A general mechanism for perceptual decision-making in the human brain

A neural code supporting prospective probabilistic reasoning for instrumental information demand in humans

When making adaptive decisions, we actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain prospectively estimates the information gains that are expected to accrue from various sources by integrating simpler quantities of prior certainty and the reliability (diagnosticity) of a source. We examine this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently in the rewards, decision uncertainty, and information diagnosticity. We show that, consistent with value of information theory, the participants' bids are sensitive to prior certainty (the certainty about the correct choice before gathering information) and expected posterior certainty (the certainty expected after gathering information). Expected posterior certainty is decoded from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation is independent of instrumental rewards and spatially overlaps with distinct representations of prior certainty and expected information gains. The findings suggest that the posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to anticipating information gains during active gathering of instrumental information.

Overlapping Central Clock Network Circuitry Regulates Circadian Feeding and Activity Rhythms in Drosophila

The circadian system coordinates multiple behavioral outputs to ensure proper temporal organization. Timing information underlying circadian regulation of behavior depends on a molecular circadian clock that operates within clock neurons in the brain. In Drosophila and other organisms, clock neurons can be divided into several molecularly and functionally discrete subpopulations that form an interconnected central clock network. It is unknown how circadian signals are coherently generated by the clock network and transmitted across output circuits that connect clock cells to downstream neurons that regulate behavior. Here, we have exhaustively investigated the contribution of clock neuron subsets to the control of two prominent behavioral outputs in Drosophila: locomotor activity and feeding. We have used cell-specific manipulations to eliminate molecular clock function or induce electrical silencing either broadly throughout the clock network or in specific subpopulations. We find that clock cell manipulations produce similar changes in locomotor activity and feeding, suggesting that overlapping central clock circuitry regulates these distinct behavioral outputs. Interestingly, the magnitude and nature of the effects depend on the clock subset targeted. Lateral clock neuron manipulations profoundly degrade the rhythmicity of feeding and activity. In contrast, dorsal clock neuron manipulations only subtly affect rhythmicity but produce pronounced changes in the distribution of activity and feeding across the day. These experiments expand our knowledge of clock regulation of activity rhythms and offer the first extensive characterization of central clock control of feeding rhythms. Despite similar effects of central clock cell disruptions on activity and feeding, we find that manipulations that prevent functional signaling in an identified output circuit preferentially degrade locomotor activity rhythms, leaving feeding rhythms relatively intact. This demonstrates that activity and feeding are indeed dissociable behaviors, and furthermore suggests that differential circadian control of these behaviors diverges in output circuits downstream of the clock network.

Face processing in animal models: implications for autism spectrum disorder

Processing facial features is crucial to identify social partners (prey, predators, or conspecifics) and recognize and accurately interpret emotional expressions. Numerous studies in both human and non-human primates provided evidence promoting the notion of inherent mechanisms for detecting facial features. These mechanisms support a representation of faces independent of prior experiences and are vital for subsequent development in social and language domains. Moreover, deficits in processing faces are a reliable biomarker of autism spectrum disorder, appearing early and correlating with symptom severity. Face processing, however, is not only a prerogative of humans: other species also show remarkable face detection abilities. In this review, we present an overview of the current literature on face detection in vertebrate models that could be relevant to the study of autism.

Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation

Efficiency of evidence accumulation (EEA), an individual's ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from 5198 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in the "task-positive" frontoparietal and dorsal attention networks. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks' flexible adaptation to cognitive demands is a key neural underpinning of EEA.

Mild exogenous inflammation blunts neural signatures of bounded evidence accumulation and reward prediction error processing in healthy male participants

BACKGROUND

Altered neural haemodynamic activity during decision making and learning has been linked to the effects of inflammation on mood and motivated behaviours. So far, it has been reported that blunted mesolimbic dopamine reward signals are associated with inflammation-induced anhedonia and apathy. Nonetheless, it is still unclear whether inflammation impacts neural activity underpinning decision dynamics. The process of decision making involves integration of noisy evidence from the environment until a critical threshold of evidence is reached. There is growing empirical evidence that such process, which is usually referred to as bounded accumulation of decision evidence, is affected in the context of mental illness.

METHODS

In a randomised, placebo-controlled, crossover study, 19 healthy male participants were allocated to placebo and typhoid vaccination. Three to four hours post-injection, participants performed a probabilistic reversal-learning task during functional magnetic resonance imaging. To capture the hidden neurocognitive operations underpinning decision-making, we devised a hybrid sequential sampling and reinforcement learning computational model. We conducted whole brain analyses informed by the modelling results to investigate the effects of inflammation on the efficiency of decision dynamics and reward learning.

RESULTS

We found that during the decision phase of the task, typhoid vaccination attenuated neural signatures of bounded evidence accumulation in the dorsomedial prefrontal cortex, only for decisions requiring short integration time. Consistent with prior work, we showed that, in the outcome phase, mild acute inflammation blunted the reward prediction error in the bilateral ventral striatum and amygdala.

CONCLUSIONS

Our study extends current insights into the effects of inflammation on the neural mechanisms of decision making and shows that exogenous inflammation alters neural activity indexing efficiency of evidence integration, as a function of choice discriminability. Moreover, we replicate previous findings that inflammation blunts striatal reward prediction error signals.

