Perceptual Decision-making Task Research Articles

Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex

We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.

Resting-state EEG dynamics help explain differences in response control in ADHD: Insight into electrophysiological mechanisms and sex differences.

Reductions in response control (greater reaction time variability and commission error rate) are consistently observed in those diagnosed with attention-deficit/hyperactivity disorder (ADHD). Previous research suggests these reductions arise from a dysregulation of large-scale cortical networks. Here, we extended our understanding of this cortical-network/response-control pathway important to the neurobiology of ADHD. First, we assessed how dynamic changes in three resting-state EEG network properties thought to be relevant to ADHD (phase-synchronization, modularity, oscillatory power) related with response control during a simple perceptual decision-making task in 112 children/adolescents (aged 8-16) with and without ADHD. Second, we tested whether these associations differed in males and females who were matched in age, ADHD-status and ADHD- subtype. We found that changes in oscillatory power (as opposed to phase-synchrony and modularity) are most related with response control, and that this relationship is stronger in ADHD compared to controls. Specifically, a tendency to dwell in an electrophysiological state characterized by high alpha/beta power (8-12/13-30Hz) and low delta/theta power (1-3/4-7Hz) supported response control, particularly in those with ADHD. Time in this state might reflect an increased initiation of alpha-suppression mechanisms, recruited by those with ADHD to suppress processing unfavourable to response control. We also found marginally significant evidence that this relationship is stronger in males compared to females, suggesting a distinct etiology for response control in the female presentation of ADHD.

Quantifying the contribution of subject and group factors in brain activation.

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

OCD symptoms are related to seeking and relying on external information even in neutral perceptual decisions

IntroductionDoubt and decision-making difficulties are very common in obsessive-compulsive disorder (OCD). In the present study, we focused on a particular feature of decision-making in OCD – increased information-seeking. Based on the Seeking Proxies for Internal States and the Intolerance for Uncertainty theoretical frameworks, we hypothesized that OCD symptoms will be positively associated with information-seeking, even for neutral perceptual decisions. MethodFifty-eight UK participants were recruited via Prolific. They performed a perceptual decision-making task and completed questionnaires assessing OCD and associated variables. The perceptual task entailed locating the exact mid-point of a brightness continuum of a specific hue. Upon request, participants could obtain objective hints (purported responses of other participants), although hints incurred time-out penalties. ResultsConsistent with our hypothesis, OCD symptom levels predicted how many hints participants requested, even after controlling for anxiety and depression symptoms. Additionally, OCD symptoms were partially related to indecisiveness in the task. DiscussionOur findings suggest that obsessive-compulsive tendencies are related to indecisiveness and to seeking external information even in a neutral context. Moreover, OCD tendencies were related to finding external information desirable enough to justify mildly aversive penalties. This need for clarity and objectivity might account for the development of compulsions despite personal costs.

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Objective. When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain–computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. Approach. We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. Main results. We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Significance. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Conspiracy beliefs are associated with a reduction in frontal beta power and biases in categorizing ambiguous stimuli

Prior beliefs, such as conspiracy beliefs, significantly influence our perception of the natural world. However, the brain activity associated with perceptual decision-making in conspiracy beliefs is not well understood. To shed light on this topic, we conducted a study examining the EEG activity of believers, and skeptics during resting state with perceptual decision-making task. Our study shows that conspiracy beliefs are related to the reduced power of beta frequency band. Furthermore, skeptics tended to misclassify ambiguous face stimuli as houses more frequently than believers. These results help to explain the differences in brain activity between believers and skeptics, especially in how conspiracy beliefs impact the categorization of ambiguous stimuli.

Metacognitive Information Theory.

The capacity that subjects have to rate confidence in their choices is a form of metacognition, and can be assessed according to bias, sensitivity and efficiency. Rich networks of domain-specific and domain-general regions of the brain are involved in the rating, and are associated with its quality and its use for regulating the processes of thinking and acting. Sensitivity and efficiency are often measured by quantities called meta-d' and the M-ratio that are based on reverse engineering the potential accuracy of the original, primary, choice that is implied by the quality of the confidence judgements. Here, we advocate a straightforward measure of sensitivity, called meta-𝓘, which assesses the mutual information between the accuracy of the subject's choices and the confidence reports, and two normalized versions of this measure that quantify efficiency in different regimes. Unlike most other measures, meta-𝓘-based quantities increase with the number of correctly assessed bins with which confidence is reported. We illustrate meta-𝓘 on data from a perceptual decision-making task, and via a simple form of simulated second-order metacognitive observer.

