title: Modelling symmetry detection with back-propagation networks.
abstract: This paper reports experimental data and results of network simulations in a project on symmetry detection in small 6 x 6 binary patterns. Patterns were symmetrical about the vertical, horizontal, positive-oblique, or negative-oblique axis, and were viewed on a computer screen. Encouraged to react quickly and accurately, subjects indicated axis of symmetry by pressing one of four designated keys. Detection times and errors were recorded. Back-propagation networks were trained to categorize the patterns on the basis of axis of symmetry, and, by employing cascaded activation functions on their output units, it was possible to compare network performance with subjects' detection times. Best correspondence between simulated and human detection-time functions was observed after the networks had been given significantly more training on patterns symmetrical about the vertical and the horizontal axes. In comparison with no pre-training and pre-training with asymmetric patterns, pre-training networks with sets of single vertical, horizontal, positive-oblique or negative-oblique bars speeded subsequent learning of symmetrical patterns. Results are discussed within the context of theories suggesting that faster detection of symmetries about the vertical and horizontal axes may be due to significantly more early experience with stimuli oriented on these axes.
authors.affiliation: Department of Psychology, University of Sydney, New South Wates, Australia.
authors.forename: C
authors.initials: C
authors.lastname: Latimer
authors.forename: W
authors.initials: W
authors.lastname: Joung
authors.forename: C
authors.initials: C
authors.lastname: Stevens
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D013028:Space Perception / Q000502:physiology*