Eating disorder symptoms and emotional arousal modulate food biases during reward learning in females DOI Creative Commons
Nina Rouhani, Cooper D. Grossman, Jamie D. Feusner

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 26, 2025

Food seeking and avoidance engage primary reward systems to drive behavior. It is nevertheless unclear whether innate or learned food biases interact with general processing interfere goal-directed choice. To this end, we recruited a large non-clinical sample of females high eating-disorder symptoms ('HED') matched low ('LED') complete reward-learning task where the calorie content stimuli was incidental goal maximizing monetary reward. We find replicate low-calorie bias in HED high-calorie LED, reflecting strength pre-experimental food-reward associations. An emotional arousal manipulation shifts group-dependent across individual differences, interoceptive awareness predicting change. Reinforcement-learning models further identify distinct cognitive components supporting these group-specific biases. Our results highlight influence reinforcement-based mechanisms eliciting potentially maladaptive Disordered eating can disrupt rewarding value food. Here, authors show female that disorder symptoms, arousal, modulate goal-irrelevant during reinforcement learning.

Language: Английский

The interpretation of computational model parameters depends on the context DOI Creative Commons
Maria K. Eckstein, Sarah L. Master, Liyu Xia

et al.

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Nov. 4, 2022

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning complex problem solving, shed light on developmental individual differences, anchor processes in specific mechanisms. However, RL literature increasingly reveals contradictory results, which might cast doubt these claims. We hypothesized that many contradictions arise two commonly-held assumptions about computational model parameters are actually often invalid: That generalize between contexts (e.g. tasks, models) they capture interpretable (i.e. unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8–30 years complete three learning tasks one experimental session, fitted each. found some (exploration / decision noise) showed significant generalization: followed similar trajectories, were reciprocally predictive tasks. Still, generalization was significantly below methodological ceiling. Furthermore, other (learning rates, forgetting) did not show evidence of generalization, sometimes even opposite trajectories. Interpretability low for all parameters. conclude systematic study context factors reward stochasticity; task volatility) will be necessary enhance generalizability interpretability models.

Language: Английский

Citations

73
Improving the Reliability of Cognitive Task Measures: A Narrative Review DOI Creative Commons
Samuel Zorowitz, Yael Niv

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2023, Volume and Issue: 8(8), P. 789 - 797

Published: Feb. 25, 2023

Language: Английский

Citations

52
Individual differences in computational psychiatry: A review of current challenges DOI Creative Commons
Povilas Karvelis, Martin P. Paulus, Andreea O. Diaconescu

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 148, P. 105137 - 105137

Published: March 20, 2023

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is development computational assays: integrating models with cognitive tasks infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements modelling cross-sectional patient studies, much less attention has been paid basic psychometric properties (reliability construct validity) measures provided by assays. In this review, we assess extent issue examining emerging empirical evidence. We find that suffer from poor properties, which poses a risk invalidating previous findings undermining ongoing research efforts using assays study (and even group) provide recommendations how address these problems and, crucially, embed them within broader perspective on key developments are needed translating clinical practice.

Language: Английский

Citations

49
Are the Futures Computable? Knightian Uncertainty and Artificial Intelligence DOI
David M. Townsend, Richard A. Hunt,

Judy Rady

et al.

Academy of Management Review, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 5, 2024

The growing sophistication of artificial intelligence (AI) tools in entrepreneurship is transforming how new ventures identify, gather, analyze, and utilize information from their internal external operating environments to automate critical choices, decisions, tasks. For many startups corporate ventures, prior research suggests that AI provides significant task performance advantages entrepreneurs addressing the problem uncertainty, part, through enhanced predictive capabilities. What less clear, however, whether enable manage problems "Knightian uncertainty"—a fundamental type uncertainty manifests a cascading set four interrelated problems: actor ignorance, practical indeterminism, agentic novelty, competitive recursion. In this study, we argue capabilities are contingent upon ability these systems grapple with Knightian uncertainty. We investigate logic approach an in-depth analysis limits foundational emerging types address problems, identifying areas computational irreducibility where manifestation use entrepreneurship.

