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

Transfer of information across repeated decisions in general and in obsessive-compulsive disorder

Real-life decisions are often repeated. Whether considering taking a job in a new city, or doing something mundane like checking if the stove is off, decisions are frequently revisited even if no new information is available. This mode of behavior takes a particularly pathological form in obsessive-compulsive disorder (OCD), which is marked by individuals' redeliberating previously resolved decisions. Surprisingly, little is known about how information is transferred across decision episodes in such circumstances, and whether and how such transfer varies in OCD. In two experiments, data from a repeated decision-making task and computational modeling revealed that both implicit and explicit memories of previous decisions affected subsequent decisions by biasing the rate of evidence integration. Further, we replicated previous work demonstrating impairments in baseline decision-making as a function of self-reported OCD symptoms, and found that information transfer effects specifically due to implicit memory were reduced, offering computational insight into checking behavior.

Neurocomputational mechanisms of prior-informed perceptual decision-making in humans

To interact successfully with diverse sensory environments, we must adapt our decision processes to account for time constraints and prior probabilities. The full set of decision-process parameters that undergo such flexible adaptation has proven to be difficult to establish using simplified models that are based on behaviour alone. Here, we utilize well-characterized human neurophysiological signatures of decision formation to construct and constrain a build-to-threshold decision model with multiple build-up (evidence accumulation and urgency) and delay components (pre- and post-decisional). The model indicates that all of these components were adapted in distinct ways and, in several instances, fundamentally differ from the conclusions of conventional diffusion modelling. The neurally informed model outcomes were corroborated by independent neural decision signal observations that were not used in the model's construction. These findings highlight the breadth of decision-process parameters that are amenable to strategic adjustment and the value in leveraging neurophysiological measurements to quantify these adjustments.

Inhibition of return: An information processing theory of its natures and significance

Inhibition of return (IOR) is an inhibitory aftereffect of visuospatial orienting, typically resulting in slower responses to targets presented in an area that has been recently attended. Since its discovery, myriad research has sought to explain the causes and effects underlying this phenomenon. Here, we briefly summarize the history of the phenomenon, and describe the early work supporting the functional significance of IOR as a foraging facilitator. We then shine a light on the discordance in the literature with respect to mechanism-in particular the lack of theoretical constructs that can consistently explain innumerable dissociations. We then describe three diagnostics (central arrow targets, locus of slack logic and the psychological refractory period, and performance in speed-accuracy space) used to support our theory that there are two forms of inhibition of return-the form which is manifest being contingent upon the activation state of the reflexive oculomotor system. The input form, which operates to decrease the salience of inputs, is generated when the reflexive oculomotor system is suppressed; the output form, which operates to bias responding, is generated when the reflexive oculomotor system is not suppressed. Then, we subject a published data set, wherein inhibitory effects had been generated while the reflexive oculomotor system was either active or suppressed, to diffusion modelling. As we hypothesized, based on the aforementioned theory, the effects of the two forms of IOR were best accounted for by different drift diffusion parameters. The paper ends with a variety of suggestions for further research.

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.

A recurrent neural network framework for flexible and adaptive decision making based on sequence learning

The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are trained to predict future events based on past events, both comprising sensory, action, and reward events. We show the networks can successfully reproduce animal and human behavior. The networks generalize the training, perform Bayesian inference in novel conditions, and adapt their choices when event contingencies vary. Importantly, units in the network encode task variables and exhibit activity patterns that match previous neurophysiology findings. Our results suggest that the neural network approach based on statistical sequence learning may reflect the brain's computational principle underlying flexible and adaptive behaviors and serve as a useful approach to understand the brain.

Robust parallel decision-making in neural circuits with nonlinear inhibition

Animals frequently need to choose the best alternative from a set of possibilities, whether it is which direction to swim in or which food source to favor. How long should a network of neurons take to choose the best of N options? Theoretical results suggest that the optimal time grows as log(N), if the values of each option are imperfectly perceived. However, standard self-terminating neural network models of decision-making cannot achieve this optimal behavior. We show how using certain additional nonlinear response properties in neurons, which are ignored in standard models, results in a decision-making architecture that both achieves the optimal scaling of decision time and accounts for multiple experimentally observed features of neural decision-making.

