the representation of task or state space. The reasons for this drift are valid, owing in part to the development of more sensitive behavioral approaches, a clear emphasis on cross-species and cross-method comparisons, as well as the elegant integration of reinforcement learning theories. However, recent evidence recording from OFC during the performance of traditional inhibitory control tasks has found new evidence supporting a role for OFC in inhibitory control. While the extent to which these findings can be integrated into existing frameworks is in its infancy, this review seeks to highlight these findings with the goal of providing new insights into function of OFC. (PsycInfo Database Record (c) 2021 APA, all rights reserved).