Social influence and social identity: A diffusion model analysis

Building on the seminal studies of Solomon Asch and Muzafer Sherif, recent research has advanced our understanding of the mechanisms underlying social influence by applying a diffusion model analysis. Here, we combined the social identity approach to social influence with a diffusion model analysis to unravel the mechanisms underlying social influence. In particular, we aimed to disentangle whether the difference between in-group and out-group influence on perceptual decision-making is driven by a judgmental bias (i.e., changes in decision criteria) or a perceptual bias (i.e., changes in the uptake of sensory information). Preregistered analyses indicated that in-groups exerted stronger social influence than out-groups because in-groups induced a stronger perceptual bias than out-groups. This finding is in line with the single process assumption of the social identity approach because it implicates that the single process driving social influence (i.e., self-categorisation) translates into a change in a single subprocess of decision-making (i.e., biased information uptake). In conclusion, our results highlight that our theoretical understanding of social influence can be expanded by integrating the social identity approach with a diffusion model analysis.

Prioritized neural processing of social threats during perceptual decision-making

Emotional signals, notably those signaling threat, benefit from prioritized processing in the human brain. Yet, it remains unclear whether perceptual decisions about the emotional, threat-related aspects of stimuli involve specific or similar neural computations compared to decisions about their non-threatening/non-emotional components. We developed a novel behavioral paradigm in which participants performed two different detection tasks (emotion vs. color) on the same, two-dimensional visual stimuli. First, electroencephalographic (EEG) activity in a cluster of central electrodes reflected the amount of perceptual evidence around 100 ms following stimulus onset, when the decision concerned emotion, not color. Second, participants' choice could be predicted earlier for emotion (240 ms) than for color (380 ms) by the mu (10 Hz) rhythm, which reflects motor preparation. Taken together, these findings indicate that perceptual decisions about threat-signaling dimensions of facial displays are associated with prioritized neural coding in action-related brain regions, supporting the motivational value of socially relevant signals.

Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning

Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. To our knowledge, however, drift-diffusion models for such scenarios have never been considered in the literature before. To address this gap, in this article, we build carefully on Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021), but now with latent response times integrated out, to derive a novel biologically interpretable class of 'inverse-probit' categorical probability models for observed categories alone. However, this new marginal model presents significant identifiability and inferential challenges not encountered originally for the joint model in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). We address these new challenges using a novel projection-based approach with a symmetry-preserving identifiability constraint that allows us to work with conjugate priors in an unconstrained space. We adapt the model for group and individual-level inference in longitudinal settings. Building again on the model's latent variable representation, we design an efficient Markov chain Monte Carlo algorithm for posterior computation. We evaluate the empirical performance of the method through simulation experiments. The practical efficacy of the method is illustrated in applications to longitudinal tone learning studies.

Expression of guanylate cyclase C in human prefrontal cortex depends on sex and feeding status

Introduction

Guanylate cyclase C (GC-C) has been detected in the rodent brain in neurons of the cerebral cortex, amygdala, midbrain, hypothalamus, and cerebellum.

Methods

In this study we determined GC-C protein expression in Brodmann areas (BA) 9, BA10, BA11, and BA32 of the human prefrontal cortex involved in regulation of feeding behavior, as well as in the cerebellar cortex, arcuate nucleus of hypothalamus and substantia nigra in brain samples of human 21 male and 13 female brains by ELISA with postmortem delay < 24 h.

Results

GC-C was found in all tested brain areas and it was expressed in neurons of the third cortical layer of BA9. The regulation of GC-C expression by feeding was found in male BA11 and BA10-M, where GC-C expression was in negative correlation to the volume of stomach content during autopsy. In female BA11 there was no correlation detected, while in BA10-M there was even positive correlation. This suggests sex differences in GC-C expression regulation in BA11 and BA10-M. The amount of GC-C was higher in female BA9 only when the death occurred shortly after a meal, while expression of GC-C was higher in BA10-O only when the stomach was empty. The expression of GC-C in female hypothalamus was lower when compared to male hypothalamus only when the stomach was full, suggesting possibly lower satiety effects of GC-C agonists in women.

Discussion

These results point toward the possible role of GC-C in regulation of feeding behavior. Since, this is first study of GC-C regulation and its possible function in prefrontal cortex, to determine exact role of GC-C in different region of prefrontal cortex, especially in humans, need further studies.

Modality-specific impacts of distractors on visual and auditory categorical decision-making: an evidence accumulation perspective

Our brain constantly processes multisensory inputs to make decisions and guide behaviors, but how goal-relevant processes are influenced by irrelevant information is unclear. Here, we investigated the effects of intermodal and intramodal task-irrelevant information on visual and auditory categorical decision-making. In both visual and auditory tasks, we manipulated the modality of irrelevant inputs (visual vs. auditory vs. none) and used linear discrimination analysis of EEG and hierarchical drift-diffusion modeling (HDDM) to identify when and how task-irrelevant information affected decision-relevant processing. The results revealed modality-specific impacts of irrelevant inputs on visual and auditory categorical decision-making. The distinct effects on the visual task were shown on the neural components, with auditory distractors amplifying the sensory processing whereas visual distractors amplifying the post-sensory process. Conversely, the distinct effects on the auditory task were shown in behavioral performance and underlying cognitive processes. Visual distractors facilitate behavioral performance and affect both stages, but auditory distractors interfere with behavioral performance and impact on the sensory processing rather than the post-sensory decision stage. Overall, these findings suggested that auditory distractors affect the sensory processing stage of both tasks while visual distractors affect the post-sensory decision stage of visual categorical decision-making and both stages of auditory categorical decision-making. This study provides insights into how humans process information from multiple sensory modalities during decision-making by leveraging modality-specific impacts.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Discrete tactile feature comparison subprocess in human brain during a decision-making process

