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 electroencephalography (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.
Armengol-Urpi et al. (Wed,) studied this question.