Neural dynamics of choice: single-trial analysis of decision-related activity in parietal cortex

Transitions in dynamical regime and neural mode underlie perceptual decision-making

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We show that contrary to prevailing hypotheses, attractors play a role only after a transition from a regime in the dynamics that is strongly driven by inputs to one dominated by the intrinsic dynamics. The initial regime mediates evidence accumulation, and the subsequent intrinsic-dominant regime subserves decision commitment. This regime transition is coupled to a rapid reorganization in the representation of the decision process in the neural population (a change in the "neural mode" along which the process develops). A simplified model approximating the coupled transition in the dynamics and neural mode allows inferring, from each trial's neural activity, the internal decision commitment time in that trial, and captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest pairing deep learning and parsimonious models as a promising approach for understanding complex data.

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition-specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single-level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

The dynamics and geometry of choice in premotor cortex

The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code. Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations. Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a common geometric principle for neural encoding of sensory and dynamic cognitive variables.

Temporal progression along discrete coding states during decision-making in the mouse gustatory cortex

The mouse gustatory cortex (GC) is involved in taste-guided decision-making in addition to sensory processing. Rodent GC exhibits metastable neural dynamics during ongoing and stimulus-evoked activity, but how these dynamics evolve in the context of a taste-based decision-making task remains unclear. Here we employ analytical and modeling approaches to i) extract metastable dynamics in ensemble spiking activity recorded from the GC of mice performing a perceptual decision-making task; ii) investigate the computational mechanisms underlying GC metastability in this task; and iii) establish a relationship between GC dynamics and behavioral performance. Our results show that activity in GC during perceptual decision-making is metastable and that this metastability may serve as a substrate for sequentially encoding sensory, abstract cue, and decision information over time. Perturbations of the model's metastable dynamics indicate that boosting inhibition in different coding epochs differentially impacts network performance, explaining a counterintuitive effect of GC optogenetic silencing on mouse behavior.

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

Perception of opposite-direction motion in random dot kinematograms

Computational models of motion perception suggest that the perceived direction of weak motion signals may sometimes be directly opposite to the true stimulus motion direction. However, this possibility cannot be assessed by using standard 2AFC motion discrimination paradigms because two opposite directions of motion were used in most studies (e.g., leftward vs. rightward). We were able to obtain robust evidence of opposite-direction motion reports by using a random-dot-kinematogram (RDK) paradigm in which the motion direction varied over 360° and observers were asked to estimate the exact motion direction. These opposite-direction motion reports were replicable across multiple display types and feedback conditions, and observers had greater confidence in their opposite-direction responses than in true guess responses. When we fed RDKs into a computational model of motion processing, we found that the model estimated substantial motion activity in the direction opposite to the coherent stimulus direction, even though no such motion was objectively present in the stimuli, suggesting that the opposite-direction motion perception may be a consequence of the properties of motion-selective neurons in visual cortex. Together, these results demonstrate that the known properties of the visual system may lead to reports of motion that are directly opposite to the true direction.

The importance of urgency in decision making based on dynamic information

A standard view in the literature is that decisions are the result of a process that accumulates evidence in favor of each alternative until such accumulation reaches a threshold and a decision is made. However, this view has been recently questioned by an alternative proposal that suggests that, instead of accumulated, evidence is combined with an urgency signal. Both theories have been mathematically formalized and supported by a variety of decision-making tasks with constant information. However, recently, tasks with changing information have shown to be more effective to study the dynamics of decision making. Recent research using one of such tasks, the tokens task, has shown that decisions are better described by an urgency mechanism than by an accumulation one. However, the results of that study could depend on a task where all fundamental information was noiseless and always present, favoring a mechanism of non-integration, such as the urgency one. Here, we wanted to address whether the same conclusions were also supported by an experimental paradigm in which sensory evidence was removed shortly after it was provided, making working memory necessary to properly perform the task. Here, we show that, under such condition, participants' behavior could be explained by an urgency-gating mechanism that low-pass filters the mnemonic information and combines it with an urgency signal that grows with time but not by an accumulation process that integrates the same mnemonic information. Thus, our study supports the idea that, under certain situations with dynamic sensory information, decisions are better explained by an urgency-gating mechanism than by an accumulation one.

Motor-like neural dynamics in two parietal areas during arm reaching

The classical view on motor control makes a clear distinction between the role of motor cortex in controlling muscles and parietal cortex in processing movement plans and goals. However, the strong parieto-frontal connections argue against such clear-cut separation of function. Modern dynamical approaches revealed that population activity in motor cortex can be captured by a limited number of patterns, called neural states that are preserved across diverse motor behaviors. Whether such dynamics are also present in parietal cortex is unclear. Here, we studied neural dynamics in the primate parietal cortex during arm movements and found three main states temporally coupled to the planning, execution and target holding epochs. Strikingly, as reported recently in motor cortex, execution was subdivided into distinct, arm acceleration- and deceleration-related, states. These results suggest that dynamics across parieto-frontal areas are highly consistent and hint that parietal population activity largely reflects timing constraints while motor actions unfold.

