Toward a functional future for the cognitive neuroscience of human aging

Neuron, Journal Year: 2025, Volume and Issue: 113(1), P. 154 - 183

Published: Jan. 1, 2025

Language: Английский

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Reliable, rapid, and remote measurement of metacognitive bias

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 28, 2024

Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of 'anxious-depression' and 'compulsivity intrusive thought', cross-sectionally. To progress our understanding the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed visuo-perceptual metacognitive (confidence) bias investigated its psychometric properties across two studies (N = 3410 unpaid citizen scientists, N 52 paid participants). assessed convergent validity, split-half test-retest reliability, identified minimum number trials capture clinical correlates. Convergent validity was moderate (r(50) 0.64, p < 0.001) it demonstrated excellent reliability 0.91, 0.001). Anxious-depression decreased confidence (β - 0.23, SE 0.02, 0.001), while compulsivity thought greater 0.07, The associations between psychiatry evident in as few 40 trials. decision-making stable sessions, exhibiting very high for 100-trial (ICC 0.86, 110) 40-trial 120) versions Meta Mind. Hybrid 'self-report cognition' tasks may be one way bridge recently discussed gap computational psychiatry.

Language: Английский

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Understanding learning through uncertainty and bias

Communications Psychology, Journal Year: 2025, Volume and Issue: 3(1)

Published: Feb. 13, 2025

Abstract Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms improve under uncertainty. Drawing models, we illustrate how should be adjusted different sources of uncertainty, including perceptual risk, uncertainty due environmental changes. Such models explain many hallmarks human in terms specific statistical considerations come into play when updating However, also display systematic biases deviate from studied computational psychiatry. Some explained conditioned inaccurate prior assumptions environment, while others reflect approximations Bayesian aimed at reducing demands. These offer insights underlying they might go awry psychiatric illness.

Language: Английский

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Day-to-day fluctuations in motivation drive effort-based decision-making

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(12)

Published: March 17, 2025

Internal states like motivation fluctuate substantially over time. However, studies of the neurocomputational mechanims motivated behavior have failed to capture this. Here, we examined how naturalistic ups and downs in state influence subjective value reward effort. In a microlongitudinal design (N = 155, timepoints 3,344, decision-making tasks 845), captured fluctuations effort-based using smartphone-based momentary assessments as people went about their daily lives. We found that both trait independent multiplicative effects on decision-making. State–behavior coupling was particularly pronounced individuals with higher apathy, meaning choices were even more dependent. Using computational modeling, demonstrate prospectively boosted sensitivity, making willing exert effort future. Our results show day-to-day cognition are tightly linked critical for understanding fundamental human behaviors mental ill-health.

Language: Английский

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Disentangling sources of variability in decision-making

Nature reviews. Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Language: Английский

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A computational cognitive neuroscience approach for characterizing individual differences in autism: Introduction to Special Issue

Personality Neuroscience, Journal Year: 2025, Volume and Issue: 8

Published: Jan. 1, 2025

Abstract Traditional psychological research has often treated inter-subject variability as statistical noise (even, nuisance variance), focusing instead on averages rather than individual differences. This approach limited our understanding of the substantial heterogeneity observed in neuropsychiatric disorders, particularly autism spectrum disorder (ASD). In this introduction to a special issue theme, we discuss recent advances cognitive computational neuroscience that can lead more systematic notion core symptom dimensions differentiate between ASD subtypes. These include large participant databases and data-sharing initiatives increase sample sizes autistic individuals across wider range cultural socioeconomic backgrounds. Our perspective helps build bridges symptomatology differences traits non-autistic population introduces finer-grained dynamic methods capture behavioral dynamics at level. We specifically focus how models have emerged powerful tools better characterize general population, with respect social decision-making. finally outline combine harness these advances, one hand, big data initiatives, other models, achieve nuanced improved diagnostic accuracy personalized interventions.

Language: Английский

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The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(10), P. 3994 - 3994

Published: May 8, 2024

The doctor–patient relationship has received widespread attention as a significant global issue affecting people’s livelihoods. In clinical practice within the medical field, applying existing artificial intelligence (AI) technology presents issues such uncontrollability, inconsistency, and lack of self-explanation capabilities, even raising concerns about ethics morality. To address problem interaction differences arising from diagnosis treatment, we collected textual content dialogues in outpatient clinics local first-class hospitals. We utilized case scenario analysis, starting two specific cases: multi-patient visits with same doctor multi-doctor patient. By capturing external interactions internal thought processes, unify expressions subjective cognition into between data, information, knowledge, wisdom, purpose (DIKWP) models. propose DIKWP semantic model for on both sides, including cognitive model, to achieve transparency throughout entire process. semantically–bidirectionally map diagnostic discrepancy space uncertainty utilize purpose-driven fusion transformation technique disambiguate problem. Finally, select four traditional methods qualitative quantitative comparison our proposed method. results show that method performs better handling. Overall, processing breaks through limitations natural language semantics terms interpretability, enhancing interpretability It will help bridge gap doctors patients, easing disputes.

Language: Английский

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Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(3), P. e1011950 - e1011950

Published: March 29, 2024

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, time. For an embodied agent a human, decisions are shaped by physical aspects actions. Beyond the effects reward outcomes on processes, to what extent can modeling behavior in reinforcement-learning task be complicated other sources variance sequential action choices? What bias (for actions per se) hysteresis determined history chosen previously? The present study addressed these questions with incremental assembly models for choice data from hierarchical structure additional complexity learning. With systematic comparison falsification computational models, human choices were tested signatures parallel modules representing enhanced form generalized hysteresis. We found evidence substantial differences across participants—even comparable magnitude individual Individuals who did learn well revealed greatest biases, those accurately significantly biased. direction varied among individuals repetition or, more commonly, alternation biases persisting multiple previous Considering that button presses trivial motor demands, idiosyncratic forces biasing sequences robust enough suggest ubiquity tasks requiring various In light how function heuristic efficient control adapts uncertainty or low motivation minimizing cost effort, phenomena broaden consilient theory mixture experts encompass expert nonexpert controllers behavior.

Language: Английский

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Adaptive Integration of Perceptual and Reward Information in an Uncertain World

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

Published: April 28, 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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Interindividual differences in Pavlovian influence on learning are consistent

Royal Society Open Science, Journal Year: 2023, Volume and Issue: 10(9)

Published: Sept. 1, 2023

Pavlovian influences impair instrumental learning. It is easier to learn approach reward-predictive signals and avoid punishment-predictive cues than their contrary. Whether the interindividual variability in this influence consistent across time has been examined by a number of recent studies met with mixed results. Here we introduce an open-source, web-based instance well-established Go-NoGo paradigm for measuring influence. We closely replicated previous laboratory-based Moreover, differences were two-week window at level (i) raw measures learning (i.e. performance accuracy), (ii) linear, descriptive estimates bias (test-retest reliability: 0.40), (iii) parameters obtained from reinforcement model fitting selection 0.25). Nonetheless, correlations reported here are still lower standards 0.7) employed psychometrics self-reported measures. Our results provide support trusting as relatively stable individual characteristic using its measure computational understanding human mental health.

Language: Английский

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