Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions

Thomas H. B. FitzGerald, Dorothea Hämmerer, Karl J. Friston, Shu-Chen Li, Raymond J. Dolan

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Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices, rendering them behaviourally dissociable. Taking data from a probabilistic reversal task we show that subjects’ choices provide strong evidence that they are representing short sequences of states. Between-subject measures of this implicit sequential inference strategy had a neurobiological underpinning and correlated with grey matter density in prefrontal and parietal cortex, as well as the hippocampus. Our findings provide, to our knowledge, the first evidence for sequential inference in human cognition, and by exploiting between subject variation in this measure we provide pointers to its neuronal substrates.
Original languageEnglish
Article numbere1005418
JournalPLoS Computational Biology
Issue number5
Publication statusPublished - 9 May 2017

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