Understanding the extent and limits of non-conscious processing is an important step on the road to a thorough understanding of the cognitive and cerebral correlates of conscious perception. In this article, we present a critical review of research on subliminal perception during masking and other related experimental conditions. Although initially controversial, the possibility that a broad variety of processes can be activated by a non-reportable stimulus is now well established. Behavioural findings of subliminal priming indicate that a masked word or digit can have an influence on perceptual, lexical and semantic levels, while neuroimaging directly visualizes the brain activation that it evokes in several cortical areas. This activation is often attenuated under subliminal presentation conditions compared to consciously reportable conditions, but there are sufficiently many exceptions, in paradigms such as the attentional blink, to indicate that high activation, per se, is not a sufficient condition for conscious access to occur. We conclude by arguing that for a stimulus to reach consciousness, two factors are jointly needed: (i) the input stimulus must have enough strength (which can be prevented by masking) and (ii) it must receive top-down attention (which can be prevented by drawing attention to another stimulus or task). This view leads to a distinction between two types of non-conscious processes, which we call subliminal and preconscious. According to us, maintaining this distinction is essential in order to make sense of the growing neuroimaging data on the neural correlates of consciousness.

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.

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Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.