Testing Separability and Independence of Perceptual Dimensions with General Recognition Theory: A Tutorial and New R Package (grtools).

Fabian A Soto,Emily Zheng,Johnny Fonseca,F Gregory Ashby

doi:10.3389/fpsyg.2017.00696

Abstract

Determining whether perceptual properties are processed independently is an important goal in perceptual science, and tools to test independence should be widely available to experimental researchers. The best analytical tools to test for perceptual independence are provided by General Recognition Theory (GRT), a multidimensional extension of signal detection theory. Unfortunately, there is currently a lack of software implementing GRT analyses that is ready-to-use by experimental psychologists and neuroscientists with little training in computational modeling. This paper presents grtools, an R package developed with the explicit aim of providing experimentalists with the ability to perform full GRT analyses using only a couple of command lines. We describe the software and provide a practical tutorial on how to perform each of the analyses available in grtools. We also provide advice to researchers on best practices for experimental design and interpretation of results when applying GRT and grtools

Highlights

In perceptual science, an important amount of effort has been dedicated to understanding what aspects of stimuli are represented independently (e.g., Bruce and Young, 1986; Ungerleider and Haxby, 1994; Haxby et al, 2000; Kanwisher, 2000; Vogels et al, 2001; Stankiewicz, 2002; Op de Beeck et al, 2008)
A summary of the results of the model fitting and selection procedures can be obtained through the following command: FIGURE 7 | (A) Summary of the results of a model-based analysis of data from a 2 × 2 identification task using traditional general recognition theory with grtools, and (B) plot of the best-fitting model resulting from such analysis, where marginal distributions for a given dimension are plotted using either solid or dotted lines depending on the level of the opposite dimension, and the bottom-left insert is a plot of the observed response proportions against those predicted by the model
We have described an R package with functions that perform a variety of statistical analyses to determine independence and separability of perceptual dimensions according to GRT

Summary

INTRODUCTION

An important amount of effort has been dedicated to understanding what aspects of stimuli are represented independently (e.g., Bruce and Young, 1986; Ungerleider and Haxby, 1994; Haxby et al, 2000; Kanwisher, 2000; Vogels et al, 2001; Stankiewicz, 2002; Op de Beeck et al, 2008). Some properties are said to be processed “holistically” or “configurally,” which is equivalent to saying that they cannot be processed independently (e.g., Thomas, 2001; Richler et al, 2008; Mestry et al, 2012). Such holistic processing is important to understand perception (see Farah et al, 1998; Maurer et al, 2002; Richler et al, 2012). Determining whether perceptual properties are processed independently is an important goal in perception science, and tools to test independence should be widely available to experimental researchers

Testing Independence with grtools

GENERAL RECOGNITION THEORY

Forms of Independence Defined within GRT

Installing grtools

DISCUSSION

AUTHOR CONTRIBUTIONS