The drift diffusion model as the choice rule in inter-temporal and risky choice: A case study in medial orbitofrontal cortex lesion patients and controls

Troubleshooting Bayesian cognitive models

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software that efficiently automates the Markov chain Monte Carlo sampling used for Bayesian model fitting-including the popular Stan and PyMC packages, which automate the dynamic Hamiltonian Monte Carlo and No-U-Turn Sampler (HMC/NUTS) algorithms that we spotlight here. Unfortunately, Bayesian cognitive models can struggle to pass the growing number of diagnostic checks required of Bayesian models. If any failures are left undetected, inferences about cognition based on the model's output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and HMC/NUTS sampling, we outline the diagnostic metrics, procedures, and plots necessary to detect problems in model output with an emphasis on how these requirements have recently been changed and extended. Throughout, we explain how uncovering the exact nature of the problem is often the key to identifying solutions. We also demonstrate the troubleshooting process for an example hierarchical Bayesian model of reinforcement learning, including supplementary code. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models in their research. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The Less Meaningful the Understanding, the Faster the Feeling: Speech Comprehension Changes Perceptual Speech Tempo

The perception of speech tempo is influenced by both the acoustic properties of speech and the cognitive state of the listener. However, there is a lack of research on how speech comprehension affects the perception of speech tempo. This study aims to disentangle the impact of speech comprehension on the perception of speech tempo by manipulating linguistic structures and measuring perceptual speech tempo at explicit and implicit levels. Three experiments were conducted to explore these relationships. In Experiment 1, two explicit speech tasks revealed that listeners tend to overestimate the speech tempo of sentences with low comprehensibility, although this effect decreased with repeated exposure to the speech. Experiment 2, utilizing an implicit speech tempo task, replicated the main findings of Experiment 1. Furthermore, the results from the drift-diffusion model eliminated the possibility that participants' responses were based on the type of sentence. In Experiment 3, non-native Chinese speakers with varying levels of language proficiency completed the implicit speech rate task. The results showed that non-native Chinese speakers exhibited distinct behavioral patterns compared to native Chinese speakers, as they did not perceive differences in speech tempo between high and low comprehensibility conditions. These findings highlight the intricate relationship between the perception of speech tempo and the comprehensibility of processed speech.

Mechanisms of increased pain discrimination by contingent reinforcement: a perceptual decision-making and instrumental learning account

ABSTRACT

Recent evidence highlights that monetary rewards can increase the precision at which healthy human volunteers can detect small changes in the intensity of thermal noxious stimuli, contradicting the idea that rewards exert a broad inhibiting influence on pain perception. This effect was stronger with contingent rewards compared with noncontingent rewards, suggesting a successful learning process. In the present study, we implemented a model comparison approach that aimed to improve our understanding of the mechanisms that underlie thermal noxious discrimination in humans. In a between-subject design, 54 healthy human volunteers took part in a pain discrimination task with monetary rewards either contingent or noncontingent on successful discrimination of small changes in the intensity painful heat stimulation. We used models from 2 traditions in decision-making research, perceptual decision-making, and instrumental learning. Replicating the previous findings, only rewards contingent on behavior enhanced pain discrimination. Drift diffusion modelling revealed increased sensory signal strength and decreased response caution and nondecision times as mechanisms underlying this effect of contingent rewards on pain discrimination. In addition, reinforcement learning models indicated a temporal evolution of discriminative abilities reflected by a trial-by-trial increase of perceived signal strength only with contingent rewards but not with noncontingent rewards. Modelling of separate learning rates for positive and negative prediction errors indicated that this temporal evolution of discriminative abilities was driven by positive reward prediction errors. These results might indicate increased sensitivity towards better-than-expected outcomes in the temporal adaptation of pain discrimination abilities to a rewarding context in humans.

