AUTHOR=Mathews Zenon , Cetnarski Ryszard , Verschure Paul F. M. J. 

TITLE=Visual anticipation biases conscious decision making but not bottom-up visual processing

JOURNAL=Frontiers in Psychology

VOLUME=Volume 5 - 2014

YEAR=2015

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.01443

DOI=10.3389/fpsyg.2014.01443

ISSN=1664-1078

ABSTRACT=Theories of consciousness can be grouped with respect to their stance on embodiment, sensori-motor contingencies, prediction and integration. In this list prediction plays a key role and it is not clear which aspects of prediction are most prominent in the conscious scene. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the conscious scene. Yet, due to the lack of efficient indirect measures, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and / or errors on the conscious scene. Using a displacement detection task combined with reverse correlation we reveal signatures of the usage of prediction at three different levels of perception: bottom-up early saccades, top-down driven late saccades and conscious decisions. Our results suggest that the brain employs multiple parallel mechanisms at different levels of information processing to restrict the sensory field using predictions. We observe that cognitive load has a quantifiable effect on this dissociation of the bottom-up sensory and top-down predictive processes. We propose a probabilistic data association model from dynamical systems theory to model this predictive bias in different information processing levels.