Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making

Active Inference: A Process Theory

This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.

Temporal coding of reward-guided choice in the posterior parietal cortex

Making a decision involves computations across distributed cortical and subcortical networks. How such distributed processing is performed remains unclear. We test how the encoding of choice in a key decision-making node, the posterior parietal cortex (PPC), depends on the temporal structure of the surrounding population activity. We recorded spiking and local field potential (LFP) activity in the PPC while two rhesus macaques performed a decision-making task. We quantified the mutual information that neurons carried about an upcoming choice and its dependence on LFP activity. The spiking of PPC neurons was correlated with LFP phases at three distinct time scales in the theta, beta, and gamma frequency bands. Importantly, activity at these time scales encoded upcoming decisions differently. Choice information contained in neural firing varied with the phase of beta and gamma activity. For gamma activity, maximum choice information occurred at the same phase as the maximum spike count. However, for beta activity, choice information and spike count were greatest at different phases. In contrast, theta activity did not modulate the encoding properties of PPC units directly but was correlated with beta and gamma activity through cross-frequency coupling. We propose that the relative timing of local spiking and choice information reveals temporal reference frames for computations in either local or large-scale decision networks. Differences between the timing of task information and activity patterns may be a general signature of distributed processing across large-scale networks.

Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.

DOI:http://dx.doi.org/10.7554/eLife.18937.001

Three challenges for connecting model to mechanism in decision-making

Recent years have seen a growing interest in understanding the neural mechanisms that support decision-making. The advent of new tools for measuring and manipulating neurons, alongside the inclusion of multiple new animal models and sensory systems has led to the generation of many novel datasets. The potential for these new approaches to constrain decision-making models is unprecedented. Here, we argue that to fully leverage these new approaches, three challenges must be met. First, experimenters must design well-controlled behavioral experiments that make it possible to distinguish competing behavioral strategies. Second, analyses of neural responses should think beyond single neurons, taking into account tradeoffs of single-trial versus trial-averaged approaches. Finally, quantitative model comparisons should be used, but must consider common obstacles.

Lesions to right posterior parietal cortex impair visual depth perception from disparity but not motion cues

The posterior parietal cortex (PPC) is understood to be active when observers perceive three-dimensional (3D) structure. However, it is not clear how central this activity is in the construction of 3D spatial representations. Here, we examine whether PPC is essential for two aspects of visual depth perception by testing patients with lesions affecting this region. First, we measured subjects' ability to discriminate depth structure in various 3D surfaces and objects using binocular disparity. Patients with lesions to right PPC (N = 3) exhibited marked perceptual deficits on these tasks, whereas those with left hemisphere lesions (N = 2) were able to reliably discriminate depth as accurately as control subjects. Second, we presented an ambiguous 3D stimulus defined by structure from motion to determine whether PPC lesions influence the rate of bistable perceptual alternations. Patients' percept durations for the 3D stimulus were generally within a normal range, although the two patients with bilateral PPC lesions showed the fastest perceptual alternation rates in our sample. Intermittent stimulus presentation reduced the reversal rate similarly across subjects. Together, the results suggest that PPC plays a causal role in both inferring and maintaining the perception of 3D structure with stereopsis supported primarily by the right hemisphere, but do not lend support to the view that PPC is a critical contributor to bistable perceptual alternations.This article is part of the themed issue 'Vision in our three-dimensional world'.

The Functional Anatomy of Time: What and When in the Brain

This Opinion article considers the implications for functional anatomy of how we represent temporal structure in our exchanges with the world. It offers a theoretical treatment that tries to make sense of the architectural principles seen in mammalian brains. Specifically, it considers a factorisation between representations of temporal succession and representations of content or, heuristically, a segregation into when and what. This segregation may explain the central role of the hippocampus in neuronal hierarchies while providing a tentative explanation for recent observations of how ordinal sequences are encoded. The implications for neuroanatomy and physiology may have something important to say about how self-organised cell assembly sequences enable the brain to exhibit purposeful behaviour that transcends the here and now.

The Behavioral Relevance of Cortical Neural Ensemble Responses Emerges Suddenly

Whereas many laboratory-studied decisions involve a highly trained animal identifying an ambiguous stimulus, many naturalistic decisions do not. Consumption decisions, for instance, involve determining whether to eject or consume an already identified stimulus in the mouth and are decisions that can be made without training. By standard analyses, rodent cortical single-neuron taste responses come to predict such consumption decisions across the 500 ms preceding the consumption or rejection itself; decision-related firing emerges well after stimulus identification. Analyzing single-trial ensemble activity using hidden Markov models, we show these decision-related cortical responses to be part of a reliable sequence of states (each defined by the firing rates within the ensemble) separated by brief state-to-state transitions, the latencies of which vary widely between trials. When we aligned data to the onset of the (late-appearing) state that dominates during the time period in which single-neuron firing is correlated to taste palatability, the apparent ramp in stimulus-aligned choice-related firing was shown to be a much more precipitous coherent jump. This jump in choice-related firing resembled a step function more than it did the output of a standard (ramping) decision-making model, and provided a robust prediction of decision latency in single trials. Together, these results demonstrate that activity related to naturalistic consumption decisions emerges nearly instantaneously in cortical ensembles. Significance statement: This paper provides a description of how the brain makes evaluative decisions. The majority of work on the neurobiology of decision making deals with "what is it?" decisions; out of this work has emerged a model whereby neurons accumulate information about the stimulus in the form of slowly increasing firing rates and reach a decision when those firing rates reach a threshold. Here, we study a different kind of more naturalistic decision--a decision to evaluate "what shall I do with it?" after the identity of a taste in the mouth has been identified--and show that this decision is not made through the gradual increasing of stimulus-related firing, but rather that this decision appears to be made in a sudden moment of "insight."

