Differential Contributions of Ventral and Dorsal Striatum to Early and Late Phases of Cognitive Set Reconfiguration

Adaptive coding of reward in schizophrenia, its change over time and relationship to apathy

Adaptive coding of reward is the process by which neurons adapt their response to the context of available compensations. Higher rewards lead to a stronger brain response, but the increase of the response depends on the range of available rewards. A steeper increase is observed in a narrow range and a more gradual slope in a wider range. In schizophrenia, adaptive coding appears to be affected in different domains, especially in the reward domain. Here, we tested adaptive coding of reward in a large group of patients with schizophrenia (n = 86) and control subjects (n = 66). We assessed: (i) the association between adaptive coding deficits and symptoms; (ii) the longitudinal stability of deficits (the same task was performed 3 months apart); and (iii) the stability of results between two experimental sites. We used functional MRI and the monetary incentive delay task to assess adaptation of participants to two different reward ranges: a narrow range and a wide range. We used a region-of-interest analysis to evaluate adaptation within striatal and visual regions. Patients and control subjects underwent a full demographic and clinical assessment. We found reduced adaptive coding in patients, with a decreased slope in the narrow reward range with respect to that of control participants, in striatal but not visual regions. This pattern was observed at both research sites. Upon retesting, patients increased their narrow-range slopes, showing improved adaptive coding, whereas control subjects slightly reduced them. At retesting, patients with overly steep slopes in the narrow range also showed higher levels of negative symptoms. Our data confirm deficits in reward adaptation in schizophrenia and reveal an effect of practice in patients, leading to improvement, with steeper slopes upon retesting. However, in some patients, an excessively steep slope may result in poor discriminability of larger rewards, owing to early saturation of the brain response. Together, the loss of precision of reward representation in new (first exposure, underadaptation) and more familiar (retest, overadaptation) situations might contribute to the multiple motivational symptoms in schizophrenia.

The role of environmental contextual cues in sequence learning: evidence from a virtual maze context

Studies on sequence learning usually focus on single, isolated stimuli that are presented sequentially. For example, in the serial reaction time (RT) task, stimuli are either presented in a predictable sequence or in a random sequence, and better performance with the predictable sequence is taken as evidence for sequence-specific learning. Yet, little is known about the role of environmental context cues in sequence learning. If the target stimuli are embedded in a meaningful context, would this facilitate learning by providing helpful contextual associations or would it hinder learning by adding distracting stimuli? This question was examined in two studies. A pilot study compared sequence learning in a virtual maze with a horizontal vs. vertical maze context, in which arrow stimuli guide spatial lever movement responses that resulted in a corresponding virtual transport on the screen. The results showed only overall somewhat better performance with the vertical maze compared to the horizontal maze, but general practice effects and sequence-specific learning effects were the same for both contexts. The main study compared sequence learning with a maze context to sequence learning of arrows without a maze context. The results showed significantly better learning with maze context than without context. These data suggest that the maze context facilitated sequence learning by inducing a meaningful spatial representation ("mental map") similar to that formed in wayfinding.

Parallel learning and cognitive flexibility impairments between Fmr1 knockout mice and individuals with fragile X syndrome

Introduction

Fragile X Syndrome (FXS) is a monogenic condition that leads to intellectual disability along with behavioral and learning difficulties. Among behavioral and learning difficulties, cognitive flexibility impairments are among the most commonly reported in FXS, which significantly impacts daily living. Despite the extensive use of the Fmr1 knockout (KO) mouse to understand molecular, synaptic and behavioral alterations related to FXS, there has been limited development of translational paradigms to understand cognitive flexibility that can be employed in both animal models and individuals with FXS to facilitate treatment development.

Methods

To begin addressing this limitation, a parallel set of studies were carried out that investigated probabilistic reversal learning along with other behavioral and cognitive tests in individuals with FXS and Fmr1 KO mice. Fifty-five adolescents and adults with FXS (67% male) and 34 age- and sex-matched typically developing controls (62% male) completed an initial probabilistic learning training task and a probabilistic reversal learning task.

