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“Intentional binding” refers to the finding that people judge voluntary actions and their effects as having occurred closer together in time than two passively observed events. If this effect reflects subjectively compressed time, then time-dependent visual illusions should be altered by voluntary initiation. To test this hypothesis, we showed participants displays that result in particular motion illusions when presented at short interstimulus intervals (ISIs). In Experiment 1 we used apparent motion, which is perceived only at very short ISIs; Experiments 2a and 2b used the Ternus display, which results in different motion illusions depending on the ISI. In support of the time compression hypothesis, when they voluntarily initiated the displays, people persisted in seeing the motion illusions associated with short ISIs at longer ISIs than had been the case during passive viewing. A control experiment indicated that this effect was not due to predictability or increased attention. Instead, voluntary action altered motion illusions, despite their purported cognitive impenetrability.
Atten Percept Psychophys

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  • Integrating prospective and retrospective cues to the sense of agency: a multi-study investigation

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Summary

—  This is a part of a series of blog posts discussing Bayesian estimation of Signal Detection models. In this post, I describe how to estimate the equal variance Gaussian SDT model’s parameters for multiple participants simultaneously, using Bayesian generalized linear and nonlinear hierarchical models. I provide a software implementation in R.

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—  Signal Detection Theory (SDT) is a common framework for modeling memory and perception. Calculating point estimates of equal variance Gaussian SDT parameters is easy using widely known formulas. More complex SDT models, such as the unequal variance SDT model, require more complicated modeling techniques. These models can be estimated using Bayesian (nonlinear and/or hierarchical) regression methods, which are sometimes difficult to implement in practice. In this post, I describe how to estimate the equal variance Gaussian SDT model’s parameters for a single participant with a Generalized Linear Model, and a nonlinear model. I describe the software implementation in R.

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Summary

—  Don’t set R’s working directory from an R script.

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