Neural and neurochemical basis of reinforcement-guided decision making

Decision making is an adaptive behavior that takes into account several internal and external input variables and leads to the choice of a course of action over other available and often competing alternatives. While it has been studied in diverse fields ranging from mathematics, economics, ecology, and ethology to psychology and neuroscience, recent cross talk among perspectives from different fields has yielded novel descriptions of decision processes. Reinforcement-guided decision making models are based on economic and reinforcement learning theories, and their focus is on the maximization of acquired benefit over a defined period of time. Studies based on reinforcement-guided decision making have implicated a large network of neural circuits across the brain. This network includes a wide range of cortical (e.g., orbitofrontal cortex and anterior cingulate cortex) and subcortical (e.g., nucleus accumbens and subthalamic nucleus) brain areas and uses several neurotransmitter systems (e.g., dopaminergic and serotonergic systems) to communicate and process decision-related information. This review discusses distinct as well as overlapping contributions of these networks and neurotransmitter systems to the processing of decision making. We end the review by touching on neural circuitry and neuromodulatory regulation of exploratory decision making.

Dynamical systems, attractors, and neural circuits

Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

Diffusion Decision Model: Current Issues and History

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology.

Neural underpinnings of the evidence accumulator

Gradual accumulation of evidence favoring one or another choice is considered a core component of many different types of decisions, and has been the subject of many neurophysiological studies in non-human primates. But its neural circuit mechanisms remain mysterious. Investigating it in rodents has recently become possible, facilitating perturbation experiments to delineate the relevant causal circuit, as well as the application of other tools more readily available in rodents. In addition, advances in stimulus design and analysis have aided studying the relevant neural encoding. In complement to ongoing non-human primate studies, these newly available model systems and tools place the field at an exciting time that suggests that the dynamical circuit mechanisms underlying accumulation of evidence could soon be revealed.

Response to Comment on "Single-trial spike trains in parietal cortex reveal discrete steps during decision-making"

Shadlen et al's Comment focuses on extrapolations of our results that were not implied or asserted in our Report. They discuss alternate analyses of average firing rates in other tasks, the relationship between neural activity and behavior, and possible extensions of the standard models we examined. Although interesting to contemplate, these points are not germane to the findings of our Report: that stepping dynamics provided a better statistical description of lateral intraparietal area spike trains than diffusion-to-bound dynamics for a majority of neurons.

Decisions reduce sensitivity to subsequent information

Behavioural studies over half a century indicate that making categorical choices alters beliefs about the state of the world. People seem biased to confirm previous choices, and to suppress contradicting information. These choice-dependent biases imply a fundamental bound of human rationality. However, it remains unclear whether these effects extend to lower level decisions, and only little is known about the computational mechanisms underlying them. Building on the framework of sequential-sampling models of decision-making, we developed novel psychophysical protocols that enable us to dissect quantitatively how choices affect the way decision-makers accumulate additional noisy evidence. We find robust choice-induced biases in the accumulation of abstract numerical (experiment 1) and low-level perceptual (experiment 2) evidence. These biases deteriorate estimations of the mean value of the numerical sequence (experiment 1) and reduce the likelihood to revise decisions (experiment 2). Computational modelling reveals that choices trigger a reduction of sensitivity to subsequent evidence via multiplicative gain modulation, rather than shifting the decision variable towards the chosen alternative in an additive fashion. Our results thus show that categorical choices alter the evidence accumulation mechanism itself, rather than just its outcome, rendering the decision-maker less sensitive to new information.

Downstream Effect of Ramping Neuronal Activity through Synapses with Short-Term Plasticity

Ramping neuronal activity refers to spiking activity with a rate that increases quasi-linearly over time. It has been observed in multiple cortical areas and is correlated with evidence accumulation processes or timing. In this work, we investigated the downstream effect of ramping neuronal activity through synapses that display short-term facilitation (STF) or depression (STD). We obtained an analytical result for a synapse driven by deterministic linear ramping input that exhibits pure STF or STD and numerically investigated the general case when a synapse displays both STF and STD. We show that the analytical deterministic solution gives an accurate description of the averaging synaptic activation of many inputs converging onto a postsynaptic neuron, even when fluctuations in the ramping input are strong. Activation of a synapse with STF shows an initial cubical increase with time, followed by a linear ramping similar to a synapse without STF. Activation of a synapse with STD grows in time to a maximum before falling and reaching a plateau, and this steady state is independent of the slope of the ramping input. For a synapse displaying both STF and STD, an increase in the depression time constant from a value much smaller than the facilitation time constant τ(F) to a value much larger than τ(F) leads to a transition from facilitation dominance to depression dominance. Therefore, our work provides insights into the impact of ramping neuronal activity on downstream neurons through synapses that display short-term plasticity. In a perceptual decision-making process, ramping activity has been observed in the parietal and prefrontal cortices, with a slope that decreases with task difficulty. Our work predicts that neurons downstream from such a decision circuit could instead display a firing plateau independent of the task difficulty, provided that the synaptic connection is endowed with short-term depression.