Cross-species translational paradigms for assessing positive valence system as defined by the RDoC matrix

Functions associated with processing reward-related information are fundamental drivers of motivation, learning, and goal-directed behavior. Such functions have been classified as the positive valence system under the Research Domain and Criteria (RDoC) criteria and are negatively impacted across a range of psychiatric disorders and mental illnesses. The positive valence system is composed of three comprehensive categories containing related but dissociable functions that are organized into either Reward Responsiveness, Reward Learning, or Reward Valuation. The presence of overlapping behavioral dysfunction across diagnostic mental disorders is in-part what motivated the RDoC initiative, which emphasized that the study of mental illness focus on investigating relevant behavior and cognitive functions and their underlying mechanisms, rather than separating efforts on diagnostic categories (i.e., transdiagnostic). Moreover, the RDoC approach is well-suited for preclinical neuroscience research, as the rise in genetic toolboxes and associated neurotechnologies enables researchers to probe specific cellular targets with high specificity. Thus, there is an opportunity to dissect whether behaviors and cognitive functions are supported by shared or distinct neural mechanisms. For preclinical research to effectively inform our understandings of human behavior however, the cognitive and behavioral paradigms should have predictive, neurobiological, and pharmacological predictive validity to the human test. Touchscreen-based testing systems provide a further advantage for this endeavor enabling tasks to be presented to animals using the same media and task design as in humans. Here, we outline the primary categories of the positive valence system and review the work that has been done cross-species to investigate the neurobiology and neurochemistry underlying reward-related functioning. Additionally, we provide clinical tasks outlined by RDoC, along with validity and/or need for further validation for analogous rodent paradigms with a focus on implementing the touchscreen-based cognitive testing systems.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

A Bayesian Hierarchical Model of Trial-To-Trial Fluctuations in Decision Criterion

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo at www.github.com/robinvloeberghs/hMFC to enable widespread application of hMFC in decision-making research.

Dopamine and acetylcholine have distinct roles in delay- and effort-based decision-making in humans

In everyday life, we encounter situations that require tradeoffs between potential rewards and associated costs, such as time and (physical) effort. The literature indicates a prominent role for dopamine in discounting of both delay and effort, with mixed findings for delay discounting in humans. Moreover, the reciprocal antagonistic interaction between dopaminergic and cholinergic transmission in the striatum suggests a potential opponent role of acetylcholine in these processes. We found opposing effects of dopamine D2 (haloperidol) and acetylcholine M1 receptor (biperiden) antagonism on specific components of effort-based decision-making in healthy humans: haloperidol decreased, whereas biperiden increased the willingness to exert physical effort. In contrast, delay discounting was reduced under haloperidol, but not affected by biperiden. Together, our data suggest that dopamine, acting at D2 receptors, modulates both effort and delay discounting, while acetylcholine, acting at M1 receptors, appears to exert a more specific influence on effort discounting only.

Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is characterized by difficulty in acting in a goal-directed manner. While most environments require a sequence of actions for goal attainment, ADHD was never studied in the context of value-based sequence learning. Here, we made use of current advancements in hierarchical reinforcement-learning algorithms to track the internal value and choice policy of individuals with ADHD performing a three-stage sequence learning task. Specifically, 54 participants (28 ADHD, 26 controls) completed a value-based reinforcement-learning task that allowed us to estimate internal action values for each trial and stage using computational modeling. We found attenuated sensitivity to action values in ADHD compared to controls, both in choice and reaction-time variability estimates. Remarkably, this was found only for first-stage actions (i.e., initiatory actions), while for actions performed just before outcome delivery the two groups were strikingly indistinguishable. These results suggest a difficulty in following value estimation for initiatory actions in ADHD.

Mechanisms of impulsive choice: Experiments to explore and models to map the empirical terrain

Impulsive choice is preference for a smaller-sooner (SS) outcome over a larger-later (LL) outcome when LL choices result in greater reinforcement maximization. Delay discounting is a model of impulsive choice that describes the decaying value of a reinforcer over time, with impulsive choice evident when the empirical choice-delay function is steep. Steep discounting is correlated with multiple diseases and disorders. Thus, understanding the processes underlying impulsive choice is a popular topic for investigation. Experimental research has explored the conditions that moderate impulsive choice, and quantitative models of impulsive choice have been developed that elegantly represent the underlying processes. This review spotlights experimental research in impulsive choice covering human and nonhuman animals across the domains of learning, motivation, and cognition. Contemporary models of delay discounting designed to explain the underlying mechanisms of impulsive choice are discussed. These models focus on potential candidate mechanisms, which include perception, delay and/or reinforcer sensitivity, reinforcement maximization, motivation, and cognitive systems. Although the models collectively explain multiple mechanistic phenomena, there are several cognitive processes, such as attention and working memory, that are overlooked. Future research and model development should focus on bridging the gap between quantitative models and empirical phenomena.

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.

