Perceptual Decision-making Task Research Articles

Behavior can be decoded across the cortex when individual differences are considered

Abstract Group-level analyses have typically linked behavioral signatures to a constrained set of brain areas. Here we show that two behavioral metrics — reaction time (RT) and confidence — can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). First, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. We then examined the decoding performance at the group and subject levels. At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the subject level, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. All results were replicated in a second dataset. These findings show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and subject levels.

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks.

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, whose behaviour was marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research.

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

AbstractCognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.

Multiple Brain Activation Patterns for the Same Perceptual Decision-Making Task.

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

Evidence for interacting but decoupled controls of decisions and movements in non-human primates.

Many recent studies indicate that control of decisions and actions is integrated during interactive behavior. Among these, several carried out in humans and monkeys conclude that there is a co-regulation of choices and movements. Another perspective, based on human data only, proposes a decoupled control of decision duration and movement speed, allowing for instance to trade decision duration for movement duration when time pressure increases. Crucially, it is not currently known whether this ability to flexibly dissociate decision duration from movement speed is specific to humans, whether it can vary depending on the context in which a task is performed, and whether it is stable over time. These are important questions to address, especially to rely on monkey electrophysiology to infer the neural mechanisms of decision-action coordination in humans. To do so, we trained two macaque monkeys in a perceptual decision-making task and analyzed data collected over multiple behavioral sessions. Our findings reveal a strong and complex relationship between decision duration and movement vigor. Decision duration and action duration can co-vary but also "compensate" each other. Such integrated but decoupled control of decisions and actions aligns with recent studies in humans, validating the monkey model in electrophysiology as a means of inferring neural mechanisms in humans. Crucially, we demonstrate for the first time that this control can evolve with experience, in an adapted manner. Together, the present findings contribute to deepening our understanding of the integrated control of decisions and actions during interactive behavior.

Global neural encoding of behavioral strategies in mice during perceptual decision-making task with two different sensory patterns

Global neural encoding of behavioral strategies in mice during perceptual decision-making task with two different sensory patterns

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that animals achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes, thereby reducing behavioral sensitivity to these outcomes. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.

Information search under uncertainty across transdiagnostic psychopathology and healthy ageing

When making decisions in everyday life, we often rely on an internally generated sense of confidence to help us revise and direct future behaviours. For instance, confidence directly informs whether further information should be sought prior to commitment to a final decision. Many studies have shown that aging and both clinical and sub-clinical symptoms of psychopathology are associated with systematic alterations in confidence. However, it remains unknown whether these confidence distortions influence information-seeking behaviour. We investigated this question in a large general population sample (N = 908). Participants completed a battery of psychiatric symptom questionnaires and performed a perceptual decision-making task with confidence ratings in which they were offered the option to seek helpful information (at a cost) before committing to a final decision. Replicating previous findings, an ‘anxious-depression’ (AD) symptom dimension was associated with systematically low confidence, despite no detriment in objective task accuracy. Conversely, a ‘compulsive behaviour and intrusive thoughts’ (CIT) dimension was associated with impaired task accuracy but paradoxical over-confidence. However, neither symptom dimension was significantly associated with an increased or decreased tendency to seek information. Hence, participants scoring highly for AD or CIT did not use the option to information seek any more than average to either increase their confidence (AD) or improve the accuracy of their decisions (CIT). In contrast, older age was associated with impaired accuracy and decreased confidence initially, but increased information seeking behaviour mediated increases in both accuracy and confidence for final decisions. Hence, older adults used the information seeking option to overcome initial deficits in objective performance and to increase their confidence accordingly. The results show an appropriate use of information seeking to overcome perceptual deficits and low confidence in healthy aging which was not present in transdiagnostic psychopathology.

Identifying a distractor produces object-based inhibition in an allocentric reference frame for saccade planning

We investigated whether distractor inhibition occurs relative to the target or fixation in a perceptual decision-making task using a purely saccadic response. Previous research has shown that during the process of discriminating a target from distractor, saccades made to a target deviate towards the distractor. Once discriminated, the distractor is inhibited, and trajectories deviate away from the distractor. Saccade deviation magnitudes provide a sensitive measure of target-distractor competition dependent on the distance between them. While saccades are planned in an egocentric reference frame (locations represented relative to fixation), object-based inhibition has been shown to occur in an allocentric reference frame (objects represented relative to each other independent of fixation). By varying the egocentric and allocentric distances of the target and distractor, we found that only egocentric distances contributed to saccade trajectories shifts towards the distractor during active decision-making. When the perceptual decision-making process was complete, and the distractor was inhibited, both ego- and allocentric distances independently contributed to saccade trajectory shifts away from the distractor. This is consistent with independent spatial and object-based inhibitory mechanisms. Therefore, we suggest that distractor inhibition is maintained in cortical visual areas with allocentric maps which then feeds into oculomotor areas for saccade planning.

