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

Neural circuit models for evidence accumulation through choice-selective sequences

Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.

A corticoamygdalar pathway controls reward devaluation and depression using dynamic inhibition code

Reward devaluation adaptively controls reward intake. It remains unclear how cortical circuits causally encode reward devaluation in healthy and depressed states. Here, we show that the neural pathway from the anterior cingulate cortex (ACC) to the basolateral amygdala (BLA) employs a dynamic inhibition code to control reward devaluation and depression. Fiber photometry and imaging of ACC pyramidal neurons reveal reward-induced inhibition, which weakens during satiation and becomes further attenuated in depression mouse models. Ablating or inhibiting these neurons desensitizes reward devaluation, causes reward intake increase and ultimate obesity, and ameliorates depression, whereas activating the cells sensitizes reward devaluation, suppresses reward consumption, and produces depression-like behaviors. Among various ACC neuron subpopulations, the BLA-projecting subset bidirectionally regulates reward devaluation and depression-like behaviors. Our study thus uncovers a corticoamygdalar circuit that encodes reward devaluation via blunted inhibition and suggests that enhancing inhibition within this circuit may offer a therapeutic approach for treating depression.

Distributed attribute representation in the superior parietal lobe during probabilistic decision-making

Several studies have examined the neural substrates of probabilistic decision-making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies (nStudy1  = 34, nStudy2  = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability-related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision-making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.

Orbitofrontal cortex control of striatum leads economic decision-making

Animals must continually evaluate stimuli in their environment to decide which opportunities to pursue, and in many cases these decisions can be understood in fundamentally economic terms. Although several brain regions have been individually implicated in these processes, the brain-wide mechanisms relating these regions in decision-making are unclear. Using an economic decision-making task adapted for rats, we find that neural activity in both of two connected brain regions, the ventrolateral orbitofrontal cortex (OFC) and the dorsomedial striatum (DMS), was required for economic decision-making. Relevant neural activity in both brain regions was strikingly similar, dominated by the spatial features of the decision-making process. However, the neural encoding of choice direction in OFC preceded that of DMS, and this temporal relationship was strongly correlated with choice accuracy. Furthermore, activity specifically in the OFC projection to the DMS was required for appropriate economic decision-making. These results demonstrate that choice information in the OFC is relayed to the DMS to lead accurate economic decision-making.

Distinct roles of the orbitofrontal cortex, ventral striatum, and dopamine neurons in counterfactual thinking of decision outcomes

Individuals often assess past decisions by comparing what was gained with what would have been gained had they acted differently. Thoughts of past alternatives that counter what actually happened are called "counterfactuals." Recent theories emphasize the role of the prefrontal cortex in processing counterfactual outcomes in decision-making, although how subcortical regions contribute to this process remains to be elucidated. Here we report a clear distinction among the roles of the orbitofrontal cortex, ventral striatum and midbrain dopamine neurons in processing counterfactual outcomes in monkeys. Our findings suggest that actually gained and counterfactual outcome signals are both processed in the cortico-subcortical network constituted by these regions but in distinct manners and integrated only in the orbitofrontal cortex in a way to compare these outcomes. This study extends the prefrontal theory of counterfactual thinking and provides key insights regarding how the prefrontal cortex cooperates with subcortical regions to make decisions using counterfactual information.

Evidence for Spinozan "Unbelieving" in the Right Inferior Prefrontal Cortex

Humans can think about possible states of the world without believing in them, an important capacity for high-level cognition. Here, we use fMRI and a novel "shell game" task to test two competing theories about the nature of belief and its neural basis. According to the Cartesian theory, information is first understood, then assessed for veracity, and ultimately encoded as either believed or not believed. According to the Spinozan theory, comprehension entails belief by default, such that understanding without believing requires an additional process of "unbelieving." Participants (n = 70) were experimentally induced to have beliefs, desires, or mere thoughts about hidden states of the shell game (e.g., believing that the dog is hidden in the upper right corner). That is, participants were induced to have specific "propositional attitudes" toward specific "propositions" in a controlled way. Consistent with the Spinozan theory, we found that thinking about a proposition without believing it is associated with increased activation of the right inferior frontal gyrus. This was true whether the hidden state was desired by the participant (because of reward) or merely thought about. These findings are consistent with a version of the Spinozan theory whereby unbelieving is an inhibitory control process. We consider potential implications of these results for the phenomena of delusional belief and wishful thinking.

Visualizing advances in the future of primate neuroscience research

Future neuroscience and biomedical projects involving non-human primates (NHPs) remain essential in our endeavors to understand the complexities and functioning of the mammalian central nervous system. In so doing, the NHP neuroscience researcher must be allowed to incorporate state-of-the-art technologies, including the use of novel viral vectors, gene therapy and transgenic approaches to answer continuing and emerging research questions that can only be addressed in NHP research models. This perspective piece captures these emerging technologies and some specific research questions they can address. At the same time, we highlight some current caveats to global NHP research and collaborations including the lack of common ethical and regulatory frameworks for NHP research, the limitations involving animal transportation and exports, and the ongoing influence of activist groups opposed to NHP research.

