Visual fixation patterns during economic choice reflect covert valuation processes that emerge with learning.

Visual fixations play a vital role in decision making. Recent studies have demonstrated that the longer subjects fixate an option, the more likely they are to choose it. However, the role of evaluating stimuli covertly (i.e., without fixating them), and how covert evaluations determine where to subsequently fixate, remains relatively unexplored. Here, we trained monkeys to perform a decision-making task where they made binary choices between reward-predictive stimuli which were well-learned ("overtrained"), recently learned ("novel"), or a combination of both ("mixed"). Subjects were free to saccade around the screen and make a choice (via joystick response) at any time. Subjects rarely fixated both options, yet choice behavior was better explained by assuming the values of both stimuli governed choices. The first fixation latency was fast (∼150 ms) but, surprisingly, its direction was value-driven. This suggests covert evaluation of stimulus values prior to first saccade. This was particularly evident for overtrained stimuli. For novel stimuli, first fixations became increasingly value-driven throughout a behavioral session. However, this improvement lagged behind learning of accurate economic choices, suggesting separate processes governed their learning. Finally, mixed trials revealed a strong bias toward fixating the novel stimulus first but no bias toward choosing it. Our results suggest that the primate brain contains fast covert evaluation mechanisms for guiding fixations toward highly valuable and novel information. By employing such covert mechanisms, fixation behavior becomes dissociable from the value comparison processes that drive final choice. This implies that primates use separable decision systems for value-guided fixations and value-guided choice.

Combined model-free and model-sensitive reinforcement learning in non-human primates.

Contemporary reinforcement learning (RL) theory suggests that potential choices can be evaluated by strategies that may or may not be sensitive to the computational structure of tasks. A paradigmatic model-free (MF) strategy simply repeats actions that have been rewarded in the past; by contrast, model-sensitive (MS) strategies exploit richer information associated with knowledge of task dynamics. MF and MS strategies should typically be combined, because they have complementary statistical and computational strengths; however, this tradeoff between MF/MS RL has mostly only been demonstrated in humans, often with only modest numbers of trials. We trained rhesus monkeys to perform a two-stage decision task designed to elicit and discriminate the use of MF and MS methods. A descriptive analysis of choice behaviour revealed directly that the structure of the task (of MS importance) and the reward history (of MF and MS importance) significantly influenced both choice and response vigour. A detailed, trial-by-trial computational analysis confirmed that choices were made according to a combination of strategies, with a dominant influence of a particular form of model sensitivity that persisted over weeks of testing. The residuals from this model necessitated development of a new combined RL model which incorporates a particular credit assignment weighting procedure. Finally, response vigor exhibited a subtly different collection of MF and MS influences. These results provide new illumination onto RL behavioural processes in non-human primates.

Correction: Approach-Induced Biases in Human Information Sampling.

[This corrects the article DOI: 10.1371/journal.pbio.2000638.].

Approach-Induced Biases in Human Information Sampling.

Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach"), the selection of which information to sample ("sampling the favorite"), and the interaction between information sampling and subsequent choices ("rejecting unsampled options"). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

Neural Signatures of Value Comparison in Human Cingulate Cortex during Decisions Requiring an Effort-Reward Trade-off.

UNLABELLED: Integrating costs and benefits is crucial for optimal decision-making. Although much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human fMRI during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area and the caudal portion of dorsal anterior cingulate cortex encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modeled effort-discounted subjective values using a novel behavioral model. This revealed that the same network of regions involving dorsal anterior cingulate cortex and supplementary motor area encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in supplementary motor area and ventromedial prefrontal cortex correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not ventromedial prefrontal cortex as typically reported for outcome-based choice. Furthermore, distinct frontal circuits seem to drive behavior toward reward maximization and effort minimization. SIGNIFICANCE STATEMENT: The neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behavior, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioral characterization of how humans trade off reward maximization with effort minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits drive behavior toward reward maximization or effort minimization.

Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation.

There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

Single-neuron mechanisms underlying cost-benefit analysis in frontal cortex.