Confidence Is Influenced by Evidence Accumulation Time in Dynamical Decision Models

Confidence judgments are closely correlated with response times across a wide range of decision tasks. Sequential sampling models offer two competing explanations for the relationship between confidence and response time: According to some models, decision time directly influences confidence. Other models explain the correlation by linking subjective confidence computation to the decision process dynamics. In previous model comparisons, drift diffusion-based confidence models that do not explicitly consider decision time in the computation of confidence provided superior model fits compared to race models that directly included decision time in the internal computation of confidence. In the present study, we present support for the assumption that confidence explicitly takes decision time and post-decisional accumulation time into account. We propose the dynamical visibility, time, and evidence (dynaViTE) model, an extension of the dynamical weighted evidence and visibility (dynWEV) model. DynaViTE assumes that confidence is not solely based on the final amount of accumulated evidence but explicitly includes time in the computation of confidence. Model comparisons using four previously published data sets with different perceptual decision tasks showed a good model fit of dynaViTE, indicating that the relationship between confidence and response time is not only due to the close link in the accumulation process but also to an explicit inclusion of time in the computation of confidence.

A jsPsych touchscreen extension for behavioral research on touch-enabled interfaces

Online experiments are increasingly gaining traction in the behavioral sciences. Despite this, behavioral researchers have largely continued to use keyboards as the primary input devices for such online studies, overlooking the ubiquity of touchscreens in everyday use. This paper presents an open-source touchscreen extension for jsPsych, a JavaScript framework designed for conducting online experiments. We additionally evaluated the touchscreen extension assessing whether typical behavioral findings from two distinct perceptual decision-making tasks – the random-dot kinematogram and the Stroop task – can similarly be observed when administered via touchscreen devices compared to keyboard devices. Our findings indicate similar performance metrics for each paradigm between the touchscreen and keyboard versions of the experiments. Specifically, we observe similar psychometric curves in the random-dot kinematogram across the touchscreen and keyboard versions. Similarly, in the Stroop task, we detect significant task, congruency, and sequential congruency effects in both experiment versions. We conclude that our open-source touchscreen extension serves as a promising tool for data collection in online behavioral experiments on forced-choice tasks.

Neural Correlates of Metacognition Impairment in Opioid Addiction

BackgroundIndividuals with substance use disorder show impaired self-awareness of ongoing behavior. This deficit suggests problems with metacognition, which has been operationalized in the cognitive neuroscience literature as the ability to monitor and evaluate the success of one’s own cognition and behavior. However, the neural mechanisms of metacognition have not been characterized in a population with drug addiction. MethodsCommunity samples of participants with opioid use disorder (OUD) (n = 27) and healthy control participants (n = 29) performed a previously validated functional magnetic resonance imaging metacognition task (perceptual decision-making task along with confidence ratings of performance). Measures of recent drug use and addiction severity were also acquired. ResultsIndividuals with OUD had lower metacognitive sensitivity (i.e., disconnection between task performance and task-related confidence) than control individuals. Trial-by-trial analyses showed that this overall group difference was driven by (suboptimally) low confidence in participants with OUD during correct trials. In functional magnetic resonance imaging analyses, the task engaged an expected network of brain regions (e.g., rostrolateral prefrontal cortex and dorsal anterior cingulate/supplementary motor area, both previously linked to metacognition); group differences emerged in a large ventral anterior cluster that included the medial and lateral orbitofrontal cortex and striatum (higher activation in OUD). Trial-by-trial functional magnetic resonance imaging analyses showed group differences in rostrolateral prefrontal cortex activation, which further correlated with metacognitive behavior across all participants. Exploratory analyses suggested that the behavioral and neural group differences were exacerbated by recent illicit opioid use and unexplained by general cognition. ConclusionsWith confirmation and extension of these findings, metacognition and its associated neural circuits could become new, promising therapeutic targets in addiction.

Endogenous preparatory control is associated with increased interaction between default mode and dorsal attention networks

Abstract It is increasingly recognized that cognitive control requires integration across large-scale brain networks anchored in frontal and parietal cortices. While the functional role of individual networks has been studied extensively, their cross-network interactions in the service of cognitive control are poorly understood. Beyond in-the-moment regulation of goal-relevant information processing (e.g., of sensory information), cognitive control encompasses preparatory processes in anticipation of upcoming stimuli and actions. Such preparatory control is often endogenous, that is, it is based on internal representations without relying on external cues or events. Here, we assessed network interactions that support such endogenously driven preparatory control. We recorded fMRI (N = 25) during a perceptual decision task with highly variable intertrial intervals. In half of the blocks, trial onset was cued, while in the remaining blocks, participants maintained readiness without relying on cues. We studied endogenous preparatory control in the intertrial period preceding uncued (vs. cued) trials. Behavioral outcomes confirmed heavier cognitive control demands in the uncued condition. Endogenous preparatory control was associated with increased activity of the dorsal attention network (DAN). This contrasted with in-the-moment control over stimulus-response processing during the trial itself, which was supported foremost by the right-hemispheric fronto-parietal network (FPN). Cross-network interactions were strengthened exclusively during endogenous preparatory control; the default mode network (DMN) showed more positive connectivity with the DAN and to a lesser degree the cingulo-opercular network (CON). Our results demonstrate that cross-networks interactions are particularly important for endogenously driven preparatory control. They further suggest that the DMN may be implicated in internally harnessing resources for cognitive control. This notion extends the DMN’s known role in internally-oriented processing to the domain of cognitive control when preparation cannot be aided by external events.