Uncertainty modulates visual maps during noninstrumental information demand

Animals are intrinsically motivated to obtain information independently of instrumental incentives. This motivation depends on two factors: a desire to resolve uncertainty by gathering accurate information and a desire to obtain positively-valenced observations, which predict favorable rather than unfavorable outcomes. To understand the neural mechanisms, we recorded parietal cortical activity implicated in prioritizing stimuli for spatial attention and gaze, in a task in which monkeys were free (but not trained) to obtain information about probabilistic non-contingent rewards. We show that valence and uncertainty independently modulated parietal neuronal activity, and uncertainty but not reward-related enhancement consistently correlated with behavioral sensitivity. The findings suggest uncertainty-driven and valence-driven information demand depend on partially distinct pathways, with the former being consistently related to parietal responses and the latter depending on additional mechanisms implemented in downstream structures.

Curiosity is motivated by uncertainty and valence, but how uncertainty and valence are encoded in the brain remains poorly understood. Here, the authors show that parietal neurons are enhanced by both factors, but that they specifically predict visual information seeking based on uncertainty.

The increased analgesic efficacy of cold therapy after an unsuccessful analgesic experience is associated with inferior parietal lobule activation

Prior experiences of successful and failed treatments are known to influence the efficacy of a newly applied treatment. However, whether that carry-over effect applies to non-pharmacological treatments is unknown. This study investigated how a failed treatment history with placebo analgesic cream affected the therapeutic outcomes of cold-pack treatment. The neural correlates underlying those effects were also explored using functional magnetic resonance imaging. The effect of the placebo analgesic cream was induced using placebo conditioning with small (44.5 °C to 43.7 °C, negative experience) and large (44.5 °C to 40.0 °C, positive experience) thermal stimuli changes. After the placebo conditioning, brain responses and self-reported evaluations of the effect of subsequent treatment with a cold-pack were contrasted between the two groups. The negative experience group reported less pain and lower anxiety scores in the cold-pack condition than the positive experience group and exhibited significantly greater activation in the right inferior parietal lobule (IPL), which is known to be involved in pain relief. These findings suggest that an unsatisfying experience with an initial pain-relief treatment could increase the expectations for the complementary treatment outcome and improve the analgesic effect of the subsequent treatment. The IPL could be associated with this expectation-induced pain relief process.

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

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

Neural mechanisms of credit assignment for inferred relationships in a structured world

Animals abstract compact representations of a task's structure, which supports accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. We develop a hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, the mPFC and lateral orbitofrontal cortex track the current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance of both tracking the current position in an abstracted task space and efficient, generalizable representations in the prefrontal cortex for supporting flexible learning and inference in structured environments.

Honey bees can store and retrieve independent memory traces after complex experiences that combine appetitive and aversive associations

Real-world experiences do often mix appetitive and aversive events. Understanding the ability of animals to extract, store and use this information is an important issue in neurobiology. We used honey bees as model organism to study learning and memory after a differential conditioning that combines appetitive and aversive training trials. First of all, we describe an aversive conditioning paradigm that constitutes a clear opposite of the well known appetitive olfactory conditioning of the proboscis extension response. A neutral odour is presented paired with the bitter substance quinine. Aversive memory is evidenced later as an odour-specific impairment in appetitive conditioning. Then we tested the effect of mixing appetitive and aversive conditioning trials distributed along the same training session. Differential conditioning protocols like this were used before to study the ability to discriminate odours, however they were not focused on whether appetitive and aversive memories are formed. We found that after a differential conditioning, honey bees establish independent appetitive and aversive memories that do not interfere with each other during acquisition or storage. Finally, we moved the question forward to retrieval and memory expression to evaluate what happens when appetitive and the aversive learned odours are mixed during test. Interestingly, opposite memories compete in a way that they do not cancel each other out. Honey bees showed the ability to switch from expressing appetitive to aversive memory depending on their satiation level.

