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passt: An R implementation of the Probability Associator Time (PASS-T) model |
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12 November 2019 |
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The passt package is an R implementation of the Probability ASSociator Time (PASS-T) model, an artificial neural network designed to explain how humans make judgments of frequency and duration [@Titz2019]. The package was developed with two purposes in mind: (1) to provide a simple way to reproduce simulation results in judgments of frequency and duration as described in @Titz2019, and (2) to explore the PASS-T model by allowing users to run their own individual simulations.
The general idea of the original PASS model [@Sedlmeier1999;@Sedlmeier2002a] is that information about the frequency of events is naturally incorporated in artificial neural networks. If an object is presented repeatedly, the weights of the network change systematically. This way, the network is able to deduce the frequency of occurrence of events based on the final weights. Put simply, the artificial neural network produces a higher activation the more often a stimulus is presented.
As an extension of the PASS model, PASS-T is also able to process the presentation time of the stimuli. One can define how often and how long different objects are presented and test whether the network is able to make valid judgments of frequency and duration in retrospect. The principal aim of PASS-T is to explain empirical results that are found in studies with humans [@Titz2018]. Specifically, two empirical results are quite robust: (1) the correlation between exposure frequency and judgment is usually larger than between exposure duration and judgment (sometimes termed frequency primacy); and (2) the amount of attention that participants pay to the stimuli moderates effect sizes. These findings have been of some interest in cognitive psychology in the last decade [@Betsch2010;@Titz2019;@Titz2018;@Winkler2015;@Winkler2009a], although its roots go back half a century [@Hintzman1970a;@Hintzman1975;@Cooper1967] and touch upon different research areas such as memory, heuristics, and perception of magnitudes. PASS-T can be seen as a summary of some of this research, cast into a formal model. The R package passt is the first concrete implementation of PASS-T to make the model available to a wider audience.