UC Irvine

[C. Koch, S. Ullman, Hum. Neurobiol.4, 219-227 (1985)] proposed a 2D topographical salience map that took feature-map outputs as its input and represented the importance saliency of the feature inputs at each location as a real number. The computation on the map, winner-take-all, was used to predict action priority. We propose that the same or a similar map is used to compute centroid judgments, the center of a cloud of diverse items. [P. Sun, V. Chu, G. Sperling, Atten. Percept. Psychophys.83, 934-955 (2021)] demonstrated that following a 250-msec exposure of a 24-dot array of 3 intermixed colors, subjects could accurately report the centroid of each dot color, thereby indicating that these subjects had at least three salience maps. Here, we use a postcue, partial-report paradigm to determine how many more salience maps subjects might have. In 11 experiments, subjects viewed 0.3-s flashes of 28 to 32 item arrays composed of M, M = 3,...,8, different features followed by a cue to mouse-click the centroid of items of just the post-cued feature. Ideal detector response analyses show that subjects utilized at least 12 to 17 stimulus items. By determining whether a subjects performance in (M-1)-feature experiments could/could-not predict performance in M-feature experiments, we conclude that one subject has at least 7 and the other two have at least five salience maps. A computational model shows that the primary performance-limiting factors are channel capacity for representing so many concurrently presented groups of items and working-memory capacity for so many computed centroids.