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes ∼N⁡log(N) time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained 2 male rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models, which flexibly accommodate diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.SIGNIFICANCE STATEMENT In everyday life, decisions are influenced by many factors, including reward structures and stimulus timing. While reward and timing have been characterized in isolation, ecologically valid decision-making involves a multiplicity of factors acting simultaneously. This raises questions about whether the same decision-making strategy is used when these two factors are concurrently manipulated. To address these questions, we trained rhesus monkeys to perform a novel decision-making task with both reward asymmetry and temporal uncertainty. In order to understand their strategy and hint at its neural mechanisms, we used the new generalized drift diffusion modeling framework to model both reward and timing mechanisms. We found two of each reward and timing mechanisms are necessary to explain our data.

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.

Human stereoEEG recordings reveal network dynamics of decision-making in a rule-switching task

The processing steps that lead up to a decision, i.e., the transformation of sensory evidence into motor output, are not fully understood. Here, we combine stereoEEG recordings from the human cortex, with single-lead and time-resolved decoding, using a wide range of temporal frequencies, to characterize decision processing during a rule-switching task. Our data reveal the contribution of rostral inferior parietal lobule (IPL) regions, in particular PFt, and the parietal opercular regions in decision processing and demonstrate that the network representing the decision is common to both task rules. We reconstruct the sequence in which regions engage in decision processing on single trials, thereby providing a detailed picture of the network dynamics involved in decision-making. The reconstructed timeline suggests that the supramarginal gyrus in IPL links decision regions in prefrontal cortex with premotor regions, where the motor plan for the response is elaborated.

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.

ChaRTr: An R toolbox for modeling choices and response times in decision-making tasks

BACKGROUND

Decision-making is the process of choosing and performing actions in response to sensory cues to achieve behavioral goals. Many mathematical models have been developed to describe the choice behavior and response time (RT) distributions of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques.

NEW METHOD

We present a toolbox - Choices and Response Times in R, or ChaRTr - that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries.

RESULTS

In three different case studies, we demonstrate how ChaRTr can be used to effortlessly discriminate between multiple models of decision-making behavior. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

COMPARISON WITH EXISTING METHOD(S)

Existing software packages surmounted some of the numerical issues but have often focused on the classical decision-making model, the diffusion decision model. Recent models that posit roles for urgency, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluation among these competing classes of decision-making models.

CONCLUSION

ChaRTr can be used to make insightful statements about the cognitive processes underlying observed decision-making behavior and ultimately for deeper insights into decision mechanisms.

Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.

The role of premature evidence accumulation in making difficult perceptual decisions under temporal uncertainty

The computations and neural processes underpinning decision making have primarily been investigated using highly simplified tasks in which stimulus onsets cue observers to start accumulating choice-relevant information. Yet, in daily life we are rarely afforded the luxury of knowing precisely when choice-relevant information will appear. Here, we examined neural indices of decision formation while subjects discriminated subtle stimulus feature changes whose timing relative to stimulus onset ('foreperiod') was uncertain. Joint analysis of behavioural error patterns and neural decision signal dynamics indicated that subjects systematically began the accumulation process before any informative evidence was presented, and further, that accumulation onset timing varied systematically as a function of the foreperiod of the preceding trial. These results suggest that the brain can adjust to temporal uncertainty by strategically modulating accumulation onset timing according to statistical regularities in the temporal structure of the sensory environment with particular emphasis on recent experience.

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.

Optimal models of decision-making in dynamic environments

Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.

Fast and slow errors: Logistic regression to identify patterns in accuracy-response time relationships

Understanding error and response time patterns is essential for making inferences in several domains of cognitive psychology. Crucial insights on cognitive performance and typical behavioral patterns are disclosed by using distributional analyses such as conditional accuracy functions (CAFs) instead of mean statistics. Several common behavioral error patterns revealed by CAFs are frequently described in the literature: response capture (associated with relatively fast errors), time pressure or urgency paradigms (slow errors), or cue-induced speed-accuracy trade-off (evenly distributed errors). Unfortunately, the standard way of computing CAFs is problematic, because accuracy is averaged in RT bins. Here we present a novel way of analyzing accuracy-RT relationships on the basis of nonlinear logistic regression, to handle these problematic aspects of RT binning. First we evaluate the parametric robustness of the logistic regression CAF through parameter recovery. Second, we apply the function to three existing data sets showing that specific parametric changes in the logistic regression CAF can consistently describe common behavioral patterns (such as response capture, time pressure, and speed-accuracy trade-off). Finally, we discuss potential modifications for future research.

Confidence predicts speed-accuracy tradeoff for subsequent decisions

When external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that internally computed neural signals of confidence predict the ongoing adjustment of decision policies.

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.

