Detecting visual texture patterns in binary sequences through pattern features.

ABSTRACT: 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, pr = 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, pd-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

SUBMITTER: Dal Martello MF 

PROVIDER: S-EPMC10627294 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature