Choosing the greater of two goods: neural currencies for valuation and decision making

Diverse motives for human curiosity

Curiosity-our desire to know-is a fundamental drive in human behaviour, but its mechanisms are poorly understood. A classical question concerns the curiosity motives. What drives individuals to become curious about some but not other sources of information?¹ Here we show that curiosity about probabilistic events depends on multiple aspects of the distribution of these events. Participants (n = 257) performed a task in which they could demand advance information about only one of two randomly selected monetary prizes that contributed to their income. Individuals differed markedly in the extent to which they requested information as a function of the ex ante uncertainty or ex ante value of an individual prize. This heterogeneity was not captured by theoretical models describing curiosity as a desire to learn about the total rewards of a situation^2,3. Instead, it could be explained by an extended model that allowed for attribute-specific anticipatory utility-the savouring of individual components of the eventual reward-and postulates that this utility increased nonlinearly with the certainty of receiving the reward. Parameter values fitting individual choices were consistent for information about gains or losses, suggesting that attribute-specific anticipatory utility captures fundamental heterogeneity in the determinants of curiosity.

Short-Term Influence of Recent Trial History on Perceptual Choice Changes with Stimulus Strength

Perceptual decisions, especially for difficult stimuli, can be influenced by choices and outcomes in previous trials. However, it is not well understood how stimulus strength modulates the temporal characteristics as well as the magnitude of trial history influence. We addressed this question using a contrast detection task in freely moving mice. We found that, at lower as compared to higher stimulus contrast, the current choice of the mice was more influenced by choices and outcomes in the past trials and the influence emerged from a longer history. To examine the neural basis of stimulus strength-dependent history influence, we recorded from the secondary motor cortex (M2), a prefrontal region that plays an important role in cue-guided actions and memory-guided behaviors. We found that more M2 neurons conveyed information about choices on the past two trials at lower than at higher contrast. Furthermore, history-trial activity in M2 was important for decoding upcoming choice at low contrast. Thus, trial history influence of perceptual choice is adaptive to the strength of sensory evidence, which may be important for action selection in a dynamic environment.

Responses of Neurons in Lateral Intraparietal Area Depend on Stimulus-Associated Reward During Binocular Flash Suppression

Discovering neural correlates of subjective perception and dissociating them from sensory input has fascinated neuroscientists for a long time. Bistable and multistable perception phenomena have exhibited great experimental potential to address this question. Here, we performed electrophysiological recordings from single neurons in lateral intraparietal area (LIP) of rhesus macaques during stimulus and perceptual transitions induced by binocular flash suppression (BFS). LIP neurons demonstrated transient bursts of activity after stimulus presentation and stimulus or perceptual switches but only a minority of cells demonstrated stimulus and perceptual selectivity. To enhance LIP neural selectivity, we performed a second experiment in which the competing stimuli were associated with asymmetric rewards. We found that transient and sustained activities substantially increased while the proportion of stimulus selective neurons remained approximately the same, albeit with increased selectivity magnitude. In addition, we observed mild increases in the proportion of perceptually selective neurons which also showed increase magnitude of selectivity. Importantly, the increased selectivity of cells after the reward manipulation was not directly reflecting the reward size per se but an enhancement in stimulus differentiation. Based on our results, we conjecture that LIP contributes to perceptual transitions and serves a modulatory role in perceptual selection taking into account the stimulus behavioral value.

Behavioral Paradigms to Probe Individual Mouse Differences in Value-Based Decision Making

Value-based decision making relies on distributed neural systems that weigh the benefits of actions against the cost required to obtain a given outcome. Perturbations of these systems are thought to underlie abnormalities in action selection seen across many neuropsychiatric disorders. Genetic tools in mice provide a promising opportunity to explore the cellular components of these systems and their molecular foundations. However, few tasks have been designed that robustly characterize how individual mice integrate differential reward benefits and cost in their selection of actions. Here we present a forced-choice, two-alternative task in which each option is associated with a specific reward outcome, and unique operant contingency. We employed global and individual trial measures to assess the choice patterns and behavioral flexibility of mice in response to differing "choice benefits" (modeled as varying reward magnitude ratios) and different modalities of "choice cost" (modeled as either increasing repetitive motor output to obtain reward or increased delay to reward delivery). We demonstrate that (1) mouse choice is highly sensitive to the relative benefit of outcomes; (2) choice costs are heavily discounted in environments with large discrepancies in relative reward; (3) divergent cost modalities are differentially integrated into action selection; (4) individual mouse sensitivity to reward benefit is correlated with sensitivity to reward costs. These paradigms reveal stable individual animal differences in value-based action selection, thereby providing a foundation for interrogating the neural circuit and molecular pathophysiology of goal-directed dysfunction.

Activity of Prefrontal Neurons Predict Future Choices during Gambling

Neuronal signals in the prefrontal cortex have been reported to predict upcoming decisions. Such activity patterns are often coupled to perceptual cues indicating correct choices or values of different options. How does the prefrontal cortex signal future decisions when no cues are present but when decisions are made based on internal valuations of past experiences with stochastic outcomes? We trained rats to perform a two-arm bandit-task, successfully adjusting choices between certain-small or possible-big rewards with changing long-term advantages. We discovered specialized prefrontal neurons, whose firing during the encounter of no-reward predicted the subsequent choice of animals, even for unlikely or uncertain decisions and several seconds before choice execution. Optogenetic silencing of the prelimbic cortex exclusively timed to encounters of no reward, provoked animals to excessive gambling for large rewards. Firing of prefrontal neurons during outcome evaluation signals subsequent choices during gambling and is essential for dynamically adjusting decisions based on internal valuations.

