Detecting visual texture patterns in binary sequences through pattern features

We measured human ability to detect texture patterns in a signal detection task. Observers viewed sequences of 20 blue or yellow tokens placed horizontally in a row. They attempted to discriminate sequences generated by a random generator (“a fair coin”) from sequences produced by a disrupted Markov sequence (DMS) generator. The DMSs were generated in two stages: first a sequence was generated using a Markov chain with probability, p(r) = 0.9, that a token would be the same color as the token to its left. The Markov sequence was then disrupted by flipping each token from blue to yellow or vice versa with probability, p(d)—the probability of disruption. Disruption played the role of noise in signal detection terms. We can frame what observers are asked to do as detecting Markov texture patterns disrupted by noise. The experiment included three conditions differing in p(d) (0.1, 0.2, 0.3). Ninety-two observers participated, each in only one condition. Overall, human observers’ sensitivities to texture patterns (d′ values) were markedly less than those of an optimal Bayesian observer. We considered the possibility that observers based their judgments not on the entire texture sequence but on specific features of the sequences such as the length of the longest repeating subsequence. We compared human performance with that of multiple optimal Bayesian classifiers based on such features. We identify the single- and multiple-feature models that best match the performance of observers across conditions and develop a pattern feature pool model for the signal detection task considered.