Results

In males with FXS, both initial probabilistic learning and reversal learning deficits were found. However, in females with FXS, we only observed reversal learning deficits. Reversal learning deficits related to more severe psychiatric features in females with FXS, whereas increased sensitivity to negative feedback (lose:shift errors) unexpectedly appear to be adaptive in males with FXS. Male Fmr1 KO mice exhibited both an initial probabilistic learning and reversal learning deficit compared to that of wildtype (WT) mice. Female Fmr1 KO mice were selectively impaired on probabilistic reversal learning. In a prepotent response inhibition test, both male and female Fmr1 KO mice were impaired in learning to choose a non-preferred spatial location to receive a food reward compared to that of WT mice. Neither male nor female Fmr1 KO mice exhibited a change in anxiety compared to that of WT mice.

Discussion

Together, our findings demonstrate strikingly similar sex-dependent learning disturbances across individuals with FXS and Fmr1 KO mice. This suggests the promise of using analogous paradigms of cognitive flexibility across species that may speed treatment development to improve lives of individuals with FXS.

Partial integration of the components of value in anterior cingulate cortex

Evaluation often involves integrating multiple determinants of value, such as the different possible outcomes in risky choice. A brain region can be placed either before or after a presumed evaluation stage by measuring how responses of its neurons depend on multiple determinants of value. A brain region could also, in principle, show partial integration, which would indicate that it occupies a middle position between (preevaluative) nonintegration and (postevaluative) full integration. Existing mathematical techniques cannot distinguish full from partial integration and therefore risk misidentifying regional function. Here we use a new Bayesian regression-based approach to analyze responses of neurons in dorsal anterior cingulate cortex (dACC) to risky offers. We find that dACC neurons only partially integrate across outcome dimensions, indicating that dACC cannot be assigned to either a pre- or postevaluative position. Neurons in dACC also show putative signatures of value comparison, thereby demonstrating that comparison does not require complete evaluation before proceeding. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A new era for executive function research: On the transition from centralized to distributed executive functioning

"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.

Rules warp feature encoding in decision-making circuits

We have the capacity to follow arbitrary stimulus-response rules, meaning simple policies that guide our behavior. Rule identity is broadly encoded across decision-making circuits, but there are less data on how rules shape the computations that lead to choices. One idea is that rules could simplify these computations. When we follow a rule, there is no need to encode or compute information that is irrelevant to the current rule, which could reduce the metabolic or energetic demands of decision-making. However, it is not clear if the brain can actually take advantage of this computational simplicity. To test this idea, we recorded from neurons in 3 regions linked to decision-making, the orbitofrontal cortex (OFC), ventral striatum (VS), and dorsal striatum (DS), while macaques performed a rule-based decision-making task. Rule-based decisions were identified via modeling rules as the latent causes of decisions. This left us with a set of physically identical choices that maximized reward and information, but could not be explained by simple stimulus-response rules. Contrasting rule-based choices with these residual choices revealed that following rules (1) decreased the energetic cost of decision-making; and (2) expanded rule-relevant coding dimensions and compressed rule-irrelevant ones. Together, these results suggest that we use rules, in part, because they reduce the costs of decision-making through a distributed representational warping in decision-making circuits.

Cognitive rigidity and BDNF-mediated frontostriatal glutamate neuroadaptations during spontaneous nicotine withdrawal