Erotic cue exposure increases physiological arousal, biases choices toward immediate rewards, and attenuates model-based reinforcement learning

Computational psychiatry focuses on identifying core cognitive processes that appear altered across distinct psychiatric disorders. Temporal discounting of future rewards and model-based control during reinforcement learning have proven as two promising candidates. Despite its trait-like stability, temporal discounting may be at least partly under contextual control. Highly arousing cues were shown to increase discounting, although evidence to date remains somewhat mixed. Whether model-based reinforcement learning is similarly affected by arousing cues remains unclear. Here, we tested cue-reactivity effects (erotic pictures) on subsequent temporal discounting and model-based reinforcement learning in a within-subjects design in n = 39 healthy heterosexual male participants. Self-reported and physiological arousal (cardiac activity and pupil dilation) were assessed before and during cue exposure. Arousal was increased during exposure of erotic versus neutral cues both on the subjective and autonomic level. Erotic cue exposure increased discounting as reflected by more impatient choices. Hierarchical drift diffusion modeling (DDM) linked increased discounting to a shift in the starting point bias of evidence accumulation toward immediate options. Model-based control during reinforcement learning was reduced following erotic cues according to model-agnostic analysis. Notably, DDM linked this effect to attenuated forgetting rates of unchosen options, leaving the model-based control parameter unchanged. Our findings replicate previous work on cue-reactivity effects in temporal discounting and for the first time show similar effects in model-based reinforcement learning in a heterosexual male sample. This highlights how environmental cues can impact core human decision processes and reveal that comprehensive modeling approaches can yield novel insights in reward-based decision processes.

Listening to the Data: Computational Approaches to Addiction and Learning

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.

Economic and social values in the brain: evidence from lesions to the human ventromedial prefrontal cortex

Making good economic and social decisions is essential for individual and social welfare. Decades of research have provided compelling evidence that damage to the ventromedial prefrontal cortex (vmPFC) is associated with dramatic personality changes and impairments in economic and social decision-making. However, whether the vmPFC subserves a unified mechanism in the social and non-social domains remains unclear. When choosing between economic options, the vmPFC is thought to guide decision by encoding value signals that reflect the motivational relevance of the options on a common scale. A recent framework, the "extended common neural currency" hypothesis, suggests that the vmPFC may also assign values to social factors and principles, thereby guiding social decision-making. Although neural value signals have been observed in the vmPFC in both social and non-social studies, it is yet to be determined whether they have a causal influence on behavior or merely correlate with decision-making. In this review, we assess whether lesion studies of patients with vmPFC damage offer evidence for such a causal role of the vmPFC in shaping economic and social behavior.

Less cortical complexity in ventromedial prefrontal cortex is associated with a greater preference for risky and immediate rewards

In our everyday lives, we are often faced with situations in which we have to make choices that involve risky or delayed rewards. However, the extent to which we are willing to accept larger risky (over smaller certain) or larger delayed (over smaller immediate) rewards vary across individuals. Here we investigated the relationship between cortical surface complexity in medial prefrontal cortex and individual differences in risky and intertemporal preferences. We found that lower cortical complexity in ventromedial prefrontal cortex (vmPFC) was associated with a greater preference for risky and immediate rewards. In addition to these common structural associations in mPFC, we also found associations between lower cortical complexity and a greater preference for immediate rewards that extended into left dorsomedial prefrontal cortex and right vmPFC. Taken together, the shared association suggests that lower cortical complexity in vmPFC may be a structural marker for individual differences in impulsive behavior.

Dopamine regulates decision thresholds in human reinforcement learning in males

Dopamine fundamentally contributes to reinforcement learning, but recent accounts also suggest a contribution to specific action selection mechanisms and the regulation of response vigour. Here, we examine dopaminergic mechanisms underlying human reinforcement learning and action selection via a combined pharmacological neuroimaging approach in male human volunteers (n = 31, within-subjects; Placebo, 150 mg of the dopamine precursor L-dopa, 2 mg of the D2 receptor antagonist Haloperidol). We found little credible evidence for previously reported beneficial effects of L-dopa vs. Haloperidol on learning from gains and altered neural prediction error signals, which may be partly due to differences experimental design and/or drug dosages. Reinforcement learning drift diffusion models account for learning-related changes in accuracy and response times, and reveal consistent decision threshold reductions under both drugs, in line with the idea that lower dosages of D2 receptor antagonists increase striatal DA release via an autoreceptor-mediated feedback mechanism. These results are in line with the idea that dopamine regulates decision thresholds during reinforcement learning, and may help to bridge action selection and response vigor accounts of dopamine.

