Using recurrent neural network to estimate irreducible stochasticity in human choice behavior.

Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others. In the current study, we examined the use of theory-independent models, specifically recurrent neural networks (RNNs), to classify the source of a predictive gap in the observed data of a single individual.

The influence of COVID-19 on attention-deficit/hyperactivity disorder diagnosis and treatment rates across age, gender, and socioeconomic status: A 20-year national cohort study.

Infection and lockdowns resulting from COVID-19 have been suggested to increase the prevalence and treatment rates of Attention Deficit/Hyperactivity Disorder (ADHD). To accurately estimate the pandemic's effects, pre-pandemic data can be used to estimate diagnosis and treatment rates during the COVID-19 years as if the COVID-19 pandemic did not occur. However, accurate predictions require a broad dataset, both in terms of the number of cases and the pre-pandemic timeframe.

Disentangling the contribution of individual and social learning processes in human advice-taking behavior.

The study of social learning examines how individuals learn from others by means of observation, imitation, or compliance with advice. However, it still remains largely unknown whether social learning processes have a distinct contribution to behavior, independent from non-social trial-and-error learning that often occurs simultaneously. 153 participants completed a reinforcement learning task, where they were asked to make choices to gain rewards.

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.

Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior.

Reinforcement learning (RL) models are used extensively to study human behavior. These rely on normative models of behavior and stress interpretability over predictive capabilities. More recently, neural network models have emerged as a descriptive modeling paradigm that is capable of high predictive power yet with limited interpretability.

Fruit bats adjust their decision-making process according to environmental dynamics.

One of the main functions of behavioral plasticity lies in the ability to contend with dynamic environments. Indeed, while numerous studies have shown that animals adapt their behavior to the environment, how they adapt their latent learning and decision strategies to changes in the environment is less understood. Here, we used a controlled experiment to examine the bats' ability to adjust their decision strategy according to the environmental dynamics.

Computational mechanisms underlying latent value updating of unchosen actions.

Current studies suggest that individuals estimate the value of their choices based on observed feedback. Here, we ask whether individuals also update the value of their unchosen actions, even when the associated feedback remains unknown. One hundred seventy-eight individuals completed a multi-armed bandit task, making choices to gain rewards.

Distinct reinforcement learning profiles distinguish between language and attentional neurodevelopmental disorders.

Background: Theoretical models posit abnormalities in cortico-striatal pathways in two of the most common neurodevelopmental disorders (Developmental dyslexia, DD, and Attention deficit hyperactive disorder, ADHD), but it is still unclear what distinct cortico-striatal dysfunction might distinguish language disorders from others that exhibit very different symptomatology. Although impairments in tasks that depend on the cortico-striatal network, including reinforcement learning (RL), have been implicated in both disorders, there has been little attempt to dissociate between different types of RL or to compare learning processes in these two types of disorders. The present study builds upon prior research indicating the existence of two learning manifestations of RL and evaluates whether these processes can be differentiated in language and attention deficit disorders.

Formation of non-veridical action-outcome associations following exposure to threat-related cues.

Acting in a goal-directed manner requires an ability to accurately predict the outcomes of one's actions. However, not much is known regarding how threat-related cues influence our ability to form action-outcome associations according to the environment's known causal structure. Here, we examined the extent to which threat-related cues influence individuals' tendency to form and act in accordance with action-outcome associations that do not exist in the external environment (i.

Working memory capacity estimates moderate value learning for outcome-irrelevant features.

To establish accurate action-outcome associations in the environment, individuals must refrain from assigning value to outcome-irrelevant features. However, studies have largely ignored the role of attentional control processes on action value updating. In the current study, we examined the extent to which working memory-a system that can filter and block the processing of irrelevant information in one's mind-also filters outcome-irrelevant information during value-based learning.

Compulsivity is linked to reduced adolescent development of goal-directed control and frontostriatal functional connectivity.

A characteristic of adaptive behavior is its goal-directed nature. An ability to act in a goal-directed manner is progressively refined during development, but this refinement can be impacted by the emergence of psychiatric disorders. Disorders of compulsivity have been framed computationally as a deficit in model-based control, and have been linked also to abnormal frontostriatal connectivity.

Credit assignment to state-independent task representations and its relationship with model-based decision making.

Model-free learning enables an agent to make better decisions based on prior experience while representing only minimal knowledge about an environment's structure. It is generally assumed that model-free state representations are based on outcome-relevant features of the environment. Here, we challenge this assumption by providing evidence that a putative model-free system assigns credit to task representations that are irrelevant to an outcome.

Effects of neurofeedback and working memory-combined training on executive functions in healthy young adults.