LAG-1: A dynamic, integrative model of learning, attention, and gaze

It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. Here we introduce LAG-1, a dynamic neural field model of learning, attention and gaze, that we fit to human learning and eye-movement data from two category learning experiments. LAG-1 comprises three control systems: one for visuospatial attention, one for saccadic timing and control, and one for category learning. The model is able to extract a kind of information gain from pairwise differences in simple associations between visual features and categories. Providing this gain as a reentrant signal with bottom-up visual information, and in top-down spatial priority, appropriately influences the initiation of saccades. LAG-1 provides a moment-by-moment simulation of the interactions of learning and gaze, and thus simultaneously produces phenomena on many timescales, from the duration of saccades and gaze fixations, to the response times for trials, to the slow optimization of attention toward task relevant information across a whole experiment. With only three free parameters (learning rate, trial impatience, and fixation impatience) LAG-1 produces qualitatively correct fits for learning, behavioural timing and eye movement measures, and also for previously unmodelled empirical phenomena (e.g., fixation orders showing stimulus-specific attention, and decreasing fixation counts during feedback). Because LAG-1 is built to capture attention and gaze generally, we demonstrate how it can be applied to other phenomena of visual cognition such as the free viewing of visual stimuli, visual search, and covert attention.

Apparent sunk cost effect in rational agents

Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats were reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments to the detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.

Neurocomputational mechanisms underlying the subjective value of information

Humans have a striking desire to actively seek new information, even when it is devoid of any instrumental utility. However, the mechanisms that drive individuals' subjective preference for information remain unclear. Here, we used fMRI to examine the processing of subjective information value, by having participants decide how much effort they were willing to trade-off for non-instrumental information. We showed that choices were best described by a model that accounted for: (1) the variability in individuals' estimates of uncertainty, (2) their desire to reduce that uncertainty, and (3) their subjective preference for positively valenced information. Model-based analyses revealed the anterior cingulate as a key node that encodes the subjective value of information across multiple stages of decision-making - including when information was prospectively valued, and when the outcome was definitively delivered. These findings emphasise the multidimensionality of information value, and reveal the neurocomputational mechanisms underlying the variability in individuals' desire to physically pursue informative outcomes.

Aversive stimuli bias corticothalamic responses to motivationally significant cues

Making predictions about future rewards or punishments is fundamental to adaptive behavior. These processes are influenced by prior experience. For example, prior exposure to aversive stimuli or stressors changes behavioral responses to negative- and positive-value predictive cues. Here, we demonstrate a role for medial prefrontal cortex (mPFC) neurons projecting to the paraventricular nucleus of the thalamus (PVT; mPFC→PVT) in this process. We found that a history of aversive stimuli negatively biased behavioral responses to motivationally relevant cues in mice and that this negative bias was associated with hyperactivity in mPFC→PVT neurons during exposure to those cues. Furthermore, artificially mimicking this hyperactive response with selective optogenetic excitation of the same pathway recapitulated the negative behavioral bias induced by aversive stimuli, whereas optogenetic inactivation of mPFC→PVT neurons prevented the development of the negative bias. Together, our results highlight how information flow within the mPFC→PVT circuit is critical for making predictions about motivationally-relevant outcomes as a function of prior experience.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality

Animals make decisions under the principle of reward value maximization and surprise minimization. It is still unclear how these principles are represented in the brain and are reflected in behavior. We addressed this question using a closed-loop virtual reality system to train adult zebrafish for active avoidance. Analysis of the neural activity of the dorsal pallium during training revealed neural ensembles assigning rules to the colors of the surrounding walls. Additionally, one third of fish generated another ensemble that becomes activated only when the real perceived scenery shows discrepancy from the predicted favorable scenery. The fish with the latter ensemble escape more efficiently than the fish with the former ensembles alone, even though both fish have successfully learned to escape, consistent with the hypothesis that the latter ensemble guides zebrafish to take action to minimize this prediction error. Our results suggest that zebrafish can use both principles of goal-directed behavior, but with different behavioral consequences depending on the repertoire of the adopted principles.

A neuronal ensemble encoding adaptive choice during sensory conflict in Drosophila

Feeding decisions are fundamental to survival, and decision making is often disrupted in disease. Here, we show that neural activity in a small population of neurons projecting to the fan-shaped body higher-order central brain region of Drosophila represents food choice during sensory conflict. We found that food deprived flies made tradeoffs between appetitive and aversive values of food. We identified an upstream neuropeptidergic and dopaminergic network that relays internal state and other decision-relevant information to a specific subset of fan-shaped body neurons. These neurons were strongly inhibited by the taste of the rejected food choice, suggesting that they encode behavioral food choice. Our findings reveal that fan-shaped body taste responses to food choices are determined not only by taste quality, but also by previous experience (including choice outcome) and hunger state, which are integrated in the fan-shaped body to encode the decision before relay to downstream motor circuits for behavioral implementation.