Predictive saccades and decision making in the beetle-predating saffron robber fly.

Internal predictions about the sensory consequences of self-motion, encoded by corollary discharge, are ubiquitous in the animal kingdom, including for fruit flies, dragonflies, and humans. In contrast, predicting the future location of an independently moving external target requires an internal model. With the use of internal models for predictive gaze control, vertebrate predatory species compensate for their sluggish visual systems and long sensorimotor latencies. This ability is crucial for the timely and accurate decisions that underpin a successful attack. Here, we directly demonstrate that the robber fly Laphria saffrana, a specialized beetle predator, also uses predictive gaze control when head tracking potential prey. Laphria uses this predictive ability to perform the difficult categorization and perceptual decision task of differentiating a beetle from other flying insects with a low spatial resolution retina. Specifically, we show that (1) this predictive behavior is part of a saccade-and-fixate strategy, (2) the relative target angular position and velocity, acquired during fixation, inform the subsequent predictive saccade, and (3) the predictive saccade provides Laphria with additional fixation time to sample the frequency of the prey's specular wing reflections. We also demonstrate that Laphria uses such wing reflections as a proxy for the wingbeat frequency of the potential prey and that consecutively flashing LEDs to produce apparent motion elicits attacks when the LED flicker frequency matches that of the beetle's wingbeat cycle.

Integrated control of non-motor and motor efforts during perceptual decision-making and action execution: a pilot study

Humans daily life is characterized by a succession of voluntary actions. Since energy resources are limited, the ability to invest the appropriate amount of effort for selecting and executing these actions is a hallmark of adapted behavior. Recent studies indicate that decisions and actions share important principles, including the optimization of their duration when the context requires it. In the present pilot study, we test the hypothesis that the management of effort-related energy resources is shared between decision and action too. Healthy human subjects performed a perceptual decision task where they had to choose between two levels of effort to invest in making the decision (i.e. two levels of perceptual difficulty), and report it with a reaching movement. Crucially, the movement accuracy requirement gradually increased from trial to trial depending on participants’ decision performance. Results indicate an overall moderate and non-significant impact of the increasing motor difficulty on the choice of the non-motor (decision) effort to invest in each trial and on decision performance. By contrast, motor performance strongly decreased depending on both the motor and decisional difficulties. Together, the results support the hypothesis of an integrated management of the effort-related energy resources between decision and action. They also suggest that in the present task, the mutualized resources are primarily allocated to the decision-making process to the detriment of movements.

Associative memory retrieval modulates upcoming perceptual decisions.

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participant's responses used both memory and sensory information, in accordance to their relative reliability. Formal model comparison showed that the sensory inference was best explained when its parameters were set dynamically at each trial by evidence sampled from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the specific content and fidelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous sampling of memory and sensory evidence.

Stabilizing expectations when shifting from analytical to intuitive reasoning: The role of prediction errors in reasoning

As humans, we rely on intuitive reasoning for most of our decisions. However, when there is a novel or atypical decision to be made, we must rely on a slower and more deliberative thought process—analytical reasoning. As we gain experience with these novel or atypical decisions, our reasoning shifts from analytical to intuitive, which parallels a reduction in the need for cognitive control. Here, we sought to confirm this claim by employing electroencephalographic (EEG) measures of cognitive control as participants performed a simple perceptual decision-making task. Specifically, we had participants categorize “blobs” into families based on their visual attributes so we could examine how their reasoning changed with learning. In a key manipulation, halfway through the experiment we introduced novel blob families to categorize, thus temporarily increasing the need for analytical reasoning (i.e., cognitive control). Congruent with past research, we focused our EEG analyses on frontal theta activity as it has been linked to cognitive control and analytical thinking. As hypothesized, we found a transition from analytical to intuitive decision-making systems with learning as indexed by a decrease in frontal theta power. Further, when the novel blobs were introduced at the midpoint of the experiment, we found that decisions about these stimuli recruited analytical reasoning as indicated by increased theta power in comparison to decisions about well-practiced stimuli. We propose our findings to reflect prediction errors to decision demands—a monitoring process that determines whether our expectations of demands are met. Shifting from analytical to intuitive reasoning thus reflects the stabilization of our expectations of decision demands, which can be violated with unexpected demands when encountering novel stimuli.