Choice history biases subsequent evidence accumulation

Perceptual choices depend not only on the current sensory input but also on the behavioral context, such as the history of one's own choices. Yet, it remains unknown how such history signals shape the dynamics of later decision formation. In models of decision formation, it is commonly assumed that choice history shifts the starting point of accumulation toward the bound reflecting the previous choice. We here present results that challenge this idea. We fit bounded-accumulation decision models to human perceptual choice data, and estimated bias parameters that depended on observers' previous choices. Across multiple task protocols and sensory modalities, individual history biases in overt behavior were consistently explained by a history-dependent change in the evidence accumulation, rather than in its starting point. Choice history signals thus seem to bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation.

Flexibility of Timescales of Evidence Evaluation for Decision Making

To understand the neural mechanisms that support decision making, it is critical to characterize the timescale of evidence evaluation. Recent work has shown that subjects can adaptively adjust the timescale of evidence evaluation across blocks of trials depending on context [1]. However, it's currently unknown if adjustments to evidence evaluation occur online during deliberations based on a single stream of evidence. To examine this question, we employed a change-detection task in which subjects report their level of confidence in judging whether there has been a change in a stochastic auditory stimulus. Using a combination of psychophysical reverse correlation analyses and single-trial behavioral modeling, we compared the time period over which sensory information has leverage on detection report choices versus confidence. We demonstrate that the length of this period differs on separate sets of trials based on what's being reported. Surprisingly, confidence judgments on trials with no detection report are influenced by evidence occurring earlier than the time period of influence for detection reports. Our findings call into question models of decision formation involving static parameters that yield a singular timescale of evidence evaluation and instead suggest that the brain represents and utilizes multiple timescales of evidence evaluation during deliberation.

Learning from the past and expecting the future in Parkinsonism: Dopaminergic influence on predictions about the timing of future events

The prolonged reaction times seen in Parkinson's disease (PD) have been linked to a dopaminergic-dependent deficit in using prior information to prepare responses, but also have been explained by an altered temporal processing. However, an underlying cognitive mechanism linking dopamine, temporal processing and response preparation remains elusive. To address this, we studied PD patients, with or without medication, and age-matched healthy individuals using a variable foreperiod task requiring speeded responses to a visual stimulus occurring at variable onset-times, with block-wise changes in the temporal predictability of visual stimuli. Compared with controls, unmedicated patients showed impaired use of prior information to prepare their responses, as reflected by slower reaction times, regardless of the level of temporal predictability. Crucially, after dopamine administration normal performance was restored, with faster responses for high temporal predictability. Using Bayesian hierarchical drift-diffusion modelling, we estimated the parameters that determine temporal preparation. In this theoretical framework, impaired temporal preparation under dopaminergic depletion was driven by inflexibly high decision boundaries (i.e. participants were always extremely cautious). This indexes high levels of uncertainty about temporal predictions irrespectively of stimulus onset predictability. Our results suggest that dopaminergic depletion in PD affects the uncertainty of predictions about the timing of future events (temporal predictions), which are crucial for the anticipatory preparation of responses. Dopamine, which is affected in PD, controls the ability to predict the timing of future events.

Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence

Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.

Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making

Ageing impacts on decision-making behaviour across a range of cognitive tasks and scenarios. Computational modelling has proved valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations remains unclear. Here, we report that age-related effects on neural signatures of decision formation are inconsistent with behavioural fits derived from a prominent accumulation-to-bound model. Most notably, model-predicted bound differences were absent neurophysiologically. However, constraining the model to match the decision-predictive elements of the brain signals provided more parsimonious fits to behaviour and generated predictions regarding the neural data that were empirically validated. These included a task-dependent slowing of evidence accumulation among older adults and reduced between-trial accumulation rate variability, which was linked to enhanced attentional engagement. Our findings highlight how combining neurophysiological measurements with computational modelling can yield unique insights into group differences in neural decision mechanisms.

Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

Sequential sampling models have provided a dominant theoretical framework guiding computational and neurophysiological investigations of perceptual decision-making. While these models share the basic principle that decisions are formed by accumulating sensory evidence to a bound, they come in many forms that can make similar predictions of choice behaviour despite invoking fundamentally different mechanisms. The identification of neural signals that reflect some of the core computations underpinning decision formation offers new avenues for empirically testing and refining key model assumptions. Here, we highlight recent efforts to explore these avenues and, in so doing, consider the conceptual and methodological challenges that arise when seeking to infer decision computations from complex neural data.