Pupil Dilation and the Slow Wave ERP Reflect Surprise about Choice Outcome Resulting from Intrinsic Variability in Decision Confidence

Central to human and animal cognition is the ability to learn from feedback in order to optimize future rewards. Such a learning signal might be encoded and broadcasted by the brain's arousal systems, including the noradrenergic locus coeruleus. Pupil responses and the positive slow wave component of event-related potentials reflect rapid changes in the arousal level of the brain. Here, we ask whether and how these variables may reflect surprise: the mismatch between one's expectation about being correct and the outcome of a decision, when expectations fluctuate due to internal factors (e.g., engagement). We show that during an elementary decision task in the face of uncertainty both physiological markers of phasic arousal reflect surprise. We further show that pupil responses and slow wave event-related potential are unrelated to each other and that prediction error computations depend on feedback awareness. These results further advance our understanding of the role of central arousal systems in decision-making under uncertainty.

Hierarchical motor adaptations negotiate failures during force field learning

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of ‘internal model adaptation’. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.

How do we improve actions after a movement failure? Although negotiating movement failures is obviously crucial, previous motor-control studies have predominantly examined human movement adaptations in the absence of failures, and it remains unclear how failures affect subsequent movement adaptations. Here we examined this issue by developing a novel force field adaptation task where the hand movement during an arm reaching is perturbed by novel forces that induce a large target error, that is a failure. Our experimental observation and computational modeling show that, in addition to the popular ‘internal model learning’ process of motor adaptations, humans also utilize a ‘failure-negotiating’ process, that enables them to quickly improve movements in the presence of failure, even at the expense of increased arm trajectory deflections, which are subsequently reduced gradually with training after the achievement of the task success. Our results suggest that a hierarchical interaction between these two processes is a key for humans to negotiate movement failures.

Apathy in small vessel cerebrovascular disease is associated with deficits in effort-based decision making

Patients with small vessel cerebrovascular disease frequently suffer from apathy, a debilitating neuropsychiatric syndrome, the underlying mechanisms of which remain to be established. Here we investigated the hypothesis that apathy is associated with disrupted decision making in effort-based decision making, and that these alterations are associated with abnormalities in the white matter network connecting brain regions that underpin such decisions. Eighty-two patients with MRI evidence of small vessel disease were assessed using a behavioural paradigm as well as diffusion weighted MRI. The decision-making task involved accepting or rejecting monetary rewards in return for performing different levels of physical effort (hand grip force). Choice data and reaction times were integrated into a drift diffusion model that framed decisions to accept or reject offers as stochastic processes approaching a decision boundary with a particular drift rate. Tract-based spatial statistics were used to assess the relationship between white matter tract integrity and apathy, while accounting for depression. Overall, patients with apathy accepted significantly fewer offers on this decision-making task. Notably, while apathetic patients were less responsive to low rewards, they were also significantly averse to investing in high effort. Significant reductions in white matter integrity were observed to be specifically related to apathy, but not to depression. These included pathways connecting brain regions previously implicated in effort-based decision making in healthy people. The drift rate to decision parameter was significantly associated with both apathy and altered white matter tracts, suggesting that both brain and behavioural changes in apathy are associated with this single parameter. On the other hand, depression was associated with an increase in the decision boundary, consistent with an increase in the amount of evidence required prior to making a decision. These findings demonstrate altered effort-based decision making for reward in apathy, and also highlight dissociable mechanisms underlying apathy and depression in small vessel disease. They provide clear potential brain and behavioural targets for future therapeutic interventions, as well as modelling parameters that can be used to measure the effects of treatment at the behavioural level.

EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks

During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing. While various research studies particularly focus on the domain of human sensorimotor control, the relation and processing between motor execution and sensory processing is not yet fully understood. Main goal of our work is to discriminate textured surfaces varying in their roughness levels during active tactile exploration using simultaneously recorded electroencephalogram (EEG) data, while minimizing the variance of distinct motor exploration movement patterns. We perform an experimental study with eight healthy participants who were instructed to use the tip of their dominant hand index finger while rubbing or tapping three different textured surfaces with varying levels of roughness. We use an adversarial invariant representation learning neural network architecture that performs EEG-based classification of different textured surfaces, while simultaneously minimizing the discriminability of motor movement conditions (i.e., rub or tap). Results show that the proposed approach can discriminate between three different textured surfaces with accuracies up to 70%, while suppressing movement related variability from learned representations.

Dynamic decision making and value computations in medial frontal cortex

Dynamic decision making requires an intact medial frontal cortex. Recent work has combined theory and single-neuron measurements in frontal cortex to advance models of decision making. We review behavioral tasks that have been used to study dynamic decision making and algorithmic models of these tasks using reinforcement learning theory. We discuss studies linking neurophysiology and quantitative decision variables. We conclude with hypotheses about the role of other cortical and subcortical structures in dynamic decision making, including ascending neuromodulatory systems.

Separable Influences of Reward on Visual Processing and Choice

Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remain unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the end points of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that, in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, although the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options, whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice, and point to the presence of multiple brain circuits for the integration of reward outcomes.