Promoting subjective preferences in simple economic choices during nap

Sleep is known to benefit consolidation of memories, especially those of motivational relevance. Yet, it remains largely unknown the extent to which sleep influences reward-associated behavior, in particular, whether and how sleep modulates reward evaluation that critically underlies value-based decisions. Here, we show that neural processing during sleep can selectively bias preferences in simple economic choices when the sleeper is stimulated by covert, reward-associated cues. Specifically, presenting the spoken name of a familiar, valued snack item during midday nap significantly improves the preference for that item relative to items not externally cued. The cueing-specific preference enhancement is sleep-dependent and can be predicted by cue-induced neurophysiological signals at the subject and item level. Computational modeling further suggests that sleep cueing accelerates evidence accumulation for cued options during the post-sleep choice process in a manner consistent with the preference shift. These findings suggest that neurocognitive processing during sleep contributes to the fine-tuning of subjective preferences in a flexible, selective manner.

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

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

Context-Sensitive Computational Mechanisms of Decision Making

Real-world information is primarily sensory in nature, and understandably people attach value to the sensory information to prepare for appropriate behavioral responses. This review presents research from value-based, perceptual, and social decision-making domains, so far studied using isolated paradigms and their corresponding computational models. For example, in perceptual decision making, the sensory evidence accumulation rather than value computation becomes central to choice behavior. Furthermore, we identify cross-linkages between the perceptual and value-based domains to help us better understand generic processes pertaining to individual decision making. The purpose of this review is 2-fold. First, we identify the need for integrated study of different domains of decision making. Second, given that both our perception and valuation are influenced by the surrounding context, we suggest the integration of different types of information in decision making could be done by studying contextual influences in decision making. Future research needs to attempt toward a system-level understanding of various subprocesses involved in decision making.

Distinguishing neural correlates of context-dependent advantageous- and disadvantageous-inequity aversion

Humans can integrate social contextual information into decision-making processes to adjust their responses toward inequity. This context dependency emerges when individuals receive more (i.e., advantageous inequity) or less (i.e., disadvantageous inequity) than others. However, it is not clear whether context-dependent processing of advantageous and disadvantageous inequity involves differential neurocognitive mechanisms. Here, we used fMRI to address this question by combining an interactive game that modulates social contexts (e.g., interpersonal guilt) with computational models that enable us to characterize individual weights on inequity aversion. In each round, the participant played a dot estimation task with an anonymous coplayer. The coplayer would receive pain stimulation with 50% probability when either of them responded incorrectly. At the end of each round, the participant completed a variant of dictator game, which determined payoffs for him/herself and the coplayer. Computational modeling demonstrated the context dependency of inequity aversion: when causing pain to the coplayer (i.e., guilt context), participants cared more about the advantageous inequity and became more tolerant of the disadvantageous inequity, compared with other conditions. Consistently, neuroimaging results suggested the two types of inequity were associated with differential neurocognitive substrates. While the context-dependent processing of advantageous inequity was associated with social- and mentalizing-related processes, involving left anterior insula, right dorsolateral prefrontal cortex, and dorsomedial prefrontal cortex, the context-dependent processing of disadvantageous inequity was primarily associated with emotion- and conflict-related processes, involving left posterior insula, right amygdala, and dorsal anterior cingulate cortex. These results extend our understanding of decision-making processes related to inequity aversion.

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.

Lateral intraparietal area (LIP) is largely effector-specific in free-choice decisions

Despite many years of intense research, there is no strong consensus about the role of the lateral intraparietal area (LIP) in decision making. One view of LIP function is that it guides spatial attention, providing a “saliency map” of the external world. If this were the case, it would contribute to target selection regardless of which action would be performed to implement the choice. On the other hand, LIP inactivation has been shown to influence spatial selection and oculomotor metrics in free-choice decisions, which are made using eye movements, arguing that it contributes to saccade decisions. To dissociate between a more general attention role and a more effector specific saccade role, we reversibly inactivated LIP while non-human primates freely selected between two targets, presented in the two hemifields, with either saccades or reaches. Unilateral LIP inactivation induced a strong choice bias to ipsilesional targets when decisions were made with saccades. Interestingly, the inactivation also caused a reduction of contralesional choices when decisions were made with reaches, albeit the effect was less pronounced. These findings suggest that LIP is part of a network for making oculomotor decisions and is largely effector-specific in free-choice decisions.

The Neural Basis of and a Common Neural Circuitry in Different Types of Pro-social Behavior

Pro-social behaviors are voluntary behaviors that benefit other people or society as a whole, such as charitable donations, cooperation, trust, altruistic punishment, and fairness. These behaviors have been widely described through non self-interest decision-making in behavioral experimental studies and are thought to be increased by social preference motives. Importantly, recent studies using a combination of neuroimaging and brain stimulation, designed to reveal the neural mechanisms of pro-social behaviors, have found that a wide range of brain areas, specifically the prefrontal cortex, anterior insula, anterior cingulate cortex, and amygdala, are correlated or causally related with pro-social behaviors. In this review, we summarize the research on the neural basis of various kinds of pro-social behaviors and describe a common shared neural circuitry of these pro-social behaviors. We introduce several general ways in which experimental economics and neuroscience can be combined to develop important contributions to understanding social decision-making and pro-social behaviors. Future research should attempt to explore the neural circuitry between the frontal lobes and deeper brain areas.

