October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Creating Visual Categories With Closed-Loop Real-Time fMRI Neurofeedback
Author Affiliations & Notes
  • Marius Cătălin Iordan
    Princeton University
  • Victoria J.H. Ritvo
    Princeton University
  • Kenneth A. Norman
    Princeton University
  • Nicholas B. Turk-Browne
    Princeton University
    Yale University
  • Jonathan D. Cohen
    Princeton University
  • Footnotes
    Acknowledgements  John Templeton Foundation, Intel Corporation, NIH R01 MH069456
Journal of Vision October 2020, Vol.20, 422. doi:https://doi.org/10.1167/jov.20.11.422
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      Marius Cătălin Iordan, Victoria J.H. Ritvo, Kenneth A. Norman, Nicholas B. Turk-Browne, Jonathan D. Cohen; Creating Visual Categories With Closed-Loop Real-Time fMRI Neurofeedback. Journal of Vision 2020;20(11):422. doi: https://doi.org/10.1167/jov.20.11.422.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Information about visual categories is widely available across the brain (Huth et al. 2012) and these representations can be modulated by learning and training (Clarke et al. 2016). However, the causal link between neural representations of categories and their perception has not been established in humans. To address this question, we sought to change neural representations via closed-loop real-time fMRI neurofeedback (deBettencourt et al. 2015) and test whether this drives categorical perception. We hypothesized that increasing neural separation between categories should also differentiate the categories perceptually. To test our hypothesis, we constructed a stimulus space of complex artificial shapes that varied along multiple dimensions simultaneously (Kok et al. 2018) and we conducted a multi-session closed-loop real-time fMRI study (n=10) in which we sought to induce plasticity in neural representations elicited by arbitrary novel visual categories from the stimulus space. Our neurofeedback procedure nudged neural representations in visual, parahippocampal, and frontal cortex of our participants, resulting in significant increases in neural separation (category log-likelihood ratio for a multivariate Gaussian decoding model) for the trained categories compared to the untrained categories. Furthermore, for each participant, the amount of increase in neural separation significantly predicted the increase in perceptual categorization ability after training, compared to before training (higher psychometric function sharpening for trained vs. untrained categories in a behavioral 2AFC task). Our results suggest considerable plasticity in the structure of cortical representations evoked by visual stimuli and begin to establish the causal role for this plasticity in human behavior. Beyond implicit category learning, this technique also opens the door to a new paradigm of fMRI research that tests causal links between neural representations in various brain regions and different behaviors. This partly overcomes the typical correlational limitations of non-invasive methods for studying the human brain, creating the possibility of targeted causal interventions.

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