Given the interest in improving executive functions, the present study examines a promising combination of two training techniques: neurofeedback training (NFT) and working memory training (WMT). NFT targeted increasing the amplitude of individual's upper Alpha frequency band at the parietal midline scalp location (Pz), and WMT consisted of an established computerized protocol with working memory updating and set-shifting components. Healthy participants (n = 140) were randomly allocated to five combinations of training, including visual search training used as an active control training for the WMT; all five groups were compared to a sixth silent control group receiving no training.

Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling.

A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability.

Can we learn to learn? The influence of procedural working-memory training on rapid instructed-task-learning.

Humans have the unique ability to efficiently execute instructions that were never practiced beforehand. In this Rapid Instructed-Task-Learning, not-yet-executed novel rules are presumably held in procedural working-memory (WM), which is assumed to hold stimulus-to-response bindings. In this study, we employed a computerized-cognitive training protocol targeting procedural WM to test this assumption and to examine whether the ability to rapidly learn novel rules can itself be learned.

Formation of abstract task representations: Exploring dosage and mechanisms of working memory training effects.

Working memory is strongly involved in human reasoning, abstract thinking and decision making. Past studies have shown that working memory training generalizes to untrained working memory tasks with similar structure (near-transfer effect). Here, we focused on two questions: First, we ask how much training might be required in order to find a reliable near-transfer effect? Second, we ask which choice- mechanism might underlie training benefits? Participants were allocated to one of three groups: working-memory training (combined set-shifting and N-back task), active-control (visual search) and no-contact control.

Computation in Psychotherapy, or How Computational Psychiatry Can Aid Learning-Based Psychological Therapies.

Learning-based therapies, such as cognitive-behavioral therapy, are used worldwide, and their efficacy is endorsed by health and research funding agencies. However, the mechanisms behind both their strengths and their weaknesses are inadequately understood. Here we describe how advances in computational modeling may help formalize and test hypotheses regarding how patients make inferences, which are core postulates of these therapies.

Examining procedural working memory processing in obsessive-compulsive disorder.

Previous research has suggested that a deficit in working memory might underlie the difficulty of obsessive-compulsive disorder (OCD) patients to control their thoughts and actions. However, a recent meta-analyses found only small effect sizes for working memory deficits in OCD. Recently, a distinction has been made between declarative and procedural working memory.

The role of working memory in rapid instructed task learning and intention-based reflexivity: An individual differences examination.

The ability to efficiently follow novel task instructions (Rapid Instructed Task Learning, RITL) appears late in evolution, is required for successful collaborative teamwork, and appears to involve maintaining instructions in working-memory (WM). RITL is indexed by the efficiency in which the instructions are performed (RITL success) and by whether the instructions operate automatically (intention-based reflexivity). Based on prior normative work employing WM-load manipulations, we predicted that individual differences in WM would positively correlate with these RITL indices.

Increased ongoing neural variability in ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) has been described as a disorder where frequent lapses of attention impair the ability of an individual to focus/attend in a sustained manner, thereby generating abnormally large intra-individual behavioral variability across trials. Indeed, increased reaction time (RT) variability is a fundamental behavioral characteristic of individuals with ADHD found across a large number of cognitive tasks. But what is the underlying neurophysiology that might generate such behavioral instability? Here, we examined trial-by-trial EEG response variability to visual and auditory stimuli while subjects' attention was diverted to an unrelated task at the fixation cross.

Memory for Action Rules and Reaction Time Variability in Attention-Deficit/Hyperactivity Disorder.

Background: Patients with attention-deficit/hyperactivity disorder (ADHD) exhibit increased reaction time (RT) variability. This finding is consistent across various choice RT tasks and is considered a core ADHD phenotype, often interpreted as expressing occasional attention lapses. This study explores the selective contribution of perceptual and working memory (WM) processes to increased RT variability in ADHD.

Learning to control actions: transfer effects following a procedural cognitive control computerized training.

Few studies have addressed action control training. In the current study, participants were trained over 19 days in an adaptive training task that demanded constant switching, maintenance and updating of novel action rules. Participants completed an executive functions battery before and after training that estimated processing speed, working memory updating, set-shifting, response inhibition and fluid intelligence.

Exploring relations between task conflict and informational conflict in the Stroop task.

In this study, we tested the proposal that the Stroop task involves two conflicts--task conflict and informational conflict. Task conflict was defined as the latency difference between color words and non-letter neutrals, and manipulated by varying the proportion of color words versus non-letter neutrals. Informational conflict was defined as the latency difference between incongruent and congruent trials and manipulated by varying the congruent-to-incongruent trial ratio.