Microstructural and neurochemical plasticity mechanisms interact to enhance human perceptual decision-making.

Experience and training are known to boost our skills and mold the brain's organization and function. Yet, structural plasticity and functional neurotransmission are typically studied at different scales (large-scale networks, local circuits), limiting our understanding of the adaptive interactions that support learning of complex cognitive skills in the adult brain. Here, we employ multimodal brain imaging to investigate the link between microstructural (myelination) and neurochemical (GABAergic) plasticity for decision-making. We test (in males, due to potential confounding menstrual cycle effects on GABA measurements in females) for changes in MRI-measured myelin, GABA, and functional connectivity before versus after training on a perceptual decision task that involves identifying targets in clutter. We demonstrate that training alters subcortical (pulvinar, hippocampus) myelination and its functional connectivity to visual cortex and relates to decreased visual cortex GABAergic inhibition. Modeling interactions between MRI measures of myelin, GABA, and functional connectivity indicates that pulvinar myelin plasticity interacts-through thalamocortical connectivity-with GABAergic inhibition in visual cortex to support learning. Our findings propose a dynamic interplay of adaptive microstructural and neurochemical plasticity in subcortico-cortical circuits that supports learning for optimized decision-making in the adult human brain.

FV 2 Mapping encoding and flow of task-relevant information from human MEG recordings in a perceptual decision-making task

FV 2 Mapping encoding and flow of task-relevant information from human MEG recordings in a perceptual decision-making task

Practice-related changes in perceptual evidence accumulation correlate with changes in working memory.

It has been proposed that evidence accumulation determines not only the speed and accuracy of simple perceptual decisions but also influences performance on tasks assessing higher-order cognitive abilities, such as working memory (WM). Accordingly, estimates of evidence accumulation based on diffusion decision modeling of perceptual decision-making tasks have been found to correlate with WM performance. Here we use diffusion decision modeling in combination with latent factor modeling to test the stronger prediction that practice-induced changes in evidence accumulation correlate with changes in WM performance. Analyses are based on data from the COGITO Study, in which 101 young adults practiced a battery of cognitive tasks, including three simple two-choice reaction time tasks and three WM tasks, in 100 day-to-day training sessions distributed over 6 months. In initial analyses, drift rates were found to correlate across the three choice tasks, such that latent factors of evidence accumulation could be established. These latent factors of evidence accumulation were positively correlated with latent factors of practiced and unpracticed WM tasks, both before and after practice. As predicted, individual differences in changes of evidence accumulation correlated positively with changes in WM performance. Our findings support the proposition that decision making and WM both rely on the active maintenance of task-relevant internal representations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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

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

Changing minds about climate change: a pervasive role for domain-general metacognition

Updating one’s beliefs about the causes and effects of climate change is crucial for altering attitudes and behaviours. Importantly, metacognitive abilities - insight into the (in)correctness of one’s beliefs- play a key role in the formation of polarised beliefs. We here aimed at investigated the role of metacognition in changing beliefs about climate change. To that end, we focused on the role of domain-general and domain-specific metacognition in updating prior beliefs about climate change across the spectrum of climate change scepticism. We also considered the role of how climate science is communicated in the form of textual or visuo-textual presentations. We asked two large US samples to perform a perceptual decision-making task (to assess domain-general decision-making and metacognitive abilities. They next performed a belief-updating task, where they were exposed to good and bad news about climate change and we asked them about their beliefs and their updating. Lastly, they completed a series of questionnaires probing their attitudes to climate change. We show that climate change scepticism is associated with differences in domain-general as well as domain-specific metacognitive abilities. Moreover, domain-general metacognitive sensitivity influenced belief updating in an asymmetric way: lower domain-general metacognition decreased the updating of prior beliefs, especially in the face of negative evidence. Our findings highlight the role of metacognitive failures in revising erroneous beliefs about climate change and point to their adverse social effects.

