Decision Making through Integration of Sensory Evidence at Prolonged Timescales

Temporal integration as an adaptive process in visual perception, attention, and working memory

I propose that temporal integration is ubiquitous in visual perception, because it serves an adaptive role. To support this idea, I draw together evidence from historically separated research fields that target different timescales. At one extreme, this concerns the detection and discrimination of successive stimuli within intervals of less than a quarter of a second. At an intermediate level, associated with attentional episodes, intervals between half a second up to a few seconds are considered. Finally, at the other extreme, this involves high-level, conceptual events across intervals of multiple seconds or even minutes. Across such varying intervals, the nature of temporal integration and the resultant perceptual events are clearly different. I nevertheless propose that temporal integration should be understood as a continuous process that serves a common adaptive goal: To maximize the amount of useful information, at minimal costs, tailored to the observer's current needs and circumstances. Emerging from this viewpoint are several research directions that might be pursued on the topic of temporal integration, and on its consequences for perception and memory.

Flexibility in choosing decision policies in gathering discrete evidence over time

The brain can remarkably adapt its decision-making process to suit the dynamic environment and diverse aims and demands. The brain's flexibility can be classified into three categories: flexibility in choosing solutions, decision policies, and actions. We employ two experiments to explore flexibility in decision policy: a visual object categorization task and an auditory object categorization task. Both tasks required participants to accumulate discrete evidence over time, with the only difference being the sensory state of the stimuli. We aim to investigate how the brain demonstrates flexibility in selecting decision policies in different sensory contexts when the solution and action remain the same. Our results indicate that the decision policy of the brain in integrating information is independent of inter-pulse interval across these two tasks. However, the decision policy based on how the brain ranks the first and second pulse of evidence changes flexibly. We show that the sequence of pulses does not affect the choice accuracy in the auditory mode. However, in the visual mode, the first pulse had the larger leverage on decisions. Our research underscores the importance of incorporating diverse contexts to improve our understanding of the brain's flexibility in real-world decision-making.

Limitation of switching sensory information flow in flexible perceptual decision making

Humans can flexibly change rules to categorize sensory stimuli, but their performance degrades immediately after a task switch. This switch cost is believed to reflect a limitation in cognitive control, although the bottlenecks remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of using sensory inputs to form a decision after a rule change. Participants classified face stimuli based on one of two rules, switching every few trials. Psychophysical reverse correlation and computational modeling reveal a reduction in sensory weighting, which recovers within a few hundred milliseconds after stimulus presentation. This reduction depends on the sensory features being switched, suggesting a constraint in routing the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on a context cue alone, but require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.

Better than expected performance effect depends on the spatial location of visual stimulus

The process of perceptual decision-making in the real world involves the aggregation of pieces of evidence into a final choice. Visual evidence is usually presented in different pieces, distributed across time and space. We wondered whether adding variation in the location of the received information would lead to differences in how subjects integrated visual information. Seven participants viewed two pulses of random dot motion stimulus, separated by time gaps and presented at different locations within the visual field. Our findings suggest that subjects accumulate discontinuous information (over space or time) differently than when it is presented continuously, in the same location or with no gaps between them. These findings indicate that the discontinuity of evidence impacts the process of evidence integration in a manner more nuanced than that presumed by the theory positing perfect integration of evidence.

Impulsive Choices Emerge When the Anterior Cingulate Cortex Fails to Encode Deliberative Strategies

Impulsive individuals excessively discount the value of delayed rewards, and this is thought to reflect deficits in brain regions critical for impulse control such as the anterior cingulate cortex (ACC). Delay discounting (DD) is an established measure of cognitive impulsivity, referring to the devaluation of rewards delayed in time. This study used male Wistar rats performing a DD task to test the hypothesis that neural activity states in ACC ensembles encode strategies that guide decision-making. Optogenetic silencing of ACC neurons exclusively increased impulsive choices at the 8 s delay by increasing the number of consecutive low-value, immediate choices. In contrast to shorter delays where animals preferred the delay option, no immediate or delay preference was detected at 8 s. These data suggest that ACC was critical for decisions requiring more deliberation between choice options. To address the role of ACC in this process, large-scale multiple single-unit recordings were performed and revealed that 4 and 8 s delays were associated with procedural versus deliberative neural encoding mechanisms, respectively. The 4 and 8 s delay differed in encoding of strategy corresponding to immediate and delay run termination. Specifically, neural ensemble states at 4 s were relatively stable throughout the choice but exhibited temporal evolution in state space during the choice epoch that resembled ramping during the 8 s delay. Collectively, these findings indicate that ensemble states in ACC facilitate strategies that guide decision-making, and impulsivity increases with disruptions of deliberative encoding mechanisms.

