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Dissociable contributions of ventromedial prefrontal and posterior parietal cortex to value-guided choice.
Two long-standing traditions have highlighted cortical decision mechanisms in the parietal and prefrontal cortices of primates, but it has not been clear how these processes differ, or when each cortical region may influence behaviour. Recent data from ventromedial prefrontal cortex (vmPFC) and posterior parietal cortex (PPC) have suggested one possible axis on which the two decision processes might be delineated. Fast decisions may be resolved primarily by parietal mechanisms, whereas decisions made without time pressure may rely on prefrontal mechanisms. Here, we report direct evidence for such dissociation. During decisions under time pressure, a value comparison process was evident in PPC, but not in vmPFC. Value-related activity was still found in vmPFC under time pressure. However, vmPFC represented overall input value rather than compared output value. In contrast, when decisions were made without time pressure, vmPFC transitioned to encode a value comparison while value-related parameters were entirely absent from PPC. Furthermore, under time pressure, decision performance was primarily governed by PPC, while it was dominated by vmPFC at longer decision times. These data demonstrate that parallel cortical mechanisms may resolve the same choices in differing circumstances, and offer an explanation of the diverse neural signals reported in vmPFC and PPC during value-guided choice.
Temporally Dissociable Contributions of Human Medial Prefrontal Subregions to Reward-Guided Learning.
UNLABELLED: In decision making, dorsal and ventral medial prefrontal cortex show a sensitivity to key decision variables, such as reward prediction errors. It is unclear whether these signals reflect parallel processing of a common synchronous input to both regions, for example from mesocortical dopamine, or separate and consecutive stages in reward processing. These two perspectives make distinct predictions about the relative timing of feedback-related activity in each of these regions, a question we address here. To reconstruct the unique temporal contribution of dorsomedial (dmPFC) and ventromedial prefrontal cortex (vmPFC) to simultaneously measured EEG activity in human subjects, we developed a novel trialwise fMRI-informed EEG analysis that allows dissociating correlated and overlapping sources. We show that vmPFC uniquely contributes a sustained activation profile shortly after outcome presentation, whereas dmPFC contributes a later and more peaked activation pattern. This temporal dissociation is expressed mainly in the alpha band for a vmPFC signal, which contrasts with a theta based dmPFC signal. Thus, our data show reward-related vmPFC and dmPFC responses have distinct time courses and unique spectral profiles, findings that support distinct functional roles in a reward-processing network. SIGNIFICANCE STATEMENT: Multiple subregions of the medial prefrontal cortex are known to be involved in decision making and learning, and expose similar response patterns in fMRI. Here, we used a novel approach to analyzing simultaneous EEG-fMRI that allows to dissociate the individual time courses of brain regions. We find that vmPFC and dmPFC have distinguishable time courses and time-frequency patterns.
Capturing the temporal evolution of choice across prefrontal cortex.
Activity in prefrontal cortex (PFC) has been richly described using economic models of choice. Yet such descriptions fail to capture the dynamics of decision formation. Describing dynamic neural processes has proven challenging due to the problem of indexing the internal state of PFC and its trial-by-trial variation. Using primate neurophysiology and human magnetoencephalography, we here recover a single-trial index of PFC internal states from multiple simultaneously recorded PFC subregions. This index can explain the origins of neural representations of economic variables in PFC. It describes the relationship between neural dynamics and behaviour in both human and monkey PFC, directly bridging between human neuroimaging data and underlying neuronal activity. Moreover, it reveals a functionally dissociable interaction between orbitofrontal cortex, anterior cingulate cortex and dorsolateral PFC in guiding cost-benefit decisions. We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making.
Approach-Induced Biases in Human Information Sampling.
Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach"), the selection of which information to sample ("sampling the favorite"), and the interaction between information sampling and subsequent choices ("rejecting unsampled options"). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.
Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice.
Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.
Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex.
Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons or by dynamic (time-varying) activity across a neural population. Here, we compare these hypotheses across four regions of prefrontal cortex (PFC) in an oculomotor-delayed-response task, where an intervening cue indicated the reward available for a correct saccade. WM representations were strongest in ventrolateral PFC neurons with higher intrinsic temporal stability (time-constant). At the population-level, although a stable mnemonic state was reached during the delay, this tuning geometry was reversed relative to cue-period selectivity, and was disrupted by the reward cue. Single-neuron analysis revealed many neurons switched to coding reward, rather than maintaining task-relevant spatial selectivity until saccade. These results imply WM is fulfilled by dynamic, population-level activity within high time-constant neurons. Rather than persistent activity supporting stable mnemonic representations that bridge subsequent salient stimuli, PFC neurons may stabilise a dynamic population-level process supporting WM.
Correction: Approach-Induced Biases in Human Information Sampling.
[This corrects the article DOI: 10.1371/journal.pbio.2000638.].
