Feature-specific interactions in salience from combined feature contrasts: Evidence for a bottom-up saliency map in V1.
, Article 6. 10.1167/7.7.6.
Items that stand out from their surroundings, that is, those that attract attention, are considered to be salient. Salience is generated by input features in many stimulus dimensions, like motion (M), color (C), orientation (O), and others. We focus on bottom-up salience generated by contrast between the feature properties of an item and its surroundings. We compare the singleton search reaction times (RTs) of items that differ from their surroundings in more than one feature (e. g., C + O, denoted as CO) against the RTs of items that differ from their surroundings in only a single feature (e. g., O or C). The measured RTs for the double-feature singletons are compared against ''race model'' predictions to evaluate whether salience in the double-feature conditions is greater than the salience of either of its feature components. Affirmative answers were found in MO and CO but not in CM. These results are consistent with some V1 neurons being conjunctively selective to MO, others to CO, but almost none to CM. They provide support for the V1 hypothesis of bottom-up salience (Z. Li, 2002) but are contrary to expectation from the ''feature summation'' hypothesis, in which different stimulus features are initially analyzed independently and subsequently summed to form a single salience map (L. ltti & C. Koch, 2001; C. Koch & S. Ullman, 1985; J. M. Wolfe, K. R. Cave, & S. L. Franzel, 1989).
|Title:||Feature-specific interactions in salience from combined feature contrasts: Evidence for a bottom-up saliency map in V1|
|Open access status:||An open access publication|
|Keywords:||salience, reaction time, feature contrast, feature combinations, salience map, V1, PRIMARY VISUAL-CORTEX, CAT STRIATE CORTEX, POP-OUT, STATISTICAL FACILITATION, TEXTURE SEGMENTATION, NEURONAL RESPONSES, REDUNDANT-SIGNALS, GUIDED SEARCH, EARLY VISION, ATTENTION|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
Archive Staff Only