Characterizing a psychiatric symptom dimension related to deficits in goal-directed control

Gillan, Claire M.; Kosinski, Michal; Whelan, Robert; Phelps, Elizabeth A.; Daw, Nathaniel D.

doi:10.7554/elife.11305

Cited by 486 publications

(810 citation statements)

References 67 publications

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“…The previous, discrete, distinction between goal-directed and habitual decision making has been used to illuminate several psychiatric disorders such as addiction (30) and obsessive-compulsive disorder (31,32). Recent interpretations in psychiatry (33) favor the notion that these and other diseases are best considered in terms of spectra rather than binary distinctions.…”

Section: Discussionmentioning

confidence: 99%

Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum

Keramati

Smittenaar

Dolan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

189

203

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Behavioral and neural evidence reveal a prospective goal-directed decision process that relies on mental simulation of the environment, and a retrospective habitual process that caches returns previously garnered from available choices. Artificial systems combine the two by simulating the environment up to some depth and then exploiting habitual values as proxies for consequences that may arise in the further future. Using a three-step task, we provide evidence that human subjects use such a normative plan-until-habit strategy, implying a spectrum of approaches that interpolates between habitual and goal-directed responding. We found that increasing time pressure led to shallower goal-directed planning, suggesting that a speed-accuracy tradeoff controls the depth of planning with deeper search leading to more accurate evaluation, at the cost of slower decision-making. We conclude that subjects integrate habit-based cached values directly into goal-directed evaluations in a normative manner.planning | habit | reinforcement learning | speed/accuracy tradeoff | tree-based evaluation B ehavioral and neural evidence suggest that the brain uses distinct goal-directed and habitual systems for decisionmaking (1-5). A goal-directed system exploits an individual's model, i.e., their knowledge of environmental dynamics, to simulate the consequences that will likely follow a choice (6) (Fig. 1A). Such evaluations, which assess a decision-tree expanding into the future to estimate the total reward, adapt flexibly to changes in environmental dynamics or the values of outcomes. Evaluating deep trees, however, is computationally expensive (in terms of time, working memory, metabolic energy, etc.) and potentially error-prone. By contrast, the habitual system simply caches the rewards received on previous trials conditional on the choice (Fig. 1C) without a representational characterization of the environment (hence being called "model-free") (6, 7). This process hinders adaptation to changes in the environment, but has advantageous computational simplicity. Previous studies show distinct behavioral and neurobiological signatures of both systems (8-18). Furthermore, consistent with the theoretical strengths and weaknesses of each system (2, 19), different experimental conditions influence the relative contributions of the two systems in controlling behavior according to their respective competencies (20)(21)(22)(23).Here, we suggest that individuals, rather than simply showing greater reliance on the more competent system in each condition, combine the relative strengths of the two systems in a normative manner by integrating habit-based cached values directly into goaldirected evaluations. Specifically, we propose that given available resources (time, working memory, etc.), individuals decide the depth k up to which they can afford full forward simulations and use cached habitual values thereafter. That is, individuals compute the value of a choice by adding the first k rewards, predicted by the explicit simulation, to the value...

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Section: Discussionmentioning

confidence: 99%

Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum

Keramati

Smittenaar

Dolan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

189

203

View full text Add to dashboard Cite

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“…14 sample was also negatively associated with model-based behavior to reward (33). The phenomenology of compulsive symptoms in OCD may also be captured by aversive avoidance habits, which may be of particular relevance.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 94%

“…Self-reported pathological eating symptoms have also been associated with impaired model-based control in a large online sample (33).…”

Section: Relevance To Dimensional Psychiatrymentioning

confidence: 96%

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Model-Based Control in Dimensional Psychiatry

Voon

Reiter

Sebold

et al. 2017

Biological Psychiatry

102

105

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“…MTurk acts as a broker between parties offering a range of Human Intelligence Tasks (HITs) and paid workers. Although MTurk has been shown to be reliable as a recruitment tool for research [66][67][68], we excluded participants from analysis if they were not performing the task with care, which we determined in multiple steps. We removed 33 participants based on missing trials (indicating technical difficulties), or too many trials (indicating that they reloaded the task part way through).…”

Section: Participants and Deployment Platformmentioning

confidence: 99%

Improving the Efficacy of Cognitive Training for Digital Mental Health Interventions Through Avatar Customization: Crowdsourced Quasi-Experimental Study (Preprint)

Birk¹,

Mandryk²

2018

Preprint

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Background: The success of Internet-based mental health interventions in practice -i.e., in-the-wild -depends on the uptake and retention of the application and the user's focused attention in the moment of use. Incorporating game-based motivational design into digital interventions delivered in-the-wild has been shown to increase uptake and retention in Internet-based training; however, there are outstanding questions about the potential of game-based motivational strategies to increase engagement with a task in the moment of use, and -as a result -improve intervention efficacy without accompanying increases in treatment exposure. Objective: Designers of Internet-based interventions need to know whether game-based motivational design strategies can increase in-the-moment engagement and thus improve intervention efficacy. We investigate the effects of one motivational design strategy (avatar customization) in an example mental health intervention (computerized cognitive training for attention bias modification). Methods: We assigned 317 participants to either a customized avatar or an assigned avatar condition. After measuring state-anxiety (STAI), we randomly assigned half of the participants in each condition to either an attentional retraining condition (ABMT) or a control condition. After training, participants were exposed to a Negative Mood Induction using images with strong negative valance (IAPS), after which we measured state-anxiety again. Results: Avatar customization improved treatment efficacy, evidenced as decreased posttraining state-anxiety when controlling for baseline state-anxiety, for those who trained attention bias modification; however, those who did not train experienced decreased resilience to the negative mood induction (F 1,252 =6.86, P=.009, η p

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Characterizing a psychiatric symptom dimension related to deficits in goal-directed control

Cited by 486 publications

References 67 publications

Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum

Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum

Model-Based Control in Dimensional Psychiatry

Improving the Efficacy of Cognitive Training for Digital Mental Health Interventions Through Avatar Customization: Crowdsourced Quasi-Experimental Study (Preprint)

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