Capturing the temporal evolution of choice across prefrontal cortex
© Hunt et al. Activity in prefrontal cortex (PFC) has been richly described using economic models of choice. Yet such descriptions fail to capture the dynamics of decision formation. Describing dynamic neural processes has proven challenging due to the problem of indexing the internal state of PFC and its trial-by-trial variation. Using primate neurophysiology and human magnetoencephalography, we here recover a single-trial index of PFC internal states from multiple simultaneously recorded PFC subregions. This index can explain the origins of neural representations of economic variables in PFC. It describes the relationship between neural dynamics and behaviour in both human and monkey PFC, directly bridging between human neuroimaging data and underlying neuronal activity. Moreover, it reveals a functionally dissociable interaction between orbitofrontal cortex, anterior cingulate cortex and dorsolateral PFC in guiding cost-benefit decisions. We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making.
A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex
Although the ventromedial prefrontal cortex (vmPFC) has long been implicated in reward-guided decision making, its exact role in this process has remained an unresolved issue. Here we show that, in accordance with models of decision making, vmPFC concentrations of GABA and glutamate in human volunteers predict both behavioral performance and the dynamics of a neural value comparison signal. These data provide evidence for a neural competition mechanism in vmPFC that supports value-guided choice. © 2012 Nature America, Inc. All rights reserved.
A neural mechanism underlying failure of optimal choice with multiple alternatives
Despite widespread interest in neural mechanisms of decision-making, most investigations focus on decisions between just two options. Here we adapt a biophysically plausible model of decision-making to predict how a key decision variable, the value difference signal - encoding how much better one choice is than another - changes with the value of a third, but unavailable, alternative. The model predicts a surprising failure of optimal decision-making: greater difficulty choosing between two options in the presence of a third very poor, as opposed to very good, alternative. Both investigation of human decision-making and functional magnetic resonance imaging-based measurements of value difference signals in ventromedial prefrontal cortex (vmPFC) bore out this prediction. The vmPFC signal decreased in the presence of low-value third alternatives, and vmPFC effect sizes predicted individual variation in suboptimal decision-making in the presence of multiple alternatives. The effect contrasts with that of divisive normalization in parietal cortex. © 2014 Nature America, Inc.
Triple dissociation of attention and decision computations across prefrontal cortex
Anatomical, neuroimaging and lesion studies indicate that prefrontal cortex (PFC) can be subdivided into different subregions supporting distinct aspects of decision making. However, explanations of neuronal computations within these subregions varies widely across studies. An integrated and mechanistic account of PFC function therefore remains elusive. Resolving these debates demands a rich dataset that directly contrasts neuronal activity across multiple PFC subregions within a single paradigm, whilst experimentally controlling factors such as the order, duration and frequency in which choice options are attended and compared. Here, we contrast neuronal population responses between macaque orbitofrontal (OFC), anterior cingulate (ACC) and dorsolateral prefrontal cortices (DLPFC) during sequential value-guided information search and choice. From the first fixation of choice-related stimuli, a strong triple dissociation of information encoding emerges in parallel across these PFC subregions. As further information is gathered, population responses in OFC reflect an attention-guided value comparison process. Meanwhile, parallel signals in ACC reflect belief updating in light of new evidence, integration of that evidence to a decision bound, and an emerging action plan for which option should be chosen. Our findings demonstrate the co-existence of multiple, distributed decision-related computations across PFC subregions during value-guided choice. They provide a synthesis of several competing accounts of PFC function.
A neural mechanism underlying failure of optimal choice with multiple alternatives
Despite widespread interest in neural mechanisms of decision-making, most investigations focus on decisions between just two options. Here we adapt a biophysically plausible model of decision-making to predict how a key decision variable, the value difference signal - encoding how much better one choice is than another - changes with the value of a third, but unavailable, alternative. The model predicts a surprising failure of optimal decision-making: greater difficulty choosing between two options in the presence of a third very poor, as opposed to very good, alternative. Both investigation of human decision-making and functional magnetic resonance imaging-based measurements of value difference signals in ventromedial prefrontal cortex (vmPFC) bore out this prediction. The vmPFC signal decreased in the presence of low-value third alternatives, and vmPFC effect sizes predicted individual variation in suboptimal decision-making in the presence of multiple alternatives. The effect contrasts with that of divisive normalization in parietal cortex. © 2014 Nature America, Inc.
Differential valuation and learning from social and non-social cues in Borderline Personality Disorder
Background: Volatile interpersonal relationships are a core feature of Borderline Personality Disorder (BPD), and lead to devastating disruption of patients' personal and professional lives. Quantitative models of social decision making and learning hold promise for defining the underlying mechanisms of this problem. In this study, we tested BPD and control subject weighting of social versus non-social information, and their learning about choices under stable and volatile conditions. We compared behavior using quantitative models. Methods: Subjects (n=20 BPD, n=23 control) played an extended reward learning task with a partner (confederate) that requires learning about non-social and social cue reward probability (The Social Valuation Task). Task experience was measured using language metrics: explicit emotions/beliefs, talk about the confederate, and implicit distress (using the previously established marker self-referentiality). Subjects' weighting of social and non-social cues was tested in mixed-effects regression models. Subjects' learning rates under stable and volatile conditions were modelled (Rescorla-Wagner approach) and group x condition interactions tested. Results: Compared to controls, BPD subject debriefings included more mentions of the confederate and less distress language. BPD subjects also weighted social cues more heavily, but had blunted learning responses to (non-social and social) volatility. Conclusions: This is the first report of patient behavior in the Social Valuation Task. The results suggest that BPD subjects expect higher volatility than do controls. These findings lay the groundwork for a neuro-computational dissection of social and non-social belief updating in BPD, which holds promise for the development of novel clinical interventions that more directly target pathophysiology.
