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

Distractors Selectively Modulate Electrophysiological Markers of Perceptual Decisions

Current models of perceptual decision-making assume that choices are made after evidence in favor of an alternative accumulates to a given threshold. This process has recently been revealed in human EEG recordings, but an unresolved issue is how these neural mechanisms are modulated by competing, yet task-irrelevant, stimuli. In this study, we tested 20 healthy participants on a motion direction discrimination task. Participants monitored two patches of random dot motion simultaneously presented on either side of fixation for periodic changes in an upward or downward motion, which could occur equiprobably in either patch. On a random 50% of trials, these periods of coherent vertical motion were accompanied by simultaneous task-irrelevant, horizontal motion in the contralateral patch. Our data showed that these distractors selectively increased the amplitude of early target selection responses over scalp sites contralateral to the distractor stimulus, without impacting on responses ipsilateral to the distractor. Importantly, this modulation mediated a decrement in the subsequent buildup rate of a neural signature of evidence accumulation and accounted for a slowing of RTs. These data offer new insights into the functional interactions between target selection and evidence accumulation signals, and their susceptibility to task-irrelevant distractors. More broadly, these data neurally inform future models of perceptual decision-making by highlighting the influence of early processing of competing stimuli on the accumulation of perceptual evidence.

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a "self-consistency" equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model "overcomplete," which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this "overcomplete" approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation

The brain is capable of processing several streams of information that bear on different aspects of the same problem. Here, we address the problem of making two decisions about one object, by studying difficult perceptual decisions about the color and motion of a dynamic random dot display. We find that the accuracy of one decision is unaffected by the difficulty of the other decision. However, the response times reveal that the two decisions do not form simultaneously. We show that both stimulus dimensions are acquired in parallel for the initial ∼0.1 s but are then incorporated serially in time-multiplexed bouts. Thus, there is a bottleneck that precludes updating more than one decision at a time, and a buffer that stores samples of evidence while access to the decision is blocked. We suggest that this bottleneck is responsible for the long timescales of many cognitive operations framed as decisions.

Flexible categorization in perceptual decision making

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Single-trial regression of spatial exploration behavior indicates posterior EEG alpha modulation to reflect egocentric coding

Learning to navigate uncharted terrain is a key cognitive ability that emerges as a deeply embodied process, with eye movements and locomotion proving most useful to sample the environment. We studied healthy human participants during active spatial learning of room-scale virtual reality (VR) mazes. In the invisible maze task, participants wearing a wireless electroencephalography (EEG) headset were free to explore their surroundings, only given the objective to build and foster a mental spatial representation of their environment. Spatial uncertainty was resolved by touching otherwise invisible walls that were briefly rendered visible inside VR, similar to finding your way in the dark. We showcase the capabilities of mobile brain/body imaging using VR, demonstrating several analysis approaches based on general linear models (GLMs) to reveal behavior-dependent brain dynamics. Confirming spatial learning via drawn sketch maps, we employed motion capture to image spatial exploration behavior describing a shift from initial exploration to subsequent exploitation of the mental representation. Using independent component analysis, the current work specifically targeted oscillations in response to wall touches reflecting isolated spatial learning events arising in deep posterior EEG sources located in the retrosplenial complex. Single-trial regression identified significant modulation of alpha oscillations by the immediate, egocentric, exploration behavior. When encountering novel walls, as well as with increasing walking distance between subsequent touches when encountering novel walls, alpha power decreased. We conclude that these oscillations play a prominent role during egocentric evidencing of allocentric spatial hypotheses.

Evidence accumulation under uncertainty - a neural marker of emerging choice and urgency

To interact meaningfully with its environment, an agent must integrate external information with its own internal states. However, information about the environment is often noisy. In this study, we identify a neural correlate that tracks how asymmetries between competing alternatives evolve over the course of a decision. In our task participants had to monitor a stream of discrete visual stimuli over time and decide whether or not to act, on the basis of either strong or ambiguous evidence. We found that the classic P3 event-related potential evoked by sequential evidence items tracked decision-making processes and predicted participants' categorical choices on a single trial level, both when evidence was strong and when it was ambiguous. The P3 amplitudes in response to evidence supporting the eventually selected option increased over trial time as decisions evolved, being maximally different from the P3 amplitudes evoked by competing evidence at the time of decision. Computational modelling showed that both the neural dynamics and behavioural primacy and recency effects can be explained by a combination of (a) competition between mutually inhibiting accumulators for the two categorical choice outcomes, and (b) a context-dependant urgency signal. In conditions where evidence was presented at a low rate, urgency increased faster than in conditions when evidence was very frequent. We also found that the readiness potential, a classic marker of endogenously initiated actions, was observed preceding movements in all conditions - even when those were strongly driven by external evidence.

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.

Confidence in predicted position error explains saccadic decisions during pursuit

A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors, and the timing of saccades can be highly variable. The observed distributions of saccade frequency and trigger time remain poorly understood, and this decision process remains imprecisely quantified. Here, we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioral observations on the frequency and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions that saccade decisions are highly sensitive to uncertainty for small predicted position errors, but this influence diminishes as the magnitude of predicted position error increases. We suggest that this predictive, confidence-based decision-making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.NEW & NOTEWORTHY This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a threshold. This model explains saccade frequency and trigger time distributions across target trajectories and makes novel predictions about the influence of sensory uncertainty in saccade decisions during pursuit.