Cognitive flexibility is the ability to switch strategic responses adaptively in changing environments. Cognitive rigidity imposed by neural circuit adaptations during nicotine abstinence may foster maladaptive nicotine taking in addicts. We systematically examined the effects of spontaneous withdrawal in mice exposed to either nicotine (6.3 or 18 mg/kg/day) or saline for 14 days on cognitive flexibility using an operant strategy set-shifting task. Because frontostriatal circuits are critical for cognitive flexibility and brain-derived neurotrophic factor (BDNF) modulates glutamate plasticity in these circuits, we also explored the effects of nicotine withdrawal on these neurochemical substrates. Mice undergoing nicotine withdrawal required more trials to attain strategy-switching criterion. Error analysis show that animals withdrawn from both nicotine doses committed higher perseverative errors, which correlated with measures of anxiety. However, animals treated with the higher nicotine dose also displayed more strategy maintenance errors that remained independent of negative affect. BDNF mRNA expression increased in the medial prefrontal cortex (mPFC) following nicotine withdrawal. Surprisingly, BDNF protein declined in mPFC but was elevated in dorsal striatum (DS). DS BDNF protein positively correlated with perseverative and maintenance errors, suggesting mPFC-DS overflow of BDNF during withdrawal. BDNF-evoked glutamate release and synapsin phosphorylation was attenuated within DS synapses, but enhanced in the nucleus accumbens, suggesting a dichotomous role of BDNF signaling in striatal regions. Taken together, these data suggest that spontaneous nicotine withdrawal impairs distinct components of cognitive set-shifting and these deficits may be linked to BDNF-mediated alterations in glutamate signaling dynamics in discrete frontostriatal circuits.

Cortical and Striatal Circuits in Huntington's Disease

Huntington's disease (HD) is a hereditary neurodegenerative disorder that typically manifests in midlife with motor, cognitive, and/or psychiatric symptoms. The disease is caused by a CAG triplet expansion in exon 1 of the huntingtin gene and leads to a severe neurodegeneration in the striatum and cortex. Classical electrophysiological studies in genetic HD mouse models provided important insights into the disbalance of excitatory, inhibitory and neuromodulatory inputs, as well as progressive disconnection between the cortex and striatum. However, the involvement of local cortical and striatal microcircuits still remains largely unexplored. Here we review the progress in understanding HD-related impairments in the cortical and basal ganglia circuits, and outline new opportunities that have opened with the development of modern circuit analysis methods. In particular, in vivo imaging studies in mouse HD models have demonstrated early structural and functional disturbances within the cortical network, and optogenetic manipulations of striatal cell types have started uncovering the causal roles of certain neuronal populations in disease pathogenesis. In addition, the important contribution of astrocytes to HD-related circuit defects has recently been recognized. In parallel, unbiased systems biology studies are providing insights into the possible molecular underpinnings of these functional defects at the level of synaptic signaling and neurotransmitter metabolism. With these approaches, we can now reach a deeper understanding of circuit-based HD mechanisms, which will be crucial for the development of effective and targeted therapeutic strategies.

The neural basis of predictive pursuit

It remains unclear whether and, if so, how nonhuman animals make on-the-fly predictions during pursuit. Here we used a novel laboratory pursuit task that incentivizes the prediction of future prey positions. We trained three macaques to perform a joystick-controlled pursuit task in which prey follow intelligent escape algorithms. Subjects aimed toward the likely future positions of the prey, which indicated that they generate internal predictions and use these to guide behavior. We then developed a generative model that explains real-time pursuit trajectories and showed that our subjects use prey position, velocity and acceleration to make predictions. We identified neurons in the dorsal anterior cingulate cortex whose responses track these three variables. These neurons multiplexed prediction-related variables with a distinct and explicit representation of the future position of the prey. Our results provide a clear demonstration that the brain can explicitly represent future predictions and highlight the critical role of anterior cingulate cortex for future-oriented cognition.

Activity in orbitofrontal neuronal ensembles reflects inhibitory control

Stopping, or inhibition, is a form of self-control that is a core element of flexible and adaptive behavior. Its neural origins remain unclear. Some views hold that inhibition decisions reflect the aggregation of widespread and diverse pieces of information, including information arising in ostensible core reward regions (i.e., outside the canonical executive system). We recorded activity of single neurons in the orbitofrontal cortex (OFC) of macaques, a region associated with economic decisions, and whose role in inhibition is debated. Subjects performed a classic inhibition task known as the stop signal task. Ensemble decoding analyses reveal a clear firing rate pattern that distinguishes successful from failed inhibition and that begins after the stop signal and before the stop signal reaction time (SSRT). We also found a different and orthogonal ensemble pattern that distinguishes successful from failed stopping before the beginning of the trial. These signals were distinct from, and orthogonal to, value encoding, which was also observed in these neurons. The timing of the early and late signals was, respectively, consistent with the idea that neuronal activity in OFC encodes inhibition both proactively and reactively.