Primary Tactile Thalamus Spiking Reflects Cognitive Signals

Little is known about whether information transfer at primary sensory thalamic nuclei is modified by behavioral context. Here we studied the influence of previous decisions/rewards on current choices and preceding spike responses of ventroposterior medial thalamus (VPm; the primary sensory thalamus in the rat whisker-related tactile system). We trained head-fixed rats to detect a ramp-like deflection of one whisker interspersed within ongoing white noise stimulation. Using generative modeling of behavior, we identify two task-related variables that are predictive of actual decisions. The first reflects task engagement on a local scale ("trial history": defined as the decisions and outcomes of a small number of past trials), whereas the other captures behavioral dynamics on a global scale ("satiation": slow dynamics of the response pattern along an entire session). Although satiation brought about a slow drift from Go to NoGo decisions during the session, trial history was related to local (trial-by-trial) patterning of Go and NoGo decisions. A second model that related the same predictors first to VPm spike responses, and from there to decisions, indicated that spiking, in contrast to behavior, is sensitive to trial history but relatively insensitive to satiation. Trial history influences VPm spike rates and regularity such that a history of Go decisions would predict fewer noise-driven spikes (but more regular ones), and more ramp-driven spikes. Neuronal activity in VPm, thus, is sensitive to local behavioral history, and may play an important role in higher-order cognitive signaling.SIGNIFICANCE STATEMENT It is an important question for perceptual and brain functions to find out whether cognitive signals modulate the sensory signal stream and if so, where in the brain this happens. This study provides evidence that decision and reward history can already be reflected in the ascending sensory pathway, on the level of first-order sensory thalamus. Cognitive signals are relayed very selectively such that only local trial history (spanning a few trials) but not global history (spanning an entire session) are reflected.

Influence of learning strategy on response time during complex value-based learning and choice

Measurements of response time (RT) have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning). Alternatively, they could learn reward values of options' features (e.g. color, shape) and combine these values to estimate reward values for individual options (feature-based learning). We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach) adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

Davida Teller Award Lecture 2017: What can be learned from natural behavior?

The essentially active nature of vision has long been acknowledged but has been difficult to investigate because of limitations in the available instrumentation, both for measuring eye and body movements and for presenting realistic stimuli in the context of active behavior. These limitations have been substantially reduced in recent years, opening up a wider range of contexts where experimental control is possible. Given this, it is important to examine just what the benefits are for exploring natural vision, with its attendant disadvantages. Work over the last two decades provides insights into these benefits. Natural behavior turns out to be a rich domain for investigation, as it is remarkably stable and opens up new questions, and the behavioral context helps specify the momentary visual computations and their temporal evolution.

Accurate step-hold tracking of smoothly varying periodic and aperiodic probability

Subjects observing many samples from a Bernoulli distribution are able to perceive an estimate of the generating parameter. A question of fundamental importance is how the current percept-what we think the probability now is-depends on the sequence of observed samples. Answers to this question are strongly constrained by the manner in which the current percept changes in response to changes in the hidden parameter. Subjects do not update their percept trial-by-trial when the hidden probability undergoes unpredictable and unsignaled step changes; instead, they update it only intermittently in a step-hold pattern. It could be that the step-hold pattern is not essential to the perception of probability and is only an artifact of step changes in the hidden parameter. However, we now report that the step-hold pattern obtains even when the parameter varies slowly and smoothly. It obtains even when the smooth variation is periodic (sinusoidal) and perceived as such. We elaborate on a previously published theory that accounts for: (i) the quantitative properties of the step-hold update pattern; (ii) subjects' quick and accurate reporting of changes; (iii) subjects' second thoughts about previously reported changes; (iv) subjects' detection of higher-order structure in patterns of change. We also call attention to the challenges these results pose for trial-by-trial updating theories.

Reward Size Informs Repeat-Switch Decisions and Strongly Modulates the Activity of Neurons in Parietal Cortex

Behavior is guided by previous experience. Good, positive outcomes drive a repetition of a previous behavior or choice, whereas poor or bad outcomes lead to an avoidance. How these basic drives are implemented by the brain has been of primary interest to psychology and neuroscience. We engaged animals in a choice task in which the size of a reward outcome strongly governed the animals' subsequent decision whether to repeat or switch the previous choice. We recorded the discharge activity of neurons implicated in reward-based choice in 2 regions of parietal cortex. We found that the tendency to retain previous choice following a large (small) reward was paralleled by a marked decrease (increase) in the activity of parietal neurons. This neural effect is independent of, and of sign opposite to, value-based modulations reported in parietal cortex previously. This effect shares the same basic properties with signals previously reported in the limbic system that detect the size of the recently obtained reward to mediate proper repeat-switch decisions. We conclude that the size of the obtained reward is a decision variable that guides the decision between retaining a choice or switching, and neurons in parietal cortex strongly respond to this novel decision variable.

Understanding active sampling strategies: Empirical approaches and implications for attention and decision research

In natural behavior we actively gather information using attention and active sensing behaviors (such as shifts of gaze) to sample relevant cues. However, while attention and decision making are naturally coordinated, in the laboratory they have been dissociated. Attention is studied independently of the actions it serves. Conversely, decision theories make the simplifying assumption that the relevant information is given, and do not attempt to describe how the decision maker may learn and implement active sampling policies. In this paper I review recent studies that address questions of attentional learning, cue validity and information seeking in humans and non-human primates. These studies suggest that learning a sampling policy involves large scale interactions between networks of attention and valuation, which implement these policies based on reward maximization, uncertainty reduction and the intrinsic utility of cognitive states. I discuss the importance of using such paradigms for formalizing the role of attention, as well as devising more realistic theories of decision making that capture a broader range of empirical observations.