Peak-at-end rule: adaptive mechanism predicts time-dependent decision weighting

Humans make decisions under various natural circumstances, integrating multiple pieces of information that are distributed over space and time. Although psychophysical and physiological studies have investigated temporal dynamics underlying perceptual decision making, weighting profiles for inliers and outliers during temporal integration have yet to be fully investigated in most studies. Here, we examined the temporal weighting profile of a computational model characterized by a leaky integrator of sensory evidence. As a corollary of its leaky nature, the model predicts the recency effect and overweights outlying elements around the end of the stream. Moreover, we found that the model underweights outlying values occurring earlier in the stream (i.e., robust averaging). We also show that human observers exhibit exactly the same weighting profile in an average estimation task. These findings suggest that the adaptive decision process in the brain results in the time-dependent decision weighting, the “peak-at-end” rule, rather than the peak-end rule in behavioral economics.

Mice Preferentially Use Increases in Cerebral Cortex Spiking to Detect Changes in Visual Stimuli

Whenever the retinal image changes, some neurons in visual cortex increase their rate of firing whereas others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice of both sexes to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements in spiking were generated in the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity has costs. This work clarifies neuronal decoding strategies used by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.SIGNIFICANCE STATEMENT Visual responses in the primary visual cortex (V1) are diverse, in that neurons can be either excited or inhibited by the onset of a visual stimulus. We selectively potentiated or suppressed V1 spiking in mice while they performed contrast change detection tasks. In other experiments, excitation or inhibition was delivered to V1 independent of visual stimuli. Mice readily detected increases in V1 spiking while equivalent reductions in V1 spiking suppressed the probability of detection, even when increases and decreases in V1 spiking were generated in the same neuronal populations. Our data raise the striking possibility that only increments in spiking are used to render information to structures downstream of V1.

Non-equilibrium landscape and flux reveal the stability-flexibility-energy tradeoff in working memory

Uncovering the underlying biophysical principles of emergent collective computational abilities, such as working memory, in neural circuits is one of the most essential concerns in modern neuroscience. Working memory system is often desired to be robust against noises. Such systems can be highly flexible for adapting environmental demands. How neural circuits reconfigure themselves according to the cognitive task requirement remains unclear. Previous studies explored the robustness and the flexibility in working memory by tracing individual dynamical trajectories in a limited time scale, where the accuracy of the results depends on the volume of the collected statistical data. Inspired by thermodynamics and statistical mechanics in physical systems, we developed a non-equilibrium landscape and flux framework for studying the neural network dynamics. Applying this approach to a biophysically based working memory model, we investigated how changes in the recurrent excitation mediated by slow NMDA receptors within a selective population and mutual inhibition mediated by GABAergic interneurons between populations affect the robustness against noises. This is realized through quantifying the underlying non-equilibrium potential landscape topography and the kinetics of state switching. We found that an optimal compromise for a working memory circuit between the robustness and the flexibility can be achieved through the emergence of an intermediate state between the working memory states. An optimal combination of both increased self-excitation and inhibition can enhance the flexibility to external signals without significantly reducing the robustness to the random fluctuations. Furthermore, we found that the enhanced performance in working memory is supported by larger energy consumption. Our approach can facilitate the design of new network structure for cognitive functions with the optimal balance between performance and cost. Our work also provides a new paradigm for exploring the underlying mechanisms of many cognitive functions based on non-equilibrium physics.

Parietal Cortex Is Required for the Integration of Acoustic Evidence

Sensory-driven decisions are formed by accumulating information over time. Although parietal cortex activity is thought to represent accumulated evidence for sensory-based decisions, recent perturbation studies in rodents and non-human primates have challenged the hypothesis that these representations actually influence behavior. Here, we asked whether the parietal cortex integrates acoustic features from auditory cortical inputs during a perceptual decision-making task. If so, we predicted that selective inactivation of this projection should impair subjects' ability to accumulate sensory evidence. We trained gerbils to perform an auditory discrimination task and obtained measures of integration time as a readout of evidence accumulation capability. Minimum integration time was calculated behaviorally as the shortest stimulus duration for which subjects could discriminate the acoustic signals. Direct pharmacological inactivation of parietal cortex increased minimum integration times, suggesting its role in the behavior. To determine the specific impact of sensory evidence, we chemogenetically inactivated the excitatory projections from auditory cortex to parietal cortex and found this was sufficient to increase minimum behavioral integration times. Our signal-detection-theory-based model accurately replicated behavioral outcomes and indicated that the deficits in task performance were plausibly explained by elevated sensory noise. Together, our findings provide causal evidence that parietal cortex plays a role in the network that integrates auditory features for perceptual judgments.

A flexible framework for simulating and fitting generalized drift-diffusion models

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

Centroparietal activity mirrors the decision variable when tracking biased and time-varying sensory evidence

Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively. We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.

