Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Recently, we have introduced a computational model, termed Selective Attention Identification Model [SAIM; Heinke, Humphreys, Attention, spatial representation and visual neglect: simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM), Psychol. Rev. 110 (1) (2003) 29]. SAIM aims at translation-invariant object identification by selecting objects and mapping them into a focus of attention (FOA). With this architecture, SAIM can model a wide range of experimental evidence on normal attention and attentional disorders. Here, we demonstrate how SAIM can produce two specially striking forms of visual disorder, within- and between-object neglect. Patients with within-object neglect tend to ignore parts of objects and patients with between-object neglect might omit complete objects presented next to another object. Importantly, both deficits can occur irrespective of the objects' positions in the visual field (translation invariant). To simulate neglect, the selection mechanism in SAIM was lesioned. Importantly, the selection mechanism selects objects and, at the same time, produces a translation-invariant representation of the objects in the FOA. Consequently, the lesion can have a translation-invariant effect, allowing SAIM to simulate within- and between-object neglect. This result gives additional credence to SAIM's integral approach to attention and translation-invariant object identification. © 2005 Published by Elsevier Inc.

Original publication

DOI

10.1016/j.cviu.2004.10.010

Type

Journal article

Journal

Computer Vision and Image Understanding

Publication Date

01/10/2005

Volume

100

Pages

172 - 197