Decision heuristics in contexts integrating action selection and execution

Heuristics can inform human decision making in complex environments through a reduction of computational requirements (accuracy-resource trade-off) and a robustness to overparameterisation (less-is-more). However, tasks capturing the efficiency of heuristics typically ignore action proficiency in determining rewards. The requisite movement parameterisation in sensorimotor control questions whether heuristics preserve efficiency when actions are nontrivial. We developed a novel action selection-execution task requiring joint optimisation of action selection and spatio-temporal skillful execution. State-appropriate choices could be determined by a simple spatial heuristic, or by more complex planning. Computational models of action selection parsimoniously distinguished human participants who adopted the heuristic from those using a more complex planning strategy. Broader comparative analyses then revealed that participants using the heuristic showed combined decisional (selection) and skill (execution) advantages, consistent with a less-is-more framework. In addition, the skill advantage of the heuristic group was predominantly in the core spatial features that also shaped their decision policy, evidence that the dimensions of information guiding action selection might be yoked to salient features in skill learning.

Computational approaches to modeling gambling behaviour: Opportunities for understanding disordered gambling

Computational modeling has become an important tool in neuroscience and psychiatry research to provide insight into the cognitive processes underlying normal and pathological behavior. There are two modeling frameworks, reinforcement learning (RL) and drift diffusion modeling (DDM), that are well-developed in cognitive science, and have begun to be applied to Gambling Disorder. RL models focus on explaining how an agent uses reward to learn about the environment and make decisions based on outcomes. The DDM is a binary choice framework that breaks down decision making into psychologically meaningful components based on choice reaction time analyses. Both approaches have begun to yield insight into aspects of cognition that are important for, but not unique to, gambling, and thus relevant to the development of Gambling Disorder. However, these approaches also oversimplify or neglect various aspects of decision making seen in real-world gambling behavior. Gambling Disorder presents an opportunity for 'bespoke' modeling approaches to consider these neglected components. In this review, we discuss studies that have used RL and DDM frameworks to investigate some of the key cognitive components in gambling and Gambling Disorder. We also include an overview of Bayesian models, a methodology that could be useful for more tailored modeling approaches. We highlight areas in which computational modeling could enable progression in the investigation of the cognitive mechanisms relevant to gambling.

Evidence for dopaminergic involvement in endogenous modulation of pain relief

Relief of ongoing pain is a potent motivator of behavior, directing actions to escape from or reduce potentially harmful stimuli. Whereas endogenous modulation of pain events is well characterized, relatively little is known about the modulation of pain relief and its corresponding neurochemical basis. Here, we studied pain modulation during a probabilistic relief-seeking task (a 'wheel of fortune' gambling task), in which people actively or passively received reduction of a tonic thermal pain stimulus. We found that relief perception was enhanced by active decisions and unpredictability, and greater in high novelty-seeking trait individuals, consistent with a model in which relief is tuned by its informational content. We then probed the roles of dopaminergic and opioidergic signaling, both of which are implicated in relief processing, by embedding the task in a double-blinded cross-over design with administration of the dopamine precursor levodopa and the opioid receptor antagonist naltrexone. We found that levodopa enhanced each of these information-specific aspects of relief modulation but no significant effects of the opioidergic manipulation. These results show that dopaminergic signaling has a key role in modulating the perception of pain relief to optimize motivation and behavior.

Temporal Construal Effects Are Independent of Episodic Future Thought

Human thought is prone to biases. Some biases serve as beneficial heuristics to free up limited cognitive resources or improve well-being, but their neurocognitive basis is unclear. One such bias is a tendency to construe events in the distant future in abstract, general terms and events in the near future in concrete, detailed terms. Temporal construal may rely on our capacity to orient toward and/or imagine context-rich future events. We tested 21 individuals with impaired episodic future thinking resulting from lesions to the hippocampus or ventromedial prefrontal cortex (vmPFC) and 57 control participants (aged 45-76 years) from Canada and Italy on measures sensitive to temporal construal. We found that temporal construal persisted in most patients, even those with impaired episodic future thinking, but was abolished in some vmPFC cases, possibly in relation to difficulties forming and maintaining future intentions. The results confirm the fractionation of future thinking and that parts of vmPFC might critically support our ability to flexibly conceive and orient ourselves toward future events.

