Showing papers by "Raymond J. Dolan published in 2014"
TL;DR: It is shown how basic principles of neuronal computation can be used to explain psychopathology, ranging from impoverished theory of mind in autism to abnormalities of smooth pursuit eye movements in schizophrenia.
376 citations
TL;DR: Using computational modeling, it is shown that emotional reactivity in the form of momentary happiness in response to outcomes of a probabilistic reward task is explained not by current task earnings, but by the combined influence of recent rewards and prediction errors arising from those expectations.
Abstract: The subjective well-being or happiness of individuals is an important metric for societies. Although happiness is influenced by life circumstances and population demographics such as wealth, we know little about how the cumulative influence of daily life events are aggregated into subjective feelings. Using computational modeling, we show that emotional reactivity in the form of momentary happiness in response to outcomes of a probabilistic reward task is explained not by current task earnings, but by the combined influence of recent reward expectations and prediction errors arising from those expectations. The robustness of this account was evident in a large-scale replication involving 18,420 participants. Using functional MRI, we show that the very same influences account for task-dependent striatal activity in a manner akin to the influences underpinning changes in happiness.
287 citations
TL;DR: The findings raise the intriguing possibility that optimistically biased updating of expectations about one's personal future is associated with mental health.
Abstract: BACKGROUND: When challenged with information about the future, healthy participants show an optimistically biased updating pattern, taking desirable information more into account than undesirable information. However, it is unknown how patients suffering from major depressive disorder (MDD), who express pervasive pessimistic beliefs, update their beliefs when receiving information about their future. Here we tested whether an optimistically biased information processing pattern found in healthy individuals is absent in MDD patients. Method MDD patients (n = 18; 13 medicated; eight with co-morbid anxiety disorder) and healthy controls (n = 19) estimated their personal probability of experiencing 70 adverse life events. After each estimate participants were presented with the average probability of the event occurring to a person living in the same sociocultural environment. This information could be desirable (i.e. average probability better than expected) or undesirable (i.e. average probability worse than expected). To assess how desirable versus undesirable information influenced beliefs, participants estimated their personal probability of experiencing the 70 events a second time. RESULTS: Healthy controls showed an optimistic bias in updating, that is they changed their beliefs more toward desirable versus undesirable information. Overall, this optimistic bias was absent in MDD patients. Symptom severity correlated with biased updating: more severely depressed individuals showed a more pessimistic updating pattern. Furthermore, MDD patients estimated the probability of experiencing adverse life events as higher than healthy controls. CONCLUSIONS: Our findings raise the intriguing possibility that optimistically biased updating of expectations about one's personal future is associated with mental health.
238 citations
TL;DR: Variational Bayes is considered as a scheme that the brain might use for approximate Bayesian inference that optimizes a free energy bound on model evidence and changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses.
Abstract: This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.
224 citations
TL;DR: M measuring how much money people will sacrifice to reduce the number of painful electric shocks delivered to either themselves or an anonymous stranger shows that most people valued others’ pain more than their own pain, and this ‟hyperaltruistic” valuation was linked to slower responding when making decisions that affected others, consistent with an engagement of deliberative processes in moral decision making.
Abstract: Concern for the suffering of others is central to moral decision making. How humans evaluate others’ suffering, relative to their own suffering, is unknown. We investigated this question by inviting subjects to trade off profits for themselves against pain experienced either by themselves or an anonymous other person. Subjects made choices between different amounts of money and different numbers of painful electric shocks. We independently varied the recipient of the shocks (self vs. other) and whether the choice involved paying to decrease pain or profiting by increasing pain. We built computational models to quantify the relative values subjects ascribed to pain for themselves and others in this setting. In two studies we show that most people valued others’ pain more than their own pain. This was evident in a willingness to pay more to reduce others’ pain than their own and a requirement for more compensation to increase others’ pain relative to their own. This ‟hyperaltruistic” valuation of others’ pain was linked to slower responding when making decisions that affected others, consistent with an engagement of deliberative processes in moral decision making. Subclinical psychopathic traits correlated negatively with aversion to pain for both self and others, in line with reports of aversive processing deficits in psychopathy. Our results provide evidence for a circumstance in which people care more for others than themselves. Determining the precise boundaries of this surprisingly prosocial disposition has implications for understanding human moral decision making and its disturbance in antisocial behavior.