Propositional Accounts of Implicit Evaluation: Taking Stock and Looking Ahead

Associative accounts suggest that implicit (indirectly measured) evaluations are sensitive primarily to co-occurrence information (e.g., pairings of gorges with positive experiences) and are represented associatively (e.g., Gorge–Nice). By contrast, recent propositional accounts have argued that implicit evaluations are also responsive to relational information (e.g., gorges causing vs. preventing ennui) and are represented propositionally (e.g., “I find gorges fascinating”). In a review of 30 empirical papers involving exposure to contradictory co-occurrence information and relational information, we found overwhelming evidence for the latter dominating the updating of implicit evaluations, supporting the propositional perspective. However, unlike explicit evaluations, implicit evaluations seem recalcitrant in the face of relational information that requires retrospective revaluation of already encoded co-occurrence information. These findings may be jointly explained by a “common currency” hypothesis under which implicit evaluations emerge from compressed summary representations, which are sensitive to relational information but are not fully propositional.

Reward foraging task and model-based analysis reveal how fruit flies learn value of available options

Foraging animals have to evaluate, compare and select food patches in order to increase their fitness. Understanding what drives foraging decisions requires careful manipulation of the value of alternative options while monitoring animals choices. Value-based decision-making tasks in combination with formal learning models have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is despite the fact that fruit flies have served as model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we also show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of reward foraging decisions.

Different temporal dynamics of object-based attentional allocation for reward and non-reward objects

Previous studies have confirmed that both non-reward objects (such as rectangles) and reward objects (such as banknotes) can guide the allocation of our attention; however, it is unclear whether the allocation mode of attention for reward objects is the same as for non-reward objects. This study aims to evaluate different modes of object-based attentional selection elicited by two types of objects: reward objects and non-reward objects. In our analysis, we used a two-rectangle paradigm in which two objects were presented visually. In a series of four experiments, we found a constant object-based effect with non-reward objects, such as rectangles and umbrellas, as stimuli in all of the stimulus onset asynchrony (SOA) conditions (Experiments 1 and 4), but the object-based effect disappeared only at longer SOA with reward objects such as monetary and food objects as stimuli (Experiments 2 and 3). Moreover, we found that monetary and food objects induced similar object-based effects. These results suggest that the temporal dynamics of object-based attentional allocation are different with respect to reward and non-reward objects, and different types of reward objects can guide attentional allocation in a similar way.

Properties and temporal dynamics of choice- and action-predictive signals during item recognition decisions

Decision-making is in the service of action regardless of whether the decision concerns perceptual information, goods or memories. Compared to recent advances in the neurobiology of perceptual or value-based decisions, however, the neural bases supporting the sampling of evidence in long-term memory, and the transformation of memory-based decisions into appropriate actions, are still poorly understood. In the present fMRI study, we used multivariate pattern analysis to investigate the temporal dynamics of choice- and action-predictive signals during an item recognition task that manipulated the association between memory choices (old/new) and motor responses (eye/hand) across subjects. Choice-predictive activity was mainly observed in striatal, lateral prefrontal and lateral parietal regions, was sensitive to the amount of decision evidence and showed a rapid increase after stimulus onset, followed by a fast decay. Action-predictive signals were found in primary sensory motor, premotor and occipito-parietal regions, were generally observed at the end of the decision phase and were not modulated by decision evidence. These findings suggest that a memory decision variable, potentially represented in a fronto-striato-parietal network, is not directly transformed into an action plan as often observed in perceptual decisions. Regions exhibiting choice predictive activity, and especially the striatum, however, also showed a second peak of decision-related activity that, unlike pure choice- or action-predictive signals, depended on the particular choice-response association. This second peak of activity in the striatum might represent the neural signature of the transformation of a memory decision into an appropriate motor response based on the specific choice-response association.

Choosing fast and simply: Construction of preferences by starlings through parallel option valuation

The integration of normative and descriptive analyses of decision processes in humans struggles with the fact that measuring preferences by different procedures yields different rankings and that humans appear irrationally impulsive (namely, show maladaptive preference for immediacy). Failure of procedure invariance has led to the widespread hypothesis that preferences are constructed "on the spot" by cognitive evaluations performed at choice time, implying that choices should take extra time in order to perform the necessary comparisons. We examine this issue in experiments with starlings (Sturnus vulgaris) and show that integrating normative and descriptive arguments is possible and may help reinterpreting human decision results. Our main findings are that (1) ranking alternatives through direct rating (response time) accurately predicts preference in choice, overcoming failures of procedure invariance; (2) preference is not constructed at choice time nor does it involve extra time (we show that the opposite is true); and (3) starlings' choices are not irrationally impulsive but are instead directly interpretable in terms of profitability ranking. Like all nonhuman research, our protocols examine decisions by experience rather than by description, and hence support the conjecture that irrationalities that prevail in research with humans may not be observed in decisions by experience protocols.

