Prior object-knowledge sharpens properties of early visual feature-detectors

• Published in: Scientific reports Vol. 8; no. 1; pp. 10853 - 12
• Main Authors: Teufel, Christoph, Dakin, Steven C., Fletcher, Paul C.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.07.2018; Nature Publishing Group
• Subjects: 631/378/2613/1875; 631/378/2613/2615; 631/378/2613/2616; 631/477/2811; Cortex; Humanities and Social Sciences; Information processing; multidisciplinary; Neural coding; Neural networks; Orientation behavior; Perception; Science; Science (multidisciplinary); Sensors; Vision; Visual pathways; Visual task performance
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-28845-5

Early stages of visual processing are carried out by neural circuits activated by simple and specific features, such as the orientation of an edge. A fundamental question in human vision is how the brain organises such intrinsically local information into meaningful properties of objects. Classic models of visual processing emphasise a one-directional flow of information from early feature-detectors to higher-level information-processing. By contrast to this view, and in line with predictive-coding models of perception, here, we provide evidence from human vision that high-level object representations dynamically interact with the earliest stages of cortical visual processing. In two experiments, we used ambiguous stimuli that, depending on the observer’s prior object-knowledge, can be perceived as either coherent objects or as a collection of meaningless patches. By manipulating object knowledge we were able to determine its impact on processing of low-level features while keeping sensory stimulation identical. Both studies demonstrate that perception of local features is facilitated in a manner consistent with an observer’s high-level object representation (i.e., with no effect on object-inconsistent features). Our results cannot be ascribed to attentional influences. Rather, they suggest that high-level object representations interact with and sharpen early feature-detectors, optimising their performance for the current perceptual context.