Weaker neural suppression in autism

Abnormal sensory processing has been observed in autism, including superior visual motion discrimination, but the neural basis for these sensory changes remains unknown. Leveraging well-characterized suppressive neural circuits in the visual system, we used behavioral and fMRI tasks to demonstrate a significant reduction in neural suppression in young adults with autism spectrum disorder (ASD) compared to neurotypical controls. MR spectroscopy measurements revealed no group differences in neurotransmitter signals. We show how a computational model that incorporates divisive normalization, as well as narrower top-down gain (that could result, for example, from a narrower window of attention), can explain our observations and divergent previous findings. Thus, weaker neural suppression is reflected in visual task performance and fMRI measures in ASD, and may be attributable to differences in top-down processing.

Sensory hypersensitivity is common in autism spectrum disorders. Using functional MRI, psychophysics, and computational modeling, Schallmo et al. show that differences in visual motion perception in ASD are accompanied by weaker neural suppression in visual cortex.

Decomposing loss aversion from gaze allocation and pupil dilation

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals.

Mechanisms of competitive selection: A canonical neural circuit framework

Competitive selection, the transformation of multiple competing sensory inputs and internal states into a unitary choice, is a fundamental component of animal behavior. Selection behaviors have been studied under several intersecting umbrellas including decision-making, action selection, perceptual categorization, and attentional selection. Neural correlates of these behaviors and computational models have been investigated extensively. However, specific, identifiable neural circuit mechanisms underlying the implementation of selection remain elusive. Here, we employ a first principles approach to map competitive selection explicitly onto neural circuit elements. We decompose selection into six computational primitives, identify demands that their execution places on neural circuit design, and propose a canonical neural circuit framework. The resulting framework has several links to neural literature, indicating its biological feasibility, and has several common elements with prominent computational models, suggesting its generality. We propose that this framework can help catalyze experimental discovery of the neural circuit underpinnings of competitive selection.

Constructing and Forgetting Temporal Context in the Human Cerebral Cortex

How does information from seconds earlier affect neocortical responses to new input? We found that when two groups of participants heard the same sentence in a narrative, preceded by different contexts, the neural responses of each group were initially different but gradually fell into alignment. We observed a hierarchical gradient: sensory cortices aligned most quickly, followed by mid-level regions, while some higher-order cortical regions took more than 10 seconds to align. What computations explain this hierarchical temporal organization? Linear integration models predict that regions that are slower to integrate new information should also be slower to forget old information. However, we found that higher-order regions could rapidly forget prior context. The data from the cortical hierarchy were instead captured by a model in which each region maintains a temporal context representation that is nonlinearly integrated with input at each moment, and this integration is gated by local prediction error.

Differentiating between integration and non-integration strategies in perceptual decision making

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker’s strategy and support solutions to achieve this goal.

Evidence accumulation during perceptual decisions in humans varies as a function of dorsal frontoparietal organization

Animal neurophysiological studies have identified neural signals within dorsal frontoparietal areas that trace a perceptual decision by accumulating sensory evidence over time and trigger action upon reaching a threshold. Although analogous accumulation-to-bound signals are identifiable on extracranial human electroencephalography, their cortical origins remain unknown. Here neural metrics of human evidence accumulation, predictive of the speed of perceptual reports, were isolated using electroencephalography and related to dorsal frontoparietal network (dFPN) connectivity using diffusion and resting-state functional magnetic resonance imaging. The build-up rate of evidence accumulation mediated the relationship between the white matter macrostructure of dFPN pathways and the efficiency of perceptual reports. This association between steeper build-up rates of evidence accumulation and the dFPN was recapitulated in the resting-state networks. Stronger connectivity between dFPN regions is thus associated with faster evidence accumulation and speeded perceptual decisions. Our findings identify an integrated network for perceptual decisions that may be targeted for neurorehabilitation in cognitive disorders.

Reaching decisions during ongoing movements

Neurophysiological studies suggest that when decisions are made between concrete actions, the selection process involves a competition between potential action representations in the same sensorimotor structures involved in executing those actions. However, it is unclear how such models can explain situations, often encountered during natural behavior, in which we make decisions while were are already engaged in performing an action. Does the process of deliberation characterized in classical studies of decision-making proceed the same way when subjects are deciding while already acting? In the present study, human subjects continuously tracked a target moving in the horizontal plane and were occasionally presented with a new target to which they could freely choose to switch at any time, whereupon it became the new tracked target. We found that the probability of choosing to switch increased with decreasing distance to the new target and increasing size of the new target relative to the tracked target, as well as when the direction to the new target was aligned (either toward or opposite) to the current tracking direction. However, contrary to our expectations, subjects did not choose targets that minimized the energetic costs of execution, as calculated by a biomechanical model of the arm. When the constraints of continuous tracking were removed in variants of the task involving point-to-point movements, the expected preference for lower cost choices was seen. These results are discussed in the context of current theories of nested feedback control, internal models of forward dynamics, and high-dimensional neural spaces.NEW & NOTEWORTHY Current theories of decision-making primarily address how subjects make decisions before executing selected actions. However, in our daily lives we often make decisions while already performing some action (e.g., while playing a sport or navigating through a crowd). To gain insight into how current theories can be extended to such "decide-while-acting" scenarios, we examined human decisions during continuous manual tracking and found some intriguing departures from how decisions are made in classical "decide-then-act" paradigms.