Digit eyes: Learning-related changes in information access in a computer game parallel those of oculomotor attention in laboratory studies

Active sensing theory is founded upon the dynamic relationship between information sampling and an observer's evolving goals. Oculomotor activity is a well studied method of sampling; a mouse or a keyboard can also be used to access information past the current screen. We examine information access patterns of StarCraft 2 players at multiple skill levels. The first measures are analogous to existing eye-movement studies: fixation frequency, fixation targets, and fixation duration all change as a function of skill, and are commensurate with known properties of eye movements in learning. Actions that require visual attention at moderate skill levels are eventually performed with little visual attention at all. This (a) confirms the generalizability of laboratory studies of attention and learning using eye movements to digital interface use, and (b) suggests that a wide variety of information access behaviors may be considered as a unified set of phenomena.

Towards Automaticity in Reinforcement Learning: A Model-Based Functional Magnetic Resonance Imaging Study

Introduction

Previous studies showed that over the course of learning many neurons in the medial prefrontal cortex adapt their firing rate towards the options with highest predicted value reward but it was showed that during later learning trials the brain switches to a more automatic processing mode governed by the basal ganglia. Based on this evidence, we hypothesized that during the early learning trials the predicted values of chosen options will be coded by a goal directed system in the medial frontal cortex but during the late trials the predicted values will be coded by the habitual learning system in the dorsal striatum.

Methods

In this study, using a 3 Tesla functional magnetic resonance imaging scanner (fMRI), blood oxygen level dependent signal (BOLD) data was collected whilst participants (N=12) performed a reinforcement learning task. The task consisted of instrumental conditioning trials wherein each trial a participant choose one of the two available options in order to win or avoid losing money. In addition to that, depending on the experimental condition, participants received either monetary reward (gain money), monetary penalty (lose money) or neural outcome.

Results

Using model-based analysis for functional magnetic resonance imaging (fMRI) event related designs; region of interest (ROI) analysis was performed to nucleus accumbens, medial frontal cortex, caudate nucleus, putamen and globus pallidus internal and external segments. In order to compare the difference in brain activity for early (goal directed) versus late learning (habitual, automatic) trials, separate ROI analyses were performed for each anatomical sub-region. For the reward condition, we found significant activity in the medial frontal cortex (p<0.05) only for early learning trials but activity is shifted to bilateral putamen (p<0.05) during later trials. However, for the loss condition no significant activity was found for early trials except globus pallidus internal segment showed a significant activity (p<0.05) for later trials.

Conclusion

We found that during reinforcement learning activation in the brain shifted from the medial frontal regions to dorsal regions of the striatum. These findings suggest that there are two separable (early goal directed and late habitual) learning systems in the brain.

Dopaminergic and Prefrontal Basis of Learning from Sensory Confidence and Reward Value

Deciding between stimuli requires combining their learned value with one's sensory confidence. We trained mice in a visual task that probes this combination. Mouse choices reflected not only present confidence and past rewards but also past confidence. Their behavior conformed to a model that combines signal detection with reinforcement learning. In the model, the predicted value of the chosen option is the product of sensory confidence and learned value. We found precise correlates of this variable in the pre-outcome activity of midbrain dopamine neurons and of medial prefrontal cortical neurons. However, only the latter played a causal role: inactivating medial prefrontal cortex before outcome strengthened learning from the outcome. Dopamine neurons played a causal role only after outcome, when they encoded reward prediction errors graded by confidence, influencing subsequent choices. These results reveal neural signals that combine reward value with sensory confidence and guide subsequent learning.

Influence of Expected Reward on Temporal Order Judgment

Perceptual decision-making has been shown to be influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing, later stages of decision-making, or both. To address this question, we conducted two experiments in which human participants made saccades to what they perceived to be either the first or second of two visually identical but asynchronously presented targets while we manipulated expected reward from correct and incorrect responses on each trial. By comparing reward-induced bias in target selection (i.e., reward bias) during the two experiments, we determined whether reward caused changes in sensory or decision-making processes. We found similar reward biases in the two experiments indicating that reward information mainly influenced later stages of decision-making. Moreover, the observed reward biases were independent of the individual's sensitivity to sensory signals. This suggests that reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our experimental observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that, during a temporal judgment task, reward exerts its influence via changing later stages of decision-making (i.e., response bias) rather than early sensory processing (i.e., perceptual bias).

