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Limitation of switching sensory information flow in flexible perceptual decision making

Author

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  • Tianlin Luo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Mengya Xu

    (Chinese Academy of Sciences)

  • Zhihao Zheng

    (Chinese Academy of Sciences)

  • Gouki Okazawa

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Humans can flexibly change rules to categorize sensory stimuli, but their performance degrades immediately after a task switch. This switch cost is believed to reflect a limitation in cognitive control, although the bottlenecks remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of using sensory inputs to form a decision after a rule change. Participants classified face stimuli based on one of two rules, switching every few trials. Psychophysical reverse correlation and computational modeling reveal a reduction in sensory weighting, which recovers within a few hundred milliseconds after stimulus presentation. This reduction depends on the sensory features being switched, suggesting a constraint in routing the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on a context cue alone, but require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.

Suggested Citation

  • Tianlin Luo & Mengya Xu & Zhihao Zheng & Gouki Okazawa, 2025. "Limitation of switching sensory information flow in flexible perceptual decision making," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55686-w
    DOI: 10.1038/s41467-024-55686-w
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    References listed on IDEAS

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    1. Paul I. Jaffe & Russell A. Poldrack & Robert J. Schafer & Patrick G. Bissett, 2023. "Modelling human behaviour in cognitive tasks with latent dynamical systems," Nature Human Behaviour, Nature, vol. 7(6), pages 986-1000, June.
    2. Geoffrey M. Ghose & John H. R. Maunsell, 2002. "Attentional modulation in visual cortex depends on task timing," Nature, Nature, vol. 419(6907), pages 616-620, October.
    3. Valerio Mante & David Sussillo & Krishna V. Shenoy & William T. Newsome, 2013. "Context-dependent computation by recurrent dynamics in prefrontal cortex," Nature, Nature, vol. 503(7474), pages 78-84, November.
    4. Gouki Okazawa & Long Sha & Braden A. Purcell & Roozbeh Kiani, 2018. "Psychophysical reverse correlation reflects both sensory and decision-making processes," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
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