Vision for the blind: visual psychophysics and blinded inference for decision models
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THEORETICAL REVIEW
Vision for the blind: visual psychophysics and blinded inference for decision models Philip L. Smith1 · Simon D. Lilburn1
© The Author(s) 2020
Abstract Evidence accumulation models like the diffusion model are increasingly used by researchers to identify the contributions of sensory and decisional factors to the speed and accuracy of decision-making. Drift rates, decision criteria, and nondecision times estimated from such models provide meaningful estimates of the quality of evidence in the stimulus, the bias and caution in the decision process, and the duration of nondecision processes. Recently, Dutilh et al. (Psychonomic Bulletin & Review 26, 1051–1069, 2019) carried out a large-scale, blinded validation study of decision models using the random dot motion (RDM) task. They found that the parameters of the diffusion model were generally well recovered, but there was a pervasive failure of selective influence, such that manipulations of evidence quality, decision bias, and caution also affected estimated nondecision times. This failure casts doubt on the psychometric validity of such estimates. Here we argue that the RDM task has unusual perceptual characteristics that may be better described by a model in which drift and diffusion rates increase over time rather than turn on abruptly. We reanalyze the Dutilh et al. data using models with abrupt and continuous-onset drift and diffusion rates and find that the continuous-onset model provides a better overall fit and more meaningful parameter estimates, which accord with the known psychophysical properties of the RDM task. We argue that further selective influence studies that fail to take into account the visual properties of the evidence entering the decision process are likely to be unproductive. Keywords Evidence accumulation · Diffusion model · Selective influence · Random dot motion The ability to make fast and accurate decisions about stimuli in the environment is the hallmark of all cognitive systems. In humans and nonhuman animals alike, evidence accumulation models like the diffusion model (Ratcliff, 1978; Ratcliff & McKoon, 2008) have provided insights into the processes that determine the speed and accuracy of decision-making (Smith & Ratcliff, 2004). The attraction of such models, for both basic and applied researchers, is that their parameters have meaningful psychological interpretations. When estimated from data, the model parameters can help researchers understand which processes are affected by experimental manipulations and, in individual differences settings, the parameters can be interpreted psychometrically
Philip L. Smith
[email protected] 1
Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, 3010, Australia
to help understand why one participant population differs from another (Ratcliff et al., 2015). The availability of thirdparty software packages for fitting the diffusion model to data, such as fast-dm (Voss & Voss, 2007), HDDM (Wiecki et al., 2013), and DMAT (Vandekerckhove
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