From sensory input to motor action, encoded sensory features flow sequentially along cortical networks for decision-making. Despite numerous studies probing the decision-making process, the subprocess that compares encoded sensory features before making a decision has not been fully elucidated in humans. In this study, we investigated sensory feature comparison by presenting two different tasks (a discrimination task, in which participants made decisions by comparing two sequential tactile stimuli; and a detection task, in which participants responded to the second tactile stimulus in two sequential stimuli) to epilepsy patients while recording electrocorticography (ECoG). By comparing tactile-specific gamma band (30-200 Hz) power between the two tasks, the decision-making process was divided into three subprocesses-categorization, comparison, and decision-consistent with a previous study (Heekeren et al., 2004). These subprocesses occurred sequentially in the dorsolateral prefrontal cortex, premotor cortex, secondary somatosensory cortex, and parietal lobe. Gamma power showed two different patterns of correlation with response time. In the inferior parietal lobule (IPL), there was a negative correlation. This means that as gamma power increased, response time decreased. In the secondary somatosensory cortex (S2), there was a positive correlation. Here, as gamma power increased, response time also increased. These results indicate that the IPL and S2 encode tactile feature comparison differently. Our connectivity analysis showed that the S2 transmitted tactile information to the IPL. Our findings suggest that multiple areas in the parietal lobe encode sensory feature comparison differently before making a decision.

Neural circuit models for evidence accumulation through choice-selective sequences

Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.

Impact of aversive affect on neural mechanisms of categorization decisions

INTRODUCTION

Many theories contend that evidence accumulation is a critical component of decision-making. Cognitive accumulation models typically interpret two main parameters: a drift rate and decision threshold. The former is the rate of accumulation, based on the quality of evidence, and the latter is the amount of evidence required for a decision. Some studies have found neural signals that mimic evidence accumulators and can be described by the two parameters. However, few studies have related these neural parameters to experimental manipulations of sensory data or memory representations. Here, we investigated the influence of affective salience on neural accumulation parameters. High affective salience has been repeatedly shown to influence decision-making, yet its effect on neural evidence accumulation has been unexamined.

METHODS

The current study used a two-choice object categorization task of body images (feet or hands). Half the images in each category were high in affective salience because they contained highly aversive features (gore and mutilation). To study such quick categorization decisions with a relatively slow technique like functional magnetic resonance imaging, we used a gradual reveal paradigm to lengthen cognitive processing time through the gradual "unmasking" of stimuli.

RESULTS

Because the aversive features were task-irrelevant, high affective salience produced a distractor effect, slowing decision time. In visual accumulation regions of interest, high affective salience produced a longer time to peak activation. Unexpectedly, the later peak appeared to be the product of changes to both drift rate and decision threshold. The drift rate for high affective salience was shallower, and the decision threshold was greater. To our knowledge, this is the first demonstration of an experimental manipulation of sensory data or memory representations that changed the neural decision threshold.

CONCLUSION

These findings advance our knowledge of the neural mechanisms underlying affective responses in general and the influence of high affective salience on object representations and categorization decisions.

TMS disruption of the lateral prefrontal cortex increases neural activity in the default mode network when naming facial expressions

Recognizing facial expressions is dependent on multiple brain networks specialized for different cognitive functions. In the current study, participants (N = 20) were scanned using functional magnetic resonance imaging (fMRI), while they performed a covert facial expression naming task. Immediately prior to scanning thetaburst transcranial magnetic stimulation (TMS) was delivered over the right lateral prefrontal cortex (PFC), or the vertex control site. A group whole-brain analysis revealed that TMS induced opposite effects in the neural responses across different brain networks. Stimulation of the right PFC (compared to stimulation of the vertex) decreased neural activity in the left lateral PFC but increased neural activity in three nodes of the default mode network (DMN): the right superior frontal gyrus, right angular gyrus and the bilateral middle cingulate gyrus. A region of interest analysis showed that TMS delivered over the right PFC reduced neural activity across all functionally localised face areas (including in the PFC) compared to TMS delivered over the vertex. These results suggest that visually recognizing facial expressions is dependent on the dynamic interaction of the face-processing network and the DMN. Our study also demonstrates the utility of combined TMS/fMRI studies for revealing the dynamic interactions between different functional brain networks.

Encoding of continuous perceptual choices in human early visual cortex

Introduction

Research on the neural mechanisms of perceptual decision-making has typically focused on simple categorical choices, say between two alternative motion directions. Studies on such discrete alternatives have often suggested that choices are encoded either in a motor-based or in an abstract, categorical format in regions beyond sensory cortex.

