Test-retest reliability of behavioral and computational measures of advice taking under volatility

Published: Nov. 9, 2023

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk to undermine use in empirical research and clinical translation. Here we investigated test-retest reliability (with 2-week interval) assay probing advice-taking under volatility with Hierarchical Gaussian Filter (HGF) model. In sample 39 healthy participants, found measures have largely poor (intra-class correlation coefficient or ICC < 0.5), par behavioral task performance. Further analysis revealed that was substantially impacted by intrinsic measurement noise (indicated parameter recovery analysis) smaller extent practice effects. large portion within-subject variance remained unexplained may be attributable state-like fluctuations. Despite reliability, face validity at group level. Overall, our work highlights different sources affecting need studied greater detail. A better understanding these would facilitate design more psychometrically sound assays, which improve quality future increase probability

Language: Английский

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A computational neuroscience perspective on subjective wellbeing within the active inference framework

Published: July 17, 2022

Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing research, due in part to its potential contributions health productivity. To date, conceptualization SWB grounded within social psychology largely focused on self-report measures. In this paper, we explore potentially complementary tools theoretical perspectives offered by computational neuroscience, with a focus active inference (AI) framework. This framework is motivated fact that brain does not have direct access world; select actions, it must instead infer most likely external causes sensory input receives from both body world. Because always consistent multiple interpretations, brain’s internal model use background knowledge, form prior expectations, make “best guess” about situation how will change taking one action or another. best guess arises minimizing an error signal representing deviation between predicted observed sensations given chosen – quantified mathematically variable called free energy (FE). Crucially, recent proposals illustrated emotional experience may emerge AI as natural consequence keeping track success selecting actions minimize FE. draw concepts mathematics highlight different strategies can be used FE some more successfully than others. affords characterization diverse individuals adopt unique for achieving high SWB. It also highlights novel ways which could effectively improved. These considerations lead us propose understanding We several parameters these models explain individual cultural differences SWB, they might inspire interventions. conclude proposing line future empirical research based modelling complement current approaches study improvement.

Language: Английский

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The path forward for modeling action-oriented cognition as active inference

Physics of Life Reviews, Journal Year: 2023, Volume and Issue: 46, P. 152 - 154

Published: June 30, 2023

Language: Английский

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