Variable Statistical Structure of Neuronal Spike Trains in Monkey Superior Colliculus

Popular models of decision-making propose that noisy sensory evidence accumulates until reaching a bound. Behavioral evidence as well as trial-averaged ramping of neuronal activity in sensorimotor regions of the brain support this idea. However, averaging activity across trials can mask other processes, such as rapid shifts in decision commitment, calling into question the hypothesis that evidence accumulation is encoded by delay period activity of individual neurons. We mined two sets of data from experiments in four monkeys in which we recorded from superior colliculus neurons during two different decision-making tasks and a delayed saccade task. We applied second-order statistical measures and spike train simulations to determine whether spiking statistics were similar or different in the different tasks and monkeys, despite similar trial-averaged activity across tasks and monkeys. During a motion direction discrimination task, single-trial delay period activity behaved statistically consistent with accumulation. During an orientation detection task, the activity behaved superficially like accumulation, but statistically consistent with stepping. Simulations confirmed both findings. Importantly, during a simple saccade task, with similar trial-averaged activity, neither process explained spiking activity, ruling out interpretations based on differences in attention, reward, or motor planning. These results highlight the need for exploring single-trial spiking dynamics to understand cognitive processing and raise the interesting hypothesis that the superior colliculus participates in different aspects of decision-making depending on task differences.SIGNIFICANCE STATEMENT How are decisions based on sensory information transformed into actions? We report that single-trial neuronal activity dynamics in the superior colliculus of monkeys show differences in decision-making tasks depending on task idiosyncrasies and requirements and despite similar trial-averaged ramping activity. These results highlight the importance of exploring single-trial spiking dynamics to understand cognitive processing and raise the interesting hypothesis that the superior colliculus participates in different aspects of decision-making depending on task requirements.

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment¹. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject's upcoming decision². Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind³. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

Second Guessing in Perceptual Decision-Making

Human subjects of both sexes were asked to make a perceptual decision between multiple directions of visual motion. In addition to reporting a primary choice, they also had to report a second guess, indicating which of the remaining options they would rather bet on, assuming that they got their primary choice wrong. The second guess was clearly informed by the amounts of sensory evidence that were provided for the different options. A single computational integration-to-threshold model, based on the assumption that the second guess is determined by the rank ordering of accumulated evidence at or shortly after the time of the decision, was able to explain the distribution of primary choices, associated response times, and the distribution of second guesses. This suggests that the decision-maker has access to how well supported unchosen options are by the sensory evidence.SIGNIFICANCE STATEMENT Perceptual decisions require conversion of sensory evidence into a discrete choice. Computational models based on the accumulation of evidence to a decision threshold can explain the distribution of choices and associated decision times. Subjects are also able to report the level of confidence in their decision. Here we show that, when making decisions between more than two alternatives, the decision-maker can even report a second guess that is clearly informed by the sensory evidence. These second guesses show a distribution that is consistent with subjects having access to how much sensory evidence was accumulated for the unchosen options. The decision-maker therefore has knowledge about the outcome of the decision process that goes beyond just the choice and an associated confidence.

The Interaction Between Elapsed Time and Decision Accuracy Differs Between Humans and Rats

A stochastic visual motion discrimination task is widely used to study rapid decision-making in humans and animals. Among trials of the same sensory difficulty within a block of fixed decision strategy, humans and monkeys are widely reported to make more errors in the individual trials with longer reaction times. This finding has posed a challenge for the drift-diffusion model of sensory decision-making, which in its basic form predicts that errors and correct responses should have the same reaction time distributions. We previously reported that rats also violate this model prediction, but in the opposite direction: for rats, motion discrimination accuracy was highest in the trials with the longest reaction times. To rule out task differences as the cause of our divergent finding in rats, the present study tested humans and rats using the same task and analyzed their data identically. We confirmed that rats' accuracy increased with reaction time, whereas humans' accuracy decreased with reaction time in the same task. These results were further verified using a new temporally local analysis method, ruling out that the observed trend was an artifact of non-stationarity in the data of either species. The main effect was found whether the signal strength (motion coherence) was varied in randomly interleaved trials or held constant within a block. The magnitude of the effects increased with motion coherence. These results provide new constraints useful for refining and discriminating among the many alternative mathematical theories of decision-making.

Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making

Neurons in LIP exhibit ramping trial-averaged responses during decision-making. Recent work sparked debate over whether single-trial LIP spike trains are better described by discrete "stepping" or continuous "ramping" dynamics. We extended latent dynamical spike train models and used Bayesian model comparison to address this controversy. First, we incorporated non-Poisson spiking into both models and found that more neurons were better described by stepping than ramping, even when conditioned on evidence or choice. Second, we extended the ramping model to include a non-zero baseline and compressive output nonlinearity. This model accounted for roughly as many neurons as the stepping model. However, latent dynamics inferred under this model exhibited high diffusion variance for many neurons, softening the distinction between continuous and discrete dynamics. Results generalized to additional datasets, demonstrating that substantial fractions of neurons are well described by either stepping or nonlinear ramping, which may be less categorically distinct than the original labels implied.

Single reach plans in dorsal premotor cortex during a two-target task

In many situations, we are faced with multiple potential actions, but must wait for more information before knowing which to perform. Movement scientists have long asked whether in these delayed-response situations the brain plans both potential movements simultaneously, or if it simply chooses one and then switches later if necessary. To answer this question, we used simultaneously recorded activity from populations of neurons in macaque dorsal premotor cortex to track moment-by-moment deliberation between two potential reach targets. We found that the neural activity only ever indicated a single-reach plan (with some targets favored more than others), and that initial plans often predicted the monkeys’ behavior on both Free-Choice trials and incorrect Cued trials. Our results suggest that premotor cortex plans only one option at a time, and that decisions are strongly influenced by the initial response to the available set of movement options.

It is debated whether motor cortical activity reflects plans for multiple potential actions. Here, the authors report that in a delayed response task with two potential reach targets, population activity in the dorsal premotor cortex at any moment in time represents only one of the targets.

Comparison of Decision-Related Signals in Sensory and Motor Preparatory Responses of Neurons in Area LIP

Neurons in the lateral intraparietal (LIP) area of Macaques exhibit both sensory and oculomotor preparatory responses. During perceptual decision making, the preparatory responses have been shown to track the state of the evolving evidence leading to the decision. The sensory responses are known to reflect categorical properties of visual stimuli, but it is not known whether these responses also track evolving evidence. We recorded neural responses from lateral intraparietal area of 2 female rhesus monkeys during a direction discrimination task. We compared sensory and oculomotor-preparatory responses in the same neurons when either the discriminandum (random dot motion) or an eye movement choice-target was in the neuron's response field. The neural responses in both configurations reflected the strength and direction of motion and were correlated with the animal's choice, albeit more prominently when the choice-target was in the response field. However, the variance and autocorrelation pattern of only the motor preparatory responses reflected the process of evidence accumulation. Simulations suggest that the task related activity of sensory responses could be inherited through lateral interactions with neurons that are carrying evidence accumulation signals in their motor-preparatory responses. The results are consistent with the proposal that evolving decision processes are supported by persistent neural activity in the service of actions or intentions, as opposed to high-order representations of stimulus properties.SIGNIFICANCE STATEMENT Perceptual decision making is the process of choosing an appropriate motor action based on perceived sensory information. Association areas of the cortex play an important role in this sensory-motor transformation. The neurons in these areas show both sensory- and motor-related activity. We show here that, in the macaque parietal association area LIP, signatures of the process of evidence accumulation that underlies the decisions are predominantly reflected in the motor-related activity. This finding supports the proposal that perceptual decision making is implemented in the brain as a process of choosing between available motor actions rather than as a process of representing the properties of the sensory stimulus.

What can neuronal populations tell us about cognition?

Nowadays, it is possible to record the activity of hundreds of cells at the same time in behaving animals. However, these data are often treated and analyzed as if they consisted of many independently recorded neurons. How can neuronal populations be uniquely used to learn about cognition? We describe recent work that shows that populations of simultaneously recorded neurons are fundamental to understand the basis of decision-making, including processes such as ongoing deliberations and decision confidence, which generally fall outside the reach of single-cell analysis. Thus, neuronal population data allow addressing novel questions, but they also come with so far unsolved challenges.