Developmental trajectories of delay discounting from childhood to young adulthood: longitudinal associations and test-retest reliability

Delay discounting (DD) indexes an individual's preference for smaller immediate rewards over larger delayed rewards, and is considered a form of cognitive impulsivity. Cross-sectional studies have demonstrated that DD peaks in adolescence; longitudinal studies are needed to validate this putative developmental trend, and to determine whether DD assesses a temporary state, or reflects a more stable behavioral trait. In this study, 140 individuals aged 9-23 completed a delay discounting (DD) task and cognitive battery at baseline and every-two years thereafter, yielding five assessments over approximately 10 years. Models fit with the inverse effect of age best approximated the longitudinal trajectory of two DD measures, hyperbolic discounting (log[k]) and area under the indifference-point curve (AUC). Discounting of future rewards increased rapidly from childhood to adolescence and appeared to plateau in late adolescence for both models of DD. Participants with greater verbal intelligence and working memory displayed reduced DD across the duration of the study, suggesting a functional interrelationship between these domains and DD from early adolescence to adulthood. Furthermore, AUC demonstrated good to excellent reliability across assessment points that was superior to log(k), with both measures demonstrating acceptable stability once participants reached late adolescence. The developmental trajectories of DD we observed from childhood through young adulthood suggest that DD may index cognitive control more than reward sensitivity, and that despite modest developmental changes with maturation, AUC may be conceptualized as a trait variable related to cognitive control vs impulsivity.

Financial incentives facilitate stronger neural computation of prosocial decisions in lower empathic adult females

Financial incentives are commonly used to motivate behaviors. However, there is also evidence that incentives can impede the behavior they are supposed to foster, for example, documented by a decrease in blood donations if a financial incentive is offered. Based on these findings, previous studies assumed that prosocial motivation is shaped by incentives. However, so far, there is no direct evidence showing an interaction between financial incentives and a specific prosocial motive. Combining drift-diffusion modeling and fMRI, we investigated the effect of financial incentives on empathy, i.e., one of the key motives driving prosocial decisions. In the empathy-alone condition, participants made prosocial decisions based on empathy. In the empathy-bonus condition, they were offered a financial bonus for prosocial decisions, in addition to empathy induction. On average, the bonus enhanced the information accumulation in empathy-based decisions. On the neural level, this enhancement was related to the anterior insula, the same region that also correlated with empathy ratings. Moreover, the effect of the financial incentive on anterior insula activation was stronger the lower a person scored on empathy. These findings show that financial incentives enhance prosocial motivation in the absence of empathy.

Rat Anterior Cingulate Cortex Continuously Signals Decision Variables in a Patch Foraging Task

In patch foraging tasks, animals must decide whether to remain with a depleting resource or to leave it in search of a potentially better source of reward. In such tasks, animals consistently follow the general predictions of optimal foraging theory (the marginal value theorem; MVT): to leave a patch when the reward rate in the current patch depletes to the average reward rate across patches. Prior studies implicate an important role for the anterior cingulate cortex (ACC) in foraging decisions based on MVT: within single trials, ACC activity increases immediately preceding foraging decisions, and across trials, these dynamics are modulated as the value of staying in the patch depletes to the average reward rate. Here, we test whether these activity patterns reflect dynamic encoding of decision-variables and whether these signals are directly involved in decision-making. We developed a leaky accumulator model based on the MVT that generates estimates of decision variables within and across trials, and tested model predictions against ACC activity recorded from male rats performing a patch foraging task. Model predicted changes in MVT decision variables closely matched rat ACC activity. Next, we pharmacologically inactivated ACC in male rats to test the contribution of these signals to decision-making. ACC inactivation had a profound effect on rats' foraging decisions and response times (RTs) yet rats still followed the MVT decision rule. These findings indicate that the ACC encodes foraging-related variables for reasons unrelated to patch-leaving decisions.SIGNIFICANCE STATEMENT The ability to make adaptive patch-foraging decisions, to remain with a depleting resource or search for better alternatives, is critical to animal well-being. Previous studies have found that anterior cingulate cortex (ACC) activity is modulated at different points in the foraging decision process, raising questions about whether the ACC guides ongoing decisions or serves a more general purpose of regulating cognitive control. To investigate the function of the ACC in foraging, the present study developed a dynamic model of behavior and neural activity, and tested model predictions using recordings and inactivation of ACC. Findings revealed that ACC continuously signals decision variables but that these signals are more likely used to monitor and regulate ongoing processes than to guide foraging decisions.