Emergent mechanisms of evidence integration in recurrent neural networks

Recent advances in machine learning have enabled neural networks to solve tasks humans typically perform. These networks offer an exciting new tool for neuroscience that can give us insight in the emergence of neural and behavioral mechanisms. A big gap remains though between the very deep neural networks that have risen in popularity and outperformed many existing shallow networks in the field of computer vision and the highly recurrently connected human brain. This trend towards ever-deeper architectures raises the question why the brain has not developed such an architecture. Besides wiring constraints we argue that the brain operates under different circumstances when performing object recognition, being confronted with noisy and ambiguous sensory input. The role of time in the process of object recognition is investigated, showing that a recurrent network trained through reinforcement learning is able to learn the amount of time needed to arrive at an accurate estimate of the stimulus and develops behavioral and neural mechanisms similar to those found in the human and non-human primate literature.

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.

Sensory and decision-making processes underlying perceptual adaptation

Perceptual systems adapt to their inputs. As a result, prolonged exposure to particular stimuli alters judgments about subsequent stimuli. This phenomenon is commonly assumed to be sensory in origin. Changes in the decision-making process, however, may also be a component of adaptation. Here, we quantify sensory and decision-making contributions to adaptation in a facial expression paradigm. As expected, exposure to happy or sad expressions shifts the psychometric function toward the adaptor. More surprisingly, response times show both an overall decline and an asymmetry, with faster responses opposite the adapting category, implicating a substantial change in the decision-making process. Specifically, we infer that sensory changes from adaptation are accompanied by changes in how much sensory information is accumulated for the two choices. We speculate that adaptation influences implicit expectations about the stimuli one will encounter, causing modifications in the decision-making process as part of a normative response to a change in context.

A Dynamical Systems Perspective on Flexible Motor Timing

A hallmark of higher brain function is the ability to rapidly and flexibly adjust behavioral responses based on internal and external cues. Here, we examine the computational principles that allow decisions and actions to unfold flexibly in time. We adopt a dynamical systems perspective and outline how temporal flexibility in such a system can be achieved through manipulations of inputs and initial conditions. We then review evidence from experiments in nonhuman primates that support this interpretation. Finally, we explore the broader utility and limitations of the dynamical systems perspective as a general framework for addressing open questions related to the temporal control of movements, as well as in the domains of learning and sequence generation.

Magnetoencephalography decoding reveals structural differences within integrative decision processes

When confronted with complex inputs consisting of multiple elements, humans use various strategies to integrate the elements quickly and accurately. For instance, accuracy may be improved by processing elements one at a time^1-4 or over extended periods^5-8; speed can increase if the internal representation of elements is accelerated^9,10. However, little is known about how humans actually approach these challenges because behavioural findings can be accounted for by multiple alternative process models¹¹ and neuroimaging investigations typically rely on haemodynamic signals that change too slowly. Consequently, to uncover the fast neural dynamics that support information integration, we decoded magnetoencephalographic signals that were recorded as human subjects performed a complex decision task. Our findings reveal three sources of individual differences in the temporal structure of the integration process-sequential representation, partial reinstatement and early computation-each having a dissociable effect on how subjects handled problem complexity and temporal constraints. Our findings shed new light on the structure and influence of self-determined neural integration processes.

Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

When decisions are made under speed pressure, "urgency" signals elevate neural activity toward action-triggering thresholds independent of the sensory evidence, thus incurring a cost to choice accuracy. While urgency signals have been observed in brain circuits involved in preparing actions, their influence at other levels of the sensorimotor pathway remains unknown. We used a novel contrast-comparison paradigm to simultaneously trace the dynamics of sensory evidence encoding, evidence accumulation, motor preparation, and muscle activation in humans. Results indicate speed pressure impacts multiple sensorimotor levels but in crucially distinct ways. Evidence-independent urgency was applied to cortical action-preparation signals and downstream muscle activation, but not directly to upstream levels. Instead, differential sensory evidence encoding was enhanced in a way that partially countered the negative impact of motor-level urgency on accuracy, and these opposing sensory-boost and motor-urgency effects had knock-on effects on the buildup and pre-response amplitude of a motor-independent representation of cumulative evidence.

