Conscious and Unconscious Perception: A Computational Theory

We propose a computational theory of consciousness and model data from three experiments in visual perception. The central idea of our theory is that the contents of consciousness correspond to temporally stable states in an interconnected network of specialized computational modules. Each module incorporates a relaxation search that is concerned with achieving semantically well-formed states. We claim that being an attractor of the relaxation search is a necessary condition for awareness. We show that the model provides sensible explanations for the results of three experiments, and makes testable predictions. The first experiment (Marcel, 1980) found that masked, ambiguous prime words facilitate lexical decision for targets related to either prime meaning, whereas consciously perceived primes facilitate only the meaning that is consistent with prior context. The second experiment (Fehrer and Raab, 1962) found that subjects can make detection responses in constant time to simple visual stimuli regardless of whether they are consciously perceived or masked by metacontrast and not consciously perceived. The third experiment (Levy and Pashler, 1996) found that visual word recognition accuracy is lower than baseline when an earlier speeded response was incorrect, and higher than baseline when the early response was correct, consistent with a causal relationship between conscious perception and subsequent processing.