MULTISCALE MOTION AND DEFORMATION OF BUMPS IN STOCHASTIC NEURAL FIELDS WITH DYNAMIC CONNECTIVITY

The distinct timescales of synaptic plasticity and neural activity dynamics play an important role in the brain's learning and memory systems. Activity-dependent plasticity reshapes neural circuit architecture, determining spontaneous and stimulus-encoding spatiotemporal patterns of neural activity. Neural activity bumps maintain short term memories of continuous parameter values, emerging in spatially organized models with short-range excitation and long-range inhibition. Previously, we demonstrated nonlinear Langevin equations derived using an interface method which accurately describe the dynamics of bumps in continuum neural fields with separate excitatory/inhibitory populations. Here we extend this analysis to incorporate effects of short term plasticity that dynamically modifies connectivity described by an integral kernel. Linear stability analysis adapted to these piecewise smooth models with Heaviside firing rates further indicates how plasticity shapes the bumps' local dynamics. Facilitation (depression), which strengthens (weakens) synaptic connectivity originating from active neurons, tends to increase (decrease) stability of bumps when acting on excitatory synapses. The relationship is inverted when plasticity acts on inhibitory synapses. Multiscale approximations of the stochastic dynamics of bumps perturbed by weak noise reveal that the plasticity variables evolve to slowly diffusing and blurred versions of their stationary profiles. Nonlinear Langevin equations associated with bump positions or interfaces coupled to slowly evolving projections of plasticity variables accurately describe how these smoothed synaptic efficacy profiles can tether or repel wandering bumps.

Negative Influence of the Hunger State on Rule-observance Behavior in Mice

Developing social strategies to share limited resources equally and maximize the long-term benefits of conflict resolution is critical for appropriate social interactions. During social interactions, social decision-making depends not only on the external environment, but also on internal factors, such as hunger, thirst, or fatigue. In particular, hunger, which is related to food as a physical need, plays a dominant role in social decision-making. However, the consequences of food deprivation on social decision-making are not well understood. We have previously shown that mice with rule-observance behavior are capable of resolving conflict during social decision-making by observing a well-established social strategy based on reward zone allocation. Here, we developed a rule-observance behavior paradigm wherein the hunger state is achieved by applying food restrictions on mice prior to social behavior experiments. We found that the hunger state in mice deteriorated the established social strategy by decreasing reaction time, implying an increase in impulsivity. In contrast, the hunger state did not affect reward zone allocation, indicating no effect on spatial memory. This decrease in reaction time led to a significant increase in the percentage of violations during rule observance and a significant decrease in the amount of reward (payoff equity). Our study proposes that the hunger state exerts a detrimental effect on appropriate social decision-making by decreasing reaction time, increasing violation, and decreasing payoff equity in rule-observance behavior.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Explaining Integration of Evidence Separated by Temporal Gaps with Frontoparietal Circuit Models

Perceptual decisions rely on accumulating sensory evidence over time. However, the accumulation process is complicated in real life when evidence resulted from separated cues over time. Previous studies demonstrate that participants are able to integrate information from two separated cues to improve their performance invariant to an interval between the cues. However, there is no neural model that can account for accuracy and confidence in decisions when there is a time interval in evidence. We used behavioral and EEG datasets from a visual choice task -Random dot motion- with separated evidence to investigate three candid distributed neural networks. We showed that decisions based on evidence accumulation by separated cues over time are best explained by the interplay of recurrent cortical dynamics of centro-parietal and frontal brain areas while an uncertainty-monitoring module included in the model.