A mathematical and biological plausible model of decision-execution regulation in "Go/No-Go" tasks: Focusing on the fronto-striatal-thalamic pathway

Discovering factors influencing the speed and accuracy of responses in tasks such as "Go/No-Go" is one of issues which have been raised in neurocognitive studies. Mathematical models are considered as tools to identify and to study decision making procedure from different aspects. In this paper, a mathematical model has been presented to show several factors can alter the output of decision making procedure before execution in a "Go/No-Go" task. The dynamic of this model has two stable fixed points, each of them corresponds to the "Press" and "Not-press" responses. This model that focuses on the fronto-striatal-thalamic direct and indirect pathways, receives planned decisions from frontal cortex and sends a regulated output to motor cortex for execution. The state-space analysis showed that several factors could affect the regulation procedure such as the input strength, noise value, initial condition, and the values of involved neurotransmitters. Some probable analytical reasons that may lead to changes in decision-execution regulation have been suggested as well. Bifurcation diagram analysis demonstrates that an optimal interaction between these factors can compensate the weaknesses of some others. It is predicted that abnormalities of response control in different brain disorders such as attention deficit hyperactivity disorder may be resolved by providing treatment techniques that target the regulation of the interaction. The model also suggests a possible justification to show why so many studies insist on the important role of dopamine in some brain disorders.

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.

Itinerancy between attractor states in neural systems

Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.

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."

Toward a theoretical role for tonic norepinephrine in the orbitofrontal cortex in facilitating flexible learning

To adaptively respond in a complex, changing world, animals need to flexibly update their understanding of the world when their expectations are violated. Though several brain regions in rodents and primates have been implicated in aspects of this updating, current models of orbitofrontal cortex (OFC) and norepinephrine neurons of the locus coeruleus (LC-NE) suggest that each plays a role in responding to environmental change, where the OFC allows updating of prior learning to occur without overwriting or unlearning one's previous understanding of the world that changed, while elevated tonic NE allows for increased flexibility in behavior that tracks an animal's uncertainty. In light of recent studies highlighting a specific LC-NE projection to the OFC, in this review we discuss current models of OFC and NE function, and their potential synergy in the updating of associations following environmental change.

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.

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

Latimeret al (Reports, 10 July 2015, p. 184) claim that during perceptual decision formation, parietal neurons undergo one-time, discrete steps in firing rate instead of gradual changes that represent the accumulation of evidence. However, that conclusion rests on unsubstantiated assumptions about the time window of evidence accumulation, and their stepping model cannot explain existing data as effectively as evidence-accumulation models.

Neural control of visual search by frontal eye field: chronometry of neural events and race model processes

We investigated the chronometry of neural processes in frontal eye fields of macaques performing double-step saccade visual search in which a conspicuous target changes location in the array on a random fraction of trials. Durations of computational processes producing a saccade to original and final target locations (GO1 and GO2, respectively) are derived from response times (RT) on different types of trials. In these data, GO2 tended to be faster than GO1, demonstrating that inhibition of the initial saccade did not delay production of the compensated saccade. Here, we measured the dynamics of visual, visuomovement, and movement neuron activity in relation to these processes by examining trials when neurons instantiated either process. First, we verified that saccades were initiated when the discharge rate of movement neurons reached a threshold that was invariant across RT and trial type. Second, the time when visual and visuomovement neurons selected the target and when movement neuron activity began to accumulate were not significantly different across trial type. Third, the interval from the beginning of accumulation to threshold of movement-related activity was significantly shorter when instantiating the GO2 relative to the GO1 process. Differences observed between monkeys are discussed. Fourth, random variation of RT was accounted for to some extent by random variation in both the onset and duration of selective activity of each neuron type but mostly by variation of movement neuron accumulation duration. These findings offer new insights into the sources of control of target selection and saccade production in dynamic environments.

Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex

When faced with a new challenge, we often reflect on related past experiences to guide our behavior. The ability to retrieve memories that overlap with current experience, a process known as pattern completion, is theorized as a critical function of the hippocampus. Although this view has influenced research for decades, there is little empirical support for hippocampal pattern completion to individual memory elements and its influence on behavior. We used pattern analysis of brain activity measured with functional magnetic resonance imaging to demonstrate that specific elements of past experiences are reinstated in the hippocampus, as well as perirhinal cortex (PRC), when making decisions about those experiences. Linking neural measures of specific memory reinstatement in the hippocampus and PRC to behavior with computational modeling revealed that reinstatement predicts the speed of memory-based decisions. Moreover, hippocampal activation during retrieval was selectively coupled to regions of occipito-temporal cortex that showed content-specific item reinstatement. These results provide evidence for hippocampal pattern completion and its role in the mechanisms of decision making.

Capturing the temporal evolution of choice across prefrontal cortex

Activity in prefrontal cortex (PFC) has been richly described using economic models of choice. Yet such descriptions fail to capture the dynamics of decision formation. Describing dynamic neural processes has proven challenging due to the problem of indexing the internal state of PFC and its trial-by-trial variation. Using primate neurophysiology and human magnetoencephalography, we here recover a single-trial index of PFC internal states from multiple simultaneously recorded PFC subregions. This index can explain the origins of neural representations of economic variables in PFC. It describes the relationship between neural dynamics and behaviour in both human and monkey PFC, directly bridging between human neuroimaging data and underlying neuronal activity. Moreover, it reveals a functionally dissociable interaction between orbitofrontal cortex, anterior cingulate cortex and dorsolateral PFC in guiding cost-benefit decisions. We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making.