Methods

In this study, we used motion stimuli that could vary anywhere between 0° and 360° to assess how the brain encodes choices for features that span the full sensory continuum. We employed a combination of neuroimaging and encoding models based on Gaussian process regression to assess how either stimuli or choices were encoded in brain responses.

Results

We found that single-voxel tuning patterns could be used to reconstruct the trial-by-trial physical direction of motion as well as the participants' continuous choices. Importantly, these continuous choice signals were primarily observed in early visual areas. The tuning properties in this region generalized between choice encoding and stimulus encoding, even for reports that reflected pure guessing.

Discussion

We found only little information related to the decision outcome in regions beyond visual cortex, such as parietal cortex, possibly because our task did not involve differential motor preparation. This could suggest that decisions for continuous stimuli take can place already in sensory brain regions, potentially using similar mechanisms to the sensory recruitment in visual working memory.

Neural segregation and integration of sensory, decision, and action processes during object categorization

Neural and computational evidence suggests that perceptual decisions depend on an evidence accumulation process. The gradual reveal fMRI method, which prolongs a decision to match the slow temporal resolution of fMRI measurements, has classified dorsal visual stream regions as "Action" (alternatively, "Moment of Recognition" or "Commitment") and ventral visual stream regions as "Accumulator." Previous gradual reveal fMRI studies, however, only tested actions that were in response to decisions and, thus, related to evidence accumulation. To fully dissociate the contribution of sensory, decision, and motor components to Action and Accumulator regions in the dorsal and ventral visual streams, we extended the gradual reveal paradigm to also include responses made to cues where no decision was necessary. We found that the lateral occipital cortex in the ventral visual stream showed a highly selective Accumulator profile, whereas regions in the fusiform gyrus were influenced by action generation. Dorsal visual stream regions showed strikingly similar profiles as classical motor regions and also as regions of the salience network. These results suggest that the dorsal and ventral visual streams may appear highly segregated because they include a small number of regions that are highly selective for Accumulator or Action. However, the streams may be more integrated than previously thought and this integration may be accomplished by regions with graded responses that are less selective (i.e., more distributed).

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.

The effect of acute high-intensity interval training and Tabata training on inhibitory control and cortical activation in young adults

Introduction

Physical exercise not only benefits peoples' health, but also improves their cognitive function. Although growing evidence suggests that high-intensity interval training (HIIT) is a time-efficient exercise regime that can improve inhibitory control performance by enhancing cortical activation in the prefrontal cortex, less is known about how Tabata training, a subset of HIIT that requires no equipment or facilities to perform, affects inhibitory control and cortical activation in young adults. Therefore, we aimed to reveal the effect of an acute bout of HIIT and Tabata training on inhibitory control and attempted to identify its potential neural substrates.

Methods

Forty-two young adults (mean age: 19.36 ± 1.36 years; 21 females) performed the Stroop task and Simon task before and after acute HIIT, Tabata training, or a control session, and cortical hemodynamic changes in the prefrontal area were monitored by functional near-infrared spectroscopy (fNIRS) during the tasks. Both HIIT and Tabata interventions lasted for a total of 12 min. The HIIT participants performed ergometer cycling at their 80% maximal aerobic power at 90-100 rpm, and the Tabata participants performed a total of 8 intense activities, such as jumping jacks, high knees, and butt kickers, without using equipment or facilities, keeping the heart rate at 80-95% of their maximum heart rate. Participants in the control group watched a sport video while sedentary. Cognitive tasks data and fNIRS data were analyzed by repeated-measures three-way ANOVA.

Results and discussion

Our results indicated that both the HIIT and Tabata groups exhibited reduced reaction times after the intervention, and there were alterations in activation patterns in the dorsolateral and ventrolateral prefrontal cortices.

The effects of transcranial electrical stimulation of the left dorsolateral prefrontal cortex on tactile spatial discrimination performance

The dorsolateral prefrontal cortex (DLPFC) plays a key role in tactile perceptual discrimination performance. Both transcranial random noise stimulation (tRNS) and anodal transcranial pulsed current stimulation (tPCS) have been shown to modulate neural activity in cortical regions. In this study, we aimed to determine whether tRNS and anodal tPCS over the left DLPFC would improve tactile perceptual discrimination performance of the right index finger in healthy neurological individuals. Subjects underwent a grating orientation task before, immediately after, and 30 min after applying tRNS in Experiment 1 or anodal tPCS in Experiment 2. tRNS application on the left DLPFC tended to enhance tactile perceptual discrimination performance. In contrast, the application of anodal tPCS over the left DLPFC did not affect tactile perceptual discrimination performance. These findings indicate that transcranial electrical stimulation to the left DLPFC may improve tactile perceptual discrimination performance, with effects that depend on stimulus modality.

Motor Decision-Making as a Common Denominator in Motor Pathology and a Possible Rehabilitation Target

Despite the substantial progress in motor rehabilitation, patient involvement and motivation remain major challenges. They are typically addressed with communicational and environmental strategies, as well as with improved goal-setting procedures. Here we suggest a new research direction and framework involving Neuroeconomics principles to investigate the role of Motor Decision-Making (MDM) parameters in motivational component and motor performance in rehabilitation. We argue that investigating NE principles could bring new approaches aimed at increasing active patient engagement in the rehabilitation process by introducing more movement choice, and adapting existing goal-setting procedures. We discuss possible MDM implementation strategies and illustrate possible research directions using examples of stroke and psychiatric disorders.