The influence of attention on value integration

People often have to make decisions based on many pieces of information. Previous work has found that people are able to integrate values presented in a rapid serial visual presentation (RSVP) stream to make informed judgements on the overall stream value (Tsetsos et al. Proceedings of the National Academy of Sciences of the United States of America, 109(24), 9659-9664, 2012). It is also well known that attentional mechanisms influence how people process information. However, it is unknown how attentional factors impact value judgements of integrated material. The current study is the first of its kind to investigate whether value judgements are influenced by attentional processes when assimilating information. Experiments 1-3 examined whether the attentional salience of an item within an RSVP stream affected judgements of overall stream value. The results showed that the presence of an irrelevant high or low value salient item biased people to judge the stream as having a higher or lower overall mean value, respectively. Experiments 4-7 directly tested Tsetsos et al.'s (Proceedings of the National Academy of Sciences of the United States of America, 109(24), 9659-9664, 2012) theory examining whether extreme values in an RSVP stream become over-weighted, thereby capturing attention more than other values in the stream. The results showed that the presence of both a high (Experiments 4, 6 and 7) and a low (Experiment 5) value outlier captures attention leading to less accurate report of subsequent items in the stream. Taken together, the results showed that valuations can be influenced by attentional processes, and can lead to less accurate subjective judgements.

Prefrontal Contribution to Decision-Making under Free-Choice Conditions

Executive function is thought to be the coordinated operation of multiple neural processes and allows to accomplish a current goal flexibly. The most important function of the prefrontal cortex is the executive function. Among a variety of executive functions in which the prefrontal cortex participates, decision-making is one of the most important. Although the prefrontal contribution to decision-making has been examined using a variety of behavioral tasks, recent studies using fMRI have shown that the prefrontal cortex participates in decision-making under free-choice conditions. Since decision-making under free-choice conditions represents the very first stage for any kind of decision-making process, it is important that we understand its neural mechanism. Although few studies have examined this issue while a monkey performed a free-choice task, those studies showed that, when the monkey made a decision to subsequently choose one particular option, prefrontal neurons showing selectivity to that option exhibited transient activation just before presentation of the imperative cue. Further studies have suggested that this transient increase is caused by the irregular fluctuation of spontaneous firing just before cue presentation, which enhances the response to the cue and biases the strength of the neuron's selectivity to the option. In addition, this biasing effect was observed only in neurons that exhibited sustained delay-period activity, indicating that this biasing effect not only influences the animal's decision for an upcoming choice, but also is linked to working memory mechanisms in the prefrontal cortex.

Choice Behavior Guided by Learned, But Not Innate, Taste Aversion Recruits the Orbitofrontal Cortex

The ability to select an appropriate behavioral response guided by previous emotional experiences is critical for survival. Although much is known about brain mechanisms underlying emotional associations, little is known about how these associations guide behavior when several choices are available. To address this, we performed local pharmacological inactivations of several cortical regions before retrieval of an aversive memory in choice-based versus no-choice-based conditioned taste aversion (CTA) tasks in rats. Interestingly, we found that inactivation of the orbitofrontal cortex (OFC), but not the dorsal or ventral medial prefrontal cortices, blocked retrieval of choice CTA. However, OFC inactivation left retrieval of no-choice CTA intact, suggesting its role in guiding choice, but not in retrieval of CTA memory. Consistently, OFC activity increased in the choice condition compared with no-choice, as measured with c-Fos immunolabeling. Notably, OFC inactivation did not affect choice behavior when it was guided by innate taste aversion. Consistent with an anterior insular cortex (AIC) involvement in storing taste memories, we found that AIC inactivation impaired retrieval of both choice and no-choice CTA. Therefore, this study provides evidence for OFC's role in guiding choice behavior and shows that this is dissociable from AIC-dependent taste aversion memory. Together, our results suggest that OFC is required and recruited to guide choice selection between options of taste associations relayed from AIC.

SIGNIFICANCE STATEMENT

Survival and mental health depend on being able to choose stimuli not associated with danger. This is particularly important when danger is associated with stimuli that we ingest. Although much is known about the brain mechanisms that underlie associations with dangerous taste stimuli, very little is known about how these stored emotional associations guide behavior when it involves choice. By combining pharmacological and immunohistochemistry tools with taste-guided tasks, our study provides evidence for the key role of orbitofrontal cortex activity in choice behavior and shows that this is dissociable from the adjacent insular cortex-dependent taste aversion memory. Understanding the brain mechanisms that underlie the impact that emotional associations have on survival choice behaviors may lead to better treatments for mental disorders characterized by emotional decision-making deficits.

Distinct Sources of Deterministic and Stochastic Components of Action Timing Decisions in Rodent Frontal Cortex

The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals.

Vision and Action

Investigation of natural behavior has contributed a number of insights to our understanding of visual guidance of actions by highlighting the importance of behavioral goals and focusing attention on how vision and action play out in time. In this context, humans make continuous sequences of sensory-motor decisions to satisfy current behavioral goals, and the role of vision is to provide the relevant information for making good decisions in order to achieve those goals. This conceptualization of visually guided actions as a sequence of sensory-motor decisions has been formalized within the framework of statistical decision theory, which structures the problem and provides the context for much recent progress in vision and action. Components of a good decision include the task, which defines the behavioral goals, the rewards and costs associated with those goals, uncertainty about the state of the world, and prior knowledge.

Collective Activity of Many Bistable Assemblies Reproduces Characteristic Dynamics of Multistable Perception

The timing of perceptual decisions depends on both deterministic and stochastic factors, as the gradual accumulation of sensory evidence (deterministic) is contaminated by sensory and/or internal noise (stochastic). When human observers view multistable visual displays, successive episodes of stochastic accumulation culminate in repeated reversals of visual appearance. Treating reversal timing as a "first-passage time" problem, we ask how the observed timing densities constrain the underlying stochastic accumulation. Importantly, mean reversal times (i.e., deterministic factors) differ enormously between displays/observers/stimulation levels, whereas the variance and skewness of reversal times (i.e., stochastic factors) keep characteristic proportions of the mean. What sort of stochastic process could reproduce this highly consistent "scaling property?" Here we show that the collective activity of a finite population of bistable units (i.e., a generalized Ehrenfest process) quantitatively reproduces all aspects of the scaling property of multistable phenomena, in contrast to other processes under consideration (Poisson, Wiener, or Ornstein-Uhlenbeck process). The postulated units express the spontaneous dynamics of attractor assemblies transitioning between distinct activity states. Plausible candidates are cortical columns, or clusters of columns, as they are preferentially connected and spontaneously explore a restricted repertoire of activity states. Our findings suggests that perceptual representations are granular, probabilistic, and operate far from equilibrium, thereby offering a suitable substrate for statistical inference.