DOI:http://dx.doi.org/10.7554/eLife.11945.001

In 1848, a railroad worker named Phineas Gage suffered an accident that was to secure him a place in neuroscience lore. While constructing a new railway line, a mistimed explosion propelled an iron bar into the base of his skull, where it passed behind his left eye before exiting through the top of his head. Gage survived the accident, but those who knew him reported significant changes in his personality and behaviour.

Gage’s ability to make decisions was particularly impaired by his injury. Decision-making involves weighing up the costs and benefits associated with alternative courses of action. It entails looking into the future to decide whether an anticipated reward will justify the effort or expense necessary to obtain it. This process is dependent on a region of the brain called the prefrontal cortex, the area that sustained the most damage in Phineas Gage.

While many studies have shown correlations between activity in particular parts of prefrontal cortex and the outcome of decisions, little is known about how this activity evolves over time as a decision is made. To explore this process, Hunt et al. trained macaque monkeys to choose between pairs of images that were associated with specific rewards (quantities of fruit juice) and costs (either amounts of work or fixed delays).

Electrode recordings revealed changes in prefrontal activity that varied over time as the monkeys deliberated over each pair of images, choosing for example between a large reward after a long delay versus a smaller reward immediately. This activity was consistent with a mathematical model of decision-making, which also explains data from brain imaging experiments in humans. This provides an important link between human data and electrode recordings in animals.

However, some of the patterns of activity observed in both macaques and humans appeared to reflect the speed at which decisions were made, rather than the outcome of the decisions themselves. By extracting information about decision speed on each decision from each region, it was shown that communication between regions of prefrontal cortex changes when choices are between two different amounts of work, as opposed to two different delays. Further experiments are needed to explore this phenomenon and to determine how other brain regions interact with the prefrontal cortex to support the decision-making process.

DOI:http://dx.doi.org/10.7554/eLife.11945.002

The Encoding of Decision Difficulty and Movement Time in the Primate Premotor Cortex

Estimating the difficulty of a decision is a fundamental process to elaborate complex and adaptive behaviour. In this paper, we show that the movement time of behaving monkeys performing a decision-making task is correlated with decision difficulty and that the activity of a population of neurons in ventral Premotor cortex correlates with the movement time. Moreover, we found another population of neurons that encodes the discriminability of the stimulus, thereby supplying another source of information about the difficulty of the decision. The activity of neurons encoding the difficulty can be produced by very different computations. Therefore, we show that decision difficulty can be encoded through three different mechanisms: 1. Switch time coding, 2. rate coding and 3. binary coding. This rich representation reflects the basis of different functional aspects of difficulty in the making of a decision and the possible role of difficulty estimation in complex decision scenarios.

Understanding how the brain produces complex cognitive functions has been a crucial question since ancient philosophical inquiries. The encoding of decision difficulty in the brain is fundamental for complex and adaptive behaviour, and can provide valuable information in uncertain environments where the future outcome of a choice must be evaluated beforehand. Here we show that neurons in premotor cortex represent the difficulty of a decision using at least three different variables: 1) the time of the neuronal response, 2) the intensity of the neuronal response, 3) the probability of switching from a low activity to a high activity profile. Moreover, we show that, by encoding the time elapsed from the end of the stimulus and commitment to a choice, another set of premotor neurons is able to provide information about the difficulty of the decision. These results show that the brain is implementing heterogeneous neural mechanisms to fulfill a complex cognitive function.

NEURONAL MODELING. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upward or downward during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence toward a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike-train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete "stepping" dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal's choice than spike counts.

Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex

When choosing actions, we can act decisively, vacillate, or suffer momentary indecision. Studying how individual decisions unfold requires moment-by-moment readouts of brain state. Here we provide such a view from dorsal premotor and primary motor cortex. Two monkeys performed a novel decision task while we recorded from many neurons simultaneously. We found that a decoder trained using 'forced choices' (one target viable) was highly reliable when applied to 'free choices'. However, during free choices internal events formed three categories. Typically, neural activity was consistent with rapid, unwavering choices. Sometimes, though, we observed presumed 'changes of mind': the neural state initially reflected one choice before changing to reflect the final choice. Finally, we observed momentary 'indecision': delay forming any clear motor plan. Further, moments of neural indecision accompanied moments of behavioral indecision. Together, these results reveal the rich and diverse set of internal events long suspected to occur during free choice.

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.

Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making

For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such "collapsing boundaries" or "urgency signals" have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results.