Evidence against perfect integration of sensory information during perceptual decision making

Perceptual decision making is often modeled as perfect integration of sequential sensory samples until the accumulated total reaches a fixed decision bound. In that view, the buildup of neural activity during perceptual decision making is attributed to temporal integration. However, an alternative explanation is that sensory estimates are computed quickly with a low-pass filter and combined with a growing signal reflecting the urgency to respond and it is the latter that is primarily responsible for neural activity buildup. These models are difficult to distinguish empirically because they make similar predictions for tasks in which sensory information is constant within a trial, as in most previous studies. Here we presented subjects with a variant of the classic constant-coherence motion discrimination (CMD) task in which we inserted brief motion pulses. We examined the effect of these pulses on reaction times (RTs) in two conditions: 1) when the CMD trials were blocked and subjects responded quickly and 2) when the same CMD trials were interleaved among trials of a variable-motion coherence task that motivated slower decisions. In the blocked condition, early pulses had a strong effect on RTs but late pulses did not, consistent with both models. However, when subjects slowed their decision policy in the interleaved condition, later pulses now became effective while early pulses lost their efficacy. This last result contradicts models based on perfect integration of sensory evidence and implies that motion signals are processed with a strong leak, equivalent to a low-pass filter with a short time constant.

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.

Sources of noise during accumulation of evidence in unrestrained and voluntarily head-restrained rats

Decision-making behavior is often characterized by substantial variability, but its source remains unclear. We developed a visual accumulation of evidence task designed to quantify sources of noise and to be performed during voluntary head restraint, enabling cellular resolution imaging in future studies. Rats accumulated discrete numbers of flashes presented to the left and right visual hemifields and indicated the side that had the greater number of flashes. Using a signal-detection theory-based model, we found that the standard deviation in their internal estimate of flash number scaled linearly with the number of flashes. This indicates a major source of noise that, surprisingly, is not consistent with the widely used 'drift-diffusion modeling' (DDM) approach but is instead closely related to proposed models of numerical cognition and counting. We speculate that this form of noise could be important in accumulation of evidence tasks generally.

DOI:http://dx.doi.org/10.7554/eLife.11308.001

Perceptual decision-making, i.e. making choices based on observed evidence, is rarely perfect. Humans and other animals tend to respond correctly on some trials and incorrectly on others. For over a century, this variability has been used to study the basis of decision-making. Most behavioral models assume that random fluctuations or 'noise' in the decision-making process is the primary source of variability and errors. However, the nature of this noise is unclear and the subject of intense scrutiny.

To investigate the sources of the behavioral variability during decision-making, Scott, Constantinople et al. trained rats to perform a visual 'accumulation of evidence' task. The animals counted flashes of light that appeared on either their left or their right. Up to 15 flashes occurred on each side, in a random order, and the rats then received a reward if they selected the side that the greatest number of flashes had occurred on. The rats chose correctly on many occasions but not on every single one.

Using a computer-controlled rat training facility or 'rat academy', Scott, Constantinople et al. collected hundreds of thousands of behavioral trials from over a dozen rats. This large dataset provided the statistical power necessary to test the assumptions of leading models of behavioral variability during decision-making, and revealed that noise grew more rapidly with the number of flashes than previously predicted. This finding explained patterns of behavior that previous models struggled with, most notably the fact that individuals make errors even on the easiest trials. The analysis also revealed that animals maintain two separate running totals – one of stimuli on the left and another of stimuli on the right – rather than a single tally of the difference between the two.

Scott, Constantinople et al. further demonstrated that rats could be trained to perform this task using a new system that enables functional brain imaging. The next step is to repeat these experiments while simultaneously recording brain activity to study the neural circuits that underlie decision-making and its variability.

DOI:http://dx.doi.org/10.7554/eLife.11308.002

How to discriminate conclusively among different models of decision making?

A popular and successful class of decision-making models (the "evidence accumulator" models) has been recently challenged by a new hypothesis called the urgency-gating model. Hawkins et al. (J Neurophysiol 114: 40-47, 2015) used a sophisticated curve-fitting procedure to show that these models are discriminable and thus testable in constant evidence tasks. In this Neuro Forum article I raise possible limitations of such an approach, discuss some of its implications, and propose alternative solutions.

Transcranial Direct Current Stimulation of the Motor Cortex Biases Action Choice in a Perceptual Decision Task

One of the multiple interacting systems involved in the selection and execution of voluntary actions is the primary motor cortex (PMC). We aimed to investigate whether the transcranial direct current stimulation (tDCS) of this area can modulate hand choice. A perceptual decision-making task was administered. Participants were asked to classify rectangles with different height-to-width ratios into horizontal and vertical rectangles using their right and left index fingers while their PMC was stimulated either bilaterally or unilaterally. Two experiments were conducted with different stimulation conditions: the first experiment (n = 12) had only one stimulation condition (bilateral stimulation), and the second experiment (n = 45) had three stimulation conditions (bilateral, anodal unilateral, and cathodal unilateral stimulations). The second experiment was designed to confirm the results of the first experiment and to further investigate the effects of anodal and cathodal stimulations alone in the observed effects. Each participant took part in two sessions. The laterality of stimulation was reversed over the two sessions. Our results showed that anodal stimulation of the PMC biases participants' responses toward using the contralateral hand whereas cathodal stimulation biases responses toward the ipsilateral hand. Brain stimulation also modulated the RT of the left hand in all stimulation conditions: Responses were faster when the response bias was in favor of the left hand and slower when the response bias was against it. We propose two possible explanations for these findings: the perceptual bias account (bottom-up effects of stimulation on perception) and the motor-choice bias account (top-down modulation of the decision-making system by facilitation of response in one hand over the other). We conclude that motor responses and the choice of hand can be modulated using tDCS.