SIGNIFICANCE STATEMENT

Spontaneous reversals of high-level perception, so-called multistable perception, conform to highly consistent and characteristic statistics, constraining plausible neural representations. We show that the observed perceptual dynamics would be reproduced quantitatively by a finite population of distinct neural assemblies, each with locally bistable activity, operating far from the collective equilibrium (generalized Ehrenfest process). Such a representation would be consistent with the intrinsic stochastic dynamics of neocortical activity, which is dominated by preferentially connected assemblies, such as cortical columns or clusters of columns. We predict that local neuron assemblies will express bistable dynamics, with spontaneous active-inactive transitions, whenever they contribute to high-level perception.

Complementary contributions of basolateral amygdala and orbitofrontal cortex to value learning under uncertainty

We make choices based on the values of expected outcomes, informed by previous experience in similar settings. When the outcomes of our decisions consistently violate expectations, new learning is needed to maximize rewards. Yet not every surprising event indicates a meaningful change in the environment. Even when conditions are stable overall, outcomes of a single experience can still be unpredictable due to small fluctuations (i.e., expected uncertainty) in reward or costs. In the present work, we investigate causal contributions of the basolateral amygdala (BLA) and orbitofrontal cortex (OFC) in rats to learning under expected outcome uncertainty in a novel delay-based task that incorporates both predictable fluctuations and directional shifts in outcome values. We demonstrate that OFC is required to accurately represent the distribution of wait times to stabilize choice preferences despite trial-by-trial fluctuations in outcomes, whereas BLA is necessary for the facilitation of learning in response to surprising events.

DOI:http://dx.doi.org/10.7554/eLife.27483.001

Nobody likes waiting – we opt for online shopping to avoid standing in lines, grow impatient in traffic, and often prefer restaurants that serve food quickly. When making decisions, humans and other animals try to maximize the benefits by weighing up the costs and rewards associated with a situation. Many regions in the brain help us choose the best options based on quality and size of rewards, and required waiting times. Even before we make decisions, the activity in these brain regions predicts what we will choose.

Sometimes, however, unexpected changes can lead to longer waiting times and our preferences suddenly become less desirable. The brain can detect such changes by comparing the outcomes we anticipate to those we experience. When the outcomes are surprising, specific areas in the brain such as the amygdala and the orbitofrontal cortex help us learn to make better choices. However, as surprising events can occur purely by chance, we need to be able to ignore irrelevant surprises and only learn from meaningful ones. Until now, it was not clear whether the amygdala and orbitofrontal cortex play specific roles in successfully learning under such conditions.

Stolyarova and Izquierdo trained rats to select between two images and rewarded them with sugar pellets after different delays. If rats chose one of these images they received the rewards after a predictable delay that was about 10 seconds, while choosing the other one produced variable delays – sometimes the time intervals were either very short or very long. Then, the waiting times for one of the alternatives changed unexpectedly. Rats with healthy brains quickly learned to choose the option with the shorter waiting time.

Stolyarova and Izquierdo repeated the experiments with rats that had damage in a part of the amygdala. These rats learned more slowly, particularly when the variable option changed for the better. Rats with damage to the orbitofrontal cortex failed to learn at all. Stolyarova and Izquierdo then examined the rats’ behavior during delays. Rats with damage to the orbitofrontal cortex could not distinguish between meaningful and irrelevant surprises and always looked for the food pellet (i.e. anticipated a reward) at the average delay interval.

These findings highlight two brain regions that help us distinguish meaningful surprises from irrelevant ones. A next step will be to examine how the amygdala and orbitofrontal cortex interact during learning and see if changes to the activity of these brain regions may affect responses. Advanced methods to non-invasively manipulate brain activity in humans may help people who find it hard to cope with changes; or individuals suffering from substance use disorders, who often struggle to give up drugs that provide them immediate and predictable rewards.

DOI:http://dx.doi.org/10.7554/eLife.27483.002

Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo-mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.

EEG correlates of sensorimotor processing: independent components involved in sensory and motor processing

Sensorimotor processing is a critical function of the human brain with multiple cortical areas specialised for sensory recognition or motor execution. Although there has been considerable research into sensorimotor control in humans, the steps between sensory recognition and motor execution are not fully understood. To provide insight into brain areas responsible for sensorimotor computation, we used complex categorization-response tasks (variations of a Stroop task requiring recognition, decision-making, and motor responses) to test the hypothesis that some functional modules are participating in both sensory as well as motor processing. We operationalize functional modules as independent components (ICs) yielded by an independent component analysis (ICA) of EEG data and measured event-related responses by means of inter-trial coherence (ITC). Our results consistently found ICs with event-related ITC responses related to both sensory stimulation and motor response onsets (on average 5.8 ICs per session). These findings reveal EEG correlates of tightly coupled sensorimotor processing in the human brain, and support frameworks like embodied cognition, common coding, and sensorimotor contingency that do not sequentially separate sensory and motor brain processes.