Adaptive coding of stimulus information in human frontoparietal cortex during visual classification

The neural mechanisms of how frontal and parietal brain regions support flexible adaptation of behavior remain poorly understood. Here, we used functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA) to investigate frontoparietal representations of stimulus information during visual classification under varying task demands. Based on prior research, we predicted that increasing perceptual task difficulty should lead to adaptive changes in stimulus coding: task-relevant category information should be stronger, while task-irrelevant exemplar-level stimulus information should become weaker, reflecting a focus on the behaviorally relevant category information. Counter to our expectations, however, we found no evidence for adaptive changes in category coding. We did find weakened coding at the exemplar-level within categories however, demonstrating that task-irrelevant information is de-emphasized in frontoparietal cortex. These findings reveal adaptive coding of stimulus information at the exemplar-level, highlighting how frontoparietal regions might support behavior even under challenging conditions.

Linking paranormal and conspiracy beliefs to illusory pattern perception through signal detection theory

Previous research indicates that irrational beliefs (Paranormal beliefs & conspiracy theory endorsement) are associated with the perception of patterns in noise, but the previous findings do not conclusively describe this relationship. This study aims to disentangle the underlying parameters of this association by applying a signal detection theory approach, thus allowing to distinguish illusory pattern perception (false alarms) from perceptual sensitivity and response tendencies-while also taking base rate information into account. Results from a large sample (N = 723) indicate that paranormal beliefs relate to a more liberal response bias and a lower perceptual sensitivity, and that this relationship is driven by illusory pattern perception. Such a clear pattern could not be observed for conspiracy beliefs, for which the increase in false alarm rates was moderated by the base rate. The associations between irrational beliefs and illusory pattern perception were however less substantial compared to other sources of variance. Implications are discussed.

Benchmarking explanation methods for mental state decoding with deep learning models

Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., experiencing anger or joy) and brain activity by identifying those spatial and temporal features of brain activity that allow to accurately identify (i.e., decode) these states. Once a DL model has been trained to accurately decode a set of mental states, neuroimaging researchers often make use of methods from explainable artificial intelligence research to understand the model's learned mappings between mental states and brain activity. Here, we benchmark prominent explanation methods in a mental state decoding analysis of multiple functional Magnetic Resonance Imaging (fMRI) datasets. Our findings demonstrate a gradient between two key characteristics of an explanation in mental state decoding, namely, its faithfulness and its alignment with other empirical evidence on the mapping between brain activity and decoded mental state: explanation methods with high explanation faithfulness, which capture the model's decision process well, generally provide explanations that align less well with other empirical evidence than the explanations of methods with less faithfulness. Based on our findings, we provide guidance for neuroimaging researchers on how to choose an explanation method to gain insight into the mental state decoding decisions of DL models.

Resolving the neural mechanism of core object recognition in space and time: A computational approach

The underlying mechanism of object recognition- a fundamental brain ability- has been investigated in various studies. However, balancing between the speed and accuracy of recognition is less explored. Most of the computational models of object recognition are not potentially able to explain the recognition time and, thus, only focus on the recognition accuracy because of two reasons: lack of a temporal representation mechanism for sensory processing and using non-biological classifiers for decision-making processing. Here, we proposed a hierarchical temporal model of object recognition using a spiking deep neural network coupled to a biologically plausible decision-making model for explaining both recognition time and accuracy. We showed that the response dynamics of the proposed model can resemble those of the brain. Firstly, in an object recognition task, the model can mimic human's and monkey's recognition time as well as accuracy. Secondly, the model can replicate different speed-accuracy trade-off regimes as observed in the literature. More importantly, we demonstrated that temporal representation of different abstraction levels (superordinate, midlevel, and subordinate) in the proposed model matched the brain representation dynamics observed in previous studies. We conclude that the accumulation of spikes, generated by a hierarchical feedforward spiking structure, to reach abound can well explain not even the dynamics of making a decision, but also the representations dynamics for different abstraction levels.

Noise and vestibular perception of passive self-motion

Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber's law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation.

Evidence for Spinozan "Unbelieving" in the Right Inferior Prefrontal Cortex

Humans can think about possible states of the world without believing in them, an important capacity for high-level cognition. Here, we use fMRI and a novel "shell game" task to test two competing theories about the nature of belief and its neural basis. According to the Cartesian theory, information is first understood, then assessed for veracity, and ultimately encoded as either believed or not believed. According to the Spinozan theory, comprehension entails belief by default, such that understanding without believing requires an additional process of "unbelieving." Participants (n = 70) were experimentally induced to have beliefs, desires, or mere thoughts about hidden states of the shell game (e.g., believing that the dog is hidden in the upper right corner). That is, participants were induced to have specific "propositional attitudes" toward specific "propositions" in a controlled way. Consistent with the Spinozan theory, we found that thinking about a proposition without believing it is associated with increased activation of the right inferior frontal gyrus. This was true whether the hidden state was desired by the participant (because of reward) or merely thought about. These findings are consistent with a version of the Spinozan theory whereby unbelieving is an inhibitory control process. We consider potential implications of these results for the phenomena of delusional belief and wishful thinking.

