Using smartphones to optimise and scale-up the assessment of model-based planning

Donegan, Kelly R; Brown, Vanessa M.; Price, Rebecca; Pringle, Andrew; Gallagher, Eoghan; Hanlon, Anna; Gillan, Claire M.

doi:10.31234/osf.io/hpm4s

Cited by 4 publications

(4 citation statements)

References 49 publications

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“…Taking this work a step further, we for the first time decompose the portion of time dedicated for resolving representational uncertainty, by utilizing another dimension of behavior -response time, both within-and across-trials. This represents an important step towards field or clinical applications which will require small numbers of trials (Donegan et al, 2023) and thus may benefit from leveraging response times which can improve quality of model fits Fontanesi et al, 2019) and reveal important dynamics with potential relevance to clinical applications (Bornstein and Pickard, 2020;Banavar et al, 2024;Chwiesko et al, 2023;Copeland, Stafford, and Field, 2024). Our discussion on open-loop vs. closed-loop planning is also a novel contribution to the two-stage task paradigm.…”

Section: Discussionmentioning

confidence: 97%

Temporal dynamics of model-based control reveal arbitration between multiple task representations

Yoo,

Bornstein

2024

Preprint

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Previous work demonstrates that people arbitrate between control algorithms – e.g. model-based or model-free – according to their relative certainty at the given moment. Here, we examined whether a similar uncertainty-based arbitration could explain the relative pattern of reliance on distinct representations. We employ a novel variant of a standard, two-stage decision task. This task allows us to behaviorally capture the within- and across-trial dynamics of model-based planning. We jointly fit choices and response times with a new computational model that revealed how people select among multiple task representations during planning in environments of differing state-space complexity. In particular, we examined how the reliance on task representations changed both as a function of experience, within-subject, and task complexity, across-subjects (total n = 426). We show that both the complexity of the environment and experience with a given contingency structure inform the kinds of representations we use to make decisions: at the early stages of the task, people start with “conjunctive” representations (combining co-occurring first-stage states) in simpler environments, but a “separated” representation (splitting states according to their second-step outcomes) is preferred in more complex environments. With experience, this pattern is reversed. We show that this shift is likely to be governed by a change in objectives: initially, people focus on minimizing uncertainty, and once this is achieved, they transition to prioritizing efficiency. Taken together, we show that people not only arbitrate between different modes of control, but also between types of representations for efficient planning.

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Section: Discussionmentioning

confidence: 97%

Temporal dynamics of model-based control reveal arbitration between multiple task representations

Yoo,

Bornstein

2024

Preprint

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“…Since being launched in 2020, Neureka has over 23,000 registered users across 139 countries as of September 2023. The Neureka project aims to enrol members of the general public in scientific research, by voluntarily playing games that tap into distinct cognitive processes underlying brain health 27 . After downloading Neureka, users provide informed consent and provide the following sociodemographic information upon registration: age, gender, levels of education and country of residence.…”

Section: Participantsmentioning

confidence: 99%

“…Uncompensated, self-selected online samples provide comparable data quality to lab-based perceptual experiments, with the advantage of speeding up and scaling up data collection 23 , and being more representative 22 . Employing cognitive tasks through smartphone applications specifically has proven particularly beneficial in ensuring high-quality data collection among citizen scientists [24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

Reliable, rapid, and remote measurement of metacognitive bias

Fox,

McDonogh,

Donegan

et al. 2023

Preprint

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Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of ‘anxious-depression’ and ‘compulsivity and intrusive thought’, cross-sectionally. To progress our understanding of the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed to measure metacognitive (confidence) bias and investigated its psychometric properties across two studies (N=3410 unpaid citizen scientists, N=52 paid participants). We assessed convergent validity, split-half and test-retest reliability, and identified the minimum number of trials required to capture its clinical correlates. Convergent validity of metacognitive bias was moderate (r(50)=0.64, p<0.001) and it demonstrated excellent split-half reliability (r(50)=0.91, p<0.001). Test-retest reliability was also very high (ICC=0.86, N=110). Anxious-depression was associated with decreased confidence (B=-0.23, SE=0.02, p<0.001), while compulsivity and intrusive thought was associated with greater confidence (B=0.07, SE=0.02, p<0.001). Metacognitive biases in decision-making are highly stable within-session, exhibiting strong reliability and associations with clinical correlates are evident in as few as 40 trials. Meta Mind exemplifies how to adapt established cognitive tasks for within-person, longitudinal assessments in computational psychiatry, facilitating the development and testing of causal models.

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“…A prevalent theme within prior research into the role of decision-making within computational psychiatry is the importance of internal world models 9 , which allow for flexible, prospective, and goal-directed planning. The use of this general "model-based" decision-making strategy has been linked across multiple studies to transdiagnostic symptom dimensions [10][11][12][13][14] . Further work has identified links between anxiety and model-based planning when seeking protection from danger specifically 15 , suggesting that task context may influence the nature of such symptom-behaviour relationships.…”

Section: Introductionmentioning

confidence: 99%

Transdiagnostic mental health symptom dimensions predict use of flexible model-based inference in complex environments

Wise,

Sookud,

Michelini

et al. 2024

Preprint

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A key goal within computational psychiatry is to identify mechanisms underpinning symptom dimensions that cut across diagnostic categories. Complex, naturalistic, situations, such as the inference of others’ mental states and how to act accordingly (e.g., social interactions), is a common junction at which mental health symptoms emerge. Such patterns may reflect the breakdown of the fundamental computational processes that ordinarily underpin these behaviours, with the use of flexible goal-directed decision-making being a prime candidate. Here, we used a validated, naturalistic threat inference task to assess the use of goal-directed decision-making in complex interactive decision problems. Participants (n=1025) completed this task alongside a battery of self-report measures of mental health symptoms and neurodevelopmental characteristics. By performing hierarchical dimensionality reduction on symptom measures, we found that task behaviour was most clearly associated with fine-grained symptom dimensions, where higher scores on an inattentive/neurodevelopmental dimension predicted more accurate inferences and higher scores on an externalising dimension predicted poorer performance. Using computational modelling, we show that these associations are mediated by the degree to which individuals use goal-directed decision-making to make inferences about the predator’s behaviour. Our results suggest that symptoms and traits that manifest in complex, social environments may result from alterations in the use of complex computational mechanisms.

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Using smartphones to optimise and scale-up the assessment of model-based planning

Cited by 4 publications

References 49 publications

Temporal dynamics of model-based control reveal arbitration between multiple task representations

Temporal dynamics of model-based control reveal arbitration between multiple task representations

Reliable, rapid, and remote measurement of metacognitive bias

Transdiagnostic mental health symptom dimensions predict use of flexible model-based inference in complex environments

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