A framework for studying the neurobiology of value-based decision making

Active information sampling in health and disease

Active information gathering is a fundamental cognitive process that enables organisms to navigate uncertainty and make adaptive decisions. Here we synthesise current knowledge on the behavioural, neural, and computational mechanisms underlying information sampling in healthy people and across several brain disorders. The role of cortical and subcortical regions spanning limbic, insular, fronto-parietal, and striatal systems is considered, along with the contributions of key neurotransmitters involving norepinephrine, dopamine, and serotonin. We also examine how various clinical conditions, including schizophrenia, obsessive-compulsive disorder, and Parkinson's disease have an impact on information gathering behaviours. To account for the findings, we outline a neuroeconomic perspective on how the brain may evaluate the costs and benefits of acquiring information to resolve uncertainty. This work highlights how active information gathering is a crucial brain process for adaptive behaviour in healthy individuals and how its breakdown is relevant to several psychiatric and neurological conditions. The findings have important implications for developing novel computational assays as well as targeted interventions in brain disorders.

A cognitive map for value-guided choice in the ventromedial prefrontal cortex

The prefrontal cortex (PFC) is crucial for economic decision-making. However, how PFC value representations facilitate flexible decisions remains unknown. We reframe economic decision-making as a navigation process through a cognitive map of choice values. We found rhesus macaques represented choices as navigation trajectories in a value space using a grid-like code. This occurred in ventromedial PFC (vmPFC) local field potential theta frequency across two datasets. vmPFC neurons deployed the same grid-like code and encoded chosen value. However, both signals depended on theta phase: occurring on theta troughs but on separate theta cycles. Finally, we found sharp-wave ripples-a key signature of planning and flexible behavior-in vmPFC. Thus, vmPFC utilizes cognitive map-based computations to organize and compare values, suggesting an alternative architecture for economic choice in PFC.

Meta-analyses of executive function deficits in chemotherapy-treated rodent models

People diagnosed with cancer who undergo chemotherapy commonly encounter cognitive changes, particularly in executive functions (EFs). EFs support goal-directed behaviours, with EF deficits implicated in various neurocognitive impairments. We conducted five meta-analyses of the rodent models to investigate the impact of chemotherapy across five EF domains. A systematic search across PubMed, Web of Science, Scopus, and PsycINFO yielded 56 eligible papers. Our findings supported the clinical literature suggesting the selective impact of chemotherapy on different EF domains. Specifically, chemotherapy-treated animals performed significantly more poorly than controls in tasks assessing working memory, behavioural flexibility, and problem-solving, with no significant group differences in inhibition or attention. Subgroup analyses revealed that alkylating agents, antitumor antibiotics, and combination therapies were strongly associated with working memory deficits, whereas mitotic inhibitors were not. Rodent species, strain, age, sex, number of treatments, and time of behavioural assessment since the end of treatment did not moderate the drug effect on any assessed EF domains. To increase the generalisability and translational validity of the results, the overall reporting quality of animal studies needs to be improved with more details on randomisation, blinding, sample sizes, and criteria for animal exclusions.

Direct and indirect striatal projecting neurons exert strategy-dependent effects on decision-making

The striatum plays a key role in decision-making, with its effects varying with anatomical location and direct and indirect pathway striatal projecting neuron (d- and iSPN) populations. Using a mouse gambling task with a reinforcement-learning model, we described individual decision-making profiles as a combination of three archetypal strategies: Optimizers, Risk-averse, and Explorers. These strategies reflected stable differences in the parameters generating decisions (sensitivity to the reward magnitude, to risk, or to punishment) derived from a reinforcement-learning model of animal choice. Chemogenetic manipulation showed that dorsomedial striatum (DMS) neurons substantially affect decision-making, while the nucleus accumbens (NAc) and dorsolateral striatum neurons (DLS) have lesser or no effects, respectively. Specifically, DMS dSPNs decrease risk aversion by increasing the perceived value of risky choices, while DMS iSPNs emphasize large gains, affecting decisions depending on decision-making profiles. Hence, we propose that striatal populations from different subregions influence distinct decision-making parameters, leading to profile-dependent choices.

Integrating optical neuroscience tools into touchscreen operant systems

Unlocking the neural regulation of complex behavior is a foundational goal of brain science. Touchscreen-based assessments of behavior have been used extensively in the pursuit of this goal, with traditional pharmacological and neurochemical approaches being employed to provide key insights into underlying neural systems. So far, optically based approaches to measure and manipulate neural function, which have begun to revolutionize our understanding of relatively simple behaviors, have been less widely adopted for more complex cognitive functions of the type assessed with touchscreen-based behavioral tasks. Here we provide guidance and procedural descriptions to enable researchers to integrate optically based manipulation and measurement techniques into their touchscreen experimental systems. We focus primarily on three techniques, optogenetic manipulation, fiber photometry and microendoscopic imaging, describing experimental design adjustments that we have found to be critical to the successful integration of these approaches with extant touchscreen behavior pipelines. These include factors related to surgical procedures and timing, alterations to touchscreen operant environments and approaches to synchronizing light delivery and task design. A detailed protocol is included for each of the three techniques, covering their use from implementation through data analysis. The procedures in this protocol can be conducted in as short a time as a few days or over the course of weeks or months.

Old Strategies, New Environments: Reinforcement Learning on Social Media

The rise of social media has profoundly altered the social world, introducing new behaviors that can satisfy our social needs. However, it is not yet known whether human social strategies, which are well adapted to the offline world we developed in, operate as effectively within this new social environment. Here, we describe how the computational framework of reinforcement learning (RL) can help us to precisely frame this problem and diagnose where behavior-environment mismatches emerge. The RL framework describes a process by which an agent can learn to maximize their long-term reward. RL, which has proven to be successful in characterizing human social behavior, consists of 3 stages: updating expected reward, valuating expected reward by integrating subjective costs such as effort, and selecting an action. Specific social media affordances, such as the quantifiability of social feedback, may interact with the RL process at each of these stages. In some cases, affordances can exploit RL biases that are beneficial offline by violating the environmental conditions under which such biases are optimal, such as when algorithmic personalization of content interacts with confirmation bias. Characterizing the impact of specific aspects of social media through this lens can improve our understanding of how digital environments shape human behavior. Ultimately, this formal framework could help address pressing open questions about social media use, including its changing role across human development and its impact on outcomes such as mental health.

SHRUNKNeural dynamics encoding risky choices during deliberation reveal separate choice subspaces

Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decisions, but not decisions without risk, converge to separate subspaces depending on the subject's preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice.

Motivational disturbances and cognitive effort-based decision-making in Parkinson's disease

BACKGROUND

Motivational disturbances, such as apathy and impulse control disorders (ICDs), frequently co-occur in patients with Parkinson's disease (PD). The assessment of these motivational disturbances has proven to be challenging due the absence of validated objective behavioral measures for evaluating motivation in older adults. This scenario may contribute to underdiagnosis. The present study aimed to investigate the clinical utility of a modified version of an existing effort-based decision-making task which required cognitive (e.g., working memory) instead of physical (e.g., finger tapping) effort.

