Individual differences in computational psychiatry: A review of current challenges

Karvelis, Povilas; Paulus, Martin P.; Diaconescu, Andreea O.

doi:10.1016/j.neubiorev.2023.105137

Cited by 36 publications

(27 citation statements)

References 187 publications

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“…Our methodology may therefore be useful in dissociating common co-occurring processes, especially if the orthogonality must be established post-hoc or used as a complement to specific task designs, such as cases involving working memory and attention (56), valuation and salience (57), or valence, arousal, and emotions (49). In computational psychiatry, our approach may aid in constructing neural biomarkers specific to a particular diagnosis without relying on covariates of no interest (58,59).…”

Section: Discussionmentioning

confidence: 99%

Nothing but lies: improving the validity of neural predictors of deception

Lee,

Niu,

Zhu

et al. 2024

Preprint

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Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to understand the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here we address these fundamental questions by applying novel machine learning methods and functional neuroimaging to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and thereby validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from confounding processes. Second, we show that the presence of confounding signals need not be fatal, and we improve the validity of our neural predictor via a novel machine learning procedure that identifies and removes these confounding signals. Together, these findings point to a scientific approach for studying a neglected class of behavior, with important methodological and societal implications.

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Section: Discussionmentioning

confidence: 99%

Nothing but lies: improving the validity of neural predictors of deception

Lee,

Niu,

Zhu

et al. 2024

Preprint

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“…Importantly, these findings were found across females and males, indicating comparable developmental patterns and similar decision-making impairments in attention problems regardless of sex. While computational psychiatry still has limited direct clinical implications (Hauser et al, 2019 ; Karvelis et al, 2023 ), it is a promising avenue for improving our understanding of cognition in psychopathology.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence

Wiker,

Pedersen,

Ferschmann

et al. 2023

Res Child Adolesc Psychopathol

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Cognitive functions and psychopathology develop in parallel in childhood and adolescence, but the temporal dynamics of their associations are poorly understood. The present study sought to elucidate the intertwined development of decision-making processes and attention problems using longitudinal data from late childhood (9–10 years) to mid-adolescence (11–13 years) from the Adolescent Brain Cognitive Development (ABCD) Study (n = 8918). We utilised hierarchical drift-diffusion modelling of behavioural data from the stop-signal task, parent-reported attention problems from the Child Behavior Checklist (CBCL), and multigroup univariate and bivariate latent change score models. The results showed faster drift rate was associated with lower levels of inattention at baseline, as well as a greater reduction of inattention over time. Moreover, baseline drift rate negatively predicted change in attention problems in females, and baseline attention problems negatively predicted change in drift rate. Neither response caution (decision threshold) nor encoding- and responding processes (non-decision time) were significantly associated with attention problems. There were no significant sex differences in the associations between decision-making processes and attention problems. The study supports previous findings of reduced evidence accumulation in attention problems and additionally shows that development of this aspect of decision-making plays a role in developmental changes in attention problems in youth.

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“…Despite the widespread use of computational phenotypes, their interpretation hinges critically on their psychometric properties 19 , which remain poorly understood 20 . This issue is even more prominent in longitudinal studies that address changes within individuals over time 21 , 22 .…”

Section: Mainmentioning

confidence: 99%

“…Only a few studies explicitly address the reliability of computational phenotypes (see review in ref. 20 ) and rarely in more than two sessions (but see ref. 23 ).…”

Section: Mainmentioning

confidence: 99%

Dynamic computational phenotyping of human cognition

Schurr,

Reznik,

Hillman

et al. 2024

Nat Hum Behav

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Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual’s computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

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Individual differences in computational psychiatry: A review of current challenges

Cited by 36 publications

References 187 publications

Nothing but lies: improving the validity of neural predictors of deception

Nothing but lies: improving the validity of neural predictors of deception

Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence

Dynamic computational phenotyping of human cognition

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