Biological markers of risk taking are prominent targets for clinical, developmental, and longitudinal research. With respect to brain function, several regions are considered central for risky choice, yet insights into the neural basis of risk taking stem primarily from studies using single measures. Considering that recent studies suggested different risk-taking measures cannot be used interchangeably, it is currently unclear whether core regions of the brain involved in risk show a measure-dependent functional dissociation. Reporting results from the imaging subsample (N = 116 young adults) of the Basel–Berlin Risk Study, we examine (1) the conjunction of average neural representations of experience-based risky choice in the Balloon Analogue Risk Task and description-based risky choice in monetary gambles, (2) the preservation of individual activation differences across the two measures, and (3) the explanatory power of the neural correlates of risky choice for behavior. Our results suggest common group-level activation increases in nucleus accumbens, inconsistent individual differences in regional activation across measures, and limited explanatory power of neural indices for behavior, within and across measures. Our findings help clarify commonalities and differences between the neural representation of experienced and described risk, and thus should inform research designs targeting individual differences in risk taking.
Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants’ choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants’ choice data from the perspective of our model set. In brief, we provide quantitative evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective symmetric reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas.
Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. For example, a medicated patient may consider partaking in a clinical trial on the effectiveness of a new drug. Taking part in the trial can provide the patient with information about the personal effectiveness of the new drug and the potential reward of a better treatment. Not taking part in the trial does not provide the patient with this information, but is associated with the reward of a (potentially less) effective treatment. In the current study, we introduce a novel information-selective reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioural models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants’ choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants’ choice data from the perspective of our model set. In brief, we provide evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas.
Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain–behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers. To address this question, we analyzed within-participant neuroimaging data for two widely used risk-taking tasks collected from the imaging subsample of the Basel–Berlin Risk Study ( N = 116 young human adults). Focusing on core brain regions implicated in risk taking (nucleus accumbens, anterior insula, and anterior cingulate cortex), for the two tasks, we examined group-level activation for risky versus safe choices, as well as associations between local functional markers and various risk-related outcomes, including psychometrically derived risk preference factors. While we observed common group-level activation in the two tasks (notably increased nucleus accumbens activation), individual differences analyses support the idea that the presence and directionality of associations between brain activation and risk taking varies as a function of the risk-taking measures used to capture individual differences. Our results have methodological implications for the use of brain markers for intervention or prevention.
Recent discussions on the reproducibility of task-related functional magnetic resonance imaging (fMRI) studies have emphasized the importance of power and sample size calculations in fMRI study planning. In general, statistical power and sample size calculations are dependent on the statistical inference framework that is used to test hypotheses. Bibliometric analyses suggest that random eld theory (RFT)-based voxel-and cluster-level fMRI inference are the most commonly used approaches for the statistical evaluation of task-related fMRI data. However, general power and sample size calculations for these inference approaches remain elusive. Based on the mathematical theory of RFT-based inference, we here develop power and positive predictive value (PPV) functions for voxel-and cluster-level inference in both uncorrected single test and corrected multiple testing scenarios. Moreover, we apply the theoretical results to evaluate the sample size necessary to achieve desired power and PPV levels based on an fMRI pilot study.
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