METHODS

Ninety-five non-demented individuals (45-85 years of age) with idiopathic PD completed a cognitive screening measure, self-report questionnaires, and a cognitive adaptation of the Effort Expenditure for Rewards Task (COG-EEfRT), which is a multi-trial game where a participant can choose whether to expend greater effort for larger rewards which vary in magnitude and probability. Patients were classified as having clinically significant symptoms of apathy and/or an ICD based on recommended cut-off scores on the Apathy Scale (AS) and Questionnaire for Impulse Control Disorders in Parkinson's Disease - Rating Scale (QUIP-RS). The methodological cutoffs defined two groups: Apathy (36.8 %), and ICD (48.4 %).

RESULTS

The level of effort expended by patients significantly predicted apathy and ICD status with high accuracy (88.2 % and 82.4 %, respectively), above and beyond age, levodopa equivalent dose and self-report measures of motivation. Additionally, we found that greater symptoms of apathy and ICD (i.e., negative urgency) were significantly correlated with patients choosing to expend greater effort. This result varied based on reward probability and outcome.

CONCLUSION

We offer preliminary evidence suggesting the clinical utility of the COG-EEfRT for identifying and quantifying motivational disturbances in PD. Additionally, anticipatory anhedonia and impulsive traits may be important predictors of cognitive effort-based decision-making. Compared to tasks requiring physical effort, the COG-EEfRT may be a more suitable tool for PD and perhaps for people with motor impairment.

Value-based decision-making deficits in non-suicidal self-injury: A systematic review and meta-analysis

Non-suicidal self-injury (NSSI) is a significant global mental health issue. However, whether the deficit in value-based decision-making processing is the consistently key mechanisms of NSSI across studies with heterogeneous decision-making tasks remains uncertain. Hence, we reviewed literature on value-based decision-making and its moderating factors in NSSI, and then conducted a meta-analysis on studies in NSSI, specifically with Iowa Gambling Task. In total, 31 studies with 8417 participants aged from 8 to 50 were included in the systematic review of and 8 studies with 657 participants aged from 10 to 37 included in the meta-analysis. We found consistent impairments in value-based decision-making processing among individuals who engage in NSSI. Importantly, age is a significant moderator, with adolescents with NSSI performance worse, exhibiting increased reward sensitivity and preference for immediate rewards compared to other age groups. These results suggest the important role of value-based decision making in NSSI behaviors, especially during the development of adolescents. Our work provides new insights and integrative evidence for decision-making based prevention and intervention of NSSI.

Do goldfish like to be informed?

Like humans, several mammalian and avian species prefer foretold over unsignalled future events, even if the information is costly and confers no direct benefit. It is unclear whether this is an epiphenomenon of basic associative learning mechanisms, or whether these preferences reflect a derived form of information-seeking that is reminiscent of human curiosity. We investigate whether a fish that shares basic reinforcement learning mechanisms with birds and mammals also shows such a preference, with the aim of elucidating whether widely shared conditioning processes are sufficient to explain paradoxical preferences resulting in unusable information. Goldfish (Carassius auratus) chose between two alternatives, both resulting in a 5 s delay and 50% reward chance. The 'informative' option immediately produced a stimulus correlated with the trial's forthcoming outcome (reward/no reward). Choosing the 'non-informative' option instead triggered an uncorrelated stimulus. Goldfish discriminated between the different contingencies but did not develop a preference for the informative option, suggesting that in goldfish associative learning mechanisms are not sufficient to generate preferences between alternatives differing only in outcome predictability. These results challenge the notion that informative preferences are a by-product of ubiquitous associative processes, and are consistent with the possibility that derived information-seeking mechanisms have evolved in some vertebrate species.

Disentangling sources of variability in decision-making

Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.

Touchscreen Response Precision Is Sensitive to the Explore/Exploit Trade-off

The explore/exploit trade-off is a fundamental property of choice selection during reward-guided decision making, where the "same" choice can reflect either of these internal cognitive states. An unanswered question is whether the execution of a decision provides an underexplored measure of internal cognitive states. Touchscreens are increasingly used across species for cognitive testing and afford the ability to measure the precise location of choice touch responses. We examined how male and female mice in a restless bandit decision making task interacted with a touchscreen to determine if the explore/exploit trade-off, prior reward, and/or sex differences change the variability in the kinetics of touchscreen choices. During exploit states, successive touch responses are closer together than those made in an explore state, suggesting exploit states reflect periods of increased motor stereotypy. Although exploit decisions might be expected to be rewarded more frequently than explore decisions, we find that immediate past reward reduces choice variability independently of explore/exploit state. Male mice are more variable in their interactions with the touchscreen than females, even in low-variability trials such as exploit or following reward. These results suggest that as exploit behavior emerges in reward-guided decision making, all mice become less variable and more automated in both their choice and the actions taken to make that choice, but this occurs on a background of increased male variability. These data uncover the hidden potential for touchscreen decision making tasks to uncover the latent neural states that unite cognition and movement.

Exploring value learning in pigeons: the role of dual pathways in the basal ganglia and synaptic plasticity

Understanding value learning in animals is a key focus in cognitive neuroscience. Current models used in research are often simple, and while more complex models have been proposed, it remains unclear which assumptions align with actual value-learning strategies of animals. This study investigated the computational mechanisms behind value learning in pigeons using a free-choice task. Three models were constructed based on different assumptions about the role of the basal ganglia's dual pathways and synaptic plasticity in value computation, followed by model comparison and neural correlation analysis. Among the three models tested, the dual-pathway reinforcement learning model with Hebbian rules most closely matched the pigeons' behavior. Furthermore, the striatal gamma band connectivity showed the highest correlation with the values estimated by this model. Additionally, enhanced beta band connectivity in the nidopallium caudolaterale supported value learning. This study provides valuable insights into reinforcement learning mechanisms in non-human animals.

Perceived food value depends on display format, preference strength, and physical accessibility

In everyday life, dietary decisions are made in response to real foods, such as at the grocery store or cafe. In stark contrast, decision-making studies in the laboratory typically measure responses to food stimuli presented as two-dimensional pictures or computer images, with the assumption that artificial displays are adequate substitutes for their real-world counterparts. Yet accumulating evidence challenges this view, including studies showing that willingness-to-pay (WTP) is higher for foods displayed as real objects versus images -a phenomenon known as the "real object advantage" in valuation. Here, we examined whether the "real object advantage" is modulated by accessibility to the stimuli, subjective food preference, or interactions between these factors. Participants placed monetary bids on snack foods displayed as real objects or computer images. Critically, on half of the trials, a transparent barrier was positioned between the participant and the stimulus. Linear mixed-effects modeling analysis revealed that, overall, WTP was ∼7 % higher for foods displayed as real objects versus images; however, this effect emerged only for foods of moderate (but not strong) preference strength. WTP was also higher when the stimuli appeared unoccluded versus behind the barrier, but this was equally so for real objects and images, suggesting that the barrier's effect on valuation was not related to stimulus actability. Our findings suggest that while eliminating perceived barriers to a good can bolster valuation regardless of display format, presenting real foods may nevertheless increase valuation and encourage healthy dietary choices.

