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Predictors of risky foraging behaviour in healthy young people

Abstract

During adolescence and early adulthood, learning when to avoid threats and when to pursue rewards becomes crucial. Using a risky foraging task, we investigated individual differences in this dynamic across 781 individuals aged 14–24 years who were split into a hypothesis-generating discovery sample and a hold-out confirmation sample. Sex was the most important predictor of cautious behaviour and performance. Males earned one standard deviation (or 20%) more reward than females, collected more reward when there was little to lose and reduced foraging to the same level as females when potential losses became high. Other independent predictors of cautiousness and performance were self-reported daringness, IQ and self-reported cognitive complexity. We found no evidence for an impact of age or maturation. Thus, maleness, a high IQ or self-reported cognitive complexity, and self-reported daringness predicted greater success in risky foraging, possibly due to better exploitation of low-risk opportunities in high-risk environments.

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Fig. 1: Risky foraging task, building on rodent approach–avoidance conflict tests.
Fig. 2: Relationship between sex and task measures.
Fig. 3: Relationship of self-reported daringness (CADS questionnaire) with task measures.
Fig. 4: Relationship of IQ (measured with WASI) and self-reported cognitive complexity (BIS questionnaire) with task measures.

Data availability

Anonymized data are available from the Open Science Framework (https://osf.io/mnbfy/). Full data are available upon reasonable request from the corresponding author or from OpenNSPN@medschl.cam.ac.uk.

Code availability

All custom code used for the analysis is available from the Open Science Framework (https://osf.io/mnbfy/). After extracting task measures using MATLAB 2017b, all discovery analyses were performed in R 3.4.1 (www.r-project.org), using the following toolboxes: R.matlab version 3.6.1, abind 1.4-5, reshape2 1.4.3, nlme 3.1-131.1, lme4 1.1-15, lmerTest 2.0-36, nFactors 2.3.3 and sem 3.1-9. Confirmation and post-hoc analyses were performed in R 3.5.2, using the following toolboxes: R.matlab version 3.6.2, abind 1.4-5, reshape2 1.4.3, psych 1.8.12, lme4 1.1-21, lmerTest 3.1-0, sem 3.1-9, pracma 2.2.5, mediation 4.5.0, gvlma 1.0.0.3, DescTools 0.99.30 and corrplot 0.84.

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Acknowledgements

We thank the NSPN management and research assistant teams. The cognitive experiments were realized using Cogent 2000 (developed by the Cogent 2000 team at the FIL and ICN) and Cogent Graphics (developed by J. Romaya at the Wellcome Department of Imaging Neuroscience). The Wellcome Trust funded the Neuroscience in Psychiatry Project (NSPN). All NSPN members (Supplementary Table 1) are supported by a Wellcome Strategic Award (095844/7/11/Z). D.R.B. is supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number ERC-2018 CoG-816564 ActionContraThreat). M.M. and D.R.B. receive support from the National Institute for Health Research (NIHR) UCLH Biomedical Research Centre. R.J.D. is supported by a Wellcome Investigator Award (098362/Z/12/Z). P. Fonagy (NSPN consortium; Supplementary Table 1) is in receipt of an NIHR Senior Investigator Award (NF-SI-0514-10157) and was in part supported by the NIHR Collaborations for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Barts Health NHS Trust. The Max Planck UCL Centre for Computational Psychiatry and Ageing is a joint initiative of the Max Planck Society and UCL. The Wellcome Centre for Human Neuroimaging is funded by core funding from the Wellcome Trust (203147/Z/16/Z). The views expressed in this article are those of the authors and not necessarily those of the NHS, NIHR or Department of Health. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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D.R.B., M.M., the NSPN consortium and R.J.D. contributed to the conception and design of this work. M.M., the NSPN consortium and R.J.D. contributed to acquisition of the data. D.R.B. and M.M. analysed the data. D.R.B., M.M., A.B. and R.J.D. contributed to interpretation of the data and to drafting and revising the manuscript.

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Correspondence to Dominik R. Bach.

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Extended data

Extended Data Fig. 1 Extraction of summary statistics from time-dependent variables.

Four summary statistics are extracted for each of 7 time-dependent task measures, and for their time-dependent weighted sum (example data). Blue: low threat probability; orange: high threat probability. Example data are averaged over the active/passive (ie. starting position) factor.

Extended Data Fig. 2 Association of individual task variables with sex.

Results from linear regressions fitted separately on discovery and confirmation sample. See supplementary table 2 for statistical tests of the individual relations. To confirm these associations collectively, we fitted a multiple logistic regression on the discovery data (registered hypothesis H1), which was confirmed. See Table 2 in main text for hypothesis summary and discovery/confirmation results. A multiple logistic regression across the entire sample weakly favoured a model with common regression weights over one with separate weights for discovery and confirmation sample (LBF = 2.8).

Extended Data Fig. 3 Association of individual task variables with CADS daringness.

Results from linear regressions fitted separately on discovery and confirmation sample. See supplementary table 2 for statistical tests of the individual relations. To confirm these associations collectively, we computed a multiple regression model on the discovery data (registered hypothesis H4), which was confirmed. See Table 2 in main text for hypothesis summary and discovery/confirmation results. A multiple logistic regression across the entire sample favoured a model with common regression weights over one with separate weights for discovery and confirmation sample (LBF = 3.2). For the association of CADS with intra-epoch trajectories shown in Fig. 3 and Supplementary Table 2, we computed a multiple regression model with these three measures on the discovery data (registered hypothesis H7), which was confirmed (see Table 2). A multiple logistic regression across the entire sample weakly favoured a model with common regression weights over one with separate weights for discovery and confirmation sample (LBF = 2.3).

Extended Data Fig. 4 Association of individual task variables with IQ and BIS cognitive complexity.

Results from linear regressions fitted separately on discovery and confirmation sample. See supplementary table 2 for statistical tests of the individual relations. To confirm the associations with IQ collectively, we computed a multiple regression model on the discovery data (registered hypothesis H3), which was confirmed. See Table 2 in main text for hypothesis summary and discovery/confirmation results. A multiple logistic regression across the entire sample weakly favoured a model with common regression weights over one with separate weights for discovery and confirmation sample (LBF = 2.5). For BIS cognitive complexity, the multiple regression model (registered hypothesis H6) was confirmed as well (see Table 2). A multiple logistic regression across the entire sample weakly favoured a model with common regression weights over one with separate weights for discovery and confirmation sample (LBF = 2.7).

Extended Data Fig. 5 Lottery (revealed economic preference) task.

The roulette task involved a choice between the sure amount (upper left) and a four-sector roulette, just complex enough to define an Expectation, Variance and Skewness over roulette outcomes. The square in the middle of the roulette indicated a timer to maintain a reasonable pace of trials.

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Bach, D.R., Moutoussis, M., Bowler, A. et al. Predictors of risky foraging behaviour in healthy young people. Nat Hum Behav 4, 832–843 (2020). https://doi.org/10.1038/s41562-020-0867-0

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