More than the end: OFF response plasticity as a mnemonic signature of a sound's behavioral salience

In studying how neural populations in sensory cortex code dynamically varying stimuli to guide behavior, the role of spiking after stimuli have ended has been underappreciated. This is despite growing evidence that such activity can be tuned, experience-and context-dependent and necessary for sensory decisions that play out on a slower timescale. Here we review recent studies, focusing on the auditory modality, demonstrating that this so-called OFF activity can have a more complex temporal structure than the purely phasic firing that has often been interpreted as just marking the end of stimuli. While diverse and still incompletely understood mechanisms are likely involved in generating phasic and tonic OFF firing, more studies point to the continuing post-stimulus activity serving a short-term, stimulus-specific mnemonic function that is enhanced when the stimuli are particularly salient. We summarize these results with a conceptual model highlighting how more neurons within the auditory cortical population fire for longer duration after a sound's termination during an active behavior and can continue to do so even while passively listening to behaviorally salient stimuli. Overall, these studies increasingly suggest that tonic auditory cortical OFF activity holds an echoic memory of specific, salient sounds to guide behavioral decisions.

Multiple timescales of sensory-evidence accumulation across the dorsal cortex

Cortical areas seem to form a hierarchy of intrinsic timescales, but the relevance of this organization for cognitive behavior remains unknown. In particular, decisions requiring the gradual accrual of sensory evidence over time recruit widespread areas across this hierarchy. Here, we tested the hypothesis that this recruitment is related to the intrinsic integration timescales of these widespread areas. We trained mice to accumulate evidence over seconds while navigating in virtual reality and optogenetically silenced the activity of many cortical areas during different brief trial epochs. We found that the inactivation of all tested areas affected the evidence-accumulation computation. Specifically, we observed distinct changes in the weighting of sensory evidence occurring during and before silencing, such that frontal inactivations led to stronger deficits on long timescales than posterior cortical ones. Inactivation of a subset of frontal areas also led to moderate effects on behavioral processes beyond evidence accumulation. Moreover, large-scale cortical Ca2+ activity during task performance displayed different temporal integration windows. Our findings suggest that the intrinsic timescale hierarchy of distributed cortical areas is an important component of evidence-accumulation mechanisms.

Strategy-dependent effects of working-memory limitations on human perceptual decision-making

Deliberative decisions based on an accumulation of evidence over time depend on working memory, and working memory has limitations, but how these limitations affect deliberative decision-making is not understood. We used human psychophysics to assess the impact of working-memory limitations on the fidelity of a continuous decision variable. Participants decided the average location of multiple visual targets. This computed, continuous decision variable degraded with time and capacity in a manner that depended critically on the strategy used to form the decision variable. This dependence reflected whether the decision variable was computed either: (1) immediately upon observing the evidence, and thus stored as a single value in memory; or (2) at the time of the report, and thus stored as multiple values in memory. These results provide important constraints on how the brain computes and maintains temporally dynamic decision variables.

Working memory, the brain’s ability to temporarily store and recall information, is a critical part of decision making – but it has its limits. The brain can only store so much information, for so long. Since decisions are not often acted on immediately, information held in working memory ‘degrades’ over time. However, it is unknown whether or not this degradation of information over time affects the accuracy of later decisions.

The tactics that people use, knowingly or otherwise, to store information in working memory also remain unclear. Do people store pieces of information such as numbers, objects and particular details? Or do they tend to compute that information, make some preliminary judgement and recall their verdict later? Does the strategy chosen impact people’s decision-making?

To investigate, Schapiro et al. devised a series of experiments to test whether the limitations of working memory, and how people store information, affect the accuracy of decisions they make. First, participants were shown an array of colored discs on a screen. Then, either immediately after seeing the disks or a few seconds later, the participants were asked to recall the position of one of the disks they had seen, or the average position of all the disks. This measured how much information degraded for a decision based on multiple items, and how much for a decision based on a single item. From this, the method of information storage used to make a decision could be inferred.

