Selective Integration during Sequential Sampling in Posterior Neural Signals

Fabrice Luyckx, Bernhard Spitzer, Annabelle Blangero, Konstantinos Tsetsos, Christopher Summerfield

Research output: Contribution to journalArticle (Academic Journal)peer-review


Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as "selective integration" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers' decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.

Original languageEnglish
Pages (from-to)4454-4464
Number of pages11
JournalCerebral Cortex
Issue number8
Publication statusPublished - 30 Jun 2020

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