A Hierarchical Attractor Network Model of perceptual versus intentional decision updates

Anne Löffler (), Anastasia Sylaidi, Zafeirios Fountas and Patrick Haggard
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Anne Löffler: Columbia University
Anastasia Sylaidi: Spike AI Research Labs
Zafeirios Fountas: Emotech Labs
Patrick Haggard: University College London

Nature Communications, 2021, vol. 12, issue 1, 1-17

Abstract: Abstract Changes of Mind are a striking example of our ability to flexibly reverse decisions and change our own actions. Previous studies largely focused on Changes of Mind in decisions about perceptual information. Here we report reversals of decisions that require integrating multiple classes of information: 1) Perceptual evidence, 2) higher-order, voluntary intentions, and 3) motor costs. In an adapted version of the random-dot motion task, participants moved to a target that matched both the external (exogenous) evidence about dot-motion direction and a preceding internally-generated (endogenous) intention about which colour to paint the dots. Movement trajectories revealed whether and when participants changed their mind about the dot-motion direction, or additionally changed their mind about which colour to choose. Our results show that decision reversals about colour intentions are less frequent in participants with stronger intentions (Exp. 1) and when motor costs of intention pursuit are lower (Exp. 2). We further show that these findings can be explained by a hierarchical, multimodal Attractor Network Model that continuously integrates higher-order voluntary intentions with perceptual evidence and motor costs. Our model thus provides a unifying framework in which voluntary actions emerge from a dynamic combination of internal action tendencies and external environmental factors, each of which can be subject to Change of Mind.

Date: 2021
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DOI: 10.1038/s41467-021-22017-2

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