J Neurosci. 2024 Apr 1:e1337232024. doi: 10.1523/JNEUROSCI.1337-23.2024. Online ahead of print.

ABSTRACT

It remains a pressing concern to understand how neural computations relate to risky decisions. However, most observations of brain-behavior relationships in the risk-taking domain lack a rigorous computational basis or fail to emulate of the dynamic, sequential nature of real-life risky decision making. Recent advances emphasize the role of neural prediction error (PE) signals. We modelled, according to prospect theory, the choices of n = 43 human participants (33 females, ten males) performing an EEG version of the hot Columbia Card Task, featuring rounds of sequential decisions between stopping (safe option) and continuing with increasing odds of a high loss (risky option). Single-trial regression EEG analyses yielded a subjective value signal at centroparietal (300-700 ms) and frontocentral (>800ms) electrodes and in the delta band, as well as PE signals tied to the feedback-related negativity, P3a, and P3b, and in the theta band. Higher risk preference (total number of risky choices) was linked to attenuated subjective value signals but increased PE signals. Higher P3-like activity associated with the most positive PE in each round predicted stopping in the present round but not risk taking in the subsequent round. Our findings indicate that decreased representation of decision values and increased sensitivity to winning despite low odds (positive PE) facilitate risky choices at the subject level Strong neural responses when gains are least expected (the most positive PE on each round) adaptively contribute to safer choices at the trial-by-trial level but do not affect risky choice at the round-by-round level Significance statement It remains an open question, most pressingly in mental health, how neural computations facilitate real-life risky behavior (e.g. drinking, criminal activities). This endeavor requires a rigorous computational basis as well as paradigms emulating the dynamic, sequential nature of real-life risk taking. We applied prospect theory to model sequentially dependent risky choices and isolated neural correlates of subjective decision values and unexpected gains (prediction errors). This allowed us to show that overall risk-taking was related to hyperactive prediction error and reduced decision value signals. Moreover, subjects responded more cautiously following higher neural responses to the least expected gains. These findings indicate multi-layered, independent roles of prediction error processing, mediating safer choices at the trial-level but risk tendencies at the subject-level.

PMID:38561225 | DOI:10.1523/JNEUROSCI.1337-23.2024