Bumblebees Express Consistent, but Flexible, Speed-Accuracy Tactics Under Different Levels of Predation Threat

A speed-accuracy trade-off (SAT) in behavioural decisions is known to occur in a wide range of vertebrate and invertebrate taxa. Accurate decisions often take longer for a given condition, while fast decisions can be inaccurate in some tasks. Speed-accuracy tactics are known to vary consistently among individuals, and show a degree of flexibility during colour discrimination tasks in bees. Such individual flexibility in speed-accuracy tactics is likely to be advantageous for animals exposed to fluctuating environments, such as changes in predation threat. We therefore test whether individual speed-accuracy tactics are fixed or flexible under different levels of predation threat in a model invertebrate, the bumblebee Bombus terrestris. The flexibility of speed-accuracy tactics in a foraging context was tested in the laboratory using a “meadow” of artificial flowers harbouring “robotic” crab spider predators. We found that while the ranking of bees along the speed and accuracy continuums was consistent across two levels of predation threat, there was some flexibility in the tactics used by individual bees – most bees became less accurate at colour discrimination when exposed to predation threat when flower types were rewarding. The relationship between decision speed and accuracy was influenced by predator detectability and the risk associated with making incorrect choices during the colour discrimination task. Predator crypsis resulted in a breakdown in the relationship between speed and accuracy, especially when making an incorrect floral choice incurred a distasteful quinine punishment. No single speed-accuracy tactic was found to be optimal in terms of foraging efficiency under either predation threat situation. However, bees that made faster decisions achieved higher nectar collection rates in predator free situations, while accurate bees achieved higher foraging rates under predation threat. Our findings show that while individual bees remain relatively consistent in terms of whether they place greater emphasis on speed or accuracy under predation threat, they can respond flexibly to the additional time costs of detecting predators.

Psychophysical reverse correlation reflects both sensory and decision-making processes

Goal-directed behavior depends on both sensory mechanisms that gather information from the outside world and decision-making mechanisms that select appropriate behavior based on that sensory information. Psychophysical reverse correlation is commonly used to quantify how fluctuations of sensory stimuli influence behavior and is generally believed to uncover the spatiotemporal weighting functions of sensory processes. Here we show that reverse correlations also reflect decision-making processes and can deviate significantly from the true sensory filters. Specifically, changes of decision bound and mechanisms of evidence integration systematically alter psychophysical reverse correlations. Similarly, trial-to-trial variability of sensory and motor delays and decision times causes systematic distortions in psychophysical kernels that should not be attributed to sensory mechanisms. We show that ignoring details of the decision-making process results in misinterpretation of reverse correlations, but proper use of these details turns reverse correlation into a powerful method for studying both sensory and decision-making mechanisms.

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

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

Neurodynamic Evidence Supports a Forced-Excursion Model of Decision-Making under Speed/Accuracy Instructions

Evolutionary pressures suggest that choices should be optimized to maximize rewards, by appropriately trading speed for accuracy. This speed-accuracy tradeoff (SAT) is commonly explained by variation in just the baseline-to-boundary distance, i.e., the excursion, of accumulation-to-bound models of perceptual decision-making. However, neural evidence is not consistent with this explanation. A compelling account of speeded choice should explain both overt behavior and the full range of associated brain signatures. Here, we reconcile seemingly contradictory behavioral and neural findings. In two variants of the same experiment, we triangulated upon the neural underpinnings of the SAT in the human brain using both EEG and transcranial magnetic stimulation (TMS). We found that distinct neural signals, namely the event-related potential (ERP) centroparietal positivity (CPP) and a smoothed motor-evoked potential (MEP) signal, which have both previously been shown to relate to decision-related accumulation, revealed qualitatively similar average neurodynamic profiles with only subtle differences between SAT conditions. These signals were then modelled from behavior by either incorporating traditional boundary variation or utilizing a forced excursion. These model variants are mathematically equivalent, in terms of their behavioral predictions, hence providing identical fits to correct and erroneous reaction time distributions. However, the forced-excursion version instantiates SAT via a more global change in parameters and implied neural activity, a process conceptually akin to, but mathematically distinct from, urgency. This variant better captured both ERP and MEP neural profiles, suggesting that the SAT may be implemented via neural gain modulation, and reconciling standard modelling approaches with human neural data.

Neural mechanisms of speed-accuracy tradeoff of visual search: saccade vigor, the origin of targeting errors, and comparison of the superior colliculus and frontal eye field

