The crack of dawn : perceptual functions and neural mechanisms that mark the transition from unconscious processing to conscious vision

There is conscious vision, and there is unconscious visual processing. So far so good. But where lies the boundary between the two? What are the visual functions that shape the transition from “processing in the dark” to having a conscious visual percept? And what are the neural mechanisms that carry that transition? I review the findings on feature detection, object categorization, interference, inference, Gestalt grouping, and perceptual organization, and examine to what extent these functions correlate with the presence or absence of conscious vision. It turns out that a surprisingly large set of visual functions is executed unconsciously, indicating that unconscious vision is much “smarter” than we might intuitively think. Only when these unconscious mechanisms fail, and more elaborate and incremental processing steps are required, is consciousness necessary. The function of conscious vision may be to add a final layer to our interpretation of the world, to solve relatively “new” visual problems, and to enable visual learning

Predictive coding is unconscious, so that consciousness happens now : a reply to Lucia Melloni

Conscious percepts depend strongly on past events. Expectations, primes, and prior experiences all shape the percept we have at any moment in time. Yet does this imply that conscious experience should be viewed as extended in time—as “flowing”—instead of as just happening now

Act quickly, decide later: long latency visual processing underlies perceptual decisions but not reflexive behavior

Jolij J, Scholte H, Van Gaal S, Hodgson TL, Lamme VAF (2011) Act quickly, decide later: Long latency visual processing underlies perceptual decisions but not reflexive behavior. Journal of Cognitive Neuroscience 23(12), p 3734-3745

The Role of the Primary Visual Cortex in Higher Level Vision

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons’ responses reflecting higher order perceptual computations related to Ullman’s (Cognition 1984;18:97–159) visual routines and Marr’s (Vision NJ: Freeman 1982) full primal sketch, 2Image D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons’ precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later part of its neurons’ activities.Mathematic

Low-level contrast statistics are diagnostic of invariance of natural textures

Texture may provide important clues for real world object and scene perception. To be reliable, these clues should ideally be invariant to common viewing variations such as changes in illumination and orientation. In a large image database of natural materials, we found textures with low-level contrast statistics that varied substantially under viewing variations, as well as textures that remained relatively constant. This led us to ask whether textures with constant contrast statistics give rise to more invariant representations compared to other textures. To test this, we selected natural texture images with either high (HV) or low (LV) variance in contrast statistics and presented these to human observers. In two distinct behavioral categorization paradigms, participants more often judged HV textures as “different” compared to LV textures, showing that textures with constant contrast statistics are perceived as being more invariant. In a separate electroencephalogram (EEG) experiment, evoked responses to single texture images (single-image ERPs) were collected. The results show that differences in contrast statistics correlated with both early and late differences in occipital ERP amplitude between individual images. Importantly, ERP differences between images of HV textures were mainly driven by illumination angle, which was not the case for LV images: there, differences were completely driven by texture membership. These converging neural and behavioral results imply that some natural textures are surprisingly invariant to illumination changes and that low-level contrast statistics are diagnostic of the extent of this invariance

Unconscious Errors Enhance Prefrontal-Occipital Oscillatory Synchrony

The medial prefrontal cortex (MFC) is critical for our ability to learn from previous mistakes. Here we provide evidence that neurophysiological oscillatory long-range synchrony is a mechanism of post-error adaptation that occurs even without conscious awareness of the error. During a visually signaled Go/No-Go task in which half of the No-Go cues were masked and thus not consciously perceived, response errors enhanced tonic (i.e., over 1–2 s) oscillatory synchrony between MFC and occipital cortex (OCC) leading up to and during the subsequent trial. Spectral Granger causality analyses demonstrated that MFC → OCC directional synchrony was enhanced during trials following both conscious and unconscious errors, whereas transient stimulus-induced occipital → MFC directional synchrony was independent of errors in the previous trial. Further, the strength of pre-trial MFC-occipital synchrony predicted individual differences in task performance. Together, these findings suggest that synchronous neurophysiological oscillations are a plausible mechanism of MFC-driven cognitive control that is independent of conscious awareness