Schapiro et al. found that the accuracy of people’s responses worsened over time, whether they remembered the position of each individual disk, or computed their average location before responding. The greater the delay between seeing the disks and reporting their location, the less accurate people’s responses tended to be. Similarly, the more disks a participant saw, the less accurate their response became. This suggests that however people store information, if working memory reaches capacity, decision-making suffers and that, over time, stored information decays.

Schapiro et al. also noticed that participants remembered location information in different ways depending on the task and how many disks they were shown at once. This suggests people adopt different strategies to retain information momentarily.

In summary, these findings help to explain how people process and store information to make decisions and how the limitations of working memory impact their decision-making ability. A better understanding of how people use working memory to make decisions may also shed light on situations or brain conditions where decision-making is impaired.

Premature commitment to uncertain decisions during human NMDA receptor hypofunction

Making accurate decisions based on unreliable sensory evidence requires cognitive inference. Dysfunction of n-methyl-d-aspartate (NMDA) receptors impairs the integration of noisy input in theoretical models of neural circuits, but whether and how this synaptic alteration impairs human inference and confidence during uncertain decisions remains unknown. Here we use placebo-controlled infusions of ketamine to characterize the causal effect of human NMDA receptor hypofunction on cognitive inference and its neural correlates. At the behavioral level, ketamine triggers inference errors and elevated decision uncertainty. At the neural level, ketamine is associated with imbalanced coding of evidence and premature response preparation in electroencephalographic (EEG) activity. Through computational modeling of inference and confidence, we propose that this specific pattern of behavioral and neural impairments reflects an early commitment to inaccurate decisions, which aims at resolving the abnormal uncertainty generated by NMDA receptor hypofunction.

Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multifeature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that human subjects (five males, eight females) categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.SIGNIFICANCE STATEMENT Although simple perceptual decision-making such as discrimination of random dot motion has been successfully explained as accumulation of sensory evidence, we lack rigorous experimental paradigms to study the mechanisms underlying complex perceptual decision-making such as discrimination of naturalistic faces. We develop a stochastic multifeature face categorization task as a systematic approach to quantify the properties and potential limitations of the decision-making processes during object recognition. We show that human face categorization could be modeled as a linear integration of sensory evidence over space and time. Our framework to study object recognition as a spatiotemporal integration process is broadly applicable to other object categories and bridges past studies of object recognition and perceptual decision-making.

The biochemical basis of mitochondrial dysfunction in Zellweger Spectrum Disorder

Peroxisomal biogenesis disorders (PBDs) are genetic disorders of peroxisome biogenesis and metabolism that are characterized by profound developmental and neurological phenotypes. The most severe class of PBDs-Zellweger spectrum disorder (ZSD)-is caused by mutations in peroxin genes that result in both non-functional peroxisomes and mitochondrial dysfunction. It is unclear, however, how defective peroxisomes contribute to mitochondrial impairment. In order to understand the molecular basis of this inter-organellar relationship, we investigated the fate of peroxisomal mRNAs and proteins in ZSD model systems. We found that peroxins were still expressed and a subset of them accumulated on the mitochondrial membrane, which resulted in gross mitochondrial abnormalities and impaired mitochondrial metabolic function. We showed that overexpression of ATAD1, a mitochondrial quality control factor, was sufficient to rescue several aspects of mitochondrial function in human ZSD fibroblasts. Together, these data suggest that aberrant peroxisomal protein localization is necessary and sufficient for the devastating mitochondrial morphological and metabolic phenotypes in ZSDs.

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.

Biochemical evidence accumulates across neurons to drive a network-level eruption

Neural network computations are usually assumed to emerge from patterns of fast electrical activity. Challenging this view, we show that a male fly's decision to persist in mating hinges on a biochemical computation that enables processing over minutes to hours. Each neuron in a recurrent network contains slightly different internal molecular estimates of mating progress. Protein kinase A (PKA) activity contrasts this internal measurement with input from the other neurons to represent accumulated evidence that the goal of the network has been achieved. When consensus is reached, PKA pushes the network toward a large-scale and synchronized burst of calcium influx that we call an eruption. Eruptions transform continuous deliberation within the network into an all-or-nothing output, after which the male will no longer sacrifice his life to continue mating. Here, biochemical activity, invisible to most large-scale recording techniques, is the key computational currency directing behavior and motivational state.

