The detection of feature singletons defined in two dimensions is based on salience summation, rather than on serial...

Influential models of visual search assume that dimension-specific feature contrast signals are summed into a master saliency map in a coactive fashion. The main source of evidence for coactivation models, and against parallel race models, is violations of the race model inequality (RMI; Miller, 1982) by redundantly defined singleton feature targets. However, RMI violations do not rule out serial exhaustive (Townsend & Nozawa, 1997) or interactive race (Mordkoff & Yantis, 1991) architectures. These alternatives were tested in two experiments. In Experiment 1, we used a double-factorial design with singleton targets defined in two dimensions and at two levels of intensity, to distinguish between serial versus parallel models and self-terminating versus exhaustive stopping rules. In Experiment 2, we manipulated contingency benefits that are expected to affect the magnitude of redundancy gains and/or RMI violations on the assumption of an interactive race. The results of both experiments revealed redundancy gains as well as violations of the RMI, but the data pattern excluded serial-exhaustive and interactive race models as possible explanations for RMI violations. This result supports saliency summation (coactivation) models of search for singleton feature targets.