Distributed Intracranial Activity Underlying Human Decision-making Behavior

Value-based decision-making involves multiple cortical and subcortical brain areas, but the distributed nature of neurophysiological activity underlying economic choices in the human brain remains largely unexplored. Specifically, the nature of the neurophysiological representation of reward-guided choices, as well as whether they are represented in a subset of reward-related regions or in a more distributed fashion, is unknown. Here, we hypothesize that reward choices, as well as choice-related computations (win probability, risk), are primarily represented in high-frequency neural activity reflecting local cortical processing and that they are highly distributed throughout the human brain, engaging multiple brain regions. To test these hypotheses, we used intracranial recordings from multiple areas (including orbitofrontal, lateral prefrontal, parietal, cingulate cortices as well as subcortical regions such as the hippocampus and amygdala) from neurosurgical patients of both sexes playing a decision-making game. We show that high-frequency activity (HFA; ɣ and HFA) represents both individual choice-related computations (e.g., risk, win probability) and choice information with different prevalence and regional representation. Choice-related computations are locally and unevenly present in multiple brain regions, whereas choice information is widely distributed and more prevalent and appears later across all regions examined. These results suggest brain-wide reward processing, with local HFA reflecting the coalescence of choice-related information into a final choice, and shed light on the distributed nature of neural activity underlying economic choices in the human brain.

Smell the Label: Odors Influence Label Perception and Their Neural Processing

Providing nutrition or health labels on product packaging can be an effective strategy to promote a conscious and healthier diet. However, such labels also have the potential to be counterproductive by creating obstructive expectations about the flavor of the food and influencing odor perception. Conversely, olfaction could significantly influence label perception, whereby negative expectations could be mitigated by pleasant odors. This study explored the neural processing of the interplay between odors and nutrition labels using fMRI in 63 participants of either sex, to whom we presented beverage labels with different nutrition-related statements either with or without a congruent odor. On a behavioral level, the products for which the label was presented together with the odor were in general perceived as more positive than the same labels without an odor. Neuroimaging results revealed that added odors significantly altered activity in brain regions associated with flavor and label processing as well as decision-making, with higher activations in the right amygdala/piriform cortex (Amy/pirC) and orbitofrontal cortex. The presentation of odors induced pattern-based encoding in the right dorsolateral prefrontal cortex, the left ventral striatum/nucleus accumbens, and the right Amy/pirC when accounting for behavioral differences. This suggests that odors influence the effects of labels both on a neural and behavioral level and may offer the possibility of compensating for obstructive associations. The detailed mechanisms of odor and statement interactions within the relevant brain areas should be further investigated, especially for labels that evoke negative expectations.

The Interplay Between Experimental Heat Pain and Noninvasive Stimulation of the Medial Prefrontal Cortex on Reinforcement Learning With Manipulated Outcome Contingencies

Pain negatively affects several cognitive abilities, but knowledge about its effect on reinforcement learning (RL) is limited. During RL, instrumental choices can be influenced by heuristic tendencies to approach rewards or inhibit actions when facing potentially aversive events, introducing "Pavlovian bias" in behavior. Recent studies suggest that compromised outcome controllability enhances Pavlovian bias, a phenomenon that may be linked to suboptimal decision-making in learned helplessness (LH). Since LH is common in chronic pain syndromes, this study sought to establish a link between experimental heat pain (EHP), disrupted reward/loss contingencies, and RL performance in healthy adults. In addition, we investigated if intermittent theta burst stimulation (iTBS) above the medial prefrontal/dorsal anterior cingulate cortex (mPFC/dACC) alleviates the deleterious effects of EHP on choice behavior. In a preregistered, 2 × 2 between-group, double-blind study (N = 100), healthy adult participants underwent three blocks of an orthogonalized Go/NoGo task with two interleaved bouts of active or sham iTBS, and either EHP or warm skin stimulation combined with manipulated response-outcome contingency during the task. Although EHP did not impact response accuracy, it invigorated actions for rewards, reflecting enhanced Pavlovian bias. Whereas two bouts of iTBS attenuated Pavlovian tendencies, this effect was counteracted by EHP, indicating antagonistic effects of pain and iTBS-modulated mPFC activity on Pavlovian-instrumental interactions. Surprisingly, EHP and iTBS exerted largely similar effects on other latent parameters of RL (go-bias, learning rate, and exploration) in a manner that resembled LH. These findings shed light on the role of experimental pain and mPFC/dACC activity in LH-like choice behavior.

Posttraumatic reexperiencing and alcohol use: Mediofrontal theta as a neural mechanism for negative reinforcement

Over half of U.S. military veterans with posttraumatic stress disorder (PTSD) use alcohol heavily, potentially to cope with their symptoms. This study investigated the neural underpinnings of PTSD symptoms and heavy drinking in veterans. We focused on brain responses to salient outcomes within predictive coding theory. This framework suggests the brain generates prediction errors (PEs) when outcomes deviate from expectations. Alcohol use might provide negative reinforcement by reducing the salience of negatively valenced PEs and dampening experiences like loss. We analyzed electroencephalography responses to unpredictable gain/loss feedback in n = 82 veterans of Operations Enduring and Iraqi Freedom. We used time-frequency principal components analysis of event-related potentials to isolate neural responses indicative of PEs, identifying mediofrontal theta linked to losses (feedback-related negativity) and central delta associated with gains (reward positivity). Intrusive reexperiencing symptoms of PTSD were associated with intensified mediofrontal theta signaling during losses, suggesting heightened negative PE sensitivity. Conversely, increased hazardous alcohol use was associated with reduced theta responses, implying a dampening of these negative PEs. The separate delta-reward positivity component showed associations with alcohol use but not PTSD symptoms. The findings suggest a common neural component of PTSD and hazardous alcohol use involving altered PE processing. We suggest that reexperiencing enhances the intensity of salient negative PEs, while chronic alcohol use may reduce their intensity, thereby providing negative reinforcement by muting posttraumatic distress and associated brain responses. Modifying the mediofrontal theta response could address the intertwined nature of PTSD symptoms and alcohol use, providing new avenues for treatment. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Pre-choice midbrain fluctuations affect self-control in food choice: A functional magnetic resonance imaging (fMRI) study

Recent studies have shown that spontaneous pre-stimulus fluctuations in brain activity affect higher-order cognitive processes, including risky decision-making, cognitive flexibility, and aesthetic judgments. However, there is currently no direct evidence to suggest that pre-choice activity influences value-based decisions that require self-control. We examined the impact of fluctuations in pre-choice activity in key regions of the reward system on self-control in food choice. In the functional magnetic resonance imaging (fMRI) scanner, 49 participants made 120 food choices that required self-control in high and low working memory load conditions. The task was designed to ensure that participants were cognitively engaged and not thinking about upcoming choices. We defined self-control success as choosing a food item that was healthier over one that was tastier. The brain regions of interest (ROIs) were the ventral tegmental area (VTA), putamen, nucleus accumbens (NAc), and caudate nucleus. For each participant and condition, we calculated the mean activity in the 3-s interval preceding the presentation of food stimuli in successful and failed self-control trials. These activities were then used as predictors of self-control success in a fixed-effects logistic regression model. The results indicate that increased pre-choice VTA activity was linked to a higher probability of self-control success in a subsequent food-choice task within the low-load condition, but not in the high-load condition. We posit that pre-choice fluctuations in VTA activity change the reference point for immediate (taste) reward evaluation, which may explain our finding. This suggests that the neural context of decisions may be a key factor influencing human behavior.

