Comparison of two weighted integration models for the cueing task: linear and likelihood.
In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.
Shimozaki
Steven S
SS
Department of Psychology, University of California, Santa Barbara, CA, USA. mshimozak@psych.ucsb.edu
Eckstein
Miguel P
MP
Abbey
Craig K
CK
D000328:Adult; D001288:Attention; D001499:Bayes Theorem; D003463:Cues; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D010364:Pattern Recognition, Visual / Q000502:physiology*