title: Optimal stimulus encoders for natural tasks.
abstract: Determining the features of natural stimuli that are most useful for specific natural tasks is critical for understanding perceptual systems. A new approach is described that involves finding the optimal encoder for the natural task of interest, given a relatively small population of noisy "neurons" between the encoder and decoder. The optimal encoder, which necessarily specifies the most useful features, is found by maximizing accuracy in the natural task, where the decoder is the Bayesian ideal observer operating on the population responses. The approach is illustrated for a patch identification task, where the goal is to identify patches of natural image, and for a foreground identification task, where the goal is to identify which side of a natural surface boundary belongs to the foreground object. The optimal features (receptive fields) are intuitive and perform well in the two tasks. The approach also provides insight into general principles of neural encoding and decoding.
authors.affiliation: Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA. geisler@psy.utexas.edu
authors.forename: Wilson S
authors.initials: WS
authors.lastname: Geisler
authors.forename: Jiri
authors.initials: J
authors.lastname: Najemnik
authors.forename: Almon D
authors.initials: AD
authors.lastname: Ing
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*