Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2021, Volume and Issue: unknown

Published: Aug. 5, 2021

Abstract Despite the importance of memories in everyday life and progress made understanding how they are encoded retrieved, neural processes by which declarative maintained or forgotten remain elusive. Part problem is that it empirically difficult to measure rate at fade, even between repeated presentations source memory. Without such a ground-truth measure, hard identify corresponding correlates. This study addresses this comparing individual patterns functional connectivity against behavioral differences forgetting speed derived from computational phenotyping. Specifically, individual-specific values long-term memory (LTM) were estimated for 33 participants using formal model fit accuracy response time data an adaptive fact learning task. Individual speeds then used examine participant-specific resting-state fMRI connectivity, machine techniques most predictive generalizable features. Our results show associated with within default mode network (DMN) as well DMN cortical sensory areas. Cross-validation showed predicted high ( r = .78) these alone. These support view activity regions actively involved maintaining preventing their decline, suggesting better understood result storage decay, rather than retrieval failure.

Language: Английский

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Attention and learning strategies reveal distinct profiles of psychiatric traits

Published: June 12, 2024

Humans presented with the same problem in environment commonly adopt wildly different strategies for attention and learning. Indeed, psychiatric conditions are defined by qualitative differences behavior. However, most tasks measure an individual's deviation from a single expected strategy rather than utilization of distinct strategies. Measuring diverse is especially important psychiatry, were qualitatively patterns We paired trait questionnaires context generalization task whose metrics goal-directed short-term memory identify Questionnaires assessed traits associated ASD, attention-deficit/hyperactivity disorder (ADHD), obsessive compulsive (OCD), depression, schizotypy psychosis. The subject population recruited online was matched self-reported sex, sample enriched those reporting formal diagnosis ASD. 744 subjects completed first session task, 584 returned after four to six weeks complete second session. found that dominated profile reduced scores relative other across all measures. During session, this particularly pronounced ADHD traits. In contrast, attending features based on frequency increased subjects, ASD OCD. again elevated traits, These results provide insight into relationship between learning

Language: Английский

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

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 31, 2024

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

Language: Английский

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Symptom clusters of depression, anxiety, and ADHD show separable dimensional effects on reinforcement learning in children and adolescents

Published: Aug. 8, 2024

Past reinforcement learning (RL) studies implicated valence and uncertainty in modulating psychopathology effects on computational parameters. Yet, gaps persist understanding their developmental trajectory, generalizability across contexts, the nuanced impact of individual symptom severity that is often overlooked case-control designs. In a sample 122 8-to-18-year-olds both clinical typically developing individuals, our study found differential depression, anxiety ADHD RL, noting reduced choice sensitivity valence-related modulations uncertainty-related changes ADHD. We further deconstructed links to RL parameters according five biologically plausible transdiagnostic clusters anhedonia, negative affect, fear, inattention hyperactivity. Unexpectedly, many identified revealed (inverted-)u-shaped instead linear relationships. Our provides evidence symptom-related alterations decision-making manifest children as young 8 years old, with increasing influence internalizing symptoms but decreasing externalizing age. Through this comprehensive approach, we aim enhance interplay between psychopathology, development, processes, ultimately informing targeted interventions.

Language: Английский

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Bayesian Priors in Active Avoidance

Published: Aug. 8, 2024

Failing to make decisions that would actively avoid negative outcomes is central helplessness. In a Bayesian framework, deciding whether act informed by beliefs about the world can be characterised as priors. However, these priors have not been previously quantified. Here we administered two tasks in which participants decided attempt active avoidance actions. The differed framing and valence, allowing us test prior generating biases behaviour problem-specific or task-independent general. We performed extensive comparisons of models offering different structural explanations data, finding model with task-invariant for provided best fit participants’ trial-by-trial behaviour. parameters this were reliable, an optimistic also reported higher levels positive affect. These results show individual differences explain engage outcomes, providing evidence conceptualization

Language: Английский

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Real-world fluctuations in motivation drive effort-based choices

Published: Aug. 12, 2024

Subjective experiences, like feeling motivated, fluctuate over time. However, we usually ignore these fluctuations when studying how feelings predict behaviour. Here, examine whether naturalistic ups and downs in states influence the subjective value of choices. In a novel microlongitudinal design (N = 155, included timepoints 3344, tasks 845, mean per person 26.4), assessed link between state effort-based choices using smartphone-based, momentary assessments 15 days. Task-based willingness to exert effort for reward was specifically boosted people felt more motivated (than they normally do). This state-behaviour coupling significantly strengthened individuals with higher trait apathy. Computational modelling revealed that changed preceded sensitivity reward, thereby driving Our results show typical, day-to-day cognition are tightly linked, critical understanding fundamental human behaviours real-world.

Language: Английский

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Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

Computational Brain & Behavior, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 8, 2024

Abstract Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics formal comparison four diffusion decision models in specifically designed perceptual decision-making task. Task difficulty speed-accuracy trade-off were systematically manipulated to induce expected changes parameters. To validate our models, assess whether inferred parameter trajectories align with patterns sequences manipulations. address computational challenges, present novel deep learning techniques amortized Bayesian estimation time-varying Our findings indicate that transition both gradual abrupt shifts best fit empirical data. Moreover, find closely mirror sequence Posterior re-simulations further underscore ability faithfully reproduce critical data patterns. Accordingly, results suggest may reflect actual targeted psychological constructs. We argue initial paves way widespread application modeling beyond.

Language: Английский

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A bounded accumulation model of temporal generalization outperforms existing models and captures modality differences and learning effects

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 17, 2024

Abstract Multiple systems in the brain track passage of time and can adapt their activity to temporal requirements (Paton & Buonomano, 2018). While neural implementation timing varies widely between substrates behavioral tasks, at algorithmic level many these behaviors be described as bounded accumulation (Balcı Simen, 2024). So far, from range psychophysical model has only been applied bisection, which participants are requested categorize an interval “long” or “short” 2014; Ofir Landau, 2022). In this work, we extend fit performance generalization task, required being same different compared a standard, reference, duration (Wearden, 1992). Previous models task focused on either group highly trained animals (Birngruber et al., Church Gibbon, 1982; Wearden, Whether few hundreds trials single participants, necessary for comparing across experimental manipulations, not tested. A drift-diffusion with two decision boundaries fits data better than previous models. We ran experiments, one vision audition another examining effect learning. found that modified independently: upper boundary was higher audition, lower decreased learning task.

Language: Английский

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Human risk recognition and prediction in manned submersible diving tasks driven by deep learning models

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102893 - 102893

Published: Oct. 1, 2024

Language: Английский

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Adaptive Integration of Perceptual and Reward Information in an Uncertain World

Published: Nov. 7, 2024

Perceptual uncertainty and salience both impact decision-making, but how these factors precisely trial-and-error reinforcement learning is not well understood. Here, we test the hypotheses that (H1) perceptual modulates reward-based (H2) economic decision-making driven by value of sensory information. For this, combined computational modeling with a uncertainty-augmented reward-learning task in human behavioral experiment ( N = 98). In line our hypotheses, found subjects regulated behavior response to which they could distinguish choice options based on information (belief state), addition errors made predicting outcomes. Moreover, considered combination expected values for decision-making. Taken together, this shows are closely intertwined share common basis real world.

Language: Английский

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