A connectionist model for visual selective attention

Summary form only given, SLAM, the selective attention model, performs visual selective attention tasks with two selective processes, object and attribute selection. SLAM is based upon connectionist models for visual word recognition with the addition of a specific response selection and evaluation mechanism. The responses may be correct or incorrect, and in particular conditions SLAM may not make a response at all. Moreover, it allows for the generation of responses in time. SLAM´s main characteristics are parallelism restricted by competition within modules, heterarchical processing in hierarchical structures, and generation of responses as a result of relaxation given the conjoint constraints of stimulation, object, and attribute selection, SLAM represents an individual subject performing filtering tasks and shows appropriate selective behavior. It is also tested quantitatively using a single set of parameters. Simulations are performed by SLAM of different filtering experiments, modeling response latencies and error proportion. Specifications are made to take account of instructions, previous trials, the effect of a barmarker cue and of asynchronies in stimulus and cue onsets. The model is extended in order to simulate a number of Stroop experiments, which can be regarded as filtering tasks with nonequivalent stimuli.<>