Tonic exploration governs both flexibility and lapses

In many cognitive tasks, lapses (spontaneous errors) are tacitly dismissed as the result of nuisance processes like sensorimotor noise, fatigue, or disengagement. However, some lapses could also be caused by exploratory noise: randomness in behavior that facilitates learning in changing environments. If so, then strategic processes would need only up-regulate (rather than generate) exploration to adapt to a changing environment. This view predicts that more frequent lapses should be associated with greater flexibility because these behaviors share a common cause. Here, we report that when rhesus macaques performed a set-shifting task, lapse rates were negatively correlated with perseverative error frequency across sessions, consistent with a common basis in exploration. The results could not be explained by local failures to learn. Furthermore, chronic exposure to cocaine, which is known to impair cognitive flexibility, did increase perseverative errors, but, surprisingly, also improved overall set-shifting task performance by reducing lapse rates. We reconcile these results with a state-switching model in which cocaine decreases exploration by deepening attractor basins corresponding to rule states. These results support the idea that exploratory noise contributes to lapses, affecting rule-based decision-making even when it has no strategic value, and suggest that one key mechanism for regulating exploration may be the depth of rule states.

Macaques are risk-averse in a freely moving foraging task

Rhesus macaques (Macaca mulatta) appear to be robustly risk-seeking in computerized gambling tasks typically used for electrophysiology. This behavior distinguishes them from many other animals, which are risk-averse, albeit measured in more naturalistic contexts. We wondered whether macaques' risk preferences reflect their evolutionary history or derive from the less naturalistic elements of task design associated with the demands of physiological recording. We assessed macaques' risk attitudes in a task that is somewhat more naturalistic than many that have previously been used: subjects foraged at four feeding stations in a large enclosure. Patches (i.e., stations), provided either stochastically or non-stochastically depleting rewards. Subjects' patch residence times were longer at safe than at risky stations, indicating a preference for safe options. This preference was not attributable to a win-stay-lose-shift heuristic and reversed as the environmental richness increased. These findings highlight the lability of risk attitudes in macaques and support the hypothesis that the ecological validity of a task can influence the expression of risk preference.

Prefrontal cortex and cognitive control: new insights from human electrophysiology

Cognitive control, the ability to regulate one's cognition and actions on the basis of over-riding goals, is impaired in many psychiatric conditions. Although control requires the coordinated function of several prefrontal cortical regions, it has been challenging to determine how they work together, in part because doing so requires simultaneous recordings from multiple regions. Here, we provide a précis of cognitive control and describe the beneficial consequences of recent advances in neurosurgical practice that make large-scale prefrontal cortical network recordings possible in humans. Such recordings implicate inter-regional theta (5-8 Hz) local field potential (LFP) synchrony as a key element in cognitive control. Major open questions include how theta might influence other oscillations within these networks, the precise timing of information flow between these regions, and how perturbations such as brain stimulation might demonstrate the causal role of LFP phenomena. We propose that an increased focus on human electrophysiology is essential for an understanding of the neural basis of cognitive control.

Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum

INTRODUCTION

Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self-regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience, 18, 86).

METHODS

To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self-regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self-regulation with and without providing neurofeedback. Ten participants freely chose one self-regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real-time based on activity in task-related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain-activation levels in the target region.

RESULTS

During gradual self-regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self-regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time-resolved single-trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self-regulation.

CONCLUSION

Overall, these findings support that neurofeedback contributes to self-regulation through task-general regions involved in feedback and reward processing.