Motor response vigour and visual fixation patterns reflect subjective valuation during intertemporal choice

Value-based decision-making is of central interest in cognitive neuroscience and psychology, as well as in the context of neuropsychiatric disorders characterised by decision-making impairments. Studies examining (neuro-)computational mechanisms underlying choice behaviour typically focus on participants’ decisions. However, there is increasing evidence that option valuation might also be reflected in motor response vigour and eye movements, implicit measures of subjective utility. To examine motor response vigour and visual fixation correlates of option valuation in intertemporal choice, we set up a task where the participants selected an option by pressing a grip force transducer, simultaneously tracking fixation shifts between options. As outlined in our preregistration (https://osf.io/k6jct), we used hierarchical Bayesian parameter estimation to model the choices assuming hyperbolic discounting, compared variants of the softmax and drift diffusion model, and assessed the relationship between response vigour and the estimated model parameters. The behavioural data were best explained by a drift diffusion model specifying a non-linear scaling of the drift rate by the subjective value differences. Replicating previous findings, we found a magnitude effect for temporal discounting, such that higher rewards were discounted less. This magnitude effect was further reflected in motor response vigour, such that stronger forces were exerted in the high vs. the low magnitude condition. Bayesian hierarchical linear regression further revealed higher grip forces, faster response times and a lower number of fixation shifts for trials with higher subjective value differences. An exploratory analysis revealed that subjective value sums across options showed an even more pronounced association with trial-wise grip force amplitudes. Our data suggest that subjective utility or implicit valuation is reflected in motor response vigour and visual fixation patterns during intertemporal choice. Taking into account response vigour might thus provide deeper insight into decision-making, reward valuation and maladaptive changes in these processes, e.g. in the context of neuropsychiatric disorders.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

Advances in modeling learning and decision-making in neuroscience

An organism's survival depends on its ability to learn about its environment and to make adaptive decisions in the service of achieving the best possible outcomes in that environment. To study the neural circuits that support these functions, researchers have increasingly relied on models that formalize the computations required to carry them out. Here, we review the recent history of computational modeling of learning and decision-making, and how these models have been used to advance understanding of prefrontal cortex function. We discuss how such models have advanced from their origins in basic algorithms of updating and action selection to increasingly account for complexities in the cognitive processes required for learning and decision-making, and the representations over which they operate. We further discuss how a deeper understanding of the real-world complexities in these computations has shed light on the fundamental constraints on optimal behavior, and on the complex interactions between corticostriatal pathways to determine such behavior. The continuing and rapid development of these models holds great promise for understanding the mechanisms by which animals adapt to their environments, and what leads to maladaptive forms of learning and decision-making within clinical populations.

Discerning Mouse Trajectory Features With the Drift Diffusion Model

Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the "mouse-tracking" design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard "keypress" design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = -.42) and boundary separation (ρ = .44)-the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity.

Does Ventromedial Prefrontal Cortex Damage Really Increase Impulsiveness? Delay and Probability Discounting in Patients with Focal Lesions

If the tendency to discount rewards reflects individuals' general level of impulsiveness, then the discounting of delayed and probabilistic rewards should be negatively correlated: The less a person is able to wait for delayed rewards, the more they should take chances on receiving probabilistic rewards. It has been suggested that damage to the ventromedial prefrontal cortex (vMPFC) increases individuals' impulsiveness, but both intertemporal choice and risky choice have only recently been assayed in the same patients with vMPFC damage. Here, we assess both delay and probability discounting in individuals with vMPFC damage (n = 8) or with medial temporal lobe (MTL) damage (n = 10), and in age- and education-matched controls (n = 30). On average, MTL-lesioned individuals discounted delayed rewards at normal rates but discounted probabilistic rewards more shallowly than controls. In contrast, vMPFC-lesioned individuals discounted delayed rewards more steeply but probabilistic rewards more shallowly than controls. These results suggest that vMPFC lesions affect the weighting of reward amount relative to delay and certainty in opposite ways. Moreover, whereas MTL-lesioned individuals and controls showed typical, nonsignificant correlations between the discounting of delayed and probabilistic rewards, vMPFC-lesioned individuals showed a significant negative correlation, as would be expected if vMPFC damage increases impulsiveness more in some patients than in others. Although these results are consistent with the hypothesis that vMPFC plays a role in impulsiveness, it is unclear how they could be explained by a single mechanism governing valuation of both delayed and probabilistic rewards.