Decision Making and Sequential Sampling from Memory

Decisions take time, and as a rule more difficult decisions take more time. But this only raises the question of what consumes the time. For decisions informed by a sequence of samples of evidence, the answer is straightforward: more samples are available with more time. Indeed, the speed and accuracy of such decisions are explained by the accumulation of evidence to a threshold or bound. However, the same framework seems to apply to decisions that are not obviously informed by sequences of evidence samples. Here, we proffer the hypothesis that the sequential character of such tasks involves retrieval of evidence from memory. We explore this hypothesis by focusing on value-based decisions and argue that mnemonic processes can account for regularities in choice and decision time. We speculate on the neural mechanisms that link sampling of evidence from memory to circuits that represent the accumulated evidence bearing on a choice. We propose that memory processes may contribute to a wider class of decisions that conform to the regularities of choice-reaction time predicted by the sequential sampling framework.

Cortical neurons multiplex reward-related signals along with sensory and motor information

Rewards are known to influence neural activity associated with both motor preparation and execution. This influence can be exerted directly upon the primary motor (M1) and somatosensory (S1) cortical areas via the projections from reward-sensitive dopaminergic neurons of the midbrain ventral tegmental areas. However, the neurophysiological manifestation of reward-related signals in M1 and S1 are not well understood. Particularly, it is unclear how the neurons in these cortical areas multiplex their traditional functions related to the control of spatial and temporal characteristics of movements with the representation of rewards. To clarify this issue, we trained rhesus monkeys to perform a center-out task in which arm movement direction, reward timing, and magnitude were manipulated independently. Activity of several hundred cortical neurons was simultaneously recorded using chronically implanted microelectrode arrays. Many neurons (9-27%) in both M1 and S1 exhibited activity related to reward anticipation. Additionally, neurons in these areas responded to a mismatch between the reward amount given to the monkeys and the amount they expected: A lower-than-expected reward caused a transient increase in firing rate in 60-80% of the total neuronal sample, whereas a larger-than-expected reward resulted in a decreased firing rate in 20-35% of the neurons. Moreover, responses of M1 and S1 neurons to reward omission depended on the direction of movements that led to those rewards. These observations suggest that sensorimotor cortical neurons corepresent rewards and movement-related activity, presumably to enable reward-based learning.

Dorsolateral prefrontal cortex contributes to the impaired behavioral adaptation in alcohol dependence

Substance-dependent individuals often lack the ability to adjust decisions flexibly in response to the changes in reward contingencies. Prediction errors (PEs) are thought to mediate flexible decision-making by updating the reward values associated with available actions. In this study, we explored whether the neurobiological correlates of PEs are altered in alcohol dependence. Behavioral, and functional magnetic resonance imaging (fMRI) data were simultaneously acquired from 34 abstinent alcohol-dependent patients (ADP) and 26 healthy controls (HC) during a probabilistic reward-guided decision-making task with dynamically changing reinforcement contingencies. A hierarchical Bayesian inference method was used to fit and compare learning models with different assumptions about the amount of task-related information subjects may have inferred during the experiment. Here, we observed that the best-fitting model was a modified Rescorla-Wagner type model, the “double-update” model, which assumes that subjects infer the knowledge that reward contingencies are anti-correlated, and integrate both actual and hypothetical outcomes into their decisions. Moreover, comparison of the best-fitting model's parameters showed that ADP were less sensitive to punishments compared to HC. Hence, decisions of ADP after punishments were loosely coupled with the expected reward values assigned to them. A correlation analysis between the model-generated PEs and the fMRI data revealed a reduced association between these PEs and the BOLD activity in the dorsolateral prefrontal cortex (DLPFC) of ADP. A hemispheric asymmetry was observed in the DLPFC when positive and negative PE signals were analyzed separately. The right DLPFC activity in ADP showed a reduced correlation with positive PEs. On the other hand, ADP, particularly the patients with high dependence severity, recruited the left DLPFC to a lesser extent than HC for processing negative PE signals. These results suggest that the DLPFC, which has been linked to adaptive control of action selection, may play an important role in cognitive inflexibility observed in alcohol dependence when reinforcement contingencies change. Particularly, the left DLPFC may contribute to this impaired behavioral adaptation, possibly by impeding the extinction of the actions that no longer lead to a reward.

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Alcohol-dependent patients (ADP) had difficulty adapting to the reversals.

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The impaired adaptation was associated with a decrease in punishment sensitivity.

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The dorsolateral prefrontal cortex (DLPFC) of ADP failed to track prediction errors.

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A reduced tracking of the negative prediction error was present in the left DLPFC.

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The clinical severity of dependence was correlated with abnormal DLPFC activity.

Dynamic modulation of decision biases by brainstem arousal systems

Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence. Variability of behavior is commonly attributed to noise in the brain's decision-making machinery. We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability. We tracked pupil responses (a proxy of phasic arousal) during sensory-motor decisions in humans, across different sensory modalities and task protocols. Large pupil responses generally predicted a reduction in decision bias. Using fMRI, we showed that the pupil-linked bias reduction was (i) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and (ii) predicted by phasic, pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state, including the noradrenergic locus coeruleus. We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis, thus accounting for a significant component of the variability of choice behavior.

Parietal neurons encode expected gains in instrumental information

In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain's information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons' visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues.