223 citations
TL;DR: This study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies, and highlights VS dysfunction is tightly linked to a reward- related reversal learning deficit in early, unmedicated schizophrenia patients.
222 citations
TL;DR: Investigating whether the processing of brief fear stimuli is selectively gated by their timing in relation to individual heartbeats shows that fearful faces were detected more easily and were rated as more intense at systole than at diastole, counter to the view that baroreceptor afferent signaling is always inhibitory to sensory perception.
Abstract: Cognitions and emotions can be influenced by bodily physiology. Here, we investigated whether the processing of brief fear stimuli is selectively gated by their timing in relation to individual heartbeats. Emotional and neutral faces were presented to human volunteers at cardiac systole, when ejection of blood from the heart causes arterial baroreceptors to signal centrally the strength and timing of each heartbeat, and at diastole, the period between heartbeats when baroreceptors are quiescent. Participants performed behavioral and neuroimaging tasks to determine whether these interoceptive signals influence the detection of emotional stimuli at the threshold of conscious awareness and alter judgments of emotionality of fearful and neutral faces. Our results show that fearful faces were detected more easily and were rated as more intense at systole than at diastole. Correspondingly, amygdala responses were greater to fearful faces presented at systole relative to diastole. These novel findings highlight a major channel by which short-term interoceptive fluctuations enhance perceptual and evaluative processes specifically related to the processing of fear and threat and counter the view that baroreceptor afferent signaling is always inhibitory to sensory perception.
220 citations
TL;DR: It is proposed that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes, and it is concluded that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting.
Abstract: The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a “model-based” (or goal-directed) system and a “model-free” (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.
211 citations
TL;DR: Boosting dopamine enhances the dominance of action over valence in the striatum and dopaminergic midbrain and decreases the extent of the behavioral coupling between action and valence.
209 citations
TL;DR: The strength of attentional alpha modulations increases with the predictability of the anticipated sensory target, regardless of current afferent drive, and the poststimulus attentional gamma enhancement is stimulus-bound and decreases when the subsequent target becomes more predictable.
Abstract: The brain adapts to dynamic environments by adjusting the attentional gain or precision afforded to salient and predictable sensory input. Previous research suggests that this involves the regulation of cortical excitability (reflected in prestimulus alpha oscillations) before stimulus onset that modulates subsequent stimulus processing (reflected in stimulus-bound gamma oscillations). We present two spatial attention experiments in humans, where we first replicate the classic finding of prestimulus attentional alpha modulation and poststimulus gamma modulation. In the second experiment, the task-relevant target was a stimulus change that occurred after stimulus onset. This enabled us to show that attentional alpha modulation reflects the predictability (precision) of an upcoming sensory target, rather than an attenuation of alpha activity induced by neuronal excitation related to stimulus onset. In particular, we show that the strength of attentional alpha modulations increases with the predictability of the anticipated sensory target, regardless of current afferent drive. By contrast, we show that the poststimulus attentional gamma enhancement is stimulus-bound and decreases when the subsequent target becomes more predictable. Hence, this pattern suggests that the strength of gamma oscillations is not merely a function of cortical excitability, but also depends on the relative mismatch of predictions and sensory evidence. Together, these findings support recent theoretical proposals for distinct roles of alpha/beta and gamma oscillations in hierarchical perceptual inference and predictive coding.
207 citations
TL;DR: The data provide the first human assay for approach-avoidance conflict akin to that of animal anxiety models and furnish a framework for addressing the neuronal underpinnings of human anxiety disorders, where the data indicate a major role for the hippocampus.