Balancing the speed-accuracy tradeoff (SAT) is necessary for successful behavior. Using a visual search task with interleaved cues emphasizing speed or accuracy, we recently reported diverse contributions of frontal eye field (FEF) neurons instantiating salience evidence and response preparation. Here, we report replication of visual search SAT performance in two macaque monkeys, new information about variation of saccade dynamics with SAT, extension of the neurophysiological investigation to describe processes in the superior colliculus (SC), and a description of the origin of search errors in this task. Saccade vigor varied idiosyncratically across SAT conditions and monkeys but tended to decrease with response time. As observed in the FEF, speed-accuracy tradeoff was accomplished through several distinct adjustments in the superior colliculus. In "Accurate" relative to "Fast" trials, visually responsive neurons in SC as in FEF had lower baseline firing rates and later target selection. The magnitude of these adjustments in SC was indistinguishable from that in FEF. Search errors occurred when visual salience neurons in the FEF and the SC treated distractors as targets, even in the Accurate condition. Unlike FEF, the magnitude of visual responses in the SC did not vary across SAT conditions. Also unlike FEF, the activity of SC movement neurons when saccades were initiated was equivalent in Fast and Accurate trials. Saccade-related neural activity in SC, but not FEF, varied with saccade peak velocity. These results extend our understanding of the cortical and subcortical contributions to SAT. NEW & NOTEWORTHY Neurophysiological mechanisms of speed-accuracy tradeoff (SAT) have only recently been investigated. This article reports the first replication of SAT performance in nonhuman primates, the first report of variation of saccade dynamics with SAT, the first description of superior colliculus contributions to SAT, and the first description of the origin of errors during SAT. These results inform and constrain new models of distributed decision making.

Optimal Readout of Correlated Neural Activity in a Decision-Making Circuit

The neural correlates of decision making have been extensively studied with tasks involving a choice between two alternatives that is guided by visual cues. While a large body of work argues for a role of the lateral intraparietal (LIP) region of cortex in these tasks, this role may be confounded by the interaction between LIP and other regions, including medial temporal (MT) cortex. Here, we describe a simplified linear model of decision making that is adapted to two tasks: a motion discrimination and a categorization task. We show that the distinct contribution of MT and LIP may indeed be confounded in these tasks. In particular, we argue that the motion discrimination task relies on a straightforward visuomotor mapping, which leads to redundant information between MT and LIP. The categorization task requires a more complex mapping between visual information and decision behavior, and therefore does not lead to redundancy between MT and LIP. Going further, the model predicts that noise correlations within LIP should be greater in the categorization compared to the motion discrimination task due to the presence of shared inputs from MT. The impact of these correlations on task performance is examined by analytically deriving error estimates of an optimal linear readout for shared and unique inputs. Taken together, results clarify the contribution of MT and LIP to decision making and help characterize the role of noise correlations in these regions.

Mechanisms Underlying Decision-Making as Revealed by Deep-Brain Stimulation in Patients with Parkinson's Disease

To optimally balance opposing demands of speed and accuracy during decision-making, we must flexibly adapt how much evidence we require before making a choice. Such adjustments in decision thresholds have been linked to the subthalamic nucleus (STN), and therapeutic STN deep-brain stimulation (DBS) has been shown to interfere with this function. Here, we performed continuous as well as closed-loop DBS of the STN while Parkinson’s disease patients performed a perceptual decision-making task. Closed-loop STN DBS allowed temporally patterned STN stimulation and simultaneous recordings of STN activity. This revealed that DBS only affected patients’ ability to adjust decision thresholds if applied in a specific temporally confined time window during deliberation. Only stimulation in that window diminished the normal slowing of response times that occurred on difficult trials when DBS was turned off. Furthermore, DBS eliminated a relative, time-specific increase in STN beta oscillations and compromised its functional relationship with trial-by-trial adjustments in decision thresholds. Together, these results provide causal evidence that the STN is involved in adjusting decision thresholds in distinct, time-limited processing windows during deliberation.

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We performed temporally patterned stimulation of the subthalamic nucleus in humans

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During stimulation, Parkinson’s patients performed a perceptual decision-making task

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Stimulation effects on behavior were confined to a short window during deliberation

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Here, stimulation affected changes in decision thresholds during difficult decisions

Post-encoding control of working memory enhances processing of relevant information in rhesus monkeys (Macaca mulatta)

Working memory is a system by which a limited amount of information can be kept available for processing after the cessation of sensory input. Because working memory resources are limited, it is adaptive to focus processing on the most relevant information. We used a retro-cue paradigm to determine the extent to which monkey working memory possesses control mechanisms that focus processing on the most relevant representations. Monkeys saw a sample array of images, and shortly after the array disappeared, they were visually cued to a location that had been occupied by one of the sample images. The cue indicated which image should be remembered for the upcoming recognition test. By determining whether the monkeys were more accurate and quicker to respond to cued images compared to un-cued images, we tested the hypothesis that monkey working memory focuses processing on relevant information. We found a memory benefit for the cued image in terms of accuracy and retrieval speed with a memory load of two images. With a memory load of three images, we found a benefit in retrieval speed but only after shortening the onset latency of the retro-cue. Our results demonstrate previously unknown flexibility in the cognitive control of memory in monkeys, suggesting that control mechanisms in working memory likely evolved in a common ancestor of humans and monkeys more than 32 million years ago. Future work should be aimed at understanding the interaction between memory load and the ability to control memory resources, and the role of working memory control in generating differences in cognitive capacity among primates.