A computational account of multiple motives guiding context-dependent prosocial behavior

Prosocial behaviors play a pivotal role for human societies, shaping critical domains such as healthcare, education, taxation, and welfare. Despite the ubiquity of norms that prescribe prosocial actions, individuals do not consistently adhere to them and often behave selfishly, thereby harming the collective good. Interventions aimed at promoting prosociality would therefore be beneficial but are often ineffective due to a limited understanding of the various motives that govern prosocial behavior across different contexts. Here we present a computational and experimental framework to identify motives underlying individual prosocial choices and to characterize how these vary across contexts with changing social norms. Using a series of experiments in which 575 participants either judge the normative appropriateness of different prosocial actions or choose between prosocial and selfish actions themselves, we first show that while most individuals are consistent in their judgements about the appropriateness of behaviors, the actual prosocial behavior varies strongly across people. We used computational decision models to quantify the conflicting motives underlying judgements and prosocial choices, combined with a clustering approach to characterize different types of individuals whose judgements and choices reflect different motivational profiles. We identified four such types: Unconditionally selfish participants never behave prosocially, Cost-sensitive participants behave selfishly when prosocial actions are costly, Efficiency-sensitive participants choose actions that maximize total wealth, and Harm-sensitive participants prioritize avoiding harming others. When these four types of participants were exposed to different social environments where norms were judged or followed more or less by a group, they responded in fundamentally different ways to this change in others' behavior. Our approach helps us better understand the origins of heterogeneity in prosocial judgments and behaviors and may have implications for policy making and the design of behavioral interventions.

Dissociation of value and confidence signals in the orbitofrontal cortex during decision-making: an intracerebral electrophysiology study in humans

Some decisions, such as selecting a food item in a novel menu, are not based on rational norms, or on trained habits, but on subjective preferences. How the human brain makes these preference-based decisions is still debated in cognitive neuroscience. Classical models focus on the comparison mechanism that achieves the selection of the option with best expected value. Recent models suggest that estimates of option values are refined until reaching sufficient confidence in the considered choice. Neuroimaging studies in humans and electrophysiology studies in animals have gathered evidence that value and confidence estimates are both represented in medial and lateral regions of the orbitofrontal cortex (OFC). Here, we took advantage of electrodes implanted within the OFC of human patients with pharmaco-resistant epilepsy (14 women, 12 men) to investigate whether value and confidence estimates can be dissociated in electrophysiology activity recorded during preference-based binary decisions. The overall value (liking ratings summed over options) and choice confidence (selection probability of the chosen option) were identified in low-frequency (4-8 Hz) OFC activity. These value and confidence signals were time-locked to the decision, showed opposite signs of correlation and were recorded in separate sites. This pattern of results is not consistent with the simulations of an attractor neural network model implementing a comparison of option values. However, it is compatible with the notion of a neural network generating sparse representations of option values and choice confidence estimates, based on which decisions can be made.Significance statement The orbitofrontal cortex (OFC) is known to play a critical role in decisions based on subjective preferences, such as choosing between food items in a menu. However, the information provided by the human OFC has remained elusive, due to limitations of neuroimaging techniques. Here, taking advantage of electrodes implanted in patients for clinical purposes, we present a rare dataset of electrophysiological activity recorded during preference-based decisions. Our analyses suggest that the OFC signals two distinct constructs on which decisions could be based: the subjective values of available options and the confidence in the intended choice.

A study of dynamic functional connectivity changes in flight trainees based on a triple network model

The time-varying functional connectivity of the Central Executive Network (CEN), Default Mode Network (DMN), and Salience Network (SN) in flight trainees during a resting state was investigated using dynamic functional network connectivity (dFNC). The study included 39 flight trainees and 37 age- and sex-matched healthy controls. Resting-state fMRI data and behavioral test outcomes were obtained from both groups. Independent component analysis (ICA), sliding window, and K-means clustering approaches were utilized for evaluating functional network connectivity (FNC) and temporal metrics based on the triple networks. Correlation analyses were performed on the behavioral assessments and these metrics. The flight trainees demonstrated a significantly enhanced functional connection linking the CEN and DMN in state 2 (P < 0.05, FDR corrected). Additionally, flight trainees spent less time in state 5, while they exhibited a protracted mean dwell time and fractional windows in state 2, which were significantly correlated with accuracy on the Berg Card Sorting Test (BCST) and Change Detection Test (all P < 0.05). The improved connectivity of flight trainees between the CEN and DMN following the completion of rigorous flight training resulted in increased stability. This enhancement may be relevant to cognitive abilities such as decision-making, memory, and information integration. When multitasking, flight trainees displayed superior visual processing skills and enhanced cognitive flexibility. dFNC research provides a unique perspective on the sophisticated cognitive capabilities that are required in high-demand, high-stress occupations such as piloting, thereby providing significant insights into the intricate brain mechanisms that are inherent in these domains.

Meta-Analysis Reveals That Explore-Exploit Decisions Are Dissociable by Activation in the Dorsal Lateral Prefrontal Cortex, Anterior Insula, and Dorsal Anterior Cingulate Cortex

Explore-exploit research faces challenges in generalizability due to a limited theoretical basis for exploration and exploitation. Neuroimaging can help identify whether explore-exploit decisions involve an opponent processing system to address this issue. Thus, we conducted a coordinate-based meta-analysis (N = 23 studies) finding activation in the dorsal lateral prefrontal cortex, anterior insula, and dorsal anterior cingulate cortex during exploration versus exploitation, which provides some evidence for opponent processing. However, the conjunction of explore-exploit decisions was associated with activation in the anterior cingulate cortex and dorsal medial prefrontal cortex, suggesting that these brain regions do not engage in opponent processing. Furthermore, exploratory analyses revealed heterogeneity in brain responses between task types during exploration and exploitation respectively. Coupled with results suggesting that activation during exploration and exploitation decisions is generally more similar than it is different suggests that there remain significant challenges in characterizing explore-exploit decision-making. Nonetheless, dorsal lateral prefrontal cortex, anterior insula, and dorsal anterior cingulate cortex activation differentiate explore and exploit decisions, and identifying these responses can aid in targeted interventions aimed at manipulating these decisions.

Attachment Security Priming Reduces Risk-Taking and Emotional Responses to Loss

We examined the effects of attachment security priming on economic risky decisions and their neural underpinnings. Participants were exposed to either attachment security primes (N = 28) or control primes (N = 29) and then completed a gambling task while connected to an electroencephalogram system. We anticipated that attachment security priming would affect risky decision-making at both the behavioral and neural levels. Feedback-related negativity (FRN) and P3 components were analyzed. At the behavioral level, participants in the attachment security priming group selected less risky choices than those in the control group. At the neural level, participants exposed to attachment security primes exhibited attenuated FRN but no significant difference in P3 amplitude. The regression analysis showed that small P3 amplitude predicted large risk-taking tendencies in the control priming group, whereas P3 amplitude did not significantly predict risk-taking tendencies in the attachment security priming group. These findings suggest how boosted attachment security affects economic risky decisions: It lowers people's affective reactions to undesirable outcomes and buffers people's underestimation of the outcome salience. Specifically, attachment security seems to attenuate risk-taking by preventing people from down playing the significance of outcomes. Our findings extend existing knowledge by demonstrating attachment security priming ability to reduce risk-taking tendencies beyond naturalistic to economic decisions.