TL;DR: Brain imaging is used in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals.
Abstract: Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus) and bad news (in right inferior parietal lobule and right inferior frontal gyrus) regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in major depression disorder, which may be essential for mental health.
TL;DR: It is found that behavior in multi-attribute choice is best explained by a model involving competition at multiple levels of representation, and this hierarchical model also explains neural signals in human brain regions previously linked to valuation.
Abstract: Valuation is a key tenet of decision neuroscience, where it is generally assumed that different attributes of competing options are assimilated into unitary values. Such values are central to current neural models of choice. By contrast, psychological studies emphasize complex interactions between choice and valuation. Principles of neuronal selection also suggest that competitive inhibition may occur in early valuation stages, before option selection. We found that behavior in multi-attribute choice is best explained by a model involving competition at multiple levels of representation. This hierarchical model also explains neural signals in human brain regions previously linked to valuation, including striatum, parietal and prefrontal cortex, where activity represents within-attribute competition, competition between attributes and option selection. This multi-layered inhibition framework challenges the assumption that option values are computed before choice. Instead, our results suggest a canonical competition mechanism throughout all stages of a processing hierarchy, not simply at a final choice stage.
TL;DR: It is established that the habenula encodes associations with aversive outcomes in humans, specifically that it tracks the dynamically changing negative values of cues paired with painful electric shocks, consistent with a role in learning.
Abstract: Learning what to approach, and what to avoid, involves assigning value to environmental cues that predict positive and negative events. Studies in animals indicate that the lateral habenula encodes the previously learned negative motivational value of stimuli. However, involvement of the habenula in dynamic trial-by-trial aversive learning has not been assessed, and the functional role of this structure in humans remains poorly characterized, in part, due to its small size. Using high-resolution functional neuroimaging and computational modeling of reinforcement learning, we demonstrate positive habenula responses to the dynamically changing values of cues signaling painful electric shocks, which predict behavioral suppression of responses to those cues across individuals. By contrast, negative habenula responses to monetary reward cue values predict behavioral invigoration. Our findings show that the habenula plays a key role in an online aversive learning system and in generating associated motivated behavior in humans.
TL;DR: The findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.
Abstract: The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses—including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects—from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.
TL;DR: It is hypothesized that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture, and a number of apparently suboptimal phenomena within the framework of approximate Bayesian inference are proposed.
Abstract: Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior.
TL;DR: The results support the hypothesis that a subcortical pathway to the amygdala plays a role in rapid sensory processing of faces, in particular during early stimulus processing, and contributes to an understanding of the amygdala as a behavioural relevance detector.
TL;DR: This study investigated whether the mass collection of experimental data using smartphone technology is valid, given the variability of data collection outside of a laboratory setting and found that the large sample size vastly outweighed the noise inherent in collecting data outside a controlled laboratory setting.
Abstract: By 2015, there will be an estimated two billion smartphone users worldwide. This technology presents exciting opportunities for cognitive science as a medium for rapid, large-scale experimentation and data collection. At present, cost and logistics limit most study populations to small samples, restricting the experimental questions that can be addressed. In this study we investigated whether the mass collection of experimental data using smartphone technology is valid, given the variability of data collection outside of a laboratory setting. We presented four classic experimental paradigms as short games, available as a free app and over the first month 20,800 users submitted data. We found that the large sample size vastly outweighed the noise inherent in collecting data outside a controlled laboratory setting, and show that for all four games canonical results were reproduced. For the first time, we provide experimental validation for the use of smartphones for data collection in cognitive science, which can lead to the collection of richer data sets and a significant cost reduction as well as provide an opportunity for efficient phenotypic screening of large populations.
TL;DR: An age-related reduction in updating beliefs when older adults are faced with undesirable, but not desirable, information about negative events is demonstrated and a greater update bias in healthy older age is shown.
