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      Bayesian adaptive stimulus selection for dissociating models of psychophysical data

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      Creators
      Cooke, J.R.H.
      Selen, L.P.J.
      Beers, R.J. van
      Medendorp, W.P.
      Date of Archiving
      2018
      Archive
      Radboud Data Repository
      Data archive handle
      https://hdl.handle.net/11633/di.dcc.DSC_2017.00053_185
      Publication type
      Dataset
      Access level
      Restricted access
      Please use this identifier to cite or link to this item: https://hdl.handle.net/2066/203788   https://hdl.handle.net/2066/203788
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      Organization
      SW OZ DCC SMN
      Audience(s)
      Life sciences
      Languages used
      English
      Key words
      model comparison; adaptive experiment design; Bayesian
      Abstract
      Comparing models facilitates testing different hypotheses regarding the computational basis of perception and action. Effective model comparison requires stimuli for which models make different predictions. Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods have limitations; a predetermined set may not contain stimuli that dissociate the models whereas random sampling may be inefficient. To overcome these limitations, we expanded the psi-algorithm (Kontsevich & Tyler, 1999) from estimating the parameters of a psychometric curve to distinguishing models. To test our algorithm, we applied it to two distinct problems. First, we investigated dissociating sensory noise models. We simulated ideal observers with different noise models performing a 2-afc task. Stimuli were selected randomly or using our algorithm. We found using our algorithm improved the accuracy of model comparison. We also validated the algorithm in subjects by inferring which noise model underlies speed perception. Our algorithm converged quickly to the model previously proposed (Stocker & Simoncelli, 2006), whereas if stimuli were selected randomly model probabilities separated slower and sometimes supported alternative models. Second, we applied our algorithm to a different problem; comparing models of target selection under body acceleration. Previous work found target choice preference is modulated by whole body acceleration (Rincon-Gonzalez et al., 2016). However, the effect is subtle making model comparison difficult. We show that selecting stimuli adaptively could have led to stronger conclusions in model comparison. We conclude that our technique is more efficient and more reliable than current methods of stimulus selection for dissociating models.
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      • Datasets [1237]
      • Faculty of Social Sciences [27123]
       
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