Toward a computational understanding of bribe-taking behavior

Understanding how corrupt behavior occurs is a critical issue at the intersection of behavioral ethics, social psychology, and other related social sciences, laying the foundation for establishing effective anticorruption policies. Despite a substantial body of studies focused on bribe-taking behavior-a typical form of corruption-and its modulators, its underlying psychological processes remain poorly understood. Drawing inspiration from recent literature on neuroeconomics and moral decision-making, we argue that bribe-taking decision-making involves a value-based computational process that can be characterized by a computational framework. We show how this framework advances our understanding of bribe-taking decision-making by (1) clarifying how the cost-benefit tradeoff determines the decision to accept or reject a bribe and its neural foundations, (2) improving the prediction of bribe-taking behaviors across contexts and individuals, and (3) enhancing our comprehension of individual differences in bribe-taking behaviors. Moreover, we delineate how this framework can benefit future research on bribery by examining the mechanisms through which various modulators impact the bribe-taking behaviors or the computational processes underlying more intricate forms of corrupt behaviors. We also discussed its potential fusion with artificial intelligence techniques in offering insights for understanding cognitive processes underlying bribe-taking behaviors and designing anticorruption strategies.

Dissociable effects of perceived control on reward-related neural dynamics under risk and ambiguity

Perceived control plays a crucial role in risk-taking behavior, but its neural effect on reward dynamics in risky and ambiguous decision making remains unclear. Here, we addressed this issue by measuring participants' (N = 40) EEG activity while they were performing a wheel-of-fortune task. Participants either made choices themselves (a high control condition) or followed the computer's choice (a low control condition) under risky or ambiguous decision contexts. Behavioral and rating data showed a stronger control effect in the risky compared to the ambiguous decision context. In parallel, we found an effect of perceived control on choice evaluation (indexed by the cue-P3) in the risky but not ambiguous context. However, the control effect was more pronounced during feedback anticipation (indexed by the stimulus-preceding negativity) and outcome appraisal (indexed by delta oscillation) in the ambiguous context compared to the risky context. Together, our findings suggest that experiencing control alters reward dynamics in uncertain decision making, with dissociable effects between risk and ambiguity.

Tuning the value of sweet food: Blocking sweet taste receptors increases the devaluation effect in a go/no-go task

Despite the apparent simplicity of the go/no-go (GNG) task, in which individuals selectively respond or withhold responses, there is strong evidence supporting its efficacy in terms of modulating food preferences. Herein, we manipulated sweet taste perception and investigated the no-go devaluation effect that is typically observed due to GNG training with respect to sweet and savory food items. Prior to engaging in a GNG task, one group of participants rinsed their mouths with a liquid solution containing gymnemic acid, thereby transiently and selectively inhibiting sweet taste perception, while another group used a placebo solution. The participants who rinsed their mouths with gymnemic acid exhibited a stronger overall decrease in food evaluations from pre to post training. Furthermore, a pronounced no-go devaluation effect was observed for sweet foods, irrespective of the rinsing solution. Overall, our results support the notion that training in the GNG task can induce changes in the valuation of food stimuli, particularly for sweet foods.

Enhancing Motivation to Reduce Restrictive Eating: A Novel Approach

OBJECTIVE

Despite growing interest in leveraging motivational techniques to address restrictive eating, it is not yet clear how to most effectively promote motivation to reduce this behavior. Drawing from a behavioral economic framework, the present study evaluates a novel approach for increasing motivation to address disordered eating by amplifying the potential benefits of reducing dietary restriction and the consequences of maintaining disordered behaviors.

METHOD

A sample of 126 undergraduate students engaging in restrictive eating participated in a 7-day online experiment. Participants were randomized to one of four conditions: Amplified Benefits and Consequences, Amplified Benefits, Amplified Consequences, or a control condition. Growth curve models were estimated to examine the extent to which experimental conditions led to changes in eating pathology and motivation over the study period.

RESULTS

Initial reductions in dietary restraint were observed in conditions where the benefits of reducing restriction were amplified; however, these reductions were not sustained over the 7-day study period. The greatest increases in motivation were observed when both the benefits of reducing restriction and the consequences of maintenance were amplified.

DISCUSSION

Interventions designed to enhance motivation alone are unlikely to yield sustained reductions in eating disorder symptoms and therefore should be accompanied or followed by targeted interventions which directly address restrictive eating behaviors and maladaptive cognitions about shape and weight.

Reaching Distance Influences Perceptual Decisions

Decision making is an integral part of everyday life. Recently, there has been a growing interest in the potential influence of action on perceptual decisions, following ideas of embodied decision making. Studies examining decisions regarding the direction of noisy visual motion have found a bias towards the least effortful response option in experiments in which the differences in motor costs associated with alternative response actions were implicit, but not in an experiment in which these differences were made explicit. It remains unclear whether the biasing effect generalizes to other perceptual tasks than motion perception and whether consciously experiencing motor costs prevents such biases. To test the generalizability of effects across perceptual tasks, we used a within-subjects design where 24 participants performed both a motion discrimination task and an orientation discrimination task. Motor costs were manipulated by presenting response buttons for the two alternative choices at different reaching distances. By varying distances randomly, we avoided implicit biases linked to specific decisions. Our findings revealed a bias towards closer response options in both tasks, indicating that explicit information of motor costs significantly impacts perceptual decisions beyond motion discrimination. Contrary to prevailing theories that consider the motor system as a mere effector of the decision, our study implies that the actions that are associated with the response options influence the decision process itself.

Distinct neural computations scale the violation of expected reward and emotion in social transgressions

Traditional decision-making models conceptualize humans as adaptive learners utilizing the differences between expected and actual rewards (prediction errors, PEs) to maximize outcomes, but rarely consider the influence of violations of emotional expectations (emotional PEs) and how it differs from reward PEs. Here, we conducted a fMRI experiment (n = 43) using a modified Ultimatum Game to examine how reward and emotional PEs affect punishment decisions in terms of rejecting unfair offers. Our results revealed that reward relative to emotional PEs exerted a stronger prediction to punishment decisions. On the neural level, the left dorsomedial prefrontal cortex (dmPFC) was strongly activated during reward receipt whereas the emotions engaged the bilateral anterior insula. Reward and emotional PEs were also encoded differently in brain-wide multivariate patterns, with a more sensitive neural signature observed within fronto-insular circuits for reward PE. We further identified a fronto-insular network encompassing the left anterior cingulate cortex, bilateral insula, left dmPFC and inferior frontal gyrus that encoded punishment decisions. In addition, a stronger fronto-insular pattern expression under reward PE predicted more punishment decisions. These findings underscore that reward and emotional violations interact to shape decisions in complex social interactions, while the underlying neurofunctional PEs computations are distinguishable.

Investigating the impact of microbiome-changing interventions on food decision-making: MIFOOD study protocol

BACKGROUND

Obesity is a multifactorial disease reaching pandemic proportions with increasing healthcare costs, advocating the development of better prevention and treatment strategies. Previous research indicates that the gut microbiome plays an important role in metabolic, hormonal, and neuronal cross-talk underlying eating behavior. We therefore aim to examine the effects of prebiotic and neurocognitive behavioral interventions on food decision-making and to assay the underlying mechanisms in a Randomized Controlled Trial (RCT).

