English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Human heading perception is robust in the presence of moving objects, except when the object crosses the focus of expansion (FoE). If the object approaches the observer, heading perception is shifted towards the object FoE [1]. If the object maintains a fixed distance, heading perception is shifted away from the object FoE [2]. This data has resulted in the theory that differential motion operators are used to determine heading. Primate neurophysiology, however, indicates that additive MT cells which combine data across pools of motion sensitive cells in V1, and not differential cells, are used to assess heading in dorsal MST [3]. The present work utilizes components of the ViSTARS (Visually-guided Steering, Tracking, and Route Selection) Biologically Inspired Cognitive Architecture (BICA) to analyze and reconcile the data.  ViSTARS BICA components relating to heading perception [4] were analyzed and minimal modifications were made to provide distance dependent weighting in the template match. The results indicate that differential motion operators are not required to explain human heading bias, provided that a detailed model of V1-MT-MSTd is used. The ViSTARS BICA indicates that heading bias in the presence of moving objects results from the representation of heading as a template and the use of a population codes to represent spatial properties within the system. Work is ongoing to fully characterize the causes and nature of this bias. This work demonstrates the utility of BICA not only in extending our computational capabilities but also in providing an overall framework with which to understand and reconcile the data on which it is built.

A Connectionist Model of MT+/Mstd Explains Human Heading Perception in the Presence of Moving Objects