Dopamine modulates the optomotor response to unreliable visual stimuli in Drosophila melanogaster

State-dependent modulation of sensory systems has been studied in many organisms and is possibly mediated through neuromodulators such as monoamine neurotransmitters. Among these, dopamine is involved in many aspects of animal behaviour, including movement control, attention, motivation and cognition. However, the precise neural mechanism underlying dopaminergic modulation of behaviour induced by sensory stimuli remains poorly understood. Here, we used Drosophila melanogaster to show that dopamine can modulate the optomotor response to moving visual stimuli including noise. The optomotor response is the head-turning response to moving objects, which is observed in most sight-reliant animals including mammals and insects. First, the effects of the dopamine system on the optomotor response were investigated in mutant flies deficient in dopamine receptors D1R1 or D1R2, which are involved in the modulation of sleep-arousal in flies. We examined the optomotor response in D1R1 knockout (D1R1 KO) and D1R2 knockout (D1R2 KO) flies and found that it was not affected in D1R1 KO flies; however, it was significantly reduced in D1R2 KO flies compared with the wild type. Using cell-type-specific expression of an RNA interference construct of D1R2, we identified the fan-shaped body, a part of the central complex, responsible for dopamine-mediated modulation of the optomotor response. In particular, pontine cells in the fan-shaped body seemed important in the modulation of the optomotor response, and their neural activity was required for the optomotor response. These results suggest a novel role of the central complex in the modulation of a behaviour based on the processing of sensory stimulations.

A novel fMRI paradigm to dissociate the behavioral and neural components of mixed-strategy decision making from non-strategic decisions in humans

During competitive interactions, such as predator-prey or team sports, the outcome of one's actions is dependent on both their own choices and those of their opponents. Success in these rivalries requires that individuals choose dynamically and unpredictably, often adopting a mixed strategy. Understanding the neural basis of strategic decision making is complicated by the fact that it recruits various cognitive processes that are often shared with non-strategic forms of decision making, such as value estimation, working memory, response inhibition, response selection, and reward processes. Although researchers have explored neural activity within key brain regions during mixed-strategy games, how brain activity differs in the context of strategic interactions versus non-strategic choices is not well understood. We developed a novel behavioral paradigm to dissociate choice behavior during mixed-strategy interactions from non-strategic choices, and we used task-based functional magnetic resonance imaging (fMRI) to contrast brain activation. In a block design, participants competed in the classic mixed-strategy game, "matching pennies," against a dynamic computer opponent designed to exploit predictability in players' response patterns. Results were contrasted with a non-strategic task that had comparable sensory input, motor output, and reward rate; thus, differences in behavior and brain activation reflect strategic processes. The mixed-strategy game was associated with activation of a distributed cortico-striatal network compared to the non-strategic task. We propose that choosing in mixed-strategy contexts requires additional cognitive demands present to a lesser degree during the control task, illustrating the strength of this design in probing function of cognitive systems beyond core sensory, motor, and reward processes.

Designing and Interpreting Psychophysical Investigations of Cognition

Scientific experimentation depends on the artificial control of natural phenomena. The inaccessibility of cognitive processes to direct manipulation can make such control difficult to realize. Here, we discuss approaches for overcoming this challenge. We advocate the incorporation of experimental techniques from sensory psychophysics into the study of cognitive processes such as decision making and executive control. These techniques include the use of simple parameterized stimuli to precisely manipulate available information and computational models to jointly quantify behavior and neural responses. We illustrate the potential for such techniques to drive theoretical development, and we examine important practical details of how to conduct controlled experiments when using them. Finally, we highlight principles guiding the use of computational models in studying the neural basis of cognition.

Decision-making in sensorimotor control

Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.

Action in auctions: neural and computational mechanisms of bidding behaviour

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty‐seven subjects (nine male) played a prototypical bidding game: a double action, with three “market” types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is “nudged” depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

Precision neuroradiology: mapping the nodes and networks that link genes to behaviour

What is the future of neuroradiology in the era of precision medicine? As with any big change, this transformation in medicine presents both challenges and opportunities, and to flourish in this new environment we will have to adapt. It is difficult to predict exactly how neuroradiology will evolve in this shifting landscape, but there will be changes in both what we image and what we do. In terms of imaging, we will need to move beyond simply imaging brain anatomy and toward imaging function, both at the molecular and circuit level. In terms of what we do, we will need to move from the periphery of the clinical enterprise toward its center, with a new emphasis on integrating imaging with genetic and clinical data to form a comprehensive picture of the patient that can be used to direct further testing and care.The payoff is that these changes will align neuroradiology with the emerging field of precision psychiatry, which promises to replace symptom-based diagnosis and trial-and-error treatment of psychiatric disorders with diagnoses based on quantifiable genetic, imaging, physiologic, and behavioural criteria and therapies targeted to the particular pathophysiology of individual patients. Here we review some of the recent developments in behavioural genetics and neuroscience that are laying the foundation for precision psychiatry. By no means comprehensive, our goal is to introduce some of the perspectives and techniques that are likely to be relevant to the precision neuroradiologist of the future.

