learNoise · The neurobiological and computational origins of behavioral variability
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-03-01 → 2022-02-28
- EU contribution
- €212,934
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
The neurobiological and computational origins of behavioral variability
The goal of this project was to investigate (neuro)computational mechanisms of behavioural variability, i.e., inconsistencies in our decisions, and how these inconsistencies translate into inter-individual differences in psychiatric traits, in particular impulsivity and compulsivity. Impulsivity and compulsivity are trait dimensions often linked to psychiatric disorders at their extreme ends but with substantial variability in a general population. Both dimensions have been associated with impaired cognitive flexibility, but their underlying computational implementations remained poorly understood. Understanding mechanisms underlying compulsive and impulsive behaviours pave the way for deciphering the mechanisms involved in mental health disorders such as obsessive-compulsive disorder (OCD) and attention deficit and hyperactivity disorder (ADHD). Previous research suggested that both impulsive and compulsive behaviours were driven by inconsistencies in the choice process. However, recent model advances showed that the introduction of learning imprecisions as a new source of variability substantially changes the interpretation of existing results and thus invited us to reconsider these findings. In this project, we used an innovative computational modelling approach to text how stochasticity in choice and imprecisions in learning contribute to compulsive and impulsive behaviours.
Data: CORDIS, © European Union
Project objective
Human behavior is inherently variable. Even when faced with the same exact choice options, we often take different actions. The causes for this inconsistency are unknown, but economic and cognitive theories assume this is due to noise that is injected when making a decision. However, I have recently demonstrated that noise might not just arise during the decision process, but that the learning process itself (i.e update of internal representations based on feedback) is subject to substantial and meaningful noise. Concretely, I have shown that noise during learning accounts for the majority of what is traditionally reported as ‘decision noise’. However, the neural mechanisms underlying this learning noise remains unknown. In this fellowship, I will examine the contributions of the locus coeruleus-noradrenaline (LC-NA) system to this learning noise. NA has previously been associated with decision noise and here I will test whether activity in the LC is the driving factor behind learning noise. I will use a cutting-edge real-time fMRI framework that allows to causally test whether ongoing LC activity directly influences learning noise. Moreover, I will examine whether this learning noise is relevant to impulsivity, which has previously been implicated in decision noise. This fellowship has the potential to overthrown the traditional view on behavioral variability in decision making and will provide a novel neurobiological, computational and psychiatric grounding for understanding why humans are consistently inconsistent.
Original text from CORDIS.
Participants
- UNIVERSITY COLLEGE LONDON · LondonCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union