Dynamics of individual perceptual decisions

Perceptual decision making is fundamental to a broad range of fields including neurophysiology, economics, medicine, advertising, law, etc. Although recent findings have yielded major advances in our understanding of perceptual decision making, decision making as a function of time and frequency (i.e., decision-making dynamics) is not well understood. To limit the review length, we focus most of this review on human findings. Animal findings, which are extensively reviewed elsewhere, are included when beneficial or necessary. We attempt to put these various findings and data sets, which can appear to be unrelated in the absence of a formal dynamic analysis, into context using published models. Specifically, by adding appropriate dynamic mechanisms (e.g., high-pass filters) to existing models, it appears that a number of otherwise seemingly disparate findings from the literature might be explained. One hypothesis that arises through this dynamic analysis is that decision making includes phasic (high pass) neural mechanisms, an evidence accumulator and/or some sort of midtrial decision-making mechanism (e.g., peak detector and/or decision boundary).

Reward modulates the effect of visual cortical microstimulation on perceptual decisions

Effective perceptual decisions rely upon combining sensory information with knowledge of the rewards available for different choices. However, it is not known where reward signals interact with the multiple stages of the perceptual decision-making pathway and by what mechanisms this may occur. We combined electrical microstimulation of functionally specific groups of neurons in visual area V5/MT with performance-contingent reward manipulation, while monkeys performed a visual discrimination task. Microstimulation was less effective in shifting perceptual choices towards the stimulus preferences of the stimulated neurons when available reward was larger. Psychophysical control experiments showed this result was not explained by a selective change in response strategy on microstimulated trials. A bounded accumulation decision model, applied to analyse behavioural performance, revealed that the interaction of expected reward with microstimulation can be explained if expected reward modulates a sensory representation stage of perceptual decision-making, in addition to the better-known effects at the integration stage.

DOI:http://dx.doi.org/10.7554/eLife.07832.001

Identifying how an object is moving in three-dimensional (3D) space depends upon a brain region known as V5/MT. The neurons that make up area V5/MT form groups that each have a ‘preference’ for a particular direction of movement and a particular 3D depth. If a group of neurons detects its preferred direction of movement and 3D depth, it will become highly active. The brain can assess which groups of neurons are active, in a process known as integration. This information can then be used to work out the object's movement in space.

The process of integration can be influenced by whether a rewarding outcome is expected to result from identifying the 3D movement correctly. This allows the brain to increase its likelihood of success in situations where a large reward is on offer. Until now, it was thought that the activity in area V5/MT, which takes place before integration, was not affected by the likelihood of receiving a reward.

As well as being ‘naturally’ stimulated by moving objects, the V5/MT neurons can also be ‘artificially’ activated by a technique called microstimulation, which uses a tiny electrode to electrically stimulate groups of neurons. Microstimulation can bias visual perception towards the movement and 3D depth ‘preference’ of the artificially activated neurons. If the V5/MT neurons do receive information about potential rewards from other areas of the brain, we would expect rewards to affect naturally and artificially stimulated neural activity in different ways. On the other hand, if the V5/MT neurons do not receive any information about reward, then it will not matter whether their activity is natural or artificial; the signal that they produce will be the same.

Cicmil et al. gave two monkeys a task in which they could receive rewards for correctly identifying a three-dimensional cylinder's direction of rotation, and applied microstimulation to specific groups of V5/MT neurons on some of the trials. When a larger reward was available, microstimulation was less able to bias the monkeys' choices about the rotation direction of the 3D cylinders.

Overall, Cicmil et al.'s results suggest that the V5/MT neurons are able to incorporate information about reward, before integration occurs. The next step will be to record the activity of area V5/MT to investigate exactly how this happens.

DOI:http://dx.doi.org/10.7554/eLife.07832.002

LIP activity in the interstimulus interval of a change detection task biases the behavioral response

When looking around at the world, we can only attend to a limited number of locations. The lateral intraparietal area (LIP) is thought to play a role in guiding both covert attention and eye movements. In this study, we tested the involvement of LIP in both mechanisms with a change detection task. In the task, animals had to indicate whether an element changed during a blank in the trial by making a saccade to it. If no element changed, they had to maintain fixation. We examine how the animal's behavior is biased based on LIP activity prior to the presentation of the stimulus the animal must respond to. When the activity was high, the animal was more likely to make an eye movement toward the stimulus, even if there was no change; when the activity was low, the animal either had a slower reaction time or maintained fixation, even if a change occurred. We conclude that LIP activity is involved in both covert and overt attention, but when decisions about eye movements are to be made, this role takes precedence over guiding covert attention.

Gambling on visual performance: neural correlates of metacognitive choice between visual lotteries

A lottery is a list of mutually exclusive outcomes together with their associated probabilities of occurrence. Decision making is often modeled as choices between lotteries and-in typical research on decision under risk-the probabilities are given to the subject explicitly in numerical form. In this study, we examined lottery decision task where the probabilities of receiving various rewards are contingent on the subjects' own visual performance in a random-dot-motion (RDM) discrimination task, a metacognitive or second order judgment. While there is a large literature concerning the RDM task and there is also a large literature on decision under risk, little is known about metacognitive decisions when the source of uncertainty is visual. Using fMRI with humans, we found distinct fronto-striatal and fronto-parietal networks representing subjects' estimates of his or her performance, reward value, and the expected value (EV) of the lotteries. The fronto-striatal network includes the dorsomedial prefrontal cortex and the ventral striatum, involved in reward processing and value-based decision-making. The fronto-parietal network includes the intraparietal sulcus and the ventrolateral prefrontal cortex, which was shown to be involved in the accumulation of sensory evidence during visual decision making and in metacognitive judgments on visual performance. These results demonstrate that-while valuation of performance-based lotteries involves a common fronto-striatal valuation network-an additional network unique to the estimation of task-related performance is recruited for the integration of probability and reward information when probability is inferred from visual performance.

