The optimal experimental design for multiple alternatives perceptual search

Abstract

Perceptual bias is inherent to all our senses, particularly in the form of visual illusions and aftereffects. However, many experiments measuring perceptual biases may be susceptible to nonperceptual factors, such as response bias and decision criteria. Here, we quantify how robust multiple alternative perceptual search (MAPS) is for disentangling estimates of perceptual biases from these confounding factors. First, our results show that while there are considerable response biases in our four-alternative forced-choice design, these are unrelated to perceptual biases estimates, and these response biases are not produced by the response modality (keyboard vs. mouse). We also show that perceptual bias estimates are reduced when feedback is given on each trial, likely due to feedback enabling observers to partially (and actively) correct for perceptual biases. However, this does not impact the reliability with which MAPS detects the presence of perceptual biases. Finally, our results show that MAPS can detect actual perceptual biases and is not a decisional bias towards choosing the target in the middle of the candidate stimulus distribution. In summary, researchers conducting a MAPS experiment should use a constant reference stimulus, but consider varying the mean of the candidate distribution. Ideally, they should not employ trial-wise feedback if the magnitude of perceptual biases is of interest.