Economic Choice as an Untangling of Options into Actions

We propose that economic choice can be understood as a gradual transformation from a domain of options to one of the actions. We draw an analogy with the idea of untangling information in the form vision system and propose that form vision and economic choice may be two aspects of a larger process that sculpts actions based on sensory inputs. From this viewpoint, choice results from the accumulated effect of repetitions of simple computations. These may consist primarily of relative valuations (evaluations relative to the value of rejection, perhaps in a manner akin to divisive normalization) applied to individual offers. With regard to economic choice, cortical brain regions differ primarily in their position and in what information they prioritize, and do not-with a few exceptions-have categorically distinct roles. Each region's specific contribution is determined largely by its inputs; thus, understanding connectivity is crucial for understanding choice. This view suggests that there is no single site of choice, that there is no meaningful distinction between pre- and post-decisionality, and that there is no explicit representation of value in the brain.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Neural correlates of subjective CS/UCS association in appetitive conditioning

Explicit knowledge of conditioned stimulus (CS)/unconditioned stimulus (UCS) associations is proposed as important factor in classical conditioning. However, while previous studies have focused on its roles in fear conditioning, it has been neglected in the context of appetitive conditioning. The present functional magnetic resonance study aimed to investigate neural activation and functional connectivity linked to subjective CS/UCS association in appetitive conditioning. In total, 85 subjects participated in an appetitive acquisition procedure in which a neutral stimulus (CS+) was paired with a monetary reward, while another neutral stimulus (CS-) was never paired with the reward. Directly afterwards, subjective CS/UCS association was assessed by measuring the extent to which the CS+ was thought to be associated with the UCS compared to the CS-. Close relationships were established between subjective CS/UCS association and activations in the primary visual cortex (V1) during the early phase of conditioning and in the striatum during the late conditioning phase. In addition, we observed inverse relationships between subjective CS/UCS association and both V1/ventromedial prefrontal cortex (vmPFC) and striatal/vmPFC connectivity. The results suggest the involvement of decoupling vmPFC connectivity in reward learning in general and the roles of attentional processes in the formation of the subjective CS/UCS association during the early phase and reward prediction during the late phase of appetitive conditioning.

A neuronal theory of sequential economic choice

Results of recent studies point towards a new framework for the neural bases of economic choice. The principles of this framework include the idea that evaluation is limited to a single option within the focus of attention and that we accept or reject that option relative to the entire set of alternatives. Rejection leads attention to a new option, although it can later switch back to a previously rejected one. The option to which a neuron's firing rate refers is determined dynamically by attention and not stably by labelled lines. Value is always computed relative to the value of rejection. Comparison results not from explicit competition between discrete populations of neurons, but indirectly, as in a horse race, from the fact that the first option whose value crosses a threshold is selected. Consequently, comparison can occur within a single pool of neurons rather than by competition between two or more neuronal populations. The computations that constitute comparison thus occur at multiple levels, including premotor levels, simultaneously (i.e. the brain uses a distributed consensus), and not in discrete stages. This framework suggests a solution to a set of otherwise unresolved neuronal binding problems that result from the need to link options to values, comparisons to actions, and choices to outcomes.

Dysregulation of Corticostriatal Connectivity in Huntington's Disease: A Role for Dopamine Modulation

Aberrant communication between striatum, the main information processing unit of the basal ganglia, and cerebral cortex plays a critical role in the emergence of Huntington’s disease (HD), a fatal monogenetic condition that typically strikes in the prime of life. Although both striatum and cortex undergo substantial cell loss over the course of HD, corticostriatal circuits become dysfunctional long before neurons die. Understanding the dysfunction is key to developing effective strategies for treating a progressively worsening triad of motor, cognitive, and psychiatric symptoms. Cortical output neurons drive striatal activity through the release of glutamate, an excitatory amino acid. Striatal outputs, in turn, release γ-amino butyric acid (GABA) and exert inhibitory control over downstream basal ganglia targets. Ample evidence from transgenic rodent models points to dysregulation of corticostriatal glutamate transmission along with corresponding changes in striatal GABA release as underlying factors in the HD behavioral phenotype. Another contributor is dysregulation of dopamine (DA), a modulator of both glutamate and GABA transmission. In fact, pharmacological manipulation of DA is the only currently available treatment for HD symptoms. Here, we review data from animal models and human patients to evaluate the role of DA in HD, including DA interactions with glutamate and GABA within the context of dysfunctional corticostriatal circuitry.