Abstract: Healthy older adults report greater well-being and life satisfaction than their younger counterparts. One potential explanation for this is enhanced optimism. We tested the influence of age on optimistic and pessimistic beliefs about the future and the associated structural neural correlates.
TL;DR: People may use Bayesian inference to update their own self-representation to update the value of an outcome, which is essentially the prior belief that it can be achieved.
TL;DR: Investigating the role of dopamine and serotonin in the interaction between action and valence during learning found that, after administration of levodopa, action learning was less affected by outcome valence when compared with the placebo and citalopram groups.
Abstract: Rationale Decision-making involves two fundamental axes of control namely valence, spanning reward and punishment, and action, spanning invigoration and inhibition. We recently exploited a go/no-go task whose contingencies explicitly decouple valence and action to show that these axes are inextricably coupled during learning. This results in a disadvantage in learning to go to avoid punishment and in learning to no-go to obtain a reward. The neuromodulators dopamine and serotonin are likely to play a role in these asymmetries: Dopamine signals anticipation of future rewards and is also involved in an invigoration of motor responses leading to reward, but it also arbitrates between different forms of control.Conversely,serotoninisimplicatedinmotor inhibition and punishment processing. Objective To investigate the role of dopamine and serotonin in the interaction between action and valence during learning. Methods We combined computational modeling with pharmacological manipul ation in 90 healthy human volunteers, using levodopa and citalopram to affect dopamine and serotonin, respectively. Results We found that, after administration of levodopa, action learning was less affected by outcome valence when compared with the placebo and citalopram groups. This highlights in this context a predominant effect of levodopa in controlling the balance between different forms of control. Citalopram had distinct effects, increasing participants’ tendency to perform active responses independent of outcome valence, consistent with a role in decreasing motor inhibition.
TL;DR: Computational modeling applied to psychophysical data (obtained from a spatial attention task) under a psychopharmacological challenge with the cholinesterase inhibitor galantamine allowed us to characterize the cholinergic modulation of selective attention formally, in terms of hierarchical Bayesian inference.
Abstract: The exact mechanisms whereby the cholinergic neurotransmitter system contributes to attentional processing remain poorly understood. Here, we applied computational modeling to psychophysical data (obtained from a spatial attention task) under a psychopharmacological challenge with the cholinesterase inhibitor galantamine (Reminyl). This allowed us to characterize the cholinergic modulation of selective attention formally, in terms of hierarchical Bayesian inference. In a placebo-controlled, within-subject, crossover design, 16 healthy human subjects performed a modified version of Posner's location-cueing task in which the proportion of validly and invalidly cued targets (percentage of cue validity, % CV) changed over time. Saccadic response speeds were used to estimate the parameters of a hierarchical Bayesian model to test whether cholinergic stimulation affected the trial-wise updating of probabilistic beliefs that underlie the allocation of attention or whether galantamine changed the mapping from those beliefs to subsequent eye movements. Behaviorally, galantamine led to a greater influence of probabilistic context (% CV) on response speed than placebo. Crucially, computational modeling suggested this effect was due to an increase in the rate of belief updating about cue validity (as opposed to the increased sensitivity of behavioral responses to those beliefs). We discuss these findings with respect to cholinergic effects on hierarchical cortical processing and in relation to the encoding of expected uncertainty or precision.
TL;DR: Functional magnetic resonance imaging is used together with psychopharmacology to demonstrate that RS effects within the mesolimbic system are differentially modulated by cholinergic and dopaminergic stimulation, and these shifts can influence memory retention.