Reach target selection in humans using ambiguous decision cues containing variable amounts of conflicting sensory evidence supporting each target choice

Human subjects chose between two color-coded reach targets using multicolored checkerboard-like decision cues (DCs) that presented variable amounts of conflicting sensory evidence supporting both target choices. Different DCs contained different numbers of small squares of the two target colors. The most ambiguous DCs contained nearly equal numbers of squares of both target colors. The subjects reached as soon as they selected a target after the appearance of the DC (“choose-and-go” task). The choice behavior of the subjects showed many similarities to prior studies using other stimulus properties (e.g., visual motion coherence, brightness), including progressively longer response times and higher target-choice error rates for more ambiguous DCs. However, certain trends in their choice behavior could not be fully captured by simple drift-diffusion models. Allowing the subjects to view the DCs for a period of time before presenting the targets (“match-to-sample” task) resulted in much shorter response times overall, but also revealed a reluctance of subjects to commit to a decision about the predominant color of the more ambiguous DCs during the initial extended observation period. Model processing and simulation analyses suggest that the subjects might adjust the dynamics of their decision-making process on a trial-to-trial basis in response to the variable level of ambiguous and conflicting evidence in different DCs between trials.

Dimensionality reduction for large-scale neural recordings

Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.

Cortical and subcortical contributions to state- and strength-based perceptual judgments

Perceptual judgments can be made on the basis of different kinds of information: state-based access to specific details that differentiate two similar images, or strength-based assessments of relational match/mismatch. We explored state- and strength-based perception in eleven right-hemisphere stroke patients, and examined lesion overlap images to gain insight into the neural underpinnings of these different kinds of perceptual judgments. Patients and healthy controls were presented with pairs of scenes that were either identical or differed in that one scene was slightly expanded or contracted relative to the other. Same/different confidence judgments were used to plot receiver-operating characteristics and estimate the contributions of state- and strength-based perception. The patient group showed a significant and selective impairment of strength-based, but not state-based, perception. This finding was not an artifact of reduced levels of overall performance, because matching perceptual discriminability levels between controls and patients revealed a double dissociation, with higher state-based, and lower strength-based, perception in patients vs.

CONTROLS

We then conducted exploratory follow-up analyses on the patient group, based on the observation of substantial individual differences in state-based perception - differences that were masked in analyses based on the group mean. Patients who were relatively spared in state-based perception (but impaired in strength-based perception) had damage that was primarily in temporo-parietal cortical regions. Patients who were relatively impaired in both state- and strength-based perception had overlapping damage in the thalamus, putamen, and adjacent white matter. These patient groups were not different in any other measure, e.g., presence of spatial neglect symptoms, age, education, lesion volume, or time since stroke. These findings shed light on the different roles of right hemisphere regions in high-level perception, suggesting that the thalamus and basal ganglia play a critical role in state- and strength-based perception, whereas temporo-parietal cortical regions are important for intact strength-based perception.

Encoding and decoding in parietal cortex during sensorimotor decision-making

The lateral intraparietal area (LIP) of macaques has been asserted to play a fundamental role in sensorimotor decision-making. Here we dissect the neural code in LIP at the level of individual trial spike trains using a statistical approach based on generalized linear models. We show that LIP responses reflect a combination of temporally-overlapping task and decision-related signals. Our model accounts for the detailed statistics of LIP spike trains, and accurately predicts spike trains from task events on single trials. Moreover, we derive an optimal decoder for heterogeneous, multiplexed LIP responses that could be implemented in biologically plausible circuits. In contrast to interpretations of LIP as providing an instantaneous code for decision variables, we show that optimal decoding requires integrating LIP spikes over two timescales. These analyses provide a detailed understanding of the neural code in LIP, and a framework for studying the coding of multiplexed signals in higher brain areas.

Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials

Decision making is a complex process in which different sources of information are combined into a decision variable (DV) that guides action [1, 2]. Neurophysiological studies have typically sought insight into the dynamics of the decision-making process and its neural mechanisms through statistical analysis of large numbers of trials from sequentially recorded single neurons or small groups of neurons [3-6]. However, detecting and analyzing the DV on individual trials has been challenging [7]. Here we show that by recording simultaneously from hundreds of units in prearcuate gyrus of macaque monkeys performing a direction discrimination task, we can predict the monkey's choices with high accuracy and decode DV dynamically as the decision unfolds on individual trials. This advance enabled us to study changes of mind (CoMs) that occasionally happen before the final commitment to a decision [8-10]. On individual trials, the decoded DV varied significantly over time and occasionally changed its sign, identifying a potential CoM. Interrogating the system by random stopping of the decision-making process during the delay period after stimulus presentation confirmed the validity of identified CoMs. Importantly, the properties of the candidate CoMs also conformed to expectations based on prior theoretical and behavioral studies [8]: they were more likely to go from an incorrect to a correct choice, they were more likely for weak and intermediate stimuli than for strong stimuli, and they were more likely earlier in the trial. We suggest that simultaneous recording of large neural populations provides a good estimate of DV and explains idiosyncratic aspects of the decision-making process that were inaccessible before.