Normative evidence accumulation in unpredictable environments

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.

DOI:http://dx.doi.org/10.7554/eLife.08825.001

Organisms gather information from their surroundings to make decisions. Traditionally, neuroscientists have investigated decision-making by first asking what would be optimal for the animal, and then seeing whether and how the brain implements the optimal process. This approach has assumed that the environment consists of noisy, but stable, signals that the brain must decipher by accumulating information over time and ‘averaging out’ the noise.

Previous research had suggested that most animals can accumulate information. However, these studies also showed that animals, including humans, often fall short of the optimal solution by being overly sensitive to noise and failing to completely average it out. Of course, in real life, the signals themselves can change abruptly and unpredictably, challenging us to distinguish noise from changes in the underlying signals. If a moving target suddenly jolts to the right, is that change part of the normal jitter that should be ignored, or does it predict where the target will be next? How do we know when to keep old information that is still relevant to the decision, and when to discard the old information because a change might have occurred that renders it irrelevant?

Glaze et al. have addressed this question by building optimal change detection into the traditional ‘information-accumulation’ framework. The model suggests that what researchers previously thought was an over-sensitivity to noise might actually be optimal for the real-life challenge of detecting change. In two different tasks, Glaze et al. tested human volunteers to see if they could make decisions in ways predicted by the model. One task involved the volunteers making decisions about which one of two possible sources of noisy signals generated a given piece of information, with the correct answer changing unpredictably every 1–20 trials. The other task involved looking at a crowd of moving dots, which jolted and wobbled as they changed direction, and the volunteers had to decide which direction the dots were moving at the end of each trial.

Both experiments showed that the volunteers were remarkably good at making decisions in the ways predicted by the new model, and incorporated learned expectations about the rate of change in underlying signals. The results suggest that humans, and potentially other organisms, are capable of detecting changes in the optimal ways suggested by the decision-making model. The study also makes predictions about what kinds of neural patterns neuroscientists might find when measuring brain activity while organisms do similar tasks.

DOI:http://dx.doi.org/10.7554/eLife.08825.002

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

Discriminating evidence accumulation from urgency signals in speeded decision making

The dominant theoretical paradigm in explaining decision making throughout both neuroscience and cognitive science is known as “evidence accumulation”--The core idea being that decisions are reached by a gradual accumulation of noisy information. Although this notion has been supported by hundreds of experiments over decades of study, a recent theory proposes that the fundamental assumption of evidence accumulation requires revision. The "urgency gating" model assumes decisions are made without accumulating evidence, using only moment-by-moment information. Under this assumption, the successful history of evidence accumulation models is explained by asserting that the two models are mathematically identical in standard experimental procedures. We demonstrate that this proof of equivalence is incorrect, and that the models are not identical, even when both models are augmented with realistic extra assumptions. We also demonstrate that the two models can be perfectly distinguished in realistic simulated experimental designs, and in two real data sets; the evidence accumulation model provided the best account for one data set, and the urgency gating model for the other. A positive outcome is that the opposing modeling approaches can be fruitfully investigated without wholesale change to the standard experimental paradigms. We conclude that future research must establish whether the urgency gating model enjoys the same empirical support in the standard experimental paradigms that evidence accumulation models have gathered over decades of study.

Hierarchical process memory: memory as an integral component of information processing

Models of working memory (WM) commonly focus on how information is encoded into and retrieved from storage at specific moments. However, in the majority of real-life processes, past information is used continuously to process incoming information across multiple timescales. Considering single-unit, electrocorticography, and functional imaging data, we argue that (i) virtually all cortical circuits can accumulate information over time, and (ii) the timescales of accumulation vary hierarchically, from early sensory areas with short processing timescales (10s to 100s of milliseconds) to higher-order areas with long processing timescales (many seconds to minutes). In this hierarchical systems perspective, memory is not restricted to a few localized stores, but is intrinsic to information processing that unfolds throughout the brain on multiple timescales.

The Lognormal Race: A Cognitive-Process Model of Choice and Latency with Desirable Psychometric Properties

We present a cognitive process model of response choice and response time performance data that has excellent psychometric properties and may be used in a wide variety of contexts. In the model there is an accumulator associated with each response option. These accumulators have bounds, and the first accumulator to reach its bound determines the response time and response choice. The times at which accumulator reaches its bound is assumed to be lognormally distributed, hence the model is race or minima process among lognormal variables. A key property of the model is that it is relatively straightforward to place a wide variety of models on the logarithm of these finishing times including linear models, structural equation models, autoregressive models, growth-curve models, etc. Consequently, the model has excellent statistical and psychometric properties and can be used in a wide range of contexts, from laboratory experiments to high-stakes testing, to assess performance. We provide a Bayesian hierarchical analysis of the model, and illustrate its flexibility with an application in testing and one in lexical decision making, a reading skill.

Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition

A hallmark of flexible behavior is the brain's ability to dynamically adjust speed and accuracy in decision-making. Recent studies suggested that such adjustments modulate not only the decision threshold, but also the rate of evidence accumulation. However, the underlying neuronal-level mechanism of the rate change remains unclear. In this work, using a spiking neural network model of perceptual decision, we demonstrate that speed and accuracy of a decision process can be effectively adjusted by manipulating a top-down control signal with balanced excitation and inhibition [balanced synaptic input (BSI)]. Our model predicts that emphasizing accuracy over speed leads to reduced rate of ramping activity and reduced baseline activity of decision neurons, which have been observed recently at the level of single neurons recorded from behaving monkeys in speed-accuracy tradeoff tasks. Moreover, we found that an increased inhibitory component of BSI skews the decision time distribution and produces a pronounced exponential tail, which is commonly observed in human studies. Our findings suggest that BSI can serve as a top-down control mechanism to rapidly and parametrically trade between speed and accuracy, and such a cognitive control signal presents both when the subjects emphasize accuracy or speed in perceptual decisions.

The challenge of understanding the brain: where we stand in 2015

Starting with the work of Cajal more than 100 years ago, neuroscience has sought to understand how the cells of the brain give rise to cognitive functions. How far has neuroscience progressed in this endeavor? This Perspective assesses progress in elucidating five basic brain processes: visual recognition, long-term memory, short-term memory, action selection, and motor control. Each of these processes entails several levels of analysis: the behavioral properties, the underlying computational algorithm, and the cellular/network mechanisms that implement that algorithm. At this juncture, while many questions remain unanswered, achievements in several areas of research have made it possible to relate specific properties of brain networks to cognitive functions. What has been learned reveals, at least in rough outline, how cognitive processes can be an emergent property of neurons and their connections.

Representation of accumulating evidence for a decision in two parietal areas

Decisions are often made by accumulating evidence for and against the alternatives. The momentary evidence represented by sensory neurons is accumulated by downstream structures to form a decision variable, linking the evolving decision to the formation of a motor plan. When decisions are communicated by eye movements, neurons in the lateral intraparietal area (LIP) represent the accumulation of evidence bearing on the potential targets for saccades. We now show that reach-related neurons from the medial intraparietal area (MIP) exhibit a gradual modulation of their firing rates consistent with the representation of an evolving decision variable. When decisions were communicated by saccades instead of reaches, decision-related activity was attenuated in MIP, whereas LIP neurons were active while monkeys communicated decisions by saccades or reaches. Thus, for decisions communicated by a hand movement, a parallel flow of sensory information is directed to parietal areas MIP and LIP during decision formation.

Choice certainty is informed by both evidence and decision time

"Degree of certainty" refers to the subjective belief, prior to feedback, that a decision is correct. A reliable estimate of certainty is essential for prediction, learning from mistakes, and planning subsequent actions when outcomes are not immediate. It is generally thought that certainty is informed by a neural representation of evidence at the time of a decision. Here we show that certainty is also informed by the time taken to form the decision. Human subjects reported simultaneously their choice and confidence about the direction of a noisy display of moving dots. Certainty was inversely correlated with reaction times and directly correlated with motion strength. Moreover, these correlations were preserved even for error responses, a finding that contradicts existing explanations of certainty based on signal detection theory. We also contrived a stimulus manipulation that led to longer decision times without affecting choice accuracy, thus demonstrating that deliberation time itself informs the estimate of certainty. We suggest that elapsed decision time informs certainty because it serves as a proxy for task difficulty.

Adaptive shaping of cortical response selectivity in the vibrissa pathway

One embodiment of context-dependent sensory processing is bottom-up adaptation, where persistent stimuli decrease neuronal firing rate over hundreds of milliseconds. Adaptation is not, however, simply the fatigue of the sensory pathway, but shapes the information flow and selectivity to stimulus features. Adaptation enhances spatial discriminability (distinguishing stimulus location) while degrading detectability (reporting presence of the stimulus), for both the ideal observer of the cortex and awake, behaving animals. However, how the dynamics of the adaptation shape the cortical response and this detection and discrimination tradeoff is unknown, as is to what degree this phenomenon occurs on a continuum as opposed to a switching of processing modes. Using voltage-sensitive dye imaging in anesthetized rats to capture the temporal and spatial characteristics of the cortical response to tactile inputs, we showed that the suppression of the cortical response, in both magnitude and spatial spread, is continuously modulated by the increasing amount of energy in the adapting stimulus, which is nonuniquely determined by its frequency and velocity. Single-trial ideal observer analysis demonstrated a tradeoff between detectability and spatial discriminability up to a moderate amount of adaptation, which corresponds to the frequency range in natural whisking. This was accompanied by a decrease in both detectability and discriminability with high-energy adaptation, which indicates a more complex coupling between detection and discrimination than a simple switching of modes. Taken together, the results suggest that adaptation operates on a continuum and modulates the tradeoff between detectability and discriminability that has implications for information processing in ethological contexts.

Dorsal premotor cortex: neural correlates of reach target decisions based on a color-location matching rule and conflicting sensory evidence

We recorded single-neuron activity in dorsal premotor (PMd) and primary motor cortex (M1) of two monkeys in a reach-target selection task. The monkeys chose between two color-coded potential targets by determining which target's color matched the predominant color of a multicolored checkerboard-like Decision Cue (DC). Different DCs contained differing numbers of colored squares matching each target. The DCs provided evidence about the correct target ranging from unambiguous (one color only) to very ambiguous and conflicting (nearly equal number of squares of each color). Differences in choice behavior (reach response times and success rates as a function of DC ambiguity) of the monkeys suggested that each applied a different strategy for using the target-choice evidence in the DCs. Nevertheless, the appearance of the DCs evoked a transient coactivation of PMd neurons preferring both potential targets in both monkeys. Reach response time depended both on how long it took activity to increase in neurons that preferred the chosen target and on how long it took to suppress the activity of neurons that preferred the rejected target, in both correct-choice and error-choice trials. These results indicate that PMd neurons in this task are not activated exclusively by a signal proportional to the net color bias of the DCs. They are instead initially modulated by the conflicting evidence supporting both response choices; final target selection may result from a competition between representations of the alternative choices. The results also indicate a temporal overlap between action selection and action initiation processes in PMd and M1.

