BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Computational modelling of reversal learning in individuals with O CD versus healthy controls DTSTART:20260612T143000Z DTEND:20260612T160000Z DTSTAMP:20260921T200637Z UID:8979faa1-44fc-4c78-8403-62f6cdd2ffdf SEQUENCE:2 CREATED:20260611T094353Z DESCRIPTION:Computational psychiatry provides mathematical tools to decode cognitive mechanisms underlying mental disorders. This dissertation inves tigates decision-making in obsessive-compulsive disorder (OCD) using a rev ersal learning task (RLT)\, aiming to identify latent behavioural markers that differentiate individuals with OCD from healthy controls.Trial-by-tri al empirical data from 142 subjects (80 OCD\, 62 Healthy) was analysed. A computational pipeline was specifically developed\, featuring seven cognit ive models categorized into three families: Associative Learning\, Bayesia n Inference\, and Heuristics. Latent parameters were estimated using a jus t-in-time compiled optimization engine performing multi-start maximum a po steriori fitting\, tested via surrogate data simulations.Model-agnostic an alysis revealed no significant differences in overarching task performance between the groups. In a model comparison via the Bayesian information cr iterion (BIC)\, two strategies emerged for both groups: the winning model for most subjects was a win-stay lose-shift heuristic\, while Bayesian mod els won when comparing average model quality across the sample. The OCD gr oup exhibited higher variance in baseline inattention. Within a subgroup t hat excluded inattentive subjects\, exploratory parameter analysis suggest ed a potential increase in choice stochasticity in OCD during associative learning. This work exemplifies how abstract psychiatric traits could be t ranslated into measurable computational variables. LAST-MODIFIED:20260611T094427Z LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building/Online URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/computational-modelling-of -reversal-learning-in-individuals-with-ocd-versus-healthy-controls/ X-ALT-DESC;FMTTYPE=text/html:

Computational psychiatry provides mathematical tools to decode c ognitive mechanisms underlying mental disorders. This dissertation investi gates decision-making in obsessive-compulsive disorder (OCD) using a rever sal learning task (RLT)\, aiming to identify latent behavioural markers th at differentiate individuals with OCD from healthy controls.

Tria l-by-trial empirical data from 142 subjects (80 OCD\, 62 Healthy) was anal ysed. A computational pipeline was specifically developed\, featuring seve n cognitive models categorized into three families: Associative Learning\, Bayesian Inference\, and Heuristics.

Latent parameters were est imated using a just-in-time compiled optimization engine performing multi- start maximum a posteriori fitting\, tested via surrogate data simulations .Model-agnostic analysis revealed no significant differences in overarchin g task performance between the groups. In a model comparison via the Bayes ian information criterion (BIC)\, two strategies emerged for both groups: the winning model for most subjects was a win-stay lose-shift heuristic\, while Bayesian models won when comparing average model quality across the sample.

The OCD group exhibited higher variance in baseline inat tention. Within a subgroup that excluded inattentive subjects\, explorator y parameter analysis suggested a potential increase in choice stochasticit y in OCD during associative learning. This work exemplifies how abstract p sychiatric traits could be translated into measurable computational variab les.

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