CoChat
Sign Up
Cited by 141Neural and Behavioral Psychology StudiesDecision-Making and Behavioral EconomicsNeurotransmitter Receptor Influence on Behavior

The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.

Neural Correlates of Expected Risks and Returns in Risky Choice across Development

Open access
van Duijvenvoorde, Anna C.K., Huizenga, Hilde M., Somerville, Leah H., Delgado, Mauricio R., Powers, Alisa, Weeda, Wouter D., Casey, B.J., Weber, Elke U., Figner, Bernd·2015·The Journal of Neuroscience
DOI 10.1523/jneurosci.1924-14.2015PMC PMC6795265
Open this paper in CoChat — free accountOpen PDF

Abstract

Adolescence is often described as a period of increased risk taking relative to both childhood and adulthood. This inflection in risky choice behavior has been attributed to a neurobiological imbalance between earlier developing motivational systems and later developing top-down control regions. Yet few studies have decomposed risky choice to investigate the underlying mechanisms or tracked their differential developmental trajectory. The current study uses a risk–return decomposition to more precisely assess the development of processes underlying risky choice and to link them more directly to specific neural mechanisms. This decomposition specifies the influence of changing risks (outcome variability) and changing returns (expected value) on the choices of children, adolescents, and adults in a dynamic risky choice task, the Columbia Card Task. Behaviorally, risk aversion increased across age groups, with adults uniformly risk averse and adolescents showing substantial individual differences in risk sensitivity, ranging from risk seeking to risk averse. Neurally, we observed an adolescent peak in risk-related activation in the anterior insula and dorsal medial PFC. Return sensitivity, on the other hand, increased monotonically across age groups and was associated with increased activation in the ventral medial PFC and posterior cingulate cortex with age. Our results implicate adolescence as a developmental phase of increased neural risk sensitivity. Importantly, this work shows that using a behaviorally validated decision-making framework allows a precise operationalization of key constructs underlying risky choice that inform the interpretation of results.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1523/jneurosci.1924-14.2015