A simple linear readout of MT supports motion direction-discrimination performance

Motion discrimination is a well-established model system for investigating how sensory signals are used to form perceptual decisions. Classic studies relating single-neuron activity in the middle temporal area (MT) to perceptual decisions have suggested that a simple linear readout could underlie motion discrimination behavior. A theoretically optimal readout, in contrast, would take into account the correlations between neurons and the sensitivity of individual neurons at each time point. However, it remains unknown how sophisticated the readout needs to be to support actual motion-discrimination behavior or to approach optimal performance. In this study, we evaluated the performance of various neurally plausible decoders, trained to discriminate motion direction from small ensembles of simultaneously recorded MT neurons. We found that decoding the stimulus without knowledge of the interneuronal correlations was sufficient to match an optimal (correlation aware) decoder. Additionally, a decoder could match the psychophysical performance of the animals with flat integration of up to half the stimulus and inherited temporal dynamics from the time-varying MT responses. These results demonstrate that simple, linear decoders operating on small ensembles of neurons can match both psychophysical performance and optimal sensitivity without taking correlations into account and that such simple read-out mechanisms can exhibit complex temporal properties inherited from the sensory dynamics themselves.NEW & NOTEWORTHY Motion perception depends on the ability to decode the activity of neurons in the middle temporal area. Theoretically optimal decoding requires knowledge of the sensitivity of neurons and interneuronal correlations. We report that a simple correlation-blind decoder performs as well as the optimal decoder for coarse motion discrimination. Additionally, the decoder could match the psychophysical performance with moderate temporal integration and dynamics inherited from sensory responses.

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

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.

Performance of normative and approximate evidence accumulation on the dynamic clicks task

The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near-ideal observers in a task of this type. We ask when and how performance depends on task parameters and design, and, in turn, what observer performance tells us about their decision-making process. In the dynamic clicks task subjects hear two streams (left and right) of Poisson clicks with different rates. Subjects are rewarded when they correctly identify the side with the higher rate, as this side switches unpredictably. We show that a reduced set of task parameters defines regions in parameter space in which optimal, but not near-optimal observers, maintain constant response accuracy. We also show that for a range of task parameters an approximate normative model must be finely tuned to reach near-optimal performance, illustrating a potential way to distinguish between normative models and their approximations. In addition, we show that using the negative log-likelihood and the 0/1-loss functions to fit these types of models is not equivalent: the 0/1-loss leads to a bias in parameter recovery that increases with sensory noise. These findings suggest ways to tease apart models that are hard to distinguish when tuned exactly, and point to general pitfalls in experimental design, model fitting, and interpretation of the resulting data.

Foraging as an evidence accumulation process

The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.

Foraging is a ubiquitous animal behavior, performed by organisms as different as worms, birds, rats, and humans. Although the behavior has been extensively studied, it is not known how the brain processes information obtained during foraging activity to make subsequent foraging decisions. We form an evidence accumulation model of foraging decisions that describes the process through which an animal gathers information and uses it to make foraging decisions. By building on studies of the neural decision mechanisms within systems neuroscience, this model connects the foraging decision process with ecological models of patch-leaving decisions, such as the marginal value theorem. The model suggests the existence of different foraging strategies, which optimize for different environmental conditions and their potential implementation by neural decision making circuits. The model also shows how state-dependence, such as satiation level, can affect evidence accumulation to lead to sub-optimal foraging decisions. Our model provides a framework for future experimental studies which seek to elucidate how neural decision making mechanisms have been shaped by evolutionary forces in an animal’s surrounding environment.

Sequence-dependent sensitivity explains the accuracy of decisions when cues are separated with a gap

Most decisions require information gathering from a stimulus presented with different gaps. However, the neural mechanism underlying this integration is ambiguous. Recently, it has been claimed that humans can optimally integrate the information of two discrete pulses independent of the temporal gap between them. Interestingly, subjects' performance on such a task, with two discrete pulses, is superior to what a perfect accumulator can predict. Although numerous neuronal and descriptive models have been proposed to explain the mechanism of perceptual decision-making, none can explain human behavior on this two-pulse task. In order to investigate the mechanism of decision-making on the noted tasks, a set of modified drift-diffusion models based on different hypotheses were used. Model comparisons clarified that, in a sequence of information arriving at different times, the accumulated information of earlier evidence affects the process of information accumulation of later evidence. It was shown that the rate of information extraction depends on whether the pulse is the first or the second one. Moreover, our findings suggest that a drift diffusion model with a dynamic drift rate can also explain the stronger effect of the second pulse on decisions as shown by Kiani et al. (Journal of Neuroscience, 33 (42), 16483-16489, 2013).

The validity and consistency of continuous joystick response in perceptual decision-making

A computer joystick is an efficient and cost-effective response device for recording continuous movements in psychological experiments. Movement trajectories and other measures from continuous responses have expanded the insights gained from discrete responses (e.g., button presses) by providing unique information about how cognitive processes unfold over time. However, few studies have evaluated the validity of joystick responses with reference to conventional key presses, and how response modality can affect cognitive processes. Here we systematically compared human participants' behavioral performance of perceptual decision-making when they responded with either joystick movements or key presses in a four-alternative motion discrimination task. We found evidence that the response modality did not affect raw behavioral measures, including decision accuracy and mean response time, at the group level. Furthermore, to compare the underlying decision processes between the two response modalities, we fitted a drift-diffusion model of decision-making to individual participants' behavioral data. Bayesian analyses of the model parameters showed no evidence that switching from key presses to continuous joystick movements modulated the decision-making process. These results supported continuous joystick actions as a valid apparatus for continuous movements, although we highlight the need for caution when conducting experiments with continuous movement responses.