The impact of natural aging on computational and neural indices of perceptual decision making: A review

It is well established that natural aging negatively impacts on a wide variety of cognitive functions and research has sought to identify core neural mechanisms that may account for these disparate changes. A central feature of any cognitive task is the requirement to translate sensory information into an appropriate action - a process commonly known as perceptual decision making. While computational, psychophysical, and neurophysiological research has made substantial progress in establishing the key computations and neural mechanisms underpinning decision making, it is only relatively recently that this knowledge has begun to be applied to research on aging. The purpose of this review is to provide an overview of this work which is beginning to offer new insights into the core psychological processes that mediate age-related cognitive decline in adults aged 65 years and over. Mathematical modelling studies have consistently reported that older adults display longer non-decisional processing times and implement more conservative decision policies than their younger counterparts. However, there are limits on what we can learn from behavioural modeling alone and neurophysiological analyses can play an essential role in empirically validating model predictions and in pinpointing the precise neural mechanisms that are impacted by aging. Although few studies to date have explicitly examined correspondences between computational models and neural data with respect to cognitive aging, neurophysiological studies have already highlighted age-related changes at multiple levels of the sensorimotor hierarchy that are likely to be consequential for decision making behaviour. Here, we provide an overview of this literature and suggest some future directions for the field.

The Role of the Lateral Intraparietal Area in (the Study of) Decision Making

Over the past two decades, neurophysiological responses in the lateral intraparietal area (LIP) have received extensive study for insight into decision making. In a parallel manner, inferred cognitive processes have enriched interpretations of LIP activity. Because of this bidirectional interplay between physiology and cognition, LIP has served as fertile ground for developing quantitative models that link neural activity with decision making. These models stand as some of the most important frameworks for linking brain and mind, and they are now mature enough to be evaluated in finer detail and integrated with other lines of investigation of LIP function. Here, we focus on the relationship between LIP responses and known sensory and motor events in perceptual decision-making tasks, as assessed by correlative and causal methods. The resulting sensorimotor-focused approach offers an account of LIP activity as a multiplexed amalgam of sensory, cognitive, and motor-related activity, with a complex and often indirect relationship to decision processes. Our data-driven focus on multiplexing (and de-multiplexing) of various response components can complement decision-focused models and provides more detailed insight into how neural signals might relate to cognitive processes such as decision making.

Not all observed actions are perceived equally

Action observation is the visual process analyzing the actions of others to determine their goals and how the actor's body (part) movements permit attaining those goals. Our recent psychophysical study demonstrated that 1) observed action (OA) perception differs from shape perception in viewpoint and duration dependence, and 2) accuracy and reaction times of OA discrimination are fitted by the proportional-rate diffusion model whereby a sensory stage provides noisy evidence that is accumulated up to a criterion or bound by a decision stage. That study was devoted to observation of manipulative actions, following a general trend of the field. Recent functional imaging studies of action observation, however, have established various OA classes as separate entities with processing routes involving distinct posterior parietal cortex (PPC) regions. Here, we show that the diffusion model applies to multiple OA classes. Even more importantly, the observers' ability to discriminate exemplars of a given class differs considerably between OA classes and these performance differences correspond to differences in model parameters. In particular, OA classes differ in the bound parameter which we propose may reflect an urgency signal originating in the PPC regions corresponding to the sensory stages of different OA classes.

Flexible timing by temporal scaling of cortical responses

Musicians can perform at different tempos, speakers can control the cadence of their speech, and children can flexibly vary their temporal expectations of events. To understand the neural basis of such flexibility, we recorded from the medial frontal cortex of nonhuman primates trained to produce different time intervals with different effectors. Neural responses were heterogeneous, nonlinear and complex, and exhibited a remarkable form of temporal invariance: firing rate profiles were temporally scaled to match the produced intervals. Recording from downstream neurons in the caudate and thalamic neurons projecting to the medial frontal cortex indicated that this phenomenon originates within cortical networks. Recurrent neural network models trained to perform the task revealed that temporal scaling emerges from nonlinearities in the network and degree of scaling is controlled by the strength of external input. These findings demonstrate a simple and general mechanism for conferring temporal flexibility upon sensorimotor and cognitive functions.

