Cognitive Reinforcement: Capturing Tacit Knowledge and Enhancing Expertise with a Biofeedback Interface for Visual Attention
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Tacit_knowledge_article.pdf
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Author(s)
Armengol-Urpi, Alexandre
Salazar-Gomez, Andres F.
Sinha, Pawan
Sarma, Sanjay E.
Date Issued
March 4, 2026
Journal
Journal of Neural Engineering
Publisher
IOP Publishing
Citation
Armengol-Urpi, Alexandre, Salazar-Gomez, Andres F., Sinha, Pawan and Sarma, Sanjay E. 2026. "Cognitive Reinforcement: Capturing Tacit Knowledge and Enhancing Expertise with a Biofeedback Interface for Visual Attention." Journal of Neural Engineering.
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Author's final manuscript
Abstract
Objective. Tacit or implicit knowledge refers to know-how that experts
possess but often cannot articulate, codify, or explicitly transfer to
others. This can present a significant challenge for learning, skill acquisition, and knowledge transfer across various domains, including those
that rely on apprenticeships, craftsmanship, sports, and medical imaging diagnosis. This study explores whether expert tacit knowledge can
be accessed and leveraged using an EEG and gaze-informed biofeedback interface to enhance expertise transfer and training. Approach.
We designed an image classification task where novices were trained
until they implicitly learned to classify images correctly, despite being
unaware of which image regions or features guided their decisions. The
task involved images with a hidden spatial asymmetry that even trained
participants did not explicitly recognize. Using combined eye-tracking
and EEG measures, we tracked both overt and covert visual attention to determine whether individuals unconsciously internalized this
asymmetry during learning. We then investigated whether providing
explicit gaze-informed feedback on their own implicit attention biases
could further improve task performance of trained participants. Main Results. Our findings reveal that as participants became trained, their
attention patterns —both overt and covert— consistently reflected an
unconscious awareness of image asymmetry, with attention biased toward
task-relevant image regions. Moreover, trained individuals who received
explicit feedback derived from their own gaze behavior showed additional improvements in classification performance compared to an equally
trained control group. Significance. These results open the door to novel
uses of biofeedback interfaces to facilitate new forms of expertise transfer, training, and collective intelligence. By extracting and conveying
tacit expert knowledge—ordinarily difficult to externalize—our interface
enables its transmission to novices, trained individuals, or even machine
learning systems. We refer to this process as cognitive reinforcement.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1088/1741-2552/ae3eb8