Abstract: Repeated processing of the same information is associated with decreased neuronal responses, termed repetition suppression (RS). Although RS effects (i.e., the difference in activity between novel and repeated stimuli) have been demonstrated within several brain regions, such as the medial temporal lobe, their precise neural mechanisms still remain unclear. Here, we used functional magnetic resonance imaging together with psychopharmacology in 48 healthy human subjects, demonstrating that RS effects within the mesolimbic system are differentially modulated by cholinergic and dopaminergic stimulation. The dopamine precursor levodopa (100 mg) attenuated RS within the hippocampus, parahippocampal cortex, and substantia nigra/ventral tegmental area, and the degree of this reduction correlated with recognition memory performance 24 h later. The acetylcholinesterase inhibitor galantamine (8 mg), in contrast, reversed RS into repetition enhancement, showing no relationship to subsequent recognition memory. This suggests that novelty sensitive neural populations of the mesolimbic system can dynamically shift their responses depending on the balance of cholinergic and dopaminergic neurotransmission, and these shifts can influence memory retention.
TL;DR: A Bayes optimal framework for modeling intersubject variability in mentalizing during interpersonal exchanges is inaugurated, and it is suggested the active inference framework lends itself to the study of psychiatric conditions where mentalizing is distorted.
Abstract: Introduction: We propose that active Bayesian inference – a general framework for decision-making – can equally be applied to interpersonal exchanges Social cognition, however, entails special challenges We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory Results: 1 Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred This inference, akin to 'mentalising' in the psychological literature, is based upon the outcomes of interpersonal exchanges 2 We show how some well-known social-psychological phenomena (eg self-serving biases) can be explained in terms of active interpersonal inference 3 Mentalising naturally entails Bayesian updating of how people value social outcomes Crucially this includes inference about one’s own qualities and preferences Conclusion: We inaugurate a Bayes optimal framework for modelling intersubject variability in mentalising during interpersonal exchanges Here, interpersonal representations are endowed with explicit functional and affective properties We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalising is distorted
TL;DR: This work investigates whether context impacts upon the neural representation of pain itself, or alternatively the transformation of pain into valuation-driven behavior, and finds no evidence of context-dependent activity within a conventional “pain matrix,” where pain-evoked activity reflected absolute stimulus intensity.
Abstract: The valuation of health-related states, including pain, is a critical issue in clinical practice, health economics, and pain neuroscience. Surprisingly the monetary value people associate with pain is highly context-dependent, with participants willing to pay more to avoid medium-level pain when presented in a context of low-intensity, rather than high-intensity, pain. Here, we ask whether context impacts upon the neural representation of pain itself, or alternatively the transformation of pain into valuation-driven behavior. While undergoing fMRI, human participants declared how much money they would be willing to pay to avoid repeated instances of painful cutaneous electrical stimuli delivered to the foot. We also implemented a contextual manipulation that involved presenting medium-level painful stimuli in blocks with either low- or high-level stimuli. We found no evidence of context-dependent activity within a conventional “pain matrix,” where pain-evoked activity reflected absolute stimulus intensity. By contrast, in right lateral orbitofrontal cortex, a strong contextual dependency was evident, and here activity tracked the contextual rank of the pain. The findings are in keeping with an architecture where an absolute pain valuation system and a rank-dependent system interact to influence willing to pay to avoid pain, with context impacting value-based behavior high in a processing hierarchy. This segregated processing hints that distinct neural representations reflect sensory aspects of pain and components that are less directly nociceptive whose integration also guides pain-related actions. A dominance of the latter might account for puzzling phenomena seen in somatization disorders where perceived pain is a dominant driver of behavior.
TL;DR: A model combining tonic and phasic DA is presented to show how different outflows triggered by either intrinsically or extrinsically motivating stimuli dynamically affect the basal ganglia by impacting on a selection process this system performs on its cortical input.
Abstract: The effects of striatal dopamine (DA) on behavior have been widely investigated over the past decades, with "phasic" burst firings considered as the key expression of a reward prediction error responsible for reinforcement learning. Less well studied is "tonic" DA, where putative functions include the idea that it is a regulator of vigor, incentive salience, disposition to exert an effort and a modulator of approach strategies. We present a model combining tonic and phasic DA to show how different outflows triggered by either intrinsically or extrinsically motivating stimuli dynamically affect the basal ganglia by impacting on a selection process this system performs on its cortical input. The model, which has been tested on the simulated humanoid robot iCub interacting with a mechatronic board, shows the putative functions ascribed to DA emerging from the combination of a standard computational mechanism coupled to a differential sensitivity to the presence of DA across the striatum.