The neural correlates of value representation: From single items to bundles

One of the core questions in Neuro-economics is to determine where value is represented. To date, most studies have focused on simple options and identified the ventromedial prefrontal cortex (VMPFC) as the common value region. We report the findings of an fMRI study in which we asked participants to make pairwise comparisons involving options of varying complexity: single items (Control condition), bundles made of the same two single items (Scaling condition) and bundles made of two different single items (Bundling condition). We construct a measure of choice consistency to capture how coherent the choices of a participant are with one another. We also record brain activity while participants make these choices. We find that a common core of regions involving the left VMPFC, the left dorsolateral prefrontal cortex (DLPFC), regions associated with complex visual processing and the left cerebellum track value across all conditions. Also, regions in the DLPFC, the ventrolateral prefrontal cortex (VLPFC) and the cerebellum are differentially recruited across conditions. Last, variations in activity in VMPFC and DLPFC value-tracking regions are associated with variations in choice consistency. This suggests that value based decision-making recruits a core set of regions as well as specific regions based on task demands. Further, correlations between consistency and the magnitude of signal change with lateral portions of the PFC suggest the possibility that activity in these regions may play a causal role in decision quality.

Neural Mechanisms That Make Perceptual Decisions Flexible

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

Visual Plasticity in Adulthood: Perspectives from Hebbian and Homeostatic Plasticity

The visual system retains profound plastic potential in adulthood. In the current review, we summarize the evidence of preserved plasticity in the adult visual system during visual perceptual learning as well as both monocular and binocular visual deprivation. In each condition, we discuss how such evidence reflects two major cellular mechanisms of plasticity: Hebbian and homeostatic processes. We focus on how these two mechanisms work together to shape plasticity in the visual system. In addition, we discuss how these two mechanisms could be further revealed in future studies investigating cross-modal plasticity in the visual system.

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

Pre-frontal stimulation does not reliably increase reward responsiveness

Depression is the leading cause of disability worldwide and its effects can be fatal, with over 800,000 people dying by suicide each year. Neuromodulatory treatments such as transcranial magnetic stimulation (TMS) are being used to treat depression. Despite its endorsement by two regulatory bodies: NICE (2016) and the FDA (2008), there are major questions about the treatment efficacy and biological mechanisms of TMS. Ahn et al.'s (2013) justified the use of TMS in a clinical context in an important study indicating that excitatory TMS increases reward responsiveness. A pseudo-replication of this study by Duprat et al., (2016) also found a similar effect of active TMS, but only with the addition of an exploratory covariate to the analyses-trait reward responsiveness. Here we replicate Ahn et al.'s (2013) key study, and to test the reliability of the effects, and their dependency on trait reward responsiveness as described by Duprat et al., (2016). Using excitatory and sham TMS, we tested volunteers using the probabilistic learning task to measure their reward responsiveness both before and after stimulation. We also examined affect (positive, negative) following stimulation. Irrespective of TMS, the task was shown to be sensitive to reward responsiveness. However, we did not show TMS to be effective in increasing reward responsiveness and we did not replicate Ahn et al., (2013) or Duprat et al., (2016)'s key findings for TMS efficacy, where we provide evidence favouring the null. Moreover, exploratory analyses suggested following active stimulation, positive affect was reduced. Given our findings, we question the basic effects, which support the use of TMS for depression, particularly considering potential deleterious effects of reduced positive affect in patients with depression.

Perception-action integration during inhibitory control is reflected in a concomitant multi-region processing of specific codes in the neurophysiological signal

The integration of perception and action has long been studied in psychological science using overarching cognitive frameworks. Despite these being very successful in explaining perception-action integration, little is known about its neurophysiological and especially the functional neuroanatomical foundations. It is unknown whether distinct brain structures are simultaneously involved in the processing of perception-action integration codes and also to what extent demands on perception-action integration modulate activities in these structures. We investigate these questions in an EEG study integrating temporal and ICA-based EEG signal decomposition with source localization. For this purpose, we used data from 32 healthy participants who performed a 'TEC Go/Nogo' task. We show that the EEG signal can be decomposed into components carrying different informational aspects or processing codes relevant for perception-action integration. Importantly, these specific codes are processed independently in different brain structures, and their specific roles during the processing of perception-action integration differ. Some regions (i.e., the anterior cingulate and insular cortex) take a 'default role' because these are not modulated in their activity by demands or the complexity of event file coding processes. In contrast, regions in the motor cortex, middle frontal, temporal, and superior parietal cortices were not activated by 'default' but revealed modulations depending on the complexity of perception-action integration (i.e., whether an event file has to be reconfigured). Perception-action integration thus reflects a multi-region processing of specific fractions of information in the neurophysiological signal. This needs to be taken into account when further developing a cognitive science framework detailing perception-action integration.

Strategically managing learning during perceptual decision making

Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats' strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process.