Control without Controllers: Toward a Distributed Neuroscience of Executive Control

Executive control refers to the regulation of cognition and behavior by mental processes and is a hallmark of higher cognition. Most approaches to understanding its mechanisms begin with the assumption that our brains have anatomically segregated and functionally specialized control modules. The modular approach is intuitive: Control is conceptually distinct from basic mental processing, so an organization that reifies that distinction makes sense. An alternative approach sees executive control as self-organizing principles of a distributed organization. In distributed systems, control and controlled processes are colocalized within large numbers of dispersed computational agents. Control then is often an emergent consequence of simple rules governing the interaction between agents. Because these systems are unfamiliar and unintuitive, here we review several well-understood examples of distributed control systems, group living insects and social animals, and emphasize their parallels with neural systems. We then reexamine the cognitive neuroscience literature on executive control for evidence that its neural control systems may be distributed.

Rule Encoding in Orbitofrontal Cortex and Striatum Guides Selection

Active maintenance of rules, like other executive functions, is often thought to be the domain of a discrete executive system. An alternative view is that rule maintenance is a broadly distributed function relying on widespread cortical and subcortical circuits. Tentative evidence supporting this view comes from research showing some rule selectivity in the orbitofrontal cortex and dorsal striatum. We recorded in these regions and in the ventral striatum, which has not been associated previously with rule representation, as macaques performed a Wisconsin Card Sorting Task. We found robust encoding of rule category (color vs shape) and rule identity (six possible rules) in all three regions. Rule identity modulated responses to potential choice targets, suggesting that rule information guides behavior by highlighting choice targets. The effects that we observed were not explained by differences in behavioral performance across rules and thus cannot be attributed to reward expectation. Our results suggest that rule maintenance and rule-guided selection of options are distributed processes and provide new insight into orbital and striatal contributions to executive control.

SIGNIFICANCE STATEMENT

Rule maintenance, an important executive function, is generally thought to rely on dorsolateral brain regions. In this study, we examined activity of single neurons in orbitofrontal cortex and in ventral and dorsal striatum of macaques in a Wisconsin Card Sorting Task. Neurons in all three areas encoded rules and rule categories robustly. Rule identity also affected neural responses to potential choice options, suggesting that stored information is used to influence decisions. These results endorse the hypothesis that rule maintenance is a broadly distributed mental operation.

Neuronal responses support a role for orbitofrontal cortex in cognitive set reconfiguration

We are often faced with the need to abandon no-longer beneficial rules and adopt new ones. This process, known as cognitive set reconfiguration, is a hallmark of executive control. Although cognitive functions like reconfiguration are most often associated with dorsal prefrontal structures, recent evidence suggests that the orbitofrontal cortex (OFC) may play an important role as well. We recorded the activity of OFC neurons while rhesus macaques performed an analogue of the Wisconsin Card Sorting Task that involved a trial and error stage. The OFC neurons demonstrated two types of switch-related activity, an early (switch-away) signal and a late (switch-to) signal, when the new task set was established. We also found a pattern of match modulation: a significant change in activity for the stimulus that matched the current perceptual rule (and would therefore be selected). These results extend our understanding of the executive functions of the OFC. They also allow us to directly compare the OFC with the complementary datasets we previously collected in the ventral (VS) and dorsal (DS) striatum. Although both effects are observed in all three areas, the timing of responses aligns the OFC more closely with DS than with VS.