Authors
Jorge Morales
Johns Hopkins University
Abstract
Studying the neural correlates of conscious awareness depends on a reliable comparison between activations associated with awareness and unawareness. One particularly difficult confound to remove is task performance capacity, i.e. the difference in performance between the conditions of interest. While ideally task performance capacity should be matched across different conditions, this is difficult to achieve experimentally. However, differences in performance could theoretically be corrected for mathematically. One such proposal is found in a recent paper by Lamy, Salti and Bar-Haim [Lamy D, Salti M, Bar-Haim Y. Neural correlates of subjective awareness and unconscious processing: an ERP study. J Cognitive Neurosci 2009,21:1435-46], who put forward a corrective method for an electroencephalography experiment. We argue that their analysis is essentially grounded in a version of High Threshold Theory, which has been shown to be inferior in general to Signal Detection Theory. We show through a series of computer simulations that their correction method only partially removes the influence of perfor- mance capacity, which can yield misleading results. We present a mathematical correction method based on Signal Detection Theory that is theoretically capable of removing performance capacity confounds. We discuss the limitations of mathemati- cally correcting for performance capacity confounds in imaging studies and its impact for theories about consciousness.
Keywords neural correlates of consciousness (NCC)  signal detection theory (SDT)  high threshold theory  EEG  unconscious perception  consciousness  neuroimaging  awareness  unawareness
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References found in this work BETA

Relative Blindsight in Normal Observers and the Neural Correlate of Visual Consciousness.Hakwan C. Lau & Richard E. Passingham - 2006 - Proceedings of the National Academy of Sciences of the United States of America 103 (49):18763-18768.

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The Oxford Handbook of the Philosophy of Consciousness.Uriah Kriegel (ed.) - 2020 - Oxford: Oxford University Press.

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