Neurocomputational mechanisms underlying subjective valuation of effort costs

In everyday life, we have to decide whether it is worth exerting effort to obtain rewards. Effort can be experienced in different domains, with some tasks requiring significant cognitive demand and others being more physically effortful. The motivation to exert effort for reward is highly subjective and varies considerably across the different domains of behaviour. However, very little is known about the computational or neural basis of how different effort costs are subjectively weighed against rewards. Is there a common, domain-general system of brain areas that evaluates all costs and benefits? Here, we used computational modelling and functional magnetic resonance imaging (fMRI) to examine the mechanisms underlying value processing in both the cognitive and physical domains. Participants were trained on two novel tasks that parametrically varied either cognitive or physical effort. During fMRI, participants indicated their preferences between a fixed low-effort/low-reward option and a variable higher-effort/higher-reward offer for each effort domain. Critically, reward devaluation by both cognitive and physical effort was subserved by a common network of areas, including the dorsomedial and dorsolateral prefrontal cortex, the intraparietal sulcus, and the anterior insula. Activity within these domain-general areas also covaried negatively with reward and positively with effort, suggesting an integration of these parameters within these areas. Additionally, the amygdala appeared to play a unique, domain-specific role in processing the value of rewards associated with cognitive effort. These results are the first to reveal the neurocomputational mechanisms underlying subjective cost–benefit valuation across different domains of effort and provide insight into the multidimensional nature of motivation.

Model-based fMRI in humans shows that cognitive and physical motivation are underpinned by overlapping neural substrates, but the amygdala plays a unique role in valuation of cognitive effort.

Rewards are rarely obtained without the motivation to exert effort. In humans, effort can be perceived in both the cognitive and physical domains, yet little is known about how the brain evaluates whether it is worth exerting different types of effort in return for rewards. In this study, we used functional magnetic resonance imaging (fMRI) to determine the neural and computational basis of effort processing. We developed two novel tasks that were either cognitively or physically effortful and had participants indicate their preference for a low-effort/low-reward versus a higher-effort/higher-reward version of each. Our results showed distinct patterns of reward devaluation across the different domains of effort. Furthermore, regardless of the type of effort involved, motivation was subserved by a large network of overlapping brain areas across the parieto-prefrontal cortex and insula. However, we also found that the amygdala plays a unique role in motivating cognitively—but not physically—effortful behaviours. These data impact current neuroeconomic theories of value-based decision making by revealing the neurocomputational signatures that underlie the variability in individuals’ motivation to exert different types of effort in return for reward.

Stroke of bad luck?

We hypothesized that distinct acute right hemisphere lesions disrupt separate components of valuation and emotional response to winning and losing money and of emotional empathy in observing a partner win or lose money. We measured skin conductance response (SCR) and ratings of emotions when acute right hemisphere stroke patients or healthy controls won or lost money in roulette, or when they watched a partner win or lose. Our results showed that percentage of damage after stroke to right anterior insula and frontal operculum negatively correlated with both SCR to winning and losing and difference between rating wins versus losses.

Basolateral amygdala supports the maintenance of value and effortful choice of a preferred option

The basolateral amygdala (BLA) is known to be involved in appetitive behavior, yet its role in cost-benefit choice of qualitatively different rewards (more/less preferred), beyond magnitude differences (larger/smaller), is poorly understood. We assessed the effects of BLA inactivations on effortful choice behavior. Rats were implanted with cannulae in BLA and trained to stable lever pressing for sucrose pellets on a progressive ratio schedule. Rats were then introduced to a choice: chow was concurrently available while they could work for the preferred sucrose pellets. Rats were infused with either vehicle control (aCSF) or baclofen/muscimol prior to test. BLA inactivations produced a significant decrease in lever presses for sucrose pellets compared to vehicle, and chow consumption was unaffected. Inactivation had no effect on sucrose pellet preference when both options were freely available. Critically, when lab chow was not concurrently available, BLA inactivations had no effect on the number of lever presses for sucrose pellets, indicating that primary motivation in the absence of choice remains intact with BLA offline. After a test under specific satiety for sucrose pellets, BLA inactivation rendered animals less sensitive to devaluation relative to control. The effects of BLA inactivations in our task are not mediated by decreased appetite, an inability to perform the task, a change in food preference, or decrements in primary motivation. Taken together, BLA supports the specific value and effortful choice of a preferred option.

Reward-based training of recurrent neural networks for cognitive and value-based tasks

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.

Interfacing to the brain's motor decisions

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject's motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components.

A Cognitive Model Based on Neuromodulated Plasticity

Associative learning, including classical conditioning and operant conditioning, is regarded as the most fundamental type of learning for animals and human beings. Many models have been proposed surrounding classical conditioning or operant conditioning. However, a unified and integrated model to explain the two types of conditioning is much less studied. Here, a model based on neuromodulated synaptic plasticity is presented. The model is bioinspired including multistored memory module and simulated VTA dopaminergic neurons to produce reward signal. The synaptic weights are modified according to the reward signal, which simulates the change of associative strengths in associative learning. The experiment results in real robots prove the suitability and validity of the proposed model.

Decision Neuroscience

Investigations of decision making have historically been undertaken by different disciplines, each using different techniques and assumptions, and few unifying efforts have been made. Economists have focused on precise mathematical models of normative decision making, psychologists have examined how decisions are actually made based on cognitive constraints, and neuroscientists have concentrated on the detailed operation of neural systems in simple choices. In recent years, however, researchers in these separate fields have joined forces in an attempt to better specify the foundations of decision making. This interdisciplinary effort has begun to use decision theory to guide the search for the neural bases of reward value and predictability. Concurrently, these formal models are beginning to incorporate processes such as social reward and emotion. The combination of these diverse theoretical approaches and methodologies is already yielding significant progress in the construction of more comprehensive decision-making models.

