Modeling the integration of expectations in visual search with centre-surround neural fields

Abstract

a b s t r a c tCentre-Surround Neural Field (CSNF) models were used to explain a possible mechanism by which information from different sources may be integrated into target likelihood maps that are then used to direct eye saccades. The CSNF model is a dynamic model in which each region in network excites nearby location and inhibits distant locations, thereby modeling competition for eye movements (saccades). The CSNF model was tested in a number of conditions analogous to a naturalistic search task in which the target was either (1) present in the expected location, (2) and Shimozaki (2006) found no significant differences. These discrepancies between model predictions and behavioural results are discussed. Additional simulations indicated that these models can also capture the qualitative flavor of eye movements in conditions with multiple targets as compared to [Findlay, J. M. (1997). Saccade target selection during visual search. Vision Research, 37(5),[617][618][619][620][621][622][623][624][625][626][627][628][629][630][631].

Can self-organisation emerge through dynamic neural fields computation?

“…The selection ability can also be used in attention, which differs from the self-organizing context. In this case, i is the response of a saliency map, and the u bump robustly emerges at the place of the appropriate focus of attention [39,40,43]. A similar mechanism can also be used for action selection [52].…”