Region-Specific Summation Patterns Inform the Role of Cortical Areas in Selecting Motor Plans

Given an instruction regarding which effector to move and what location to move to, simply adding the effector and spatial signals together will not lead to movement selection. For this, a nonlinearity is required. Thresholds, for example, can be used to select a particular response and reject others. Here we consider another useful nonlinearity, a supralinear multiplicative interaction. To help select a motor plan, spatial and effector signals could multiply and thereby amplify each other. Such an amplification could constitute one step within a distributed network involved in response selection, effectively boosting one response while suppressing others. We therefore asked whether effector and spatial signals sum supralinearly for planning eye versus arm movements from the parietal reach region (PRR), the lateral intraparietal area (LIP), the frontal eye field (FEF), and a portion of area 5 (A5) lying just anterior to PRR. Unlike LIP neurons, PRR, FEF, and, to a lesser extent, A5 neurons show a supralinear interaction. Our results suggest that selecting visually guided eye versus arm movements is likely to be mediated by PRR and FEF but not LIP.

Risk should be objectively defined: reply to Zentall and Smith

Zentall and Smith (2014) have published a comment on Pelé and Sueur (Anim Cogn 16:543-556, 2013) in which they raise two issues, one about the definition of risk and a second concerning the optimality of decisions. When making a choice, subjects are faced not only with several possible alternatives but also with the risk of opting for an unsuitable choice which depends on several variables (context, internal state, knowledge and perception). Although it is true that animals might learn about their environment and adapt their decisions to the context and to their experience, strong constraints make some behavioural traits stable over individual lifetime and even generations. We therefore consider that experience has limited impact on the variability of temporal discounting. These behavioural traits make the difference between perceived and actual risk. If the perceived risk strongly differs from the actual risk, a decision should be considered as suboptimal. If we want to lead individual and collective cognition to a common decision science, it is crucial to use the same definitions for terms implied in decision-making.

Visuomotor transformations underlying hunting behavior in zebrafish

Visuomotor circuits filter visual information and determine whether or not to engage downstream motor modules to produce behavioral outputs. However, the circuit mechanisms that mediate and link perception of salient stimuli to execution of an adaptive response are poorly understood. We combined a virtual hunting assay for tethered larval zebrafish with two-photon functional calcium imaging to simultaneously monitor neuronal activity in the optic tectum during naturalistic behavior. Hunting responses showed mixed selectivity for combinations of visual features, specifically stimulus size, speed, and contrast polarity. We identified a subset of tectal neurons with similar highly selective tuning, which show non-linear mixed selectivity for visual features and are likely to mediate the perceptual recognition of prey. By comparing neural dynamics in the optic tectum during response versus non-response trials, we discovered premotor population activity that specifically preceded initiation of hunting behavior and exhibited anatomical localization that correlated with motor variables. In summary, the optic tectum contains non-linear mixed selectivity neurons that are likely to mediate reliable detection of ethologically relevant sensory stimuli. Recruitment of small tectal assemblies appears to link perception to action by providing the premotor commands that release hunting responses. These findings allow us to propose a model circuit for the visuomotor transformations underlying a natural behavior.

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Zebrafish hunting responses are triggered by conjunctions of visual features

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Tectal neurons show non-linear mixed selectivity for prey-like visual stimuli

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Tectal assemblies show premotor activity specifically preceding hunting responses

Early evidence affects later decisions: why evidence accumulation is required to explain response time data

Models of decision making differ in how they treat early evidence as it recedes in time. Standard models, such as the drift diffusion model, assume that evidence is gradually accumulated until it reaches a boundary and a decision is initiated. One recent model, the urgency gating model, has proposed that decision making does not require the accumulation of evidence at all. Instead, accumulation could be replaced by a simple urgency factor that scales with time. To distinguish between these fundamentally different accounts of decision making, we performed an experiment in which we manipulated the presence, duration, and valence of early evidence. We simulated the associated response time and error rate predictions from the drift diffusion model and the urgency gating model, fitting the models to the empirical data. The drift diffusion model predicted that variations in the evidence presented early in the trial would affect decisions later in that same trial. The urgency gating model predicted that none of these variations would have any effect. The behavioral data showed clear effects of early evidence on the subsequent decisions, in a manner consistent with the drift diffusion model. Our results cannot be explained by the urgency gating model, and they provide support for an evidence accumulation account of perceptual decision making.

A neural implementation of Wald's sequential probability ratio test

Difficult decisions often require evaluation of samples of evidence acquired sequentially. A sensible strategy is to accumulate evidence, weighted by its reliability, until sufficient support is attained. An optimal statistical approach would accumulate evidence in units of logarithms of likelihood ratios (logLR) to a desired level. Studies of perceptual decisions suggest that the brain approximates an analogous procedure, but a direct test of accumulation, in units of logLR, to a threshold in units of cumulative logLR is lacking. We trained rhesus monkeys to make decisions based on a sequence of evanescent, visual cues assigned different logLR, hence different reliability. Firing rates of neurons in the lateral intraparietal area (LIP) reflected the accumulation of logLR and reached a stereotyped level before the monkeys committed to a decision. The monkeys' choices and reaction times, including their variability, were explained by LIP activity in the context of accumulation of logLR to a threshold.