Cites

68 of 68 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Example of a game round in the fMRI-CCT (with two turned cards) ending because a loss card is drawn. Within each game round, we modeled the decision and the feedback phase.
Figure 1 Example of a game round in the fMRI-CCT (with two turned cards) ending because a loss card is drawn. Within each game round, we modeled the decision and the feedback phase.CC-BY-NC-SA · From the PDF
Figure 2 Top, Participant-specific random effects (fixed effect plus participant-specific random adjustment). Each dot represents one participant, x -axis shows age in years. A , Risk taking (intercept). B , Return sensitivity (random EV slope). C , Risk sensitivity (random SD slope). Bottom, Correlations (including 95% confidence intervals) between the participant-specific random effects estimated in the behavioral and the fMRI sessions. D , Risk taking (intercept). E , Return sensitivity (random EV slope). F , Risk sensitivity (random SD slope).
Figure 2 Top, Participant-specific random effects (fixed effect plus participant-specific random adjustment). Each dot represents one participant, x -axis shows age in years. A , Risk taking (intercept). B , Return sensitivity (random EV slope). C , Risk sensitivity (random SD slope). Bottom, Correlations (including 95% confidence intervals) between the participant-specific random effects estimated in the behavioral and the fMRI sessions. D , Risk taking (intercept). E , Return sensitivity (random EV slope). F , Risk sensitivity (random SD slope).CC-BY-NC-SA · From the PDF
Figure 3 A , Whole-brain maps for the risk regressor, showing regions with peak activation in adolescents ∩ . Displays thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected). The line graph is only for visualization purposes and displays extracted percentage signal change per age group for the risk-related activation in the anterior insula (functional activation is anatomically masked), including 95% confidence intervals. B , Scatterplots showing the correlation between adolescents' behaviorally estimated risk sensitivity in the fMRI session and neural activation in response to risk in anterior insula and dmPFC.
Figure 3 A , Whole-brain maps for the risk regressor, showing regions with peak activation in adolescents ∩ . Displays thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected). The line graph is only for visualization purposes and displays extracted percentage signal change per age group for the risk-related activation in the anterior insula (functional activation is anatomically masked), including 95% confidence intervals. B , Scatterplots showing the correlation between adolescents' behaviorally estimated risk sensitivity in the fMRI session and neural activation in response to risk in anterior insula and dmPFC.CC-BY-NC-SA · From the PDF
Figure 4 A , Whole-brain maps for the return regressor, showing regions with significant activation for a linear age contrast. Displays thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected). The line graph is only for visualization purposes and displays extracted percentage signal change per age group for the return-related activation in vmPFC, including 95% confidence intervals. B , Scatterplots showing the correlation between individuals' behaviorally estimated return sensitivity and neural activation in response to return in the superior parietal cortex (fMRI session) and vmPFC (fMRI session and behavioral session).
Figure 4 A , Whole-brain maps for the return regressor, showing regions with significant activation for a linear age contrast. Displays thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected). The line graph is only for visualization purposes and displays extracted percentage signal change per age group for the return-related activation in vmPFC, including 95% confidence intervals. B , Scatterplots showing the correlation between individuals' behaviorally estimated return sensitivity and neural activation in response to return in the superior parietal cortex (fMRI session) and vmPFC (fMRI session and behavioral session).CC-BY-NC-SA · From the PDF
Figure 5 Whole-brain maps display per age group Gain > Baseline and Loss > Baseline contrasts, thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected). Note that whole-brain maps are highly similar at the more peaked thresholding criterion of Z > 2.6, p < 0.05; FWE cluster-corrected.
Figure 5 Whole-brain maps display per age group Gain > Baseline and Loss > Baseline contrasts, thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected). Note that whole-brain maps are highly similar at the more peaked thresholding criterion of Z > 2.6, p < 0.05; FWE cluster-corrected.CC-BY-NC-SA · From the PDF
Figure 6 Whole-brain maps for the parametric gain regressors, showing regions with significant activation across age groups (left; also significant at the more peaked thresholding criterion of Z > 2.6, p < 0.05; FWE cluster-corrected), and regions with significant activation for a quadratic age contrast ∩ (right). Displays thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected).
Figure 6 Whole-brain maps for the parametric gain regressors, showing regions with significant activation across age groups (left; also significant at the more peaked thresholding criterion of Z > 2.6, p < 0.05; FWE cluster-corrected), and regions with significant activation for a quadratic age contrast ∩ (right). Displays thresholded at Z > 2.3, p < 0.05 (FWE cluster-corrected).CC-BY-NC-SA · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 2

  1. 10.1037//0033-2909.125.3.367 · DOI · Paper page
  2. 10.1016/S1053-8119(02)91132-8 · DOI · Paper page

Cited by

Sorted by citations

See all 141

Related

See all

Metadata

Authors
van Duijvenvoorde, A. C. K., Huizenga, H. M., Somerville, L. H., Delgado, M. R., Powers, A., Weeda, W. D., Casey, B. J., Weber, E. U., & Figner, B.
Affiliations
  • University of Amsterdam (NL)
  • Harvard University (US)
  • Rutgers, The State University of New Jersey (US)
  • Cornell University (US)
  • Vrije Universiteit Amsterdam (NL)
  • Columbia University (US)
  • Radboud University Nijmegen (NL)
ORCID
Venue
The Journal of Neuroscience
Publisher
Society for Neuroscience
Published
2015-01-28
Volume
35
Issue
4
Pages
1549-1560
ISSN
  • 0270-6474 (ISSN-L)
  • 1529-2401
DOI
10.1523/jneurosci.1924-14.2015
PMC
PMC6795265
OpenAlex
W2086045708
Cited by
141
References
68
Citations by year
2026: 6 · 2025: 11 · 2024: 12 · 2023: 9 · 2022: 10 · 2021: 13 · 2020: 11 · 2019: 18 · 2018: 17 · 2017: 10 · 2016: 11 · 2015: 12
FWCI
10.02
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • VU Research Portal (publishedVersion)
  • Radboud Repository (Radboud University) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • National Science Foundation (0922743)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (15327)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
Keywords
decision making under risk, adolescence, risk aversion, risk sensitivity, Columbia Card Task, anterior insula, dorsomedial prefrontal cortex, ventromedial prefrontal cortex, posterior cingulate cortex, developmental trajectories, individual differences
MeSH terms
Adolescent, Adult, Aging, Brain, Brain Mapping, Child, Choice Behavior, Female, Games, Experimental, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motivation, Oxygen, Risk-Taking, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review