Drift-diffusion models for multiple-alternative forced-choice decision making

The canonical computational model for the cognitive process underlying two-alternative forced-choice decision making is the so-called drift-diffusion model (DDM). In this model, a decision variable keeps track of the integrated difference in sensory evidence for two competing alternatives. Here I extend the notion of a drift-diffusion process to multiple alternatives. The competition between n alternatives takes place in a linear subspace of [Formula: see text] dimensions; that is, there are [Formula: see text] decision variables, which are coupled through correlated noise sources. I derive the multiple-alternative DDM starting from a system of coupled, linear firing rate equations. I also show that a Bayesian sequential probability ratio test for multiple alternatives is, in fact, equivalent to these same linear DDMs, but with time-varying thresholds. If the original neuronal system is nonlinear, one can once again derive a model describing a lower-dimensional diffusion process. The dynamics of the nonlinear DDM can be recast as the motion of a particle on a potential, the general form of which is given analytically for an arbitrary number of alternatives.

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.

Confidence Representation of Perceptual Decision by EEG and Eye Data in a Random Dot Motion Task

The Confidence of a decision could be considered as the internal estimate of decision accuracy. This variable has been studied extensively by different types of recording data such as behavioral, electroencephalography (EEG), eye and electrophysiology data. Although the value of the reported confidence is considered as one of the most important parameters in decision making, the confidence reporting phase might be considered as a restrictive element in investigating the decision process. Thus, decision confidence should be extracted by means of other provided types of information. Here, we proposed eight confidence related properties in EEG and eye data which are significantly descriptive of the defined confidence levels in a random dot motion (RDM) task. As a matter of fact, our proposed EEG and eye data properties are capable of recognizing more than nine distinct levels of confidence. Among our proposed features, the latency of the pupil maximum diameter through the stimulus presentation was established to be the most associated one to the confidence levels. Through the time-dependent analysis of these features, we recognized the time interval of 500-600 ms after the stimulus onset as an important time in correlating features to the confidence levels.

Enhanced Theta-Band Coherence Between Midfrontal and Posterior Parietal Areas Reflects Post-feedback Adjustments in the State of Outcome Uncertainty

Medial frontal cortex is currently viewed as the main hub of the performance monitoring system; upon detection of an error committed, it establishes functional connections with brain regions involved in task performance, thus leading to neural adjustments in them. Previous research has identified targets of such adjustments in the dorsolateral prefrontal cortex, posterior cortical regions, motor cortical areas, and subthalamic nucleus. Yet most of such studies involved visual tasks with relatively moderate cognitive load and strong dependence on motor inhibition - thus highlighting sensory, executive and motor effects while underestimating sensorimotor transformation and related aspects of decision making. Currently there is ample evidence that posterior parietal cortical areas are involved in task-specific neural processes of decision making (including evidence accumulation, sensorimotor transformation, attention, etc.) - yet, to our knowledge, no EEG studies have demonstrated post-error increase in functional connectivity in the theta-band between midfrontal and posterior parietal areas during performance on non-visual tasks. In the present study, we recorded EEG while subjects were performing an auditory version of the cognitively demanding attentional condensation task; this task involves rather non-straightforward stimulus-to-response mapping rules, thus, creating increased load on sensorimotor transformation. We observed strong pre-response alpha-band suppression in the left parietal area, which presumably reflected involvement of the posterior parietal cortex in task-specific decision-making processes. Negative feedback was followed by increased midfrontal theta-band power and increased functional coupling in the theta band between midfrontal and left parietal regions. This could be interpreted as activation of the performance monitoring system and top-down influence of this system on the posterior parietal regions involved in decision making, respectively. This inter-site coupling related to negative feedback was stronger for subjects who tended to commit errors with slower response times. Generally, current findings support the idea that slower errors are related to the state of outcome uncertainty caused by failures of task-specific processes, associated with posterior parietal regions.

Combined mechanisms of neural firing rate homeostasis

Spikes in the membrane potential of neurons comprise the currency of information processing in the brain. The ability of neurons to convert any information present across their multiple inputs into a significant modification to the pattern of their emitted spikes depends on the rate at which they emit spikes. If the mean rate is near the neuron's maximum, or if the rate is near zero, then changes in the inputs have minimal impact on the neuron's firing rate. Therefore, a neuron needs to control its mean rate. Protocols that either dramatically increase or decrease a neuron's firing rate lead to multiple compensatory changes that return the neuron's mean rate toward its prior value. In this primer, first as a summary of our previous work (Cannon and Miller in J Neurophysiol 116(5):2004-2022, 2016; Cannon and Miller in J Math Neurosci 7(1):1, 2017), we describe the advantages and disadvantages of having more than one such control mechanism responding to the neuron's firing rate. We suggest how problems of two, coexisting, potentially competing mechanisms can be overcome. Key requirements are: (1) the control be of a distribution of values, which the controlled variable achieves over a fast timescale compared to the timescale of the control system; (2) at least one of the control mechanisms be nonlinear; and (3) the two control systems are satisfied by a stable distribution or range of values that can be achieved by the variable. We show examples of functional control systems, including the previously studied integral feedback controller and new simulations of a "bang-bang" controller, that allow for compensation when inputs to the system change. Finally, we present new results describing how the underlying signal processing pathways would produce mechanisms of dual control, as opposed to a single mechanism with two outputs, and compare the responses of these systems to changes of input statistics.