Individual differences in sensory responses influence decision making by Drosophila melanogaster larvae on exposure to contradictory cues

Animals make decisions on behavioral choice by evaluating internal and external signals. Individuals often make decisions in different ways, but the underlying neural mechanisms are not well understood. Here, we describe a system for observing the behavior of individual Drosophila melanogaster larvae simultaneously presented with contradictory signals, in this case attractive (yeast paste) and aversive (NaCl) signals. Olfaction was used to detect the yeast paste, whereas the ENaC/Pickpocket channel was important for NaCl detection. We found that wild-type (Canton-S) larvae fall into two decision making groups: one group decided to approach the yeast paste by overcoming the aversive signal, whereas the other group decided to forgo the yeast paste because of the aversive signal. Our findings indicate that different endogenous sensitivities to NaCl contribute to make differences between two groups and that diverse decision making steps occur in individual animals.

Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour

In recent years, simple GO/NOGO behavioural tasks have become popular due to the relative ease with which they can be combined with technologies such as in vivo multiphoton imaging. To date, it has been assumed that behavioural performance can be captured by the average performance across a session, however this neglects the effect of motivation on behaviour within individual sessions. We investigated the effect of motivation on mice performing a GO/NOGO visual discrimination task. Performance within a session tended to follow a stereotypical trajectory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with many false positives, and transitioning through a more or less optimal regime to end with a low hit rate after satiation. Our observations are reproduced by a new model, the Motivated Actor-Critic, introduced here. Our results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.

Playing the electric light orchestra--how electrical stimulation of visual cortex elucidates the neural basis of perception

Vision research has the potential to reveal fundamental mechanisms underlying sensory experience. Causal experimental approaches, such as electrical microstimulation, provide a unique opportunity to test the direct contributions of visual cortical neurons to perception and behaviour. But in spite of their importance, causal methods constitute a minority of the experiments used to investigate the visual cortex to date. We reconsider the function and organization of visual cortex according to results obtained from stimulation techniques, with a special emphasis on electrical stimulation of small groups of cells in awake subjects who can report their visual experience. We compare findings from humans and monkeys, striate and extrastriate cortex, and superficial versus deep cortical layers, and identify a number of revealing gaps in the ‘causal map′ of visual cortex. Integrating results from different methods and species, we provide a critical overview of the ways in which causal approaches have been used to further our understanding of circuitry, plasticity and information integration in visual cortex. Electrical stimulation not only elucidates the contributions of different visual areas to perception, but also contributes to our understanding of neuronal mechanisms underlying memory, attention and decision-making.

Auditory Processing in the Posterior Parietal Cortex

Goal-directed behavior can be characterized as a dynamic link between a sensory stimulus and a motor act. Neural correlates of many of the intermediate events of goal-directed behavior are found in the posterior parietal cortex. Although the parietal cortex’s role in guiding visual behaviors has received considerable attention, relatively little is known about its role in mediating auditory behaviors. Here, the authors review recent studies that have focused on how neurons in the lateral intraparietal area (area LIP) differentially process auditory and visual stimuli. These studies suggest that area LIP contains a modality-dependent representation that is highly dependent on behavioral context.

Emotor control: computations underlying bodily resource allocation, emotions, and confidence

Emotional processes are central to behavior, yet their deeply subjective nature has been a challenge for neuroscientific study as well as for psychiatric diagnosis. Here we explore the relationships between subjective feelings and their underlying brain circuits from a computational perspective. We apply recent insights from systems neuroscience—approaching subjective behavior as the result of mental computations instantiated in the brain—to the study of emotions. We develop the hypothesis that emotions are the product of neural computations whose motor role is to reallocate bodily resources mostly gated by smooth muscles. This “emotor” control system is analagous to the more familiar motor control computations that coordinate skeletal muscle movements. To illustrate this framework, we review recent research on “confidence.” Although familiar as a feeling, confidence is also an objective statistical quantity: an estimate of the probability that a hypothesis is correct. This model-based approach helped reveal the neural basis of decision confidence in mammals and provides a bridge to the subjective feeling of confidence in humans. These results have important implications for psychiatry, since disorders of confidence computations appear to contribute to a number of psychopathologies. More broadly, this computational approach to emotions resonates with the emerging view that psychiatric nosology may be best parameterized in terms of disorders of the cognitive computations underlying complex behavior.

Inactivation of Parietal Reach Region Affects Reaching But Not Saccade Choices in Internally Guided Decisions

The posterior parietal cortex (PPC) has traditionally been considered important for awareness, spatial perception, and attention. However, recent findings provide evidence that the PPC also encodes information important for making decisions. These findings have initiated a running argument of whether the PPC is critically involved in decision making. To examine this issue, we reversibly inactivated the parietal reach region (PRR), the area of the PPC that is specialized for reaching movements, while two monkeys performed a memory-guided reaching or saccade task. The task included choices between two equally rewarded targets presented simultaneously in opposite visual fields. Free-choice trials were interleaved with instructed trials, in which a single cue presented in the peripheral visual field defined the reach and saccade target unequivocally. We found that PRR inactivation led to a strong reduction of contralesional choices, but only for reaches. On the other hand, saccade choices were not affected by PRR inactivation. Importantly, reaching and saccade movements to single instructed targets remained largely intact. These results cannot be explained as an effector-nonspecific deficit in spatial attention or awareness, since the temporary "lesion" had an impact only on reach choices. Hence, the PPR is a part of a network for reach decisions and not just reach planning.

SIGNIFICANCE STATEMENT

There has been an ongoing debate on whether the posterior parietal cortex (PPC) represents only spatial awareness, perception, and attention or whether it is also involved in decision making for actions. In this study we explore whether the parietal reach region (PRR), the region of the PPC that is specialized for reaches, is involved in the decision process. We inactivated the PRR while two monkeys performed reach and saccade choices between two targets presented simultaneously in both hemifields. We found that inactivation affected only the reach choices, while leaving saccade choices intact. These results cannot be explained as a deficit in attention, since the temporary lesion affected only the reach choices. Thus, PRR is a part of a network for making reach decisions.