Keyword search (4,163 papers available)

"Reinforcement learning" Keyword-tagged Publications:

Title Authors PubMed ID
1 Disentangling prediction error and value in a formal test of dopamine s role in reinforcement learning Usypchuk AA; Maes EJP; Lozzi M; Avramidis DK; Schoenbaum G; Esber GR; Gardner MPH; Iordanova MD; 40738112
CSBN
2 Comprehensive review of reinforcement learning for medical ultrasound imaging Elmekki H; Islam S; Alagha A; Sami H; Spilkin A; Zakeri E; Zanuttini AM; Bentahar J; Kadem L; Xie WF; Pibarot P; Mizouni R; Otrok H; Singh S; Mourad A; 40567264
ENCS
3 Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques Islam S; Rjoub G; Elmekki H; Bentahar J; Pedrycz W; Cohen R; 40336660
ENCS
4 Computational neuroscience across the lifespan: Promises and pitfalls van den Bos W; Bruckner R; Nassar MR; Mata R; Eppinger B; 29066078
PSYCHOLOGY
5 Does phasic dopamine release cause policy updates? Carter F; Cossette MP; Trujillo-Pisanty I; Pallikaras V; Breton YA; Conover K; Caplan J; Solis P; Voisard J; Yaksich A; Shizgal P; 38039083
PSYCHOLOGY
6 Nonlinear dynamic modeling and model-based AI-driven control of a magnetoactive soft continuum robot in a fluidic environment Moezi SA; Sedaghati R; Rakheja S; 37932207
ENCS
7 Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate Sartori I; Walnum HT; Skeie KS; Georges L; Knudsen MD; Bacher P; Candanedo J; Sigounis AM; Prakash AK; Pritoni M; Granderson J; Yang S; Wan MP; 37153123
ENCS
8 Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach Pan J; Huang J; Cheng G; Zeng Y; 36375347
ENCS
9 Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection Rjoub G; Wahab OA; Bentahar J; Cohen R; Bataineh AS; 35875592
ENCS
10 Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. Khalilpourazari S, Hashemi Doulabi H 33424076
ENCS
11 Cue-Evoked Dopamine Neuron Activity Helps Maintain but Does Not Encode Expected Value. Mendoza JA, Lafferty CK, Yang AK, Britt JP 31693885
CSBN
12 Metacontrol of decision-making strategies in human aging. Bolenz F, Kool W, Reiter AM, Eppinger B 31397670
PERFORM
13 Developmental Changes in Learning: Computational Mechanisms and Social Influences. Bolenz F, Reiter AMF, Eppinger B 29250006
PERFORM

 

Title:Metacontrol of decision-making strategies in human aging.
Authors:Bolenz FKool WReiter AMEppinger B
Link:https://www.ncbi.nlm.nih.gov/pubmed/31397670?dopt=Abstract
DOI:10.7554/eLife.49154
Publication:eLife
Keywords:decision makinghumanlifespan developmentmodel-basedmodel-freeneurosciencereinforcement learningreward
PMID:31397670 Category:Elife Date Added:2019-09-23
Dept Affiliation: PERFORM
1 Chair of Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.
2 Department of Psychology, Harvard University, Cambridge, United States.
3 Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, United States.
4 Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom.
5 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.
6 Department of Psychology, Concordia University, Montreal, Canada.
7 PERFORM centre, Concordia University, Montreal, Canada.

Description:

Metacontrol of decision-making strategies in human aging.

Elife. 2019 Aug 09;8:

Authors: Bolenz F, Kool W, Reiter AM, Eppinger B

Abstract

Humans employ different strategies when making decisions. Previous research has reported reduced reliance on model-based strategies with aging, but it remains unclear whether this is due to cognitive or motivational factors. Moreover, it is not clear how aging affects the metacontrol of decision making, that is the dynamic adaptation of decision-making strategies to varying situational demands. In this cross-sectional study, we tested younger and older adults in a sequential decision-making task that dissociates model-free and model-based strategies. In contrast to previous research, model-based strategies led to higher payoffs. Moreover, we manipulated the costs and benefits of model-based strategies by varying reward magnitude and the stability of the task structure. Compared to younger adults, older adults showed reduced model-based decision making and less adaptation of decision-making strategies. Our findings suggest that aging affects the metacontrol of decision-making strategies and that reduced model-based strategies in older adults are due to limited cognitive abilities.

PMID: 31397670 [PubMed - in process]





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