Galvanic vs. pulsatile effects on decision-making networks: reshaping the neural activation landscape

Objective. Primarily due to safety concerns, biphasic pulsatile stimulation (PS) is the present standard for electrical excitation of neural tissue with a diverse set of applications. While pulses have been shown to be effective to achieve functional outcomes, they have well-known deficits. Due to recent technical advances, galvanic stimulation (GS), delivery of current for extended periods of time (>1 s), has re-emerged as an alternative to PS. Approach. In this paper, we use a winner-take-all decision-making cortical network model to investigate differences between pulsatile and GS in the context of a perceptual decision-making task. Main results. Based on previous work, we hypothesized that GS would produce more spatiotemporally distributed, network-sensitive neural responses, while PS would produce highly synchronized activation of a limited group of neurons. Our results in-silico support these hypotheses for low-amplitude GS but deviate when galvanic amplitudes are large enough to directly activate or block nearby neurons. Significance. We conclude that with careful parametrization, GS could overcome some limitations of PS to deliver more naturalistic firing patterns in the group of targeted neurons.

Threat Expectation Does Not Improve Perceptual Discrimination despite Causing Heightened Priority Processing in the Frontoparietal Network.

Threat cues have been widely shown to elicit increased sensory and attentional neural processing. However, whether this enhanced recruitment leads to measurable behavioral improvements in perception is still in question. Here, we adjudicate between two opposing theories: that threat cues do or do not enhance perceptual sensitivity. We created threat stimuli by pairing one direction of motion in a random dot kinematogram with an aversive sound. While in the MRI scanner, 46 subjects (both men and women) completed a cued (threat/safe/neutral) perceptual decision-making task where they indicated the perceived motion direction of each moving dot stimulus. We found strong evidence that threat cues did not increase perceptual sensitivity compared with safe and neutral cues. This lack of improvement in perceptual decision-making ability occurred despite the threat cue resulting in widespread increases in frontoparietal BOLD activity, as well as increased connectivity between the right insula and the frontoparietal network. These results call into question the intuitive claim that expectation automatically enhances our perception of threat and highlight the role of the frontoparietal network in prioritizing the processing of threat-related environmental cues.

A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal

Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.

Passive exposure to task-relevant stimuli enhances categorization learning.

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.

Passive exposure to task-relevant stimuli enhances categorization learning

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.

Individual differences in the evaluation of ambiguous visual and auditory threat-related expressions.

This study investigated the neural correlates of the judgement of auditory and visual ambiguous threat-related information, and the influence of state anxiety on this process. Healthy subjects were scanned using a fast, high-resolution functional magnetic resonance imaging (fMRI) multiband sequence while they performed a two-alternative forced-choice emotion judgement task on faces and vocal utterances conveying explicit anger or fear, as well as ambiguous ones. Critically, the latter was specific to each subject, obtained through a morphing procedure and selected prior to scanning following a perceptual decision-making task. Behavioural results confirmed a greater task-difficulty for subject-specific ambiguous stimuli and also revealed a judgement bias for visual fear, and, to a lesser extent, for auditory anger. Imaging results showed increased activity in regions of the salience and frontoparietal control networks (FPCNs) and deactivation in areas of the default mode network for ambiguous, relative to explicit, expressions. In contrast, the right amygdala (AMG) responded more strongly to explicit stimuli. Interestingly, its response to the same ambiguous stimulus depended on the subjective judgement of the expression. Finally, we found that behavioural and neural differences between ambiguous and explicit expressions decreased as a function of state anxiety scores. Taken together, our results show that behavioural and brain responses to emotional expressions are determined not only by emotional clarity but also modality and the subjects' subjective perception of the emotion expressed, and that some of these responses are modulated by state anxiety levels.

Preferences for advisor agreement and accuracy.

Previous research has shown that people are more influenced by advisors who are objectively more accurate, but also by advisors who tend to agree with their own initial opinions. The present experiments extend these ideas to consider people's choices of who they receive advice from-the process of source selection. Across a series of nine experiments, participants were first exposed to advisors who differed in objective accuracy, the likelihood of agreeing with the participants' judgments, or both, and then were given choice over who would advise them across a series of decisions. Participants saw these advisors in the context of perceptual decision and general knowledge tasks, sometimes with feedback provided and sometimes without. We found evidence that people can discern accurate from inaccurate advice even in the absence of feedback, but that without feedback they are biased to select advisors who tend to agree with them. When choosing between advisors who are accurate vs. likely to agree with them, participants overwhelmingly choose accurate advisors when feedback is available, but show wide individual differences in preference when feedback is absent. These findings extend previous studies of advice influence to characterise patterns of advisor choice, with implications for how people select information sources and learn accordingly.