METHOD

This study uses a parallel arm RCT design with a 26-week intervention period. We plan to enroll 90 participants (male/diverse/female) living with overweight or obesity, defined as either a Waist-to-Hip Ratio (WHR) ≥ 0.9 (male)/0.85 (diverse, female) or a Body Mass Index (BMI) ≥ 25 kg/m2. Key inclusion criteria are 18-60 years of age and exclusion criteria are type 2 diabetes, psychiatric disease, and Magnetic Resonance Imaging (MRI) contraindications. The interventions comprise either a daily supplementary intake of 30 g soluble fiber (inulin), or weekly neurocognitive behavioral group sessions, compared to placebo (equicaloric maltodextrin). At baseline and follow-up, food decision-making is assessed utilizing task-based MRI. Secondary outcome measures include structural MRI, eating habits, lifestyle factors, personality traits, and mood. Further, we obtain fecal and blood samples to investigate gut microbiome composition and related metabolites.

DISCUSSION

This study relies on expanding research suggesting that dietary prebiotics could improve gut microbiome composition, leading to beneficial effects on gut-brain signaling and higher-order cognitive functions. In parallel, neurocognitive behavioral interventions have been proposed to improve unhealthy eating habits and metabolic status. However, causal evidence on how these "bottom-up" and "top-down" processes affect food decision-making and neuronal correlates in humans is still scarce. In addition, microbiome, and gut-brain-axis-related mediating mechanisms remain unclear. The present study proposes a comprehensive approach to assess the effects of these gut-brain-related processes influencing food decision-making in overweight and obesity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05353504. Retrospectively registered on 29 April 2022.

Diminished valuation in the brain: how repeated exposure reduces health message engagement

BACKGROUND

Public health campaigns traditionally aim to maximize message reach, assuming that more exposure leads to greater intended effects. However, recent research on message fatigue suggests that repeated exposure can lead to diminished message engagement. The goal of the current study is to understand the neuropsychological processes involved in repeated health message exposure in key brain regions known to facilitate or inhibit persuasion.

PURPOSE

This study examines how repeated exposure to health campaign messages is linked to neural activity in brain regions implicated in valuation, which is in turn linked to key processes of message engagement, including attention, elaboration, and counterarguing.

METHODS

Thirty-seven adolescent nonsmokers viewed 12 public service announcements (PSAs) from The Real Cost anti-tobacco campaign in a functional magnetic resonance imaging scan session. We examined the relationship between the number of PSA exposures and neural activity in the hypothesized brain regions of interest (ROIs). We also investigated whether the observed neural activity, in turn, was associated with the levels of message engagement reflected in participants' subsequent verbal descriptions of the PSAs, assessed with automated linguistic coding.

RESULTS

Increased message exposure was negatively associated with neural activity in the valuation ROI, which was positively associated with activity in the attention, elaboration, and counterarguing ROIs. Neural activity in the attention ROI was linked to higher message engagement, while activity in the counterarguing ROI was negatively associated with message engagement. No significant link was found between neural activity in the elaboration ROI and message engagement. Mediation analysis suggested that increased exposure may indirectly reduce message engagement by diminishing positive valuation of the messages, thereby decreasing attention to and elaboration of the messages.

CONCLUSIONS

This study elucidates the counterproductive effects of repeated message exposure on message engagement by examining the neurocognitive and psychological processes involved when target audiences are repeatedly exposed to similar anti-tobacco campaign messages. These insights are crucial for planning and designing public health campaigns that typically are repeated via various media channels.

Accelerated Neurocomputation in Human Decision-Making Under Time Pressure

It is common to make risky decisions under time pressure. However, there are ongoing debates regarding the interpretation of the intrinsic mechanisms through which time pressure influences risky decision-making. The current study, combining a sequential risk-taking task, behavioral modeling, and time-resolved multivariate pattern analysis on electroencephalography signals, explored the intrinsic mechanisms underlying the influence of time pressure on risky decision-making. Results from both the behavioral and neural levels indicated that under time pressure, decision-makers enhanced their computation of the value of different options, with this computation primarily based on the potential benefits of options, and made more conservative decision. Additionally, under time pressure, decision-makers' emotion experience was related to the indicator of valuation stage (i.e., decoding accuracy) of decision-making and they spent less time on the subsequent selection stage. The current study highlights that, during risky decision-making under time pressure, the brain does not suppress a particular information processing process; instead, it operates in an accelerated manner.

Representation of Anticipated Rewards and Punishments in the Human Brain

Subjective value is a core concept in neuroeconomics, serving as the basis for decision making. Despite the extensive literature on the neural encoding of subjective reward value in humans, the neural representation of punishment value remains relatively understudied. This review synthesizes current knowledge on the neural representation of reward value, including methodologies, involved brain regions, and the concept of a common currency representation of diverse reward types in decision-making and learning processes. We then critically examine existing research on the neural representation of punishment value, highlighting conceptual and methodological challenges in human studies and insights gained from animal research. Finally, we explore how individual differences in reward and punishment processing may be linked to various mental illnesses, with a focus on stress-related psychopathologies. This review advocates for the integration of both rewards and punishments within value-based decision-making and learning frameworks, leveraging insights from cross-species studies and utilizing ecological gamified paradigms to reflect real-life scenarios.

Mental Time Travel: A Retrospective

Because imagination activates the same neural circuits used in understanding the present, one can access that imagination even in non-linguistic animals through decoding techniques applied to large neural ensembles. This personal retrospective traces the history of the initial discovery that hippocampal theta sequences sweep forward to goals during moments of deliberation and discusses the history that was necessary to put ourselves in the position to recognize this signal. It also discusses how that discovery fits into the larger picture of hippocampal function and the concept of cognition as computation.

Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task

Despite progress in smoking reduction in the past several decades, cigarette smoking remains a significant public health concern world-wide, with many smokers attempting but ultimately failing to maintain abstinence. However, little is known about how decision-making evolves in quitting smokers. Based on preregistered hypotheses and analysis plan ( https://osf.io/yq5th ), we examined the evolution of reinforcement learning (RL), a key component of decision-making, in smokers during acute and extended nicotine abstinence. In a longitudinal, within-subject design, we used a probabilistic reward task (PRT) to assess RL in twenty smokers who successfully refrained from smoking for at least 30 days. We evaluated changes in reward-based decision-making using signal-detection analysis and five RL models across three sessions during 30 days of nicotine abstinence. Contrary to our preregistered hypothesis, punishment sensitivity emerged as the only parameter that changed during smoking cessation. While it is plausible that some changes in task performance could be attributed to task repetition effects, we observed a clear impact of the Nicotine Withdrawal Syndrome (NWS) on RL, and a dynamic relationship between craving and reward and punishment sensitivity over time, suggesting a significant recalibration of cognitive processes during abstinence. In this context, the heightened sensitivity to negative outcomes observed at the last session (30 days after quitting) compared to the previous sessions, may be interpreted as a cognitive adaptation aimed at fostering long-term abstinence. While further studies are needed to clarify the mechanisms underlying punishment sensitivity during nicotine abstinence, these results highlight the need for personalized treatment approaches tailored to individual needs.

The effect of transcranial Direct Current Stimulation on the Iowa Gambling Task: a scoping review

Introduction

Among the tasks employed to investigate decisional processes, the Iowa Gambling Task (IGT) appears to be the most effective since it allows for deepening the progressive learning process based on feedback on previous choices. Recently, the study of decision making through the IGT has been combined with the application of transcranial direct current stimulation (tDCS) to understand the cognitive mechanisms and the neural structures involved. However, to date no review regarding the effects of tDCS on decisional processes assessed through the IGT is available. This scoping review aims to provide a comprehensive exploration of the potential effects of tDCS in enhancing decisional processes, assessed with the IGT, through the evaluation of the complete range of target cases.

