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MathWorks Inc reinforcement learning model
Reinforcement Learning Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 94/100, based on 76 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/reinforcement+learning+models/Reinforcement+Learning+Toolbox/pmc11998638-105-13-21
Average 94 stars, based on 76 article reviews

reinforcement learning model - by Bioz Stars, 2026-09

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Article Title: Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia
Article Snippet: To parse out factors influencing choices, we fit variants of reinforcement learning models to participants choice behaviour using a constrained search algorithm (fmincon in MATLAB 2021b), which computed a set of parameters that maximized the total log posterior probability of choice behaviour.

Article Title: Callousness, exploitativeness, and tracking of cooperation incentives in the human default network.
Article Snippet: Reinforcement learning models were fit using the variational Bayesian analysis toolbox in MATLAB, which leverages parameter estimates from the full sample to constrain individual parameter estimates, reducing the risk of misestimation in poorly performing participants (64).

Article Title: The Dark Side of Mentalizing: Learning Signals in the Default Network During Social Exchanges Support Cooperation and Exploitation
Article Snippet: Reinforcement learning models were fit using the variational Bayesian analysis toolbox in MATLAB, which leverages parameter estimates from the full sample to constrain individual parameter estimates, reducing the risk of misestimation in poorly performing participants (Daunizeau et al., 2014).

Article Title: Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance
Article Snippet: We fit variants of reinforcement learning models to participants’ choice behavior using a constrained search algorithm (fmincon in MATLAB 2021b), which computed a set of parameters that maximized the total log posterior probability of choice behavior.

Sequencing:

Article Title: Prediction-error-dependent processing of immediate and delayed positive feedback
Article Snippet: .. To derive trial-by-trial values of reward PEs for each participant in each feedback timing condition, reinforcement learning models were fitted to the behavioural data (i.e. the participants’ sequence of choices) and the given feedback using MATLAB R2018b (MathWorks Inc., Natick, USA). ..

Saline:

Article Title: Dopamine Modulates Novelty Seeking Behavior During Decision Making
Article Snippet: .. Mean parameter estimates for reinforcement learning models fit to choice behavior for drug and saline sessions Statistical analyses Each dependent variable was entered into a full factorial, mixed effects ANOVA model implemented in MATLAB. ..

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Article Title: Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia.
Article Snippet: .. To parse out factors influencing choices, we fit variants of reinforcement learning models to participants choice behaviour using a constrained search algorithm (fmincon in MATLAB 2021b), which computed a set of parameters that maximized the Table 1 Demographic, cognitive, and clinical measures of controls and patients included in the final analyses Measure HC (n = 33), mean (SD) MDD (n = 31), mean (SD) SZ (n = 24), mean (SD) Inferential statistic Demographic Age 34.81 (10.83) 33.85 (8.68) 35.00 (9.17) F = 0.12 Gender 17 F; 16 M 18 F; 13 M 7 F; 17 M – Educationa 2.52 (0.62) 2.53 (0.51) 2.00 (0.83)b,c F = 5.70* Cognitive TAP-Alertness 25.45 (14.34) 22.25 (14.06) 23.45 (17.56) F = 0.37 TAP-Working memory 51.00 (27.63) 47.37 (29.76)c 30.50 (23.83)b,c F = 3.93* TAP-Divided attention 47.81 (24.51) 45.41 (25.68) 34.58 (30.80) F = 1.85 Clinical GAF 93.76 (3.49) 67.41 (11.38)b 63.46 (12.53)b F = 88.74** BDI-II 2.18 (2.87) 24.81 (10.17)b,c 16.42 (10.13)b,c F = 62.59** HAMD – 15.18 (6.77) – – PANSS Positive subscale – – 10.50 (4.68) – Negative subscale – – 13.88 (5.08) – General subscale – – 24.79 (4.71) – BDI-II = Beck depressions inventar-revision II39; F = female; GAF = Global Assessment of Functioning38; HAMD = Hamilton Rating Scale for Depression40; HC = healthy controls; M = male; MDD = patients diagnosed with major depression; PANSS = Positive and Negative Syndrome Scale for Schizophrenia41; SD = standard deviation; SZ = patients diagnosed with schizophrenia; TAP = computer-supported test series to assess attention.37 aRanging from 1 (primary education) to 3 (higher education). bSignificant difference to HC. cSignificant difference between patients. ..

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