The gradient model of brain organization in decisions involving “empathy for pain”

Influential models of cortical organization propose a close relationship between heteromodal association areas and highly connected hubs in the default mode network. The “gradient model” of cortical organization proposes a close relationship between these areas and highly connected hubs in the default mode network, a set of cortical areas deactivated by demanding tasks. Here, we used a decision-making task and representational similarity analysis with classic “empathy for pain” stimuli to probe the relationship between high-level representations of imminent pain in others and these areas. High-level representations were colocalized with task deactivations or the transitions from activations to deactivations. These loci belonged to 2 groups: those that loaded on the high end of the principal cortical gradient and were associated by meta-analytic decoding with the default mode network, and those that appeared to accompany functional repurposing of somatosensory cortex in the presence of visual stimuli. These findings suggest that task deactivations may set out cortical areas that host high-level representations. We anticipate that an increased understanding of the cortical correlates of high-level representations may improve neurobiological models of social interactions and psychopathology.

Adaptive coding occurs in object categorization and may not be associated with schizotypal personality traits

Processing more likely inputs with higher sensitivity (adaptive coding) enables the brain to represent the large range of inputs coming in from the world. Healthy individuals high in schizotypy show reduced adaptive coding in the reward domain but it is an open question whether these deficits extend to non-motivational domains, such as object categorization. Here, we develop a novel variant of a classic task to test range adaptation for face/house categorization in healthy participants on the psychosis spectrum. In each trial of this task, participants decide whether a presented image is a face or a house. Images vary on a face-house continuum and appear in both wide and narrow range blocks. The wide range block includes most of the face-house continuum (2.50-97.5% face), while the narrow range blocks limit inputs to a smaller section of the continuum (27.5-72.5% face). Adaptive coding corresponds to better performance for the overlapping smaller section of the continuum in the narrow range than in the wide range block. We find that participants show efficient use of the range in this task, with more accurate responses in the overlapping section for the narrow range blocks relative to the wide range blocks. However, we find little evidence that range adaptation in our object categorization task is reduced in healthy individuals scoring high on schizotypy. Thus, reduced range adaptation may not be a domain-general feature of schizotypy.

Explainable AI: A Neurally-Inspired Decision Stack Framework

European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called "decision stacks" that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made.

Evidence accumulation, not 'self-control', explains dorsolateral prefrontal activation during normative choice

What role do regions like the dorsolateral prefrontal cortex (dlPFC) play in normative behavior (e.g., generosity, healthy eating)? Some models suggest that dlPFC activation during normative choice reflects controlled inhibition or modulation of default hedonistic preferences. Here, we develop an alternative account, showing that evidence accumulation models predict trial-by-trial variation in dlPFC response across three fMRI paradigms and two self-control contexts (altruistic sacrifice and healthy eating). Using these models to simulate a variety of self-control dilemmas generated a novel prediction: although dlPFC activity might typically increase for norm-consistent choices, deliberate self-regulation focused on normative goals should decrease or even reverse this pattern (i.e., greater dlPFC response for hedonistic, self-interested choices). We confirmed these predictions in both altruistic and dietary choice contexts. Our results suggest that dlPFC response during normative choice may depend more on value-based evidence accumulation than inhibition of our baser instincts.

The effects of Transcranial Direct Current Stimulation on food craving and food intake in individuals affected by obesity and overweight: a mini review of the magnitude of the effects

Obesity represents one of the wellness diseases concurring to increase the incidence of diabetes, cardiovascular diseases, and cancer. One of the main perpetuating factors of obesity is food craving, which is characterized by an urgent desire to eat a large and various amount of food, regardless of calories requirement or satiety signals, and it might be addressed to the alteration of the dorsolateral prefrontal cortex (DLPFC) activity. Despite most of the gold-standard therapies focus on symptom treatment only, non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) could help treat overeating by modulating specific neural pathways. The current systematic review was conducted to identify whether convergent evidence supporting the usefulness of tDCS to deal with food craving are present in the literature. The review was conducted by searching articles published up to January 1^st 2022 on MEDLINE, Scopus and PsycInfo databases. We included studies investigating the effects of tDCS on food craving in subjects affected by overweight and obesity. According to eligibility criteria, 5 articles were included. Results showed that tDCS targeting left DLPFC with unipolar montage induced ameliorating effects on food craving. Controversial results were shown for the other studies, that might be ascribable to the use of bipolar montage, and the choice of other target areas. Further investigations including expectancy effect control, larger sample sizes and follow-up are needed to support more robust conclusions. To conclude, tDCS combined with the use of psychoeducative intervention, diet and physical activity, might represents a potential to manage food craving in individuals with overweight and obesity.

The neural stability of perception-motor representations affects action outcomes and behavioral adaptation

Actions can fail - even though this is well known, little is known about what distinguishes neurophysiological processes preceding errors and correct actions. In this study, relying on the Theory of Event Coding, we test the assumption that only specific aspects of information coded in EEG activity are relevant for understanding processes leading to response errors. We examined N = 69 healthy participants who performed a mental rotation task and combined temporal EEG signal decomposition with multivariate pattern analysis (MVPA) and source localization analyses. We show that fractions of the EEG signal, primarily representing stimulus-response translation (event file) processes and motor response representations, are essential. Stimulus representations were less critical. The source localization results revealed widespread activity modulations in structures including the frontopolar, the middle and superior frontal, the anterior cingulate cortex, the cuneus, the inferior parietal cortex, and the ventral stream regions. These are associated with differential effects of the neural dynamics preceding correct/erroneous responses. The temporal-generalization MVPA showed that event file representations and representations of the motor response were already distinct 200 ms after stimulus presentation and this lasted till around 700 ms. The stability of this representational content was predictive for the magnitude of posterror slowing, which was particularly strong when there was no clear distinction between the neural activity profile of event file representations associated with a correct or an erroneous response. The study provides a detailed analysis of the dynamics leading to an error/correct response in connection to an overarching framework on action control.