Methods

The existing literature was analyzed through the PRISMA approach.

Results

Results reported that tDCS can enhance performance in the IGT and highlighted a pivotal role of the dorsolateral prefrontal cortex and the orbitofrontal cortex in risky and ambiguous decisions.

Discussion

Thus, tDCS over the brain regions identified improves the decisional processes in healthy subjects and patients, confirming its potential to enhance decision making in everyday contexts and deepen the neural correlates. Suggestions for further studies are provided to delve into decisional mechanisms and how to better support them.

Efficient value synthesis in the orbitofrontal cortex explains how loss aversion adapts to the ranges of gain and loss prospects

Is irrational behavior the incidental outcome of biological constraints imposed on neural information processing? In this work, we consider the paradigmatic case of gamble decisions, where gamble values integrate prospective gains and losses. Under the assumption that neurons have a limited firing response range, we show that mitigating the ensuing information loss within artificial neural networks that synthetize value involves a specific form of self-organized plasticity. We demonstrate that the ensuing efficient value synthesis mechanism induces value range adaptation. We also reveal how the ranges of prospective gains and/or losses eventually determine both the behavioral sensitivity to gains and losses and the information content of the network. We test these predictions on two fMRI datasets from the OpenNeuro.org initiative that probe gamble decision-making but differ in terms of the range of gain prospects. First, we show that peoples' loss aversion eventually adapts to the range of gain prospects they are exposed to. Second, we show that the strength with which the orbitofrontal cortex (in particular: Brodmann area 11) encodes gains and expected value also depends upon the range of gain prospects. Third, we show that, when fitted to participant's gambling choices, self-organizing artificial neural networks generalize across gain range contexts and predict the geometry of information content within the orbitofrontal cortex. Our results demonstrate how self-organizing plasticity aiming at mitigating information loss induced by neurons' limited response range may result in value range adaptation, eventually yielding irrational behavior.

Deepening the decisional processes under value-based conditions in patients affected by Parkinson's disease: A comparative study

Patients affected by Parkinson's disease (PD) display a tendency toward making risky choices in value-based conditions. Possible causes may encompass the pathophysiologic characteristics of PD that affect neural structures pivotal for decision making (DM) and the dopaminergic medications that may bias choices. Nevertheless, excluding patients with concurrent impulse control disorders, results are few and mixed. Conversely, other factors, such as individual differences (e.g., emotional state, impulsivity, consideration for future consequences) and cognitive functioning, in particular executive functions (EFs), are involved, even though few studies investigated their possible role. The present study investigated (1) the differences in value-based DM between 33 patients with PD without impulse control disorders and 33 matched healthy controls, and (2) the relationships among decisional performances, EFs, and individual differences in a group of 42 patients with PD who regularly undertake dopaminergic medications. All participants underwent an individual assessment to investigate value-based DM, cognitive abilities, and individual differences associated with DM. Nonparametric analyses showed the presence of riskier decisions in patients compared with healthy controls, depending on the characteristics of the decisional situation. Moreover, parameters of the decisional tasks involving the number of risky choices were significantly related to the posology of dopaminergic medications, EFs, and individual differences. Findings were discussed, highlighting possible clinical implications.

The affective gradient hypothesis: an affect-centered account of motivated behavior

Everyone agrees that feelings and actions are intertwined, but cannot agree how. According to dominant models, actions are directed by estimates of value and these values shape or are shaped by affect. I propose instead that affect is the only form of value that drives actions. Our mind constantly represents potential future states and how they would make us feel. These states collectively form a gradient reflecting feelings we could experience depending on actions we take. Motivated behavior reflects the process of traversing this affective gradient, towards desirable states and away from undesirable ones. This affective gradient hypothesis solves the puzzle of where values and goals come from, and offers a parsimonious account of apparent conflicts between emotion and cognition.

Emotional Working Memory Training Treatment for Young Adult Problem Online Sports Bettors: A Preliminary Randomized Controlled Trial

One of the key features of gambling disorder (GD) is impairment in cognitive-emotional control. Considering the negative consequences of GD, the present study investigated the effectiveness of emotional working memory training (eWMT) in improving cognitive control, attention, working memory capacity, and cognitive emotion regulation strategies (CERS) among young adults with GD compared to a placebo group. Following the initial assessment in the pre-test phase, eligible participants were randomly assigned to one of two groups: experimental (n = 34) and placebo (n = 30). These groups completed eWMT and a feature matching task for 20 sessions respectively. The post-test and follow-up measures indicated that eWMT significantly improved cognitive control, attention, working memory capacity, and the use of maladaptive cognitive emotion regulation strategies, but it had no significant effect on adaptive CERS. The promising results of the present study suggest the use of eWMT as a new intervention to improve cognitive-emotional control among individuals with online gambling problems.

Speed is associated with polarization during subjective evaluation: no tradeoff, but an effect of the ease of processing

In human perceptual decision-making, the speed-accuracy tradeoff establishes a causal link between urgency and reduced accuracy. Less is known about how speed relates to the subjective evaluation of visual images. Here, we conducted a set of four experiments to tease apart two alternative hypotheses for the relation between speed and subjective evaluation. The hypothesis of "Speed-Polarization Tradeoff" implies that urgency causes more polarized evaluations. In contrast, the "Ease-of-Processing" hypothesis suggests that any association between speed and polarization is due to the salience of evaluation-relevant image content. The more salient the content, the easier to process, and therefore the faster and more extreme the evaluation. In each experiment, we asked participants to evaluate images on a continuous scale from - 10 to + 10 and measured their response times; in Experiments 1-3, the participants rated real-world images in terms of morality (from "very immoral," -10, to "very moral," +10); in Experiment 4, the participants rated food images in terms of appetitiveness (from "very disgusting," -10, to "very attractive," +10). In Experiments 1, 3, and 4, we used a cueing procedure to inform the participants on a trial-by-trial basis whether they could make a self-paced (SP) evaluation or whether they had to perform a time-limited (TL) evaluation within 2 s. In Experiment 2, we asked participants to rate the easiness of their SP moral evaluations. Compared to the SP conditions, the responses in the TL condition were consistently much faster, indicating that our urgency manipulation was successful. However, comparing the SP versus TL conditions, we found no significant differences in any of the evaluations. Yet, the reported ease of processing of moral evaluation covaried strongly with both the response speed and the polarization of evaluation. The overall pattern of data indicated that, while speed is associated with polarization, urgency does not cause participants to make more extreme evaluations. Instead, the association between speed and polarization reflects the ease of processing. Images that are easy to evaluate evoke faster and more extreme scores than images for which the interpretation is uncertain.