Differential Valuation and Learning From Social and Nonsocial Cues in Borderline Personality Disorder.
BACKGROUND: Volatile interpersonal relationships are a core feature of borderline personality disorder (BPD) and lead to devastating disruption of patients' personal and professional lives. Quantitative models of social decision making and learning hold promise for defining the underlying mechanisms of this problem. In this study, we tested BPD and control subject weighting of social versus nonsocial information and their learning about choices under stable and volatile conditions. We compared behavior using quantitative models. METHODS: Subjects (n = 20 BPD, n = 23 control subjects) played an extended reward learning task with a partner (confederate) that requires learning about nonsocial and social cue reward probability (the social valuation task). Task experience was measured using language metrics: explicit emotions/beliefs, talk about the confederate, and implicit distress (using the previously established marker self-referentiality). Subjects' weighting of social and nonsocial cues was tested in mixed-effect regression models. Subjects' learning rates under stable and volatile conditions were modeled (Rescorla-Wagner approach) and group × condition interactions tested. RESULTS: Compared to control subjects, BPD subject debriefings included more mentions of the confederate and less distress language. BPD subjects also weighted social cues more heavily but had blunted learning responses to (nonsocial and social) volatility. CONCLUSIONS: This is the first report of patient behavior in the social valuation task. The results suggest that BPD subjects expect higher volatility than control subjects. These findings lay the groundwork for a neurocomputational dissection of social and nonsocial belief updating in BPD, which holds promise for the development of novel clinical interventions that more directly target pathophysiology.
Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex.
Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons or by dynamic (time-varying) activity across a neural population. Here, we compare these hypotheses across four regions of prefrontal cortex (PFC) in an oculomotor-delayed-response task, where an intervening cue indicated the reward available for a correct saccade. WM representations were strongest in ventrolateral PFC neurons with higher intrinsic temporal stability (time-constant). At the population-level, although a stable mnemonic state was reached during the delay, this tuning geometry was reversed relative to cue-period selectivity, and was disrupted by the reward cue. Single-neuron analysis revealed many neurons switched to coding reward, rather than maintaining task-relevant spatial selectivity until saccade. These results imply WM is fulfilled by dynamic, population-level activity within high time-constant neurons. Rather than persistent activity supporting stable mnemonic representations that bridge subsequent salient stimuli, PFC neurons may stabilise a dynamic population-level process supporting WM.
Triple dissociation of attention and decision computations across prefrontal cortex.
Naturalistic decision-making typically involves sequential deployment of attention to choice alternatives to gather information before a decision is made. Attention filters how information enters decision circuits, thus implying that attentional control may shape how decision computations unfold. We recorded neuronal activity from three subregions of the prefrontal cortex (PFC) while monkeys performed an attention-guided decision-making task. From the first saccade to decision-relevant information, a triple dissociation of decision- and attention-related computations emerged in parallel across PFC subregions. During subsequent saccades, orbitofrontal cortex activity reflected the value comparison between currently and previously attended information. In contrast, the anterior cingulate cortex carried several signals reflecting belief updating in light of newly attended information, the integration of evidence to a decision bound and an emerging plan for what action to choose. Our findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.
A systematic review of calcium channel antagonists in bipolar disorder and some considerations for their future development.
l-type calcium channel (LTCC) antagonists have been used in bipolar disorder for over 30 years, without becoming an established therapeutic approach. Interest in this class of drugs has been rekindled by the discovery that LTCC genes are part of the genetic aetiology of bipolar disorder and related phenotypes. We have therefore conducted a systematic review of LTCC antagonists in the treatment and prophylaxis of bipolar disorder. We identified 23 eligible studies, with six randomised, double-blind, controlled clinical trials, all of which investigated verapamil in acute mania, and finding no evidence that it is effective. Data for other LTCC antagonists (diltiazem, nimodipine, nifedipine, methyoxyverapamil and isradipine) and for other phases of the illness are limited to observational studies, and therefore no robust conclusions can be drawn. Given the increasingly strong evidence for calcium signalling dysfunction in bipolar disorder, the therapeutic candidacy of this class of drugs has become stronger, and hence we also discuss issues relevant to their future development and evaluation. In particular, we consider how genetic, molecular and pharmacological data can be used to improve the selectivity, efficacy and tolerability of LTCC antagonists. We suggest that a renewed focus on LTCCs as targets, and the development of 'brain-selective' LTCC ligands, could be one fruitful approach to innovative pharmacotherapy for bipolar disorder and related phenotypes.