TL;DR: Within 2 separate experiments, 1 involving an extremely large cohort of 3,247 participants, a dissociation between encoding and delay distractor-filtering is shown, indicating that separate mechanisms may contribute to working memory capacity.
Abstract: The effectiveness of distractor-filtering is a potentially important determinant of working memory capacity (WMC). However, a distinction between the contributions of distractor-filtering at WM encoding as opposed to filtering during maintenance has not been made and the assumption is that these rely on the same mechanism. Within 2 experiments, 1 conducted in the laboratory with 21 participants, and the other played as a game on smartphones (n = 3,247) we measure WMC without distractors, and present distractors during encoding or during the delay period of a WM task to determine performance associated with distraction at encoding and during maintenance. Despite differences in experimental setting and paradigm design between the 2 studies, we show a unique contribution to WMC from both encoding and delay distractor performance in both experiments, while controlling for performance in the absence of distraction. Thus, within 2 separate experiments, 1 involving an extremely large cohort of 3,247 participants, we show a dissociation between encoding and delay distractor-filtering, indicating that separate mechanisms may contribute to WMC.
TL;DR: A sequential task whereby the decision to exploit or forego an available offer was contingent on comparing immediate value and a state-dependent future cost of expending a limited resource, and the results suggest a choice architecture whereby interactions between ACC and vmPFC underpin an integration of immediate and delayed components of value to support flexible policy switching.
Abstract: Actions can lead to an immediate reward or punishment and a complex set of delayed outcomes. Adaptive choice necessitates the brain track and integrate both of these potential consequences. Here, we designed a sequential task whereby the decision to exploit or forego an available offer was contingent on comparing immediate value and a state-dependent future cost of expending a limited resource. Crucially, the dynamics of the task demanded frequent switches in policy based on an online computation of changing delayed consequences. We found that human subjects choose on the basis of a near-optimal integration of immediate reward and delayed consequences, with the latter computed in a prefrontal network. Within this network, anterior cingulate cortex (ACC) was dynamically coupled to ventromedial prefrontal cortex (vmPFC) when adaptive switches in choice were required. Our results suggest a choice architecture whereby interactions between ACC and vmPFC underpin an integration of immediate and delayed components of value to support flexible policy switching that accommodates the potential delayed consequences of an action.
TL;DR: Subliminal motor priming is used to test for the predicted motor locus of the attenuated perceived intensity of an action-outcome by compatible priming, which is known to enhance explicit agency judgements.
TL;DR: It is concluded that ventral striatum activity during decision making is dynamically modulated by behavioral context, indexed here by task relevance and action selection.
Abstract: Multiple features of the environment are often imbued with motivational significance, and the relative importance of these can change across contexts. The ability to flexibly adjust evaluative processes so that currently important features of the environment alone drive behavior is critical to adaptive routines. We know relatively little about the neural mechanisms involved, including whether motivationally significant features are obligatorily evaluated or whether current relevance gates access to value-sensitive regions. We addressed these questions using functional magnetic resonance imaging data and a task design where human subjects had to choose whether to accept or reject an offer indicated by visual and auditory stimuli. By manipulating, on a trial-by-trial basis, which stimulus determined the value of the offer, we show choice activity in the ventral striatum solely reflects the value of the currently relevant stimulus, consistent with a model wherein behavioral relevance modulates the impact of sensory stimuli on value processing. Choice outcome signals in this same region covaried positively with wins on accept trials, and negatively with wins on reject trials, consistent with striatal activity at feedback reflecting correctness of response rather than reward processing per se. We conclude that ventral striatum activity during decision making is dynamically modulated by behavioral context, indexed here by task relevance and action selection.