Neural abstraction of evidence accumulation enables flexible multidimensional perceptual decisions

Abstract

Human perceptual decisions adapt to rich and changing contexts but are typically tested under impoverished conditions. To probe this flexibility, we examined decisions requiring both the selection and the combination of multiple sensory features across sequences of objects. Here we show that human decision flexibility extends beyond feature selection to combination, even across sensory modalities, supported by the abstraction of underlying neural representations. Rapid magnetoencephalographic (MEG) responses reveal a hierarchy of object representations that become increasingly abstract and gated according to their decision relevance. A shared abstract representation of updates reflects evidence accumulation collapsed onto the relevant axis across unidimensional (selection-based) and multidimensional (combination-based) contexts. Recurrent neural networks tested under the same conditions acquire and benefit from abstract representations only when operating under human levels of computational noise. Together, these findings provide a flexibility through abstraction account of human decisions across a broader range of everyday perceptual conditions.