Parietal neurons encode information sampling based on decision uncertainty

In natural behavior animals actively gather information that is relevant for learning or actions, but the mechanisms of active sampling are rarely investigated. We tested parietal neurons involved in oculomotor control in a task in which monkeys made saccades to gather visual information before reporting a decision based on the information. We show that the neurons encode, before the saccade, the information gains (reduction in decision uncertainty) that the saccade was expected to bring, correlating with the monkeys’ efficiency in processing the information in the post-saccadic fixation. Informational sensitivity is independent of the neurons’ reward sensitivity, which is unreliable across task contexts, inconsistent with the view that the cells encode economic utility. Instead, we suggest that parietal cells are involved in implementing active sampling policies, showing uncertainty-dependent boosts of neural gain that facilitate the selection of relevant cues and the efficient use of the information delivered by these cues.

Sensory and decisional components of endogenous attention are dissociable

Endogenous cueing of attention enhances sensory processing of the attended stimulus (perceptual sensitivity) and prioritizes information from the attended location for guiding behavioral decisions (spatial choice bias). Here, we test whether sensitivity and bias effects of endogenous spatial attention are under the control of common or distinct mechanisms. Human observers performed a multialternative visuospatial attention task with probabilistic spatial cues. Observers' behavioral choices were analyzed with a recently developed multidimensional signal detection model (the m-ADC model). The model effectively decoupled the effects of spatial cueing on sensitivity from those on spatial bias and revealed striking dissociations between them. Sensitivity was highest at the cued location and not significantly different among uncued locations, suggesting a spotlight-like allocation of sensory resources at the cued location. On the other hand, bias varied systematically with cue validity, suggesting a graded allocation of decisional priority across locations. Cueing-induced modulations of sensitivity and bias were uncorrelated within and across subjects. Bias, but not sensitivity, correlated with key metrics of prioritized decision-making, including reaction times and decision optimality indices. In addition, we developed a novel metric, differential risk curvature, for distinguishing bias effects of attention from those of signal expectation. Differential risk curvature correlated selectively with m-ADC model estimates of bias but not with estimates of sensitivity. Our results reveal dissociable effects of endogenous attention on perceptual sensitivity and choice bias in a multialternative choice task and motivate the search for the distinct neural correlates of each.NEW & NOTEWORTHY Attention is often studied as a unitary phenomenon. Yet, attention can both enhance the perception of important stimuli (sensitivity) and prioritize such stimuli for decision-making (bias). Employing a multialternative spatial attention task with probabilistic cueing, we show that attention affects sensitivity and bias through dissociable mechanisms. Specifically, the effects on sensitivity alone match the notion of an attentional "spotlight." Our behavioral model enables quantifying component processes of attention, and identifying their respective neural correlates.

The Neurobiology of Confidence: From Beliefs to Neurons

How confident are you? As humans, aware of our subjective sense of confidence, we can readily answer. Knowing your level of confidence helps to optimize both routine decisions such as whether to go back and check if the front door was locked and momentous ones like finding a partner for life. Yet the inherently subjective nature of confidence has limited investigations by neurobiologists. Here, we provide an overview of recent advances in this field and lay out a conceptual framework that lets us translate psychological questions about subjective confidence into the language of neuroscience. We show how statistical notions of confidence provide a bridge between our subjective sense of confidence and confidence-guided behaviors in nonhuman animals, thus enabling the study of the underlying neurobiology. We discuss confidence as a core cognitive process that enables organisms to optimize behavior such as learning or resource allocation and that serves as the basis of metacognitive reasoning. These approaches place confidence on a solid footing and pave the way for a mechanistic understanding of how the brain implements confidence-based algorithms to guide behavior.

Parallel Emergence of a Compartmentalized Striatum with the Phylogenetic Development of the Cerebral Cortex

The intricate neuronal architecture of the striatum plays a pivotal role in the functioning of the basal ganglia circuits involved in the control of various aspects of motor, cognitive, and emotional functions. Unlike the cerebral cortex, which has a laminar structure, the striatum is primarily composed of two functional subdivisions (i.e., the striosome and matrix compartments) arranged in a mosaic fashion. This review addresses whether striatal compartmentalization is present in non-mammalian vertebrates, in which simple cognitive and behavioral functions are executed by primitive sensori-motor systems. Studies show that neuronal subpopulations that share neurochemical and connective properties with striosomal and matrix neurons are present in the striata of not only anamniotes (fishes and amphibians), but also amniotes (reptiles and birds). However, these neurons do not form clearly segregated compartments in these vertebrates, suggesting that such compartmentalization is unique to mammals. In the ontogeny of the mammalian forebrain, the later-born matrix neurons disperse the early-born striosome neurons into clusters to form the compartments in tandem with the development of striatal afferents from the cortex. We propose that striatal compartmentalization in mammals emerged in parallel with the evolution of the cortex and possibly enhanced complex processing of sensory information and behavioral flexibility phylogenetically.