Functions of delay-period activity in the prefrontal cortex and mnemonic scotomas revisited

Working memory (WM) is one of key concepts to understand functions of the prefrontal cortex. Delay-period activity is an important neural correlate to understand the role of WM in prefrontal functions. The importance of delay-period activity is that this activity can encode not only visuospatial information but also a variety of information including non-spatial visual features, auditory and tactile stimuli, task rules, expected reward, and numerical quantity. This activity also participates in a variety of information processing including sensory-to-motor information transformation. These mnemonic features of delay-period activity enable to perform various important operations that the prefrontal cortex participates in, such as executive controls, and therefore, support the notion that WM is an important function to understand prefrontal functions. On the other hand, although experiments using manual versions of the delayed-response task had revealed many important findings, an oculomotor version of this task enabled us to use multiple cue positions, exclude postural orientation during the delay period, and further prove the importance of mnemonic functions of the prefrontal cortex. In addition, monkeys with unilateral lesions exhibited specific impairment only in the performance of memory-guided saccades directed toward visual cues in the visual field contralateral to the lesioned hemisphere. This result indicates that memories for visuospatial coordinates in each hemifield are processed primarily in the contralateral prefrontal cortex. This result further strengthened the idea of mnemonic functions of the prefrontal cortex. Thus, the mnemonic functions of the prefrontal cortex and delay-period activity may not need to be reconsidered, but should be emphasized.

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

Neural correlates of auditory scene analysis and perception

The auditory system is designed to transform acoustic information from low-level sensory representations into perceptual representations. These perceptual representations are the computational result of the auditory system's ability to group and segregate spectral, spatial and temporal regularities in the acoustic environment into stable perceptual units (i.e., sounds or auditory objects). Current evidence suggests that the cortex-specifically, the ventral auditory pathway-is responsible for the computations most closely related to perceptual representations. Here, we discuss how the transformations along the ventral auditory pathway relate to auditory percepts, with special attention paid to the processing of vocalizations and categorization, and explore recent models of how these areas may carry out these computations.

Structured synaptic inhibition has a critical role in multiple-choice motion-discrimination tasks

Neural network models have been constructed to explore the underlying neural mechanisms for decision-making in multiple-choice motion-discrimination tasks. Despite great progress made, several key experimental observations have not been interpreted. In contrast to homogeneous connectivity between pyramidal cells and interneurons in previous models, here their connectivity is totally structured in a continuous recurrent network model. Specifically, we assume two types of inhibitory connectivity: opposite-feature and similar-feature inhibition, representing that the connectivity strength has a maximum between neural pairs with opposite and identical preferred directions, respectively. With a common parameter set, the model accounted for a wide variety of physiological and behavioral data from monkey experiments, including those that previous models failed to reproduce. We found that the opposite-feature inhibition endows the decision-making circuit with an elimination strategy, which effectively reduces the number of choice alternatives for inspection to speed up the decision process at the cost of decision accuracy. Conversely, the similar-feature inhibition markedly enhances the ability of the network to make a choice among multiple options and improves the accuracy of decisions, while slowing down the decision process. A simplified mean-field model was also presented to analytically characterize the effect of structured inhibition on fine discrimination. We made a testable prediction: only the combination of cross-feature and similar-feature inhibition enables the circuit to make a categorical choice among 12 alternatives. Together, the current work highlights the importance of structured synaptic inhibition in multiple-choice decision-making processes and sheds light on the neural mechanisms for visual motion perception.

Distinct relationships of parietal and prefrontal cortices to evidence accumulation

Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated evidence, and apply it to distinguish the encoding of decision variables in posterior parietal cortex and prefrontal cortex (frontal orienting fields, FOF). We recorded the firing rates of neurons in posterior parietal cortex and FOF from rats performing a perceptual decision-making task. Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions. However, tuning curve assays revealed that while the posterior parietal cortex encodes a graded value of the accumulating evidence, the FOF has a more categorical encoding that indicates, throughout the trial, the decision provisionally favoured by the evidence accumulated so far. Contrary to current views, this suggests that premotor activity in the frontal cortex does not have a role in the accumulation process, but instead has a more categorical function, such as transforming accumulated evidence into a discrete choice. To probe causally the role of FOF activity, we optogenetically silenced it during different time points of the trial. Consistent with a role in committing to a categorical choice at the end of the evidence accumulation process, but not consistent with a role during the accumulation itself, a behavioural effect was observed only when FOF silencing occurred at the end of the perceptual stimulus. Our results place important constraints on the circuit logic of brain regions involved in decision-making.

Jumping to conclusions in schizophrenia

Schizophrenia is a mental disorder associated with a variety of symptoms, including hallucinations, delusions, social withdrawal, and cognitive dysfunction. Impairments on decision-making tasks are routinely reported: evidence points to a particular deficit in learning from and revising behavior following feedback. In addition, patients tend to make hasty decisions when probabilistic judgments are required. This is known as "jumping to conclusions" (JTC) and has typically been demonstrated by presenting participants with colored beads drawn from one of two "urns" until they claim to be sure which urn the beads are being drawn from (the proportions of colors vary in each urn). Patients tend to make early decisions on this task, and there is evidence to suggest that a hasty decision-making style might be linked to delusion formation and thus be of clinical relevance. Various accounts have been proposed regarding what underlies this behavior. In this review, we briefly introduce the disorder and the decision-making deficits associated with it. We then explore the evidence for each account of JTC in the context of a wider decision-making deficit and then go on to summarize work exploring JTC in healthy controls using pharmacological manipulations and functional imaging. Finally, we assess whether JTC might have a role in therapy.