title: Quantifying the effect of intertrial dependence on perceptual decisions.
abstract: In the perceptual sciences, experimenters study the causal mechanisms of perceptual systems by probing observers with carefully constructed stimuli. It has long been known, however, that perceptual decisions are not only determined by the stimulus, but also by internal factors. Internal factors could lead to a statistical influence of previous stimuli and responses on the current trial, resulting in serial dependencies, which complicate the causal inference between stimulus and response. However, the majority of studies do not take serial dependencies into account, and it has been unclear how strongly they influence perceptual decisions. We hypothesize that one reason for this neglect is that there has been no reliable tool to quantify them and to correct for their effects. Here we develop a statistical method to detect, estimate, and correct for serial dependencies in behavioral data. We show that even trained psychophysical observers suffer from strong history dependence. A substantial fraction of the decision variance on difficult stimuli was independent of the stimulus but dependent on experimental history.We discuss the strong dependence of perceptual decisions on internal factors and its implications for correct data interpretation.
authors.affiliation: Bernstein Center for Computational Neuroscience, Technical University Berlin, GermanyCenter for Vision Research, York University, Toronto, ON, Canada.
authors.forename: Ingo
authors.initials: I
authors.lastname: Fründ
authors.affiliation: Neural Information Processing Group, Eberhard Karls Universität, Max Planck Institute for Intelligent Systems, Bernstein Center for Computational Neuroscience, Tübingen, Germany.
authors.forename: Felix A
authors.initials: FA
authors.lastname: Wichmann
authors.affiliation: Max Planck Institute for Biological Cybernetics, Bernstein Center for Computational Neuroscience, Werner Reichardt Centre for Integrative Neuroscience, Tübingen, GermanyGatsby Computational Neuroscience Unit, University College London, UK.
authors.forename: Jakob H
authors.initials: JH
authors.lastname: Macke
mesh_terms: D003657:Decision Making / Q000502:physiology*; D006801:Humans; D015233:Models, Statistical; D010775:Photic Stimulation / Q000379:methods; D011601:Psychophysics; D014796:Visual Perception / Q000502:physiology*
keywords: internal variability; perceptual decisions; psychophysics; serial dependence; statistical modeling