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment “input” to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This “myopic” controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this control’s utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic control’s utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

Stimulating a neural system and observing its effect through simultaneous observation offers the promise to better understand how neural systems perform computations, as well as for the treatment of neurological disorders. A powerful perspective for understanding a neural system’s behavior undergoing stimulation is to conceptualize them as dynamical systems, which considers the global effect that stimulation has on the brain, rather than only assessing what impact it has on the recorded signal from the brain. With this more comprehensive perspective comes a central challenge of determining what requirements need to be satisfied to harness neural observations and then stimulate to make one dynamical system function as another one entirely. This could lead to applications such as neural stimulators that make a diseased brain behave like its healthy counterpart, or to make a neural system previously capable of only hasty decision making to wait and accumulate more evidence for a more informed decision. In this work we explore the implications of this new perspective on neural stimulation and derive a simple prescription for using neural observations to inform stimulation protocol that makes one neural system behave like another one.

Prospective decision making for randomly moving visual stimuli

Humans persist in their attempts to predict the future in spite of the fact that natural events often involve a fundamental element of uncertainty. The present study explored computational mechanisms underlying biases in prospective decision making by using a simple psychophysical task. Observers viewed a randomly moving Gabor target for T sec and anticipated its future position ΔT sec following stimulus offset. Applying reverse correlation analysis, we found that observer decisions focused heavily on the last part of target velocity and especially on velocity information following the last several direction reversals. If target random motion explicitly contained an additional linear trend, observers tended to utilize information of the linear trend as well. These behavioral data are well explained by a leaky-integrator model of perceptual decision making based on evidence accumulation with adaptive gain control. The results raise the possibility that prospective decision making toward future events follows principles similar to those involved in retrospective decision making toward past events.

Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus

The distributed nature of information processing in the brain creates a complex variety of decision making behavior. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competitive decision making in the superior colliculus (SC) in order to investigate how independent neural signals may converge to interact and guide saccade selection, and to test if systematic variations can better replicate the variability in responses that are part of normal human behavior. The model was tasked with performing pro- and anti-saccades in order to replicate specific attributes of healthy human saccade behavior. Participants (ages 18-39) were instructed to either look toward (pro-saccade, well-practiced automated response) or away from (anti-saccade, combination of inhibitory and voluntary responses) a peripheral visual stimulus. They generated express and regular latency saccades in the pro-saccade task. In the anti-saccade task, correct reaction times were longer and participants occasionally looked at the stimulus (direction error) at either express or regular latencies. To gain a better understanding of the underlying neural processes that lead to saccadic action selection and response inhibition, we implemented 8 inputs inspired by systems neuroscience. These inputs reflected known sensory, automated, voluntary, and inhibitory components of cortical and basal ganglia activity that coalesces in the intermediate layers of the SC (SCi). The model produced bimodal reaction time distributions, where express and regular latency saccades had distinct modes, for both correct pro-saccades and direction errors in the anti-saccade task. Importantly, express and regular latency direction errors resulted from interactions of different inputs in the model. Express latency direction errors were due to a lack of pre-emptive fixation and inhibitory activity, which aloud sensory and automated inputs to initiate a stimulus-driven saccade. Regular latency errors occurred when the automated motor signals were stronger than the voluntary motor signals. While previous models have emulated fewer aspects of these behavioral findings, the focus of the simulations here is on the interaction of a wide variety of physiologically-based information integration producing a richer set of natural behavioral variability.

On the Neural and Mechanistic Bases of Self-Control

Intertemporal choice requires a dynamic interaction between valuation and deliberation processes. While evidence identifying candidate brain areas for each of these processes is well established, the precise mechanistic role carried out by each brain region is still debated. In this article, we present a computational model that clarifies the unique contribution of frontoparietal cortex regions to intertemporal decision making. The model we develop samples reward and delay information stochastically on a moment-by-moment basis. As preference for the choice alternatives evolves, dynamic inhibitory processes are executed by way of asymmetric lateral inhibition. We find that it is these lateral inhibition processes that best explain the contribution of frontoparietal regions to intertemporal decision making exhibited in our data.

Glutamatergic facilitation of neural responses in MT enhances motion perception in humans

There is large individual variability in human neural responses and perceptual abilities. The factors that give rise to these individual differences, however, remain largely unknown. To examine these factors, we measured fMRI responses to moving gratings in the motion-selective region MT, and perceptual duration thresholds for motion direction discrimination. Further, we acquired MR spectroscopy data, which allowed us to quantify an index of neurotransmitter levels in the region of area MT. These three measurements were conducted in separate experimental sessions within the same group of male and female subjects. We show that stronger Glx (glutamate + glutamine) signals in the MT region are associated with both higher fMRI responses and superior psychophysical task performance. Our results suggest that greater baseline levels of glutamate within MT facilitate motion perception by increasing neural responses in this region.