A neural mechanism of speed-accuracy tradeoff in macaque area LIP

Decision making often involves a tradeoff between speed and accuracy. Previous studies indicate that neural activity in the lateral intraparietal area (LIP) represents the gradual accumulation of evidence toward a threshold level, or evidence bound, which terminates the decision process. The level of this bound is hypothesized to mediate the speed-accuracy tradeoff. To test this, we recorded from LIP while monkeys performed a motion discrimination task in two speed-accuracy regimes. Surprisingly, the terminating threshold levels of neural activity were similar in both regimes. However, neurons recorded in the faster regime exhibited stronger evidence-independent activation from the beginning of decision formation, effectively reducing the evidence-dependent neural modulation needed for choice commitment. Our results suggest that control of speed vs accuracy may be exerted through changes in decision-related neural activity itself rather than through changes in the threshold applied to such neural activity to terminate a decision.

DOI:http://dx.doi.org/10.7554/eLife.02260.001

Many actions involve a trade-off between speed and accuracy, with typing being a good example: the faster you try to type a sentence, the more mistakes you are likely to make. Mathematical models have successfully reproduced the speed-accuracy trade-off, but it is not clear how the brain represents and weighs up these two factors. Now, Hanks et al. have shown how single neurons in a region of the brain called the lateral intraparietal cortex vary their firing rate to optimize the balance between speed and accuracy.

Two macaque monkeys were trained to fixate on a single dot on a screen and then move their eyes in one of two directions in response to movies of random dots on a video screen. Initially, the monkeys received a reward immediately after every correct response, whereas incorrect responses were punished with a very short time-out. Under these conditions, the optimal strategy is to respond quickly at the expense of accuracy. In a separate block of trials, the monkeys were again rewarded for correct responses, but this time their reward was delayed if they responded too quickly. The most effective strategy now is to respond accurately, but more slowly.

In both the ‘high speed’ and ‘high accuracy’ conditions, the firing of neurons in lateral intraparietal cortex increased while the dots were on the screen. As soon as the firing rate reached a threshold—representing the point at which the monkey had accumulated enough evidence to make a decision about the direction of movement—the monkey moved its eyes. Previous theories had suggested that when speed was the priority, the level of activity required to trigger a decision would be lower than when accuracy was emphasized. Surprisingly, however, the threshold did not differ between the ‘high speed’ and ‘high accuracy’ conditions. Instead, neurons displayed a higher initial firing rate whenever speed was prioritized, enabling the monkey to make a decision on the basis of less evidence.

This finding is consistent with human brain imaging studies that have shown increased baseline activity in decision-making circuitry when speed is prioritized over accuracy. Studying these mechanisms could help to reveal why some individuals are more impulsive decision-makers than others.

DOI:http://dx.doi.org/10.7554/eLife.02260.002

Response times from ensembles of accumulators

Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique ensemble model of RT, called e pluribus unum, which embodies the well-known dictum "out of many, one." We used the e pluribus unum model to analyze the RTs produced by ensembles of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of ensembles of varying size. We found that predicted RT distributions are largely invariant to ensemble size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of ensemble RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high.

Decision making as a window on cognition

A decision is a commitment to a proposition or plan of action based on information and values associated with the possible outcomes. The process operates in a flexible timeframe that is free from the immediacy of evidence acquisition and the real time demands of action itself. Thus, it involves deliberation, planning, and strategizing. This Perspective focuses on perceptual decision making in nonhuman primates and the discovery of neural mechanisms that support accuracy, speed, and confidence in a decision. We suggest that these mechanisms expose principles of cognitive function in general, and we speculate about the challenges and directions before the field.