Measuring the response to visually presented faces in the human lateral prefrontal cortex

Neuroimaging studies identify multiple face-selective areas in the human brain. In the current study, we compared the functional response of the face area in the lateral prefrontal cortex to that of other face-selective areas. In Experiment 1, participants (n = 32) were scanned viewing videos containing faces, bodies, scenes, objects, and scrambled objects. We identified a face-selective area in the right inferior frontal gyrus (rIFG). In Experiment 2, participants (n = 24) viewed the same videos or static images. Results showed that the rIFG, right posterior superior temporal sulcus (rpSTS), and right occipital face area (rOFA) exhibited a greater response to moving than static faces. In Experiment 3, participants (n = 18) viewed face videos in the contralateral and ipsilateral visual fields. Results showed that the rIFG and rpSTS showed no visual field bias, while the rOFA and right fusiform face area (rFFA) showed a contralateral bias. These experiments suggest two conclusions; firstly, in all three experiments, the face area in the IFG was not as reliably identified as face areas in the occipitotemporal cortex. Secondly, the similarity of the response profiles in the IFG and pSTS suggests the areas may perform similar cognitive functions, a conclusion consistent with prior neuroanatomical and functional connectivity evidence.

Imperfect integration: Congruency between multiple sensory sources modulates decision-making processes

Decision-making on the basis of multiple information sources is common. However, to what extent such decisions differ from those with a single source remains unclear. We combined cognitive modelling and neural-mass modelling to characterise the neurocognitive process underlying perceptual decision-making with single or double information sources. Ninety-four human participants performed binary decisions to discriminate the coherent motion direction averaged across two independent apertures. Regardless of the angular distance of the apertures, separating motion information into two apertures resulted in a reduction in accuracy. Our cognitive and neural-mass modelling results are consistent with the hypotheses that the addition of the second information source led to a lower signal-to-noise ratio of evidence accumulation with two congruent information sources, and a change in the decision strategy of speed–accuracy trade-off with two incongruent sources. Thus, our findings support a robust behavioural change in relation to multiple information sources, which have congruency-dependent impacts on selective decision-making subcomponents.

A meta-learning BCI for estimating decision confidence

OBJECTIVE

We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods.

APPROACH

We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from EEG and EOG data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called Domain Adversarial Neural Networks (DANN), a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm.

MAIN RESULTS

The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period.

SIGNIFICANCE

Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.

Humans utilize sensory evidence of others' intended action to make online decisions

We often acquire sensory information from another person's actions to make decisions on how to move, such as when walking through a crowded hallway. Past interactive decision-making research has focused on cognitive tasks that did not allow for sensory information exchange between humans prior to a decision. Here, we test the idea that humans accumulate sensory evidence of another person's intended action to decide their own movement. In a competitive sensorimotor task, we show that humans exploit time to accumulate sensory evidence of another's intended action and utilize this information to decide how to move. We captured this continuous interactive decision-making behaviour with a drift-diffusion model. Surprisingly, aligned with a 'paralysis-by-analysis' phenomenon, we found that humans often waited too long to accumulate sensory evidence and failed to make a decision. Understanding how humans engage in interactive and online decision-making has broad implications that spans sociology, athletics, interactive technology, and economics.

The relationships between subclinical OCD symptoms, beta/gamma-band power, and the rate of evidence integration during perceptual decision making

Previous studies have demonstrated that the rate of evidence integration during perceptual decision making, a specific computationally defined parameter, is negatively correlated with both subclinical symptoms of OCD measured on a continuum and categorically diagnosed patient status. However, the neural mechanisms underlying this deficit are unknown. Separate work has shown that both gamma and beta-band power are related to evidence integration, and differences in beta-band power in particular have been hypothesized to hinder flexible behavioral control. We sought to unify these two disparate literatures, one on OCD-related information processing differences constrained by behavioral data alone, and the other on the neural correlates of evidence integration. Using computational modeling and scalp EEG, we tested (N = 67) the relationships between subclinical symptom scores, drift rate, and gamma/beta-band activity during perceptual decision making. We replicated both prior work showing deficits in evidence integration as a function of OCD symptoms, and work showing a relationship between evidence integration and gamma and beta-band power. As predicted, the slope of beta-band power was correlated with OCD symptoms. However, the relationships between OCD symptoms and drift rate and the slopes of gamma and beta-band power and drift rate remained unchanged when simultaneously accounting for all variables, speaking against the hypothesis that differences in band-band power explain drift rate deficits.

Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches

As interest in the temporal dynamics of decision-making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value-based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well-understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice-triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300-500 ms after stimulus onset. These findings can inform psychological theories of decision-making, providing empirical support for attribute weighting in value-based choice while suggesting theoretical alternatives to dual-process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment-by-moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision-Making.