Methods for eliciting and measuring behavioral and physiological consequences of stress and uncertainty in virtual reality

Military operations are characterized by high levels of stress and uncertainty, and these states can influence cognitive and physical performance outcomes. These states, however, can be difficult to reliably induce in laboratory contexts, making it challenging to quantify and model their influences on perceptual and cognitive processes underlying performance on applied tasks. Herein we describe the development and validation of a novel scenario-based virtual reality methodology, the decision making under uncertainty and stress (DeMUS) scenario, that accomplishes four primary goals. First, it induces physiological and biochemical stress responses through a threat of shock manipulation. Second, it induces transient states of uncertainty by manipulating stimulus clarity in a perceptual decision-making task. Third, it generates several performance metrics regarding recognition memory, spatial orienting, threat classification, and marksmanship decision making. Finally, the task combines behavioral, physiological, and biochemical measures to provide a more comprehensive understanding of how stress and uncertainty influence applied task performance. To provide an initial validation of the scenario and its associated tasks and measures, we conducted a pilot study (n= 18) involving stress induction and cognitive performance assessment. Analyses revealed that: 1) the DeMUS scenario elicited tonic and phasic biochemical (salivary alpha amylase and cortisol) and physiological (heart rate, pupil diameter) stress responses, 2) the scenario elicited variable sympathetic autonomic nervous system and hypothalamic-pituitary adrenal (HPA) axis responses, and 3) stress influenced some measures of memory and decision-making in both negative and positive directions. Continuing research will assess individual- and group-level predictors of performance on these virtual reality tasks, and emerging performance enhancement techniques that can help military personnel sustain performance during stressful operations.

Residual dynamics resolves recurrent contributions to neural computation.

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

Are you an empiricist or a believer? Neural signatures of predictive strategies in humans.

Predictive coding theory suggests that prior knowledge assists human behavior, from simple perceptual formation to complex decision-making processes. Here, we manipulate prior knowledge by inducing uninformative vs. informative (low and high) target probability expectation in a perceptual decision-making task while simultaneously recording EEG. We found that priors did not impact sensitivity (d') but did shape response criterion (c), being more liberal for high expected trials and more conservative for low expected trials. Importantly, we mapped the neural signature of this criterion shift, with liberal and conservative trials characterized by low and high posterior alpha amplitude, respectively. Moreover, we demonstrated that inter-areas communication along the fronto-parietal-occipital pathway is linked to the strategic tuning of sensory areas. Specifically, whereas parieto-occipital alpha synchronization facilitates the exploitation of expectancy-type information by shaping pre-stimulus alpha amplitude in a prior-dependent fashion, fronto-parietal theta coupling mediates a supervisory process on the predictive machinery, attenuating the impact of prior on sensory processing. These findings aided us in tracing the neurofunctional mechanisms underlying the differences in predictive styles existing in the general population. Crucially, an imbalance between alpha and theta synchronization leads to interindividual differences favoring priors overweighting (believers) vs. prioritization of sensory input (empiricist) strategy, respectively.

Enhanced perceptual task performance without deprivation in mice using medial forebrain bundle stimulation

Perceptual decision-making tasks are essential to many fields of neuroscience. Current protocols generally reward deprived animals with water. However, balancing animals' deprivation level with their well-being is challenging, and trial number is limited by satiation. Here, we present electrical stimulation of the medial forebrain bundle (MFB) as an alternative that avoids deprivation while yielding stable motivation for thousands of trials. Using licking or lever press as a report, MFB animals learnt auditory discrimination tasks at similar speed to water-deprived mice. Moreover, they more reliably reached higher accuracy in harder tasks, performing up to 4,500 trials per session without loss of motivation. MFB stimulation did not impact the underlying sensory behavior since psychometric parameters and response times are preserved. MFB mice lacked signs of metabolic or behavioral stress compared with water-deprived mice. Overall, MFB stimulation is a highly promising tool for task learning because it enhances task performance while avoiding deprivation.