Neural correlates of state- and strength-based perception

Perceptual judgments can be based on two kinds of information: state-based perception of specific, detailed visual information, or strength-based perception of global or relational information. State-based perception is discrete in the sense that it either occurs or fails, whereas strength-based perception is continuously graded from weak to strong. The functional characteristics of these types of perception have been examined in some detail, but whether state- and strength-based perception are supported by different brain regions has been largely unexplored. A consideration of empirical work and recent theoretical proposals suggests that parietal and occipito-temporal regions may be differentially associated with state- and strength-based signals, respectively. We tested this parietal/occipito-temporal state/strength hypothesis using fMRI and a visual perception task that allows separation of state- and strength-based perception. Participants made same/different judgments on pairs of faces and scenes using a 6-point confidence scale where "6" responses indicated a state of perceiving specific details that had changed, and "1" to "5" responses indicated judgments based on varying strength of relational match/mismatch. Regions in the lateral and medial posterior parietal cortex (supramarginal gyrus, posterior cingulate cortex, and precuneus) were sensitive to state-based perception and were not modulated by varying levels of strength-based perception. In contrast, bilateral fusiform gyrus activation was increased for strength-based "different" responses compared with misses and did not show state-based effects. Finally, the lateral occipital complex showed increased activation for state-based responses and additionally showed graded activation across levels of strength-based perception. These results offer support for a state/strength distinction between parietal and temporal regions, with the lateral occipital complex at the intersection of state- and strength-based processing.

Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study

In a previous modeling study, Leblois et al. (2006) demonstrated an action selection mechanism in cortico-basal ganglia loops based on competition between the positive feedback, direct pathway through the striatum and the negative feedback, hyperdirect pathway through the subthalamic nucleus. The present study investigates how multiple level action selection could be performed by the basal ganglia. To do this, the model is extended in a manner consistent with known anatomy and electrophysiology in three main areas. First, two-level decision making has been incorporated, with a cognitive level selecting based on cue shape and a motor level selecting based on cue position. We show that the decision made at the cognitive level can be used to bias the decision at the motor level. We then demonstrate that, for accurate transmission of information between decision-making levels, low excitability of striatal projection neurons is necessary, a generally observed electrophysiological finding. Second, instead of providing a biasing signal between cue choices as an external input to the network, we show that the action selection process can be driven by reasonable levels of noise. Finally, we incorporate dopamine modulated learning at corticostriatal synapses. As learning progresses, the action selection becomes based on learned visual cue values and is not interfered with by the noise that was necessary before learning.

Dynamics of multiple-choice decision making

Neuroscientists have carried out comprehensive experiments to reveal the neural mechanisms underlying the perceptual decision making that pervades daily life. These experiments have illuminated salient features of decision making, including probabilistic choice behavior, the ramping activity of decision-related neurons, and the dependence of decision time and accuracy on the difficulty of the task. Spiking network models have reproduced these features, and a two-dimensional mean field model has demonstrated that the saddle node structure underlies two-alternative decision making. Here, we reduced a spiking network model to an analytically tractable, partial integro-differential system and characterized not only multiple-choice decision behaviors but also the time course of neural activities underlying decisions, providing a mechanistic explanation for the observations noted in the experiments. First, we observed that a two-bump unstable steady state of the system is responsible for two-choice decision making, similar to the saddle node structure in the two-dimensional mean field model. However, for four-choice decision making, three types of unstable steady states collectively predominate the time course of the evolution from the initial state to the stable states. Second, the time constant of the unstable steady state can explain the fact that four-choice decision making requires a longer time than two-choice decision making. However, the quicker decision, given a stronger motion strength, cannot be explained by the time constant of the unstable steady state. Rather, the decision time can be attributed to the projection coefficient of the difference between the initial state and the unstable steady state on the eigenvector corresponding to the largest positive eigenvalue.

Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits

Animals choose actions based on imperfect, ambiguous data. “Noise” inherent in neural processing adds further variability to this already-noisy input signal. Mathematical analysis has suggested that the optimal apparatus (in terms of the speed/accuracy trade-off) for reaching decisions about such noisy inputs is perfect accumulation of the inputs by a temporal integrator. Thus, most highly cited models of neural circuitry underlying decision-making have been instantiations of a perfect integrator. Here, in accordance with a growing mathematical and empirical literature, we describe circumstances in which perfect integration is rendered suboptimal. In particular we highlight the impact of three biological constraints: (1) significant noise arising within the decision-making circuitry itself; (2) bounding of integration by maximal neural firing rates; and (3) time limitations on making a decision. Under conditions (1) and (2), an attractor system with stable attractor states can easily best an integrator when accuracy is more important than speed. Moreover, under conditions in which such stable attractor networks do not best the perfect integrator, a system with unstable initial states can do so if readout of the system’s final state is imperfect. Ubiquitously, an attractor system with a nonselective time-dependent input current is both more accurate and more robust to imprecise tuning of parameters than an integrator with such input. Given that neural responses that switch stochastically between discrete states can “masquerade” as integration in single-neuron and trial-averaged data, our results suggest that such networks should be considered as plausible alternatives to the integrator model.

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP). In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1) empirical questions not yet answered by existing data; (2) implementation issues related to how neural circuits could actually implement the mechanisms suggested by both extracellular neurophysiology and psychophysics; and (3) ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general "encoding-decoding framework" that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.