Redundancy gain : manifestations, causes and predictions
Response times in a visual object recognition task decrease significantly if targets can be distinguished by two redundant attributes. Redundancy gain for two attributes is a common finding, but redundancy gain from three attributes has been found only for stimuli from three different modalities (tactile, auditory, and visual). This study extends those results by showing that redundancy gain from three attributes within the visual modality is possible. It also provides a more detailed investigation of the characteristics of redundancy gain. Apart from a decrease in response times for redundant targets, these include a decrease in minimal response times and an increase in symmetry of the response time distribution.   This study further presents evidence that neither race models nor coactivation models can account for all characteristics of redundancy gain. In this context, we discuss the problem of calculating an upper limit for the performance of race models for triple redundant targets, and introduce a new method of evaluating triple redundancy gain based on performance for double redundant targets. In order to explain the results from this study, the cascade race model is introduced. The cascade race model consists of several input channels, which are triggered by a cascade of activations before satisfying a single decision criterion, and is able to provide a unifying approach to previous research on the causes of redundancy gain.   The analysis of the characteristics of response time distributions, including their mean, symmetry, onset, and scale, is an essential tool in this study. It was therefore important to find an adequate statistical test capable of reflecting differences in all these characteristics. We discuss the problem and importance of analysing response times without data loss, as well as the inadequacy of common methods of analysis such as the pooling of response times across participants (e.g. Vincentizing) in the present context.   We present tests of distributions as an alternative method for comparing distributions, response time distributions in particular, the most common of these being the Kolmogorov-Smirnoff test. We also introduce a test yet unknown in psychology: the two-sample Anderson-Darling test of goodness of fit. We compare both tests, presenting conclusive evidence that the Anderson-Darling test is more accurate and powerful: when comparing two distributions that vary (1) in onset only, (2) in scale only, (3) in symmetry only, or (4) that have the same mean and standard deviation but differ on the tail ends only, the Anderson-Darling test proves to detect differences better than the Kolmogorov-Smirnoff test. Finally, the Anderson-Darling test has a type I error rate corresponding to alpha whereas the Kolmogorov-Smirnoff test is overly conservative. Consequently, the Anderson- Darling test requires less data than the Kolmogorov-Smirnoff test to reach sufficient statistical power.
Coactivation Redundancy gain Analysis of response time distributions Coactivation