A unified online test battery for cognitive impulsivity reveals relationships with real-world impulsive behaviours

Impulsive behaviours are a major contributor to the global burden of disease, but existing measures of cognitive impulsivity have suboptimal reliability and validity. Here, we introduce the Cognitive Impulsivity Suite, comprising three computerized/online tasks using a gamified interface. We conceptualize rapid-response impulsive behaviours (disinhibition) as arising from the failure of three distinct cognitive mechanisms: attentional control, information gathering and monitoring/shifting. We demonstrate the construct and criterion validity of the Cognitive Impulsivity Suite in an online community sample (N = 1,056), show test-retest reliability and between-subjects variability in a face-to-face community sample (N = 63), and replicate the results in a community and clinical sample (N = 578). The results support the theoretical architecture of the attentional control, information gathering and monitoring/shifting constructs. The Cognitive Impulsivity Suite demonstrated incremental criterion validity for prediction of real-world, addiction-related problems and is a promising tool for large-scale research on cognitive impulsivity.

Reliability assessment of temporal discounting measures in virtual reality environments

In recent years the emergence of high-performance virtual reality (VR) technology has opened up new possibilities for the examination of context effects in psychological studies. The opportunity to create ecologically valid stimulation in a highly controlled lab environment is especially relevant for studies of psychiatric disorders, where it can be problematic to confront participants with certain stimuli in real life. However, before VR can be confidently applied widely it is important to establish that commonly used behavioral tasks generate reliable data within a VR surrounding. One field of research that could benefit greatly from VR-applications are studies assessing the reactivity to addiction related cues (cue-reactivity) in participants suffering from gambling disorder. Here we tested the reliability of a commonly used temporal discounting task in a novel VR set-up designed for the concurrent assessment of behavioral and psychophysiological cue-reactivity in gambling disorder. On 2 days, thirty-four healthy non-gambling participants explored two rich and navigable VR-environments (neutral: café vs. gambling-related: casino and sports-betting facility), while their electrodermal activity was measured using remote sensors. In addition, participants completed the temporal discounting task implemented in each VR environment. On a third day, participants performed the task in a standard lab testing context. We then used comprehensive computational modeling using both standard softmax and drift diffusion model (DDM) choice rules to assess the reliability of discounting model parameters assessed in VR. Test-retest reliability estimates were good to excellent for the discount rate log(k), whereas they were poor to moderate for additional DDM parameters. Differences in model parameters between standard lab testing and VR, reflecting reactivity to the different environments, were mostly numerically small and of inconclusive directionality. Finally, while exposure to VR generally increased tonic skin conductance, this effect was not modulated by the neutral versus gambling-related VR-environment. Taken together this proof-of-concept study in non-gambling participants demonstrates that temporal discounting measures obtained in VR are reliable, suggesting that VR is a promising tool for applications in computational psychiatry, including studies on cue-reactivity in addiction.

Motivational competition and the paraventricular thalamus

Although significant progress has been made in understanding the behavioral and brain mechanisms for motivational systems, much less is known about competition between motivational states or motivational conflict (e.g., approach - avoidance conflict). Despite being produced under diverse conditions, behavior during motivational competition has two signatures: bistability and metastability. These signatures reveal the operation of positive feedback mechanisms in behavioral selection. Different neuronal architectures can instantiate this selection to achieve bistability and metastability in behavior, but each relies on circuit-level inhibition to achieve rapid and stable selection between competing tendencies. Paraventricular thalamus (PVT) is identified as critical to this circuit level inhibition, resolving motivational competition via its extensive projections to local inhibitory networks in the ventral striatum and extended amygdala, enabling adaptive responding under motivational conflict.