Topographically selective motor inhibition under threat of pain

ABSTRACT

Pain-related motor adaptations may be enacted predictively at the mere threat of pain, before pain occurrence. Yet, in humans, the neurophysiological mechanisms underlying motor adaptations in anticipation of pain remain poorly understood. We tracked the evolution of changes in corticospinal excitability (CSE) as healthy adults learned to anticipate the occurrence of lateralized, muscle-specific pain to the upper limb. Using a Pavlovian threat conditioning task, different visual stimuli predicted pain to the right or left forearm (experiment 1) or hand (experiment 2). During stimuli presentation before pain occurrence, single-pulse transcranial magnetic stimulation was applied over the left primary motor cortex to probe CSE and elicit motor evoked potentials from target right forearm and hand muscles. The correlation between participants' trait anxiety and CSE was also assessed. Results showed that threat of pain triggered corticospinal inhibition specifically in the limb where pain was expected. In addition, corticospinal inhibition was modulated relative to the threatened muscle, with threat of pain to the forearm inhibiting the forearm and hand muscles, whereas threat of pain to the hand inhibited the hand muscle only. Finally, stronger corticospinal inhibition correlated with greater trait anxiety. These results advance the mechanistic understanding of pain processes showing that pain-related motor adaptations are enacted at the mere threat of pain, as sets of anticipatory, topographically organized motor changes that are associated with the expected pain and are shaped by individual anxiety levels. Including such anticipatory motor changes into models of pain may lead to new treatments for pain-related disorders.

Neural mechanisms of altruistic decision-making: EEG functional connectivity network analysis

Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significant interindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the "empathy-altruism" hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can further be used to identify neurophysiological markers of prosociality in behavior.

Modality-specific and modality-general representations of subjective value in frontal cortex

Neuroeconomics theories propose that the value associated with diverse rewards or reward-predicting stimuli is encoded along a common reference scale, irrespective of their sensory properties. However, in a dynamic environment with changing stimulus-reward pairings, the brain must also represent the sensory features of rewarding stimuli. The mechanism by which the brain balances these needs-deriving a common reference scale for valuation while maintaining sensitivity to sensory contexts-remains unclear. To investigate this, we conducted an fMRI study with human participants engaged in a dynamic foraging task, which required integrating the reward history of auditory or visual choice options and updating the subjective value for each sensory modality. Univariate fMRI analysis revealed modality-specific value representations in the orbitofrontal cortex (OFC) and modality-general value representations in the ventromedial prefrontal cortex (vmPFC), confirmed by an exploratory multivariate pattern classification approach. Crucially, modality-specific value representations were absent when the task involved instruction-based rather than value-based choices. Effective connectivity analysis showed that modality-specific value representations emerged from selective bidirectional interactions across the auditory and visual sensory cortices, the corresponding OFC clusters, and the vmPFC. These results illustrate how the brain enables a valuation process that is sensitive to the sensory context of rewarding stimuli.

Attention-dependent attribute comparisons underlie multi-attribute decision-making in orbitofrontal cortex

Economic decisions often require weighing multiple dimensions, or attributes. The orbitofrontal cortex FC) is thought to be important for computing the integrated value of an option from its attributes and comparing lues to make a choice. Although OFC neurons are known to encode integrated values, evidence for value mparison has been limited. Here, we used a multi-attribute choice task for monkeys to investigate how OFC eurons integrate and compare multi-attribute options. Attributes were represented separately and eye tracking as used to measure attention. We found that OFC neurons encode the value of attended attributes, dependent of other attributes in the same option. Encoding was negatively weighted by the value of the same tribute in the other option, consistent with a comparison between the two like attributes. These results indicate at OFC computes comparisons among attributes rather than integrated values, and does so dynamically, ifting with the focus of attention.

Childhood socioeconomic position relates to adult decision-making: Evidence from a large cross-cultural investigation

Early exposure to poverty may have profound and enduring impacts on developmental trajectories over the lifespan. This study investigated potential links between childhood socioeconomic position, recent economic change, and temporal discounting in a large international cohort (N = 12,951 adults from 61 countries). Temporal discounting refers to the tendency to prefer smaller immediate rewards over larger rewards delivered after a delay, and connects to consequential outcomes including academic achievement, occupational success, and risk-taking behaviors. Consistent with multiple theories about the impacts of stress exposure, individuals who reported lower socioeconomic positions in childhood exhibited greater temporal discounting in adulthood compared to peers who did not. Furthermore, an interaction emerged between childhood socioeconomic position and recent economic change, such that the steepest temporal discounting was found among those from lower childhood socioeconomic positions who also recently experienced negative economic change as a result of the COVID pandemic. These associations remained significant even when accounting for potentially confounding factors like education level and current employment. Findings provide new evidence that childhood socioeconomic position relates to greater temporal discounting and steeper devaluation of future rewards later in adulthood, particularly in response to contemporaneous economic change. This suggests childhood socioeconomic position may have longer-term impacts on developmental trajectories. Speculatively, childhood socioeconomic position may shape adult behavior through increased life stress, diminished access to resources, and lower perceived trust and reliability in social systems. These findings underscore the long-term implications of socioeconomic gaps, cycles of disadvantage and economic marginalization.

Semi-orthogonal subspaces for value mediate a binding and generalization trade-off

When choosing between options, we must associate their values with the actions needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. Here, in macaques performing a choice task, we show that neural populations in five reward-sensitive regions encode the values of offers presented on the left and right in distinct subspaces. This encoding is sufficient to bind offer values to their locations while preserving abstract value information. After offer presentation, all areas encode the value of the first and second offers in orthogonal subspaces; this orthogonalization also affords binding. Our binding-by-subspace hypothesis makes two new predictions confirmed by the data. First, behavioral errors should correlate with spatial, but not temporal, neural misbinding. Second, behavioral errors should increase when offers have low or high values, compared to medium values, even when controlling for value difference. Together, these results support the idea that the brain uses semi-orthogonal subspaces to bind features.

Touchscreen response precision is sensitive to the explore/exploit tradeoff

The explore/exploit tradeoff is a fundamental property of choice selection during reward-guided decision making. In perceptual decision making, higher certainty decisions are more motorically precise, even when the decision does not require motor accuracy. However, while we can parametrically control uncertainty in perceptual tasks, we do not know what variables - if any - shape motor precision and reflect subjective certainty during reward-guided decision making. Touchscreens are increasingly used across species to measure choice, but provide no tactile feedback on whether an action is precise or not, and therefore provide a valuable opportunity to determine whether actions differ in precision due to explore/exploit state, reward, or individual variables. We find all three of these factors exert independent drives towards increased precision. During exploit states, successive touches to the same choice are closer together than those made in an explore state, consistent with exploit states reflecting higher certainty and/or motor stereotypy in responding. However, exploit decisions might be expected to be rewarded more frequently than explore decisions. We find that exploit choice precision is increased independently of a separate increase in precision due to immediate past reward, suggesting multiple mechanisms regulating choice precision. Finally, we see evidence that male mice in general are less precise in their interactions with the touchscreen than females, even when exploiting a choice. These results suggest that as exploit behavior emerges in reward-guided decision making, individuals become more motorically precise reflecting increased certainty, even when decision choice does not require additional motor accuracy, but this is influenced by individual differences and prior reward. These data uncover the hidden potential for touchscreen tasks in any species to uncover the latent neural states that unite cognition and movement.

Decision-making and attention deficit hyperactivity disorder: neuroeconomic perspective

The decision-making process involves various cognitive procedures influenced by the interplay between cognition, motivation, and attention, forming a complex neural framework. Attention is a fundamental cognitive element within decision-making mechanisms, and one of the conditions affecting the attentional system is attention deficit hyperactivity disorder (ADHD). Decision-making impairments in ADHD have significant economic consequences, necessitating effective policies and interventions to address this critical issue. Research from computational models and neuroscience suggests how cognitive functions' workings and problems affect decision-making and provide insights into the neural implications of decision-making. This article explores the intersection of decision-making, ADHD, and neuroeconomics, highlighting research gaps, potential contributions, and implications for future policies.