Characterization of decision commitment rule alterations during an auditory change detection task

A critical component of decision making is determining when to commit to a choice. This involves stopping rules that specify the requirements for decision commitment. Flexibility of decision stopping rules provides an important means of control over decision-making processes. In many situations, these stopping rules establish a balance between premature decisions and late decisions. In this study we use a novel change detection paradigm to examine how subjects control this balance when invoking different decision stopping rules. The task design allows us to estimate the temporal weighting of sensory information for the decisions, and we find that different stopping rules did not result in systematic differences in that weighting. We also find bidirectional post-error alterations of decision strategy that depend on the type of error and effectively reduce the probability of making consecutive mistakes of the same type. This is a generalization to change detection tasks of the widespread observation of unidirectional post-error slowing in forced-choice tasks. On the basis of these results, we suggest change detection tasks as a promising paradigm to study the neural mechanisms that support flexible control of decision rules.NEW & NOTEWORTHY Flexible decision stopping rules confer control over decision processes. Using an auditory change detection task, we found that alterations of decision stopping rules did not result in systematic changes in the temporal weighting of sensory information. We also found that post-error alterations of decision stopping rules depended on the type of mistake subjects make. These results provide guidance for understanding the neural mechanisms that control decision stopping rules, one of the critical components of decision making and behavioral flexibility.

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.

Action history influences subsequent movement via two distinct processes

The characteristics of goal-directed actions tend to resemble those of previously executed actions, but it is unclear whether such effects depend strictly on action history, or also reflect context-dependent processes related to predictive motor planning. Here we manipulated the time available to initiate movements after a target was specified, and studied the effects of predictable movement sequences, to systematically dissociate effects of the most recently executed movement from the movement required next. We found that directional biases due to recent movement history strongly depend upon movement preparation time, suggesting an important contribution from predictive planning. However predictive biases co-exist with an independent source of bias that depends only on recent movement history. The results indicate that past experience influences movement execution through a combination of temporally-stable processes that are strictly use-dependent, and dynamically-evolving and context-dependent processes that reflect prediction of future actions.

Removal of inhibition uncovers latent movement potential during preparation

The motor system prepares for movements well in advance of their execution. In the gaze control system, the dynamics of preparatory neural activity have been well described by stochastic accumulation-to-threshold models. However, it is unclear whether this activity has features indicative of a hidden movement command. We explicitly tested whether preparatory neural activity in premotor neurons of the primate superior colliculus has 'motor potential'. We removed downstream inhibition on the saccadic system using the trigeminal blink reflex, triggering saccades at earlier-than-normal latencies. Accumulating low-frequency activity was predictive of eye movement dynamics tens of milliseconds in advance of the actual saccade, indicating the presence of a latent movement command. We also show that reaching a fixed threshold level is not a necessary condition for movement initiation. The results bring into question extant models of saccade generation and support the possibility of a concurrent representation for movement preparation and generation.

Piercing of Consciousness as a Threshold-Crossing Operation

Many decisions arise through an accumulation of evidence to a terminating threshold. The process, termed bounded evidence accumulation (or drift diffusion), provides a unified account of decision speed and accuracy, and it is supported by neurophysiology in human and animal models. In many situations, a decision maker may not communicate a decision immediately and yet feel that at some point she had made up her mind. We hypothesized that this occurs when an accumulation of evidence reaches a termination threshold, registered, subjectively, as an “aha” moment. We asked human participants to make perceptual decisions about the net direction of dynamic random dot motion. The difficulty and viewing duration were controlled by the experimenter. After indicating their choice, participants adjusted the setting of a clock to the moment they felt they had reached a decision. The subjective decision times (t_SDs) were faster on trials with stronger (easier) motion, and they were well fit by a bounded drift-diffusion model. The fits to the t_SDs alone furnished parameters that fully predicted the choices (accuracy) of four of the five participants. The quality of the prediction provides compelling evidence that these subjective reports correspond to the terminating process of a decision rather than a post hoc inference or arbitrary report. Thus, conscious awareness of having reached a decision appears to arise when the brain’s representation of accumulated evidence reaches a threshold or bound. We propose that such a mechanism might play a more widespread role in the “piercing of consciousness” by non-conscious thought processes.

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Perceptual decisions can arise through an accumulation of evidence to a threshold

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After a stimulus, participants set a clock to the moment they had reached a decision

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An evidence accumulation model fit to these times allowed predictions of accuracy

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The sense of having decided is mediated by a threshold on accumulated evidence