A regression method for estimating performance in a rapid serial visual presentation target detection task

Background: Estimating target detection performance in the rapid serial visual presentation (RSVP) target detection paradigm can be challenging when the inter-stimulus interval is small relative to the variability in human response time. The challenge arises because assigning a particular response to the correct image cannot be done with certainty. Existing solutions to this challenge establish a heuristic for assigning responses to images and thereby determining which responses are hits and which are false alarms. New method: We developed a regression-based method for estimating hit rate and false alarm rate that corrects for expected errors in a likelihood-based assignment of responses to stimuli. Results: Simulations show that this regression method results in an unbiased and accurate estimate of target detection performance. Comparison with existing methods: The regression method had lower estimation error compared to three existing methods, and in contrast to the existing methods, the errors made by the regression method do not depend strongly on the true values of hit rate and false alarm rate. The most commonly used existing method performed well when simulated performance was nearly perfect, but not when behavioral error rates increased. Conclusions: Based on its better estimation of hit rate and false alarm rate, the regression method proposed here would seem the best choice when estimating the hit rate and false alarm rate is the primary interest. (C) 2015 The Authors. Published by Elsevier B.V.