Discrimination of Regular and Irregular Rhythms Explained by a Time Difference Accumulation Model

Perceiving the temporal regularity in a sequence of repetitive sensory events facilitates the preparation and execution of relevant behaviors with tight temporal constraints. How we estimate temporal regularity from repeating patterns of sensory stimuli is not completely understood. We developed a decision-making task in which participants had to decide whether a train of visual, auditory, or tactile pulses, had a regular or an irregular temporal pattern. We tested the hypothesis that subjects categorize stimuli as irregular by accumulating the time differences between the predicted and observed times of sensory pulses defining a temporal rhythm. Results suggest that instead of waiting for a single large temporal deviation, participants accumulate timing-error signals and judge a pattern as irregular when the amount of evidence reaches a decision threshold. Model fits of bounded integration showed that this accumulation occurs with negligible leak of evidence. Consistent with previous findings, we show that participants perform better when evaluating the regularity of auditory pulses, as compared with visual or tactile stimuli. Our results suggest that temporal regularity is estimated by comparing expected and measured pulse onset times, and that each prediction error is accumulated towards a threshold to generate a behavioral choice.

A circuit mechanism for decision-making biases and NMDA receptor hypofunction

Decision-making biases can be features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases during evidence integration. Monkeys showed a pro-variance bias (PVB): a preference to choose options with more variable evidence. The PVB was also present in a spiking circuit model, revealing a potential neural mechanism for this behaviour. To model possible effects of NMDA receptor (NMDA-R) antagonism on this behaviour, we simulated the effects of NMDA-R hypofunction onto either excitatory or inhibitory neurons in the model. These were then tested experimentally using the NMDA-R antagonist ketamine, a pharmacological model of schizophrenia. Ketamine yielded an increase in subjects' PVB, consistent with lowered cortical excitation/inhibition balance from NMDA-R hypofunction predominantly onto excitatory neurons. These results provide a circuit-level mechanism that bridges across explanatory scales, from the synaptic to the behavioural, in neuropsychiatric disorders where decision-making biases are prominent.

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.

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.

Designing and Interpreting Psychophysical Investigations of Cognition

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

Evidence for the Rhythmic Perceptual Sampling of Auditory Scenes

Converging results suggest that perception is controlled by rhythmic processes in the brain. In the auditory domain, neuroimaging studies show that the perception of sounds is shaped by rhythmic activity prior to the stimulus, and electrophysiological recordings have linked delta and theta band activity to the functioning of individual neurons. These results have promoted theories of rhythmic modes of listening and generally suggest that the perceptually relevant encoding of acoustic information is structured by rhythmic processes along auditory pathways. A prediction from this perspective-which so far has not been tested-is that such rhythmic processes also shape how acoustic information is combined over time to judge extended soundscapes. The present study was designed to directly test this prediction. Human participants judged the overall change in perceived frequency content in temporally extended (1.2-1.8 s) soundscapes, while the perceptual use of the available sensory evidence was quantified using psychophysical reverse correlation. Model-based analysis of individual participant's perceptual weights revealed a rich temporal structure, including linear trends, a U-shaped profile tied to the overall stimulus duration, and importantly, rhythmic components at the time scale of 1-2 Hz. The collective evidence found here across four versions of the experiment supports the notion that rhythmic processes operating on the delta time scale structure how perception samples temporally extended acoustic scenes.

Perceptual Decision-Making: A Field in the Midst of a Transformation

Major changes are underway in the field of perceptual decision-making. Single-neuron studies have given way to population recordings with identified cell types, traditional analyses have been extended to accommodate these large and diverse collections of neurons, and novel methods of neural disruption have provided insights about causal circuits. Further, the field has expanded to include multiple new species: rodents and invertebrates, for example, have been instrumental in demonstrating the importance of internal state on neural responses. Finally, a renewed interest in ethological stimuli prompted development of new behaviors, frequently analyzed by new, automated movement tracking methods. Taken together, these advances constitute a seismic shift in both our approach and understanding of how incoming sensory signals are used to guide decisions.