Effective decision-making requires consideration of costs and benefits. Previous studies have implicated orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC) in cost-benefit decision-making. Yet controversy remains about whether different decision costs are encoded by different brain areas, and whether single neurons integrate costs and benefits to derive a subjective value estimate for each choice alternative. To address these issues, we trained four subjects to perform delay- and effort-based cost-benefit decisions and recorded neuronal activity in OFC, ACC, DLPFC, and the cingulate motor area (CMA). Although some neurons, mainly in ACC, did exhibit integrated value signals as if performing cost-benefit computations, they were relatively few in number. Instead, the majority of neurons in all areas encoded the decision type; that is whether the subject was required to perform a delay- or effort-based decision. OFC and DLPFC neurons tended to show the largest changes in firing rate for delay- but not effort-based decisions; whereas, the reverse was true for CMA neurons. Only ACC contained neurons modulated by both effort- and delay-based decisions. These findings challenge the idea that OFC calculates an abstract value signal to guide decision-making. Instead, our results suggest that an important function of single PFC neurons is to categorize sensory stimuli based on the consequences predicted by those stimuli.

Distributional reinforcement learning in prefrontal cortex.

The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.

Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies.

Hebb proposed that neuronal cell assemblies are critical for effective perception, cognition, and action. However, evidence for brain mechanisms that coordinate multiple coactive assemblies remains lacking. Neuronal oscillations have been suggested as one possible mechanism for cell assembly coordination. Prior studies have shown that spike timing depends upon local field potential (LFP) phase proximal to the cell body, but few studies have examined the dependence of spiking on distal LFP phases in other brain areas far from the neuron or the influence of LFP-LFP phase coupling between distal areas on spiking. We investigated these interactions by recording LFPs and single-unit activity using multiple microelectrode arrays in several brain areas and then used a unique probabilistic multivariate phase distribution to model the dependence of spike timing on the full pattern of proximal LFP phases, distal LFP phases, and LFP-LFP phase coupling between electrodes. Here we show that spiking activity in single neurons and neuronal ensembles depends on dynamic patterns of oscillatory phase coupling between multiple brain areas, in addition to the effects of proximal LFP phase. Neurons that prefer similar patterns of phase coupling exhibit similar changes in spike rates, whereas neurons with different preferences show divergent responses, providing a basic mechanism to bind different neurons together into coordinated cell assemblies. Surprisingly, phase-coupling-based rate correlations are independent of interneuron distance. Phase-coupling preferences correlate with behavior and neural function and remain stable over multiple days. These findings suggest that neuronal oscillations enable selective and dynamic control of distributed functional cell assemblies.

A cognitive map for value-guided choice in ventromedial prefrontal cortex.

The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.

Reward-dependent modulation of working memory in lateral prefrontal cortex.

Although research implicates lateral prefrontal cortex (PFC) in executive control and goal-directed behavior, it remains unclear how goals influence executive processes. One possibility is that goal-relevant information, such as expected rewards, could modulate the representation of information relating to executive control, thereby ensuring the efficient allocation of cognitive resources. To investigate this, we examined how reward modulated spatial working memory. Past studies investigating spatial working memory have focused on dorsolateral PFC, but this area only weakly connects with areas processing reward. Ventrolateral PFC has better connections in this regard. Thus, we contrasted the functional properties of single neurons in ventrolateral and dorsolateral PFC as two subjects performed a task that required them to hold spatial information in working memory under different expectancies of reward for correct performance. We balanced the order of presentation of spatial and reward information so we could assess the neuronal encoding of the two pieces of information independently and conjointly. Neurons in ventrolateral PFC encoded both spatial and reward information earlier, stronger and in a more sustained manner than neurons in dorsolateral PFC. Within ventrolateral PFC, spatial selectivity was more prevalent on the inferior convexity than within the principal sulcus. Finally, when reward increased spatial selectivity, behavioral performance improved, whereas when reward decreased spatial selectivity, behavioral performance deteriorated. These results suggest that ventrolateral PFC may be a locus whereby information about expected rewards can modulate information in working memory. The pattern of results is consistent with a role for ventrolateral PFC in attentional control.

Encoding of gustatory working memory by orbitofrontal neurons.

The content model regarding the functional organization of working memory in prefrontal cortex (PFC) states that different PFC areas encode different types of information in working memory depending on their afferent connections with other brain areas. Previous studies that tested this model focused on visual, auditory and somatosensory information. However, posterior areas processing this information project to widespread and overlapping regions of lateral PFC, making it difficult to establish the veracity of the model. In contrast, gustatory information enters PFC via orbitofrontal cortex (OFC), and so the content model would argue that OFC should be responsible for maintaining gustatory information in working memory. To test this, we recorded the activity of single neurons throughout PFC and gustatory cortex (GUS) from two subjects while they performed a gustatory delayed-match-to-sample task with intervening gustatory distraction. Neurons that encoded the identity of the gustatory stimulus across the delay, consistent with a role in gustatory working memory, were most prevalent in OFC and GUS compared with dorsolateral PFC and ventrolateral PFC. Gustatory information in OFC was more resilient to intervening distraction, paralleling previous findings regarding visual working memory processes in PFC and posterior sensory cortex. Our findings provide support for the content model of working memory organization. Maintaining gustatory information may be one aspect of a wider function for OFC in reward working memory that could contribute to its role in decision-making.

