The Roles of Endpoints and Closures in a Detection Task

Abstract

The purpose of this study is to investigate the relative detectability of endpoints and closures in a given random figure. A new detection task is introduced in which participants were requested to judge the state (i.e., presence or absence) of a pre-designated endpoint or a closure in a singly presented random figure. Rigorous control was exerted over the selection of stimulus figures possessing these features. The results seem somewhat confusing: the presence of closures and the absence of endpoints were identical in terms of stimulus states but elicited different levels of detectability depending upon the designated target feature (i.e., an asymmetry in detectability), the mental set for closure detection seemed to be more efficient than the mental set for endpoint detection, and the assumption of feature detection via feature search did not appear viable. To comprehensively account for these results, this paper proposes an explanation that assumes a default decision state (i.e., the presence of closures when the target feature is closures, and the absence of an endpoint when the target feature is an endpoint) and quick responses for situations that deviate from the default state.