Dopaminergic Modulation of Human Intertemporal Choice: A Diffusion Model Analysis Using the D2-Receptor Antagonist Haloperidol

The neurotransmitter dopamine is implicated in diverse functions, including reward processing, reinforcement learning, and cognitive control. The tendency to discount future rewards over time has long been discussed in the context of potential dopaminergic modulation. Here we examined the effect of a single dose of the D2 receptor antagonist haloperidol (2 mg) on temporal discounting in healthy female and male human participants. Our approach extends previous pharmacological studies in two ways. First, we applied combined temporal discounting drift diffusion models to examine choice dynamics. Second, we examined dopaminergic modulation of reward magnitude effects on temporal discounting. Hierarchical Bayesian parameter estimation revealed that the data were best accounted for by a temporal discounting drift diffusion model with nonlinear trialwise drift rate scaling. This model showed good parameter recovery, and posterior predictive checks revealed that it accurately reproduced the relationship between decision conflict and response times in individual participants. We observed reduced temporal discounting and substantially faster nondecision times under haloperidol compared with placebo. Discounting was steeper for low versus high reward magnitudes, but this effect was largely unaffected by haloperidol. Results were corroborated by model-free analyses and modeling via more standard approaches. We previously reported elevated caudate activation under haloperidol in this sample of participants, supporting the idea that haloperidol elevated dopamine neurotransmission (e.g., by blocking inhibitory feedback via presynaptic D2 auto-receptors). The present results reveal that this is associated with an augmentation of both lower-level (nondecision time) and higher-level (temporal discounting) components of the decision process.SIGNIFICANCE STATEMENT Dopamine is implicated in reward processing, reinforcement learning, and cognitive control. Here we examined the effects of a single dose of the D2 receptor antagonist haloperidol on temporal discounting and choice dynamics during the decision process. We extend previous studies by applying computational modeling using the drift diffusion model, which revealed that haloperidol reduced the nondecision time and reduced impulsive choice compared with placebo. These findings are compatible with a haloperidol-induced increase in striatal dopamine (e.g., because of a presynaptic mechanism). Our data provide novel insights into the contributions of dopamine to value-based decision-making and highlight how comprehensive model-based analyses using sequential sampling models can inform the effects of pharmacological modulation on choice processes.

Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults

Understanding how adult humans learn nonnative speech categories such as tone information has shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Scientists have traditionally examined these questions using longitudinal learning experiments under a multi-category decision making paradigm. Drift-diffusion processes are popular in such contexts for their ability to mimic underlying neural mechanisms. Motivated by these problems, we develop a novel Bayesian semiparametric inverse Gaussian drift-diffusion mixed model for multi-alternative decision making in longitudinal settings. We design a Markov chain Monte Carlo algorithm for posterior computation. We evaluate the method's empirical performances through synthetic experiments. Applied to our motivating longitudinal tone learning study, the method provides novel insights into how the biologically interpretable model parameters evolve with learning, differ between input-response tone combinations, and differ between well and poorly performing adults. supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Dopamine and Risky Decision-Making in Gambling Disorder

Gambling disorder is a behavioral addiction associated with impairments in value-based decision-making and cognitive control. These functions are thought to be regulated by dopamine within fronto-striatal circuits, but the role of altered dopamine neurotransmission in the etiology of gambling disorder remains controversial. Preliminary evidence suggests that increasing frontal dopamine tone might improve cognitive functioning in gambling disorder. We therefore examined whether increasing frontal dopamine tone via a single dose of the catechol-O-methyltransferase (COMT) inhibitor tolcapone would reduce risky choice in human gamblers (n = 14) in a randomized double-blind placebo-controlled crossover study. Data were analyzed using hierarchical Bayesian parameter estimation and a combined risky choice drift diffusion model (DDM). Model comparison revealed a nonlinear mapping from value differences to trial-wise drift rates, confirming recent findings. An increase in risk-taking under tolcapone versus placebo was about five times more likely, given the data, than a decrease [Bayes factor (BF) = 0.2]. Examination of drug effects on diffusion model parameters revealed that an increase in the value dependency of the drift rate under tolcapone was about thirteen times more likely than a decrease (BF = 0.073). In contrast, a reduction in the maximum drift rate under tolcapone was about seven times more likely than an increase (BF = 7.51). Results add to previous work on COMT inhibitors in behavioral addictions and to mounting evidence for the applicability of diffusion models in value-based decision-making. Future work should focus on individual genetic, clinical and cognitive factors that might account for heterogeneity in the effects of COMT inhibition.