Optimal decision making and the anterior cingulate cortex.

Learning the value of options in an uncertain environment is central to optimal decision making. The anterior cingulate cortex (ACC) has been implicated in using reinforcement information to control behavior. Here we demonstrate that the ACC's critical role in reinforcement-guided behavior is neither in detecting nor in correcting errors, but in guiding voluntary choices based on the history of actions and outcomes. ACC lesions did not impair the performance of monkeys (Macaca mulatta) immediately after errors, but made them unable to sustain rewarded responses in a reinforcement-guided choice task and to integrate risk and payoff in a dynamic foraging task. These data suggest that the ACC is essential for learning the value of actions.

A Diversity of Intrinsic Timescales Underlie Neural Computations.

Neural processing occurs across a range of temporal scales. To facilitate this, the brain uses fast-changing representations reflecting momentary sensory input alongside more temporally extended representations, which integrate across both short and long temporal windows. The temporal flexibility of these representations allows animals to behave adaptively. Short temporal windows facilitate adaptive responding in dynamic environments, while longer temporal windows promote the gradual integration of information across time. In the cognitive and motor domains, the brain sets overarching goals to be achieved within a long temporal window, which must be broken down into sequences of actions and precise movement control processed across much shorter temporal windows. Previous human neuroimaging studies and large-scale artificial network models have ascribed different processing timescales to different cortical regions, linking this to each region's position in an anatomical hierarchy determined by patterns of inter-regional connectivity. However, even within cortical regions, there is variability in responses when studied with single-neuron electrophysiology. Here, we review a series of recent electrophysiology experiments that demonstrate the heterogeneity of temporal receptive fields at the level of single neurons within a cortical region. This heterogeneity appears functionally relevant for the computations that neurons perform during decision-making and working memory. We consider anatomical and biophysical mechanisms that may give rise to a heterogeneity of timescales, including recurrent connectivity, cortical layer distribution, and neurotransmitter receptor expression. Finally, we reflect on the computational relevance of each brain region possessing a heterogeneity of neuronal timescales. We argue that this architecture is of particular importance for sensory, motor, and cognitive computations.

Frontal cortex subregions play distinct roles in choices between actions and stimuli.

The orbitofrontal cortex (OFC) has been implicated in reinforcement-guided decision making, error monitoring, and the reversal of behavior in response to changing circumstances. The anterior cingulate cortex sulcus (ACC(S)), however, has also been implicated in similar aspects of behavior. Dissociating the unique functions of these areas would improve our understanding of the decision-making process. The effect of selective OFC lesions on how monkeys used the history of reinforcement to guide choices of either particular actions or particular stimuli was studied and compared with the effects of ACC(S) lesions. Both lesions disrupted decision making, but their effects were differentially modulated by the dependence on action- or stimulus-value contingencies. OFC lesions caused a deficit in stimulus but not action selection, whereas ACC(S) lesions had the opposite effect, disrupting action but not stimulus selection. Furthermore, OFC lesions that have previously been found to impair decision making when deterministic stimulus-reward contingencies are switched were found to cause a more general learning impairment in more naturalistic situations in which reward was stochastic. Both OFC and ACC(S) are essential for reinforcement-guided decision making rather than just error monitoring or behavioral reversal. The OFC and ACC(S) are both, however, more concerned with learning and making decisions, but their roles in selecting between stimulus and action values are distinct.

Encoding of reward and space during a working memory task in the orbitofrontal cortex and anterior cingulate sulcus.