Language: Английский

Citations

19
Goal-directed learning in adolescence: neurocognitive development and contextual influences DOI
Linda Wilbrecht, Juliet Y. Davidow

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: 25(3), P. 176 - 194

Published: Jan. 23, 2024

Language: Английский

Citations

16
The computational challenge of social learning DOI Creative Commons
Oriel FeldmanHall, Matthew R. Nassar

Trends in Cognitive Sciences, Journal Year: 2021, Volume and Issue: 25(12), P. 1045 - 1057

Published: Sept. 27, 2021

Language: Английский

Citations

62
Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal DOI Creative Commons
Maria K. Eckstein, Sarah L. Master, Ronald E. Dahl

et al.

Developmental Cognitive Neuroscience, Journal Year: 2022, Volume and Issue: 55, P. 101106 - 101106

Published: April 22, 2022

During adolescence, youth venture out, explore the wider world, and are challenged to learn how navigate novel uncertain environments. We investigated performance changes across adolescent development in a stochastic, volatile reversal-learning task that uniquely taxes balance of persistence flexibility. In sample 291 participants aged 8-30, we found mid-teen years, adolescents outperformed both younger older participants. developed two independent cognitive models, based on Reinforcement learning (RL) Bayesian inference (BI). The RL parameter for from negative outcomes BI parameters specifying participants' mental models were closest optimal adolescents, suggesting central role processing. By contrast, noise improved monotonically with age. distilled insights using principal component analysis three shared components interacted form peak: adult-like behavioral quality, child-like time scales, developmentally-unique processing positive feedback. This research highlights adolescence as neurodevelopmental window can create advantages It also shows detailed be gleaned by new ways.

Language: Английский

Citations

54
Early adversity and the development of explore–exploit tradeoffs DOI
Willem E. Frankenhuis, Alison Gopnik

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(7), P. 616 - 630

Published: May 2, 2023

Language: Английский

Citations

33
Reliability of Decision-Making and Reinforcement Learning Computational Parameters DOI Creative Commons
Anahit Mkrtchian, Vincent Valton, Jonathan P. Roiser

et al.

Computational Psychiatry, Journal Year: 2023, Volume and Issue: 7(1), P. 30 - 30

Published: Feb. 8, 2023

Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders their treatment. For translational efforts be successful, it is imperative that computational measures capture individual characteristics reliably. To date, this issue has received little consideration. Here we examine reliability reinforcement learning economic derived from two commonly used tasks. Healthy individuals (N=50) completed a restless four-armed bandit calibrated gambling task twice, weeks apart. Reward punishment processing parameters model showed fair-to-good reliability, while risk/loss aversion prospect theory exhibited good-to-excellent reliability. Both were further able predict future behaviour above chance within individuals. This prediction was better when based on participants’ own than other parameter estimates. These results suggest learning, particularly parameters, measured reliably assess decision-making mechanisms, these processes may represent relatively distinct profiles across Overall, findings indicate clinically-relevant for precision psychiatry.

Language: Английский

Citations

28
The promise and pitfalls of a strength-based approach to child poverty and neurocognitive development: Implications for policy DOI Creative Commons
Meriah Lee DeJoseph, Monica E. Ellwood‐Lowe, Dana Miller‐Cotto

et al.

Developmental Cognitive Neuroscience, Journal Year: 2024, Volume and Issue: 66, P. 101375 - 101375

Published: April 1, 2024

There has been significant progress in understanding the effects of childhood poverty on neurocognitive development. This captured attention policymakers and promoted progressive policy reform. However, prevailing emphasis harms associated with may have inadvertently perpetuated a deficit-based narrative, focused presumed shortcomings children families poverty. focus can unintended consequences for (e.g., overlooking strengths) as well public discourse focusing individual rather than systemic factors). Here, we join scientists across disciplines arguing more well-rounded, "strength-based" approach, which incorporates positive and/or adaptive developmental responses to experiences social disadvantage. Specifically, first show value this approach normative brain development diverse human environments. We then highlight its application educational policy, explore pitfalls ethical considerations, offer practical solutions conducting strength-based research responsibly. Our paper re-ignites old recent calls paradigm shift, cognitive neuroscience. also unique perspective from new generation early-career researchers engaged work, several whom themselves grown up conditions Ultimately, argue that balanced scientific will be essential building effective policies.

Language: Английский

Citations

10