Quantifying Motor Task Performance by Bounded Rational Decision Theory

Expected utility models are often used as a normative baseline for human performance in motor tasks. However, this baseline ignores computational costs that are incurred when searching for the optimal strategy. In contrast, bounded rational decision-theory provides a normative baseline that takes computational effort into account, as it describes optimal behavior of an agent with limited information-processing capacity to change a prior motor strategy (before information-processing) into a posterior strategy (after information-processing). Here, we devised a pointing task where subjects had restricted reaction and movement time. In particular, we manipulated the permissible reaction time as a proxy for the amount of computation allowed for planning the movements. Moreover, we tested three different distributions over the target locations to induce different prior strategies that would influence the amount of required information-processing. We found that movement endpoint precision generally decreases with limited planning time and that non-uniform prior probabilities allow for more precise movements toward high-probability targets. Considering these constraints in a bounded rational decision model, we found that subjects were generally close to bounded optimal. We conclude that bounded rational decision theory may be a promising normative framework to analyze human sensorimotor performance.

A mechanistic account of bodily resonance and implicit bias

Implicit social biases play a critical role in shaping our attitudes towards other people. Such biases are thought to arise, in part, from a comparison between features of one's own self-image and those of another agent, a process known as 'bodily resonance'. Recent data have demonstrated that implicit bias can be remarkably plastic, being modulated by brief immersive virtual reality experiences that place participants in a virtual body with features of an out-group member. Here, we provide a mechanistic account of bodily resonance and implicit bias in terms of a putative self-image network that encodes associations between different features of an agent. When subsequently perceiving another agent, the output of this self-image network is proportional to the overlap between their respective features, providing an index of bodily resonance. By combining the self-image network with a drift diffusion model of decision making, we simulate performance on the implicit association test (IAT) and show that the model captures the ubiquitous implicit bias towards in-group members. We subsequently demonstrate that this implicit bias can be modulated by a simulated illusory body ownership experience, consistent with empirical data; and that the magnitude and plasticity of implicit bias correlates with self-esteem. Hence, we provide a simple mechanistic account of bodily resonance and implicit bias which could contribute to the development of interventions for reducing the negative evaluation of social out-groups.

Decision Making through Integration of Sensory Evidence at Prolonged Timescales

When multiple pieces of information bear on a decision, the best approach is to combine the evidence provided by each one. Evidence integration models formalize the computations underlying this process [1-3], explain human perceptual discrimination behavior [4-9], and correspond to neuronal responses elicited by discrimination tasks [10-14]. These findings suggest that evidence integration is key to understanding the neural basis of decision making [15-18]. But while evidence integration has most often been studied with simple tasks that limit deliberation to relatively brief periods, many natural decisions unfold over much longer durations. Neural network models imply acute limitations on the timescale of evidence integration [19-23], and it is currently unknown whether existing computational insights can generalize beyond rapid judgments. Here, we introduce a new psychophysical task and report model-based analyses of human behavior that demonstrate evidence integration at long timescales. Our task requires probabilistic inference using brief samples of visual evidence that are separated in time by long and unpredictable gaps. We show through several quantitative assays how decision making can approximate a normative integration process that extends over tens of seconds without accruing significant memory leak or noise. These results support the generalization of evidence integration models to a broader class of behaviors while posing new challenges for models of how these computations are implemented in biological networks.

Strategic and Dynamic Temporal Weighting for Perceptual Decisions in Humans and Macaques

Perceptual decision-making is often modeled as the accumulation of sensory evidence over time. Recent studies using psychophysical reverse correlation have shown that even though the sensory evidence is stationary over time, subjects may exhibit a time-varying weighting strategy, weighting some stimulus epochs more heavily than others. While previous work has explained time-varying weighting as a consequence of static decision mechanisms (e.g., decision bound or leak), here we show that time-varying weighting can reflect strategic adaptation to stimulus statistics, and thus can readily take a number of forms. We characterized the temporal weighting strategies of humans and macaques performing a motion discrimination task in which the amount of information carried by the motion stimulus was manipulated over time. Both species could adapt their temporal weighting strategy to match the time-varying statistics of the sensory stimulus. When early stimulus epochs had higher mean motion strength than late, subjects adopted a pronounced early weighting strategy, where early information was weighted more heavily in guiding perceptual decisions. When the mean motion strength was greater in later stimulus epochs, in contrast, subjects shifted to a marked late weighting strategy. These results demonstrate that perceptual decisions involve a temporally flexible weighting process in both humans and monkeys, and introduce a paradigm with which to manipulate sensory weighting in decision-making tasks.

Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice

The ability to manipulate neural activity with precision is an asset in uncovering neural circuits for decision-making. Diverse tools for manipulating neurons are available for mice, but their feasibility remains unclear, especially when decisions require accumulating visual evidence. For example, whether mice' decisions reflect leaky accumulation is unknown, as are the relevant/irrelevant factors that influence decisions. Further, causal circuits for visual evidence accumulation are poorly understood. To address this, we measured decisions in mice judging the fluctuating rate of a flash sequence. An initial analysis (>500,000 trials, 29 male and female mice) demonstrated that information throughout the 1000 ms trial influenced choice, with early information most influential. This suggests that information persists in neural circuits for ∼1000 ms with minimal accumulation leak. Next, in a subset of animals, we probed strategy more extensively and found that although animals were influenced by stimulus rate, they were unable to entirely suppress the influence of stimulus brightness. Finally, we identified anteromedial (AM) visual area via retinotopic mapping and optogenetically inhibited it using JAWS. Light activation biased choices in both injected and uninjected animals, demonstrating that light alone influences behavior. By varying stimulus-response contingency while holding stimulated hemisphere constant, we surmounted this obstacle to demonstrate that AM suppression biases decisions. By leveraging a large dataset to quantitatively characterize decision-making behavior, we establish mice as suitable for neural circuit manipulation studies. Further, by demonstrating that mice accumulate visual evidence, we demonstrate that this strategy for reducing uncertainty in decision-making is used by animals with diverse visual systems.SIGNIFICANCE STATEMENT To connect behaviors to their underlying neural mechanism, a deep understanding of behavioral strategy is needed. This understanding is incomplete for mice. To surmount this, we measured the outcome of >500,000 decisions made by 29 mice trained to judge visual stimuli and performed behavioral/optogenetic manipulations in smaller subsets. Our analyses offer new insights into mice' decision-making strategies and compares them with those of other species. We then disrupted neural activity in a candidate neural structure and examined the effect on decisions. Our findings establish mice as suitable for visual accumulation of evidence decisions. Further, the results highlight similarities in decision-making strategies across very different species.

Lawful tracking of visual motion in humans, macaques, and marmosets in a naturalistic, continuous, and untrained behavioral context

We characterize spatiotemporal integration of naturalistic, continuous visual motion of three primate species (humans, macaques, and marmosets). All three species volitionally, but naturally, track the center of expansion of a dynamic optic flow field. Detailed analysis of this flow-tracking behavior reveals lawful and repeatable dependencies of the behavior on nuances in the stimulus, revealing that even unconstrained and continuous behavior can exhibit the sort of precise dependencies typically studied only in artificial and constrained tasks.

Much study of the visual system has focused on how humans and monkeys integrate moving stimuli over space and time. Such assessments of spatiotemporal integration provide fundamental grounding for the interpretation of neurophysiological data, as well as how the resulting neural signals support perceptual decisions and behavior. However, the insights supported by classical characterizations of integration performed in humans and rhesus monkeys are potentially limited with respect to both generality and detail: Standard tasks require extensive amounts of training, involve abstract stimulus–response mappings, and depend on combining data across many trials and/or sessions. It is thus of concern that the integration observed in classical tasks involves the recruitment of brain circuits that might not normally subsume natural behaviors, and that quantitative analyses have limited power for characterizing single-trial or single-session processes. Here we bridge these gaps by showing that three primate species (humans, macaques, and marmosets) track the focus of expansion of an optic flow field continuously and without substantial training. This flow-tracking behavior was volitional and reflected substantial temporal integration. Most strikingly, gaze patterns exhibited lawful and nuanced dependencies on random perturbations in the stimulus, such that repetitions of identical flow movies elicited remarkably similar eye movements over long and continuous time periods. These results demonstrate the generality of spatiotemporal integration in natural vision, and offer a means for studying integration outside of artificial tasks while maintaining lawful and highly reliable behavior.

Causal contribution and dynamical encoding in the striatum during evidence accumulation

A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicate that cortical regions that are prominently associated with accumulating evidence, such as the posterior parietal cortex and the frontal orienting fields, may not be directly involved in this computation. Which, then, are the regions involved? Regions that are directly involved in evidence accumulation should directly influence the accumulation-based decision-making behavior, have a graded neural encoding of accumulated evidence and contribute throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy the three criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation.

Single Trial Plasticity in Evidence Accumulation Underlies Rapid Recalibration to Asynchronous Audiovisual Speech

Asynchronous arrival of audiovisual information at the peripheral sensory organs is a ubiquitous property of signals in the natural environment due to differences in the propagation time of light and sound. As these cues are constantly changing their distance from the observer, rapid adaptation to asynchronies is crucial for their appropriate integration. We investigated the neural basis of rapid recalibration to asynchronous audiovisual speech in humans using a combination of psychophysics, drift diffusion modeling, and electroencephalography (EEG). Consistent with previous reports, we found that perception of audiovisual temporal synchrony depends on the temporal ordering of the previous trial. Drift diffusion modelling indicated that this recalibration effect is well accounted for by changes in the rate of evidence accumulation (i.e. drift rate). Neural responses as indexed via evoked potentials were similarly found to vary based on the temporal ordering of the previous trial. Within and across subject correlations indicated that the observed changes in drift rate and the modulation of evoked potential magnitude were related. These results indicate that the rate and direction of evidence accumulation are affected by immediate sensory history and that these changes contribute to single trial recalibration to audiovisual temporal asynchrony.