Several lines of research indicate that emotional and motivational information may be useful in guiding the allocation of attentional resources. Two areas of the frontal lobe that are particularly implicated in the encoding of motivational information are the orbital prefrontal cortex (PFo) and the dorsomedial region of prefrontal cortex, specifically the anterior cingulate sulcus (PFcs). However, it remains unclear whether these areas use this information to influence spatial attention. We used single-unit neurophysiology to examine whether, at the level of individual neurons, there was evidence for integration between reward information and spatial attention. We trained two subjects to perform a task that required them to attend to a spatial location across a delay under different expectancies of reward for correct performance. We balanced the order of presentation of spatial and reward information so we could assess the neuronal encoding of the two pieces of information independently and conjointly. We found little evidence for encoding of the spatial location in either PFo or PFcs. In contrast, both areas encoded the expected reward. Furthermore, PFo consistently encoded reward more quickly than PFcs, although reward encoding was subsequently more prevalent and stronger in PFcs. These results suggest a differential contribution of PFo and PFcs to reward encoding, with PFo potentially more important for initially determining the value of rewards predicted by sensory stimuli. They also suggest that neither PFo nor PFcs play a direct role in the control of spatial attention.

Evaluating choices by single neurons in the frontal lobe: outcome value encoded across multiple decision variables.

Damage to the frontal lobe can cause severe decision-making impairments. A mechanism that may underlie this is that neurons in the frontal cortex encode many variables that contribute to the valuation of a choice, such as its costs, benefits and probability of success. However, optimal decision-making requires that one considers these variables, not only when faced with the choice, but also when evaluating the outcome of the choice, in order to adapt future behaviour appropriately. To examine the role of the frontal cortex in encoding the value of different choice outcomes, we simultaneously recorded the activity of multiple single neurons in the anterior cingulate cortex (ACC), orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) while subjects evaluated the outcome of choices involving manipulations of probability, payoff and cost. Frontal neurons encoded many of the parameters that enabled the calculation of the value of these variables, including the onset and offset of reward and the amount of work performed, and often encoded the value of outcomes across multiple decision variables. In addition, many neurons encoded both the predicted outcome during the choice phase of the task as well as the experienced outcome in the outcome phase of the task. These patterns of selectivity were more prevalent in ACC relative to OFC and LPFC. These results support a role for the frontal cortex, principally ACC, in selecting between choice alternatives and evaluating the outcome of that selection thereby ensuring that choices are optimal and adaptive.

Callosotomy patients exhibit temporal uncoupling during continuous bimanual movements.

Rhythmic bimanual movements are highly constrained in the temporal domain, with the gestures of the two hands tightly synchronized. Previous studies have implicated a subcortical locus for temporal coupling based on the observation that these constraints persist in callosotomy patients. We now report that such coupling is restricted to movements entailing a discrete event (such as a movement onset). Three callosotomy patients exhibited a striking lack of temporal coupling during continuous movements, with the two hands oscillating at non-identical frequencies. We propose a subcortical locus of temporal coupling for movements involving discrete events. In contrast, synchronization between the hands during continuous movements depends on interhemispheric transmission across the corpus callosum.

Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex.

Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons or by dynamic (time-varying) activity across a neural population. Here, we compare these hypotheses across four regions of prefrontal cortex (PFC) in an oculomotor-delayed-response task, where an intervening cue indicated the reward available for a correct saccade. WM representations were strongest in ventrolateral PFC neurons with higher intrinsic temporal stability (time-constant). At the population-level, although a stable mnemonic state was reached during the delay, this tuning geometry was reversed relative to cue-period selectivity, and was disrupted by the reward cue. Single-neuron analysis revealed many neurons switched to coding reward, rather than maintaining task-relevant spatial selectivity until saccade. These results imply WM is fulfilled by dynamic, population-level activity within high time-constant neurons. Rather than persistent activity supporting stable mnemonic representations that bridge subsequent salient stimuli, PFC neurons may stabilise a dynamic population-level process supporting WM.

Triple dissociation of attention and decision computations across prefrontal cortex.

Naturalistic decision-making typically involves sequential deployment of attention to choice alternatives to gather information before a decision is made. Attention filters how information enters decision circuits, thus implying that attentional control may shape how decision computations unfold. We recorded neuronal activity from three subregions of the prefrontal cortex (PFC) while monkeys performed an attention-guided decision-making task. From the first saccade to decision-relevant information, a triple dissociation of decision- and attention-related computations emerged in parallel across PFC subregions. During subsequent saccades, orbitofrontal cortex activity reflected the value comparison between currently and previously attended information. In contrast, the anterior cingulate cortex carried several signals reflecting belief updating in light of newly attended information, the integration of evidence to a decision bound and an emerging plan for what action to choose. Our findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.