Marmosets adaptively accumulate dynamic sequential evidence to make decisions

Abstract

The bounded accumulation model is a dominant theoretical framework to characterize evidence accumulation during decision-making. Using a dynamic sequential tone accumulation (DSTA) task in marmosets, we found that the contribution of late tones to decisions was greater for weaker than for stronger evidence strength-an effect we term evidence-strength-dependent temporal weighting (ETW). Rigorous exclusionary analyses and drift-diffusion modeling further revealed that the bounded accumulation mechanism alone fails to account for this effect, and the online modulation of attentional engagement was identified as the necessary mechanism in addition to the bounded accumulation. Our work thus establishes that attentional engagement is adaptively modulated by evidence strength during complex decision-making.