Computational and neural mechanisms of awareness

What are the computational and neural mechanisms underlying metacognitive judgments? Using a careful psychophysical task design motivated by signal detection theory analysis, my work has demonstrated that human observers exhibit a “response-congruent evidence” bias in confidence rating (Maniscalco, Peters, & Lau, 2016). If an ideal observer’s goal in rating confidence for a perceptual decision is to accurately reflect the probability that the decision was correct, then the confidence rating should weight perceptual evidence for and against the decision equally. However, in practice, this is not what is observed. Instead, confidence rating depends primarily on the amount of evidence that supports one’s decision, while downplaying or ignoring the evidence that contradicts one’s decision.

A subsequent study using intracranial electrophysiology revealed that perceptual decisions and confidence depend on distinct neural mechanisms, consistent with their differing dependence on perceptual evidence (Peters et al., 2017).

I have also contributed to work suggesting that separate neural mechanisms support metacognition for visual and memory tasks (using voxel-based morphometry or VBM; McCurdy et al., 2013), and that visual metacognition depends on action-specific representations in premotor cortex (using transcranial magnetic stimulation or TMS; Fleming et al., 2015). These results further refine our understanding of the neural mechanisms of metacognition in somewhat surprising ways: even though visual metacognition is modality-specific, it also depends on motor representations.

Finally, I have also investigated the neural correlates of stimulus detection using magnetoencephalography (MEG), showing that stimulus detection is associated with brain-wide robust transient dynamics (Baria, Maniscalco, & He, 2017). However, the experimental design in this study precluded dissociation of type 1 and type 2 processes, so it remains unclear what aspects of the neural findings are specific to stimulus awareness per se.

References

Maniscalco, B., Peters, M. A. K., & Lau, H. (2016). Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity. Attention, Perception & Psychophysics, 78(3), 923–937. https://doi.org/10.3758/s13414-016-1059-x [supplementary material]

Peters, M. A. K.*, Thesen, T.*, Ko, Y. D.*, Maniscalco, B., Carlson, C., Davidson, M., Doyle, W., Kuzniecky, R., Devinsky, O., Halgren, E., Lau, H. (2017). Human subjects under-utilize decision-incongruent evidence in the brain when computing perceptual confidence. Nature Human Behavior, 1(7), s41562-017. https://doi.org/10.1038/s41562-017-0139 [supplementary material]

McCurdy, L. Y., Maniscalco, B., Metcalfe, J., Liu, K. Y., De Lange, F. P., & Lau, H. (2013). Anatomical Coupling between Distinct Metacognitive Systems for Memory and Visual Perception. The Journal of Neuroscience, 33(5), 1897–1906. https://doi.org/10.1523/JNEUROSCI.1890-12.2013

Fleming, S. M., Maniscalco, B., Amendi, N., Ro, T., Lau, H. (2015). Action specific disruption of visual metacognition. Psychological Science, 26(1), 89-98. https://doi.org/10.1177/0956797614557697 [supplementary material]

Baria, A. T.*, Maniscalco, B.*, He, B. J. (2017). Initial-state-dependent, robust, transient neural dynamics encode conscious visual perception. PLOS Computational Biology, 13(11), e1005806. https://doi.org/10.1371/journal.pcbi.1005806 [supplementary material]