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

Soto, Fabián A.; Zheng, Emily; Fonseca, Johnny; Ashby, F. Gregory

doi:10.3389/fpsyg.2017.00696

Cited by 20 publications

(41 citation statements)

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“…In particular, the specific morphed face dimensions illustrated in Figure 1, and used in the present study, have been previously shown to be perceptually integral ahead of training according to a variety of tests (see . Evidence of the integrality of those specific dimensions comes from a strong Garner interference effect (Garner, 1974), a failure of marginal accuracy invariance, a failure of response time invariance, and a model-based analysis of data using general recognition theory (for reviews of these tests, see Soto et al, 2017). That is only the evidence showing integrality in the specific stimuli used here.…”

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confidence: 92%

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Soto

Ashby

2019

Psychological Research

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Humans learn categorization rules that are aligned with separable dimensions through a rulebased learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than one hour) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rulebased system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.

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confidence: 92%

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Soto

Ashby

2019

Psychological Research

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“…There are two reasons to believe that the perceptual changes observed after categorization training with morphed faces and other objects are not due to readout mechanisms. First, the effects of categorization are not task-specific, but they transfer from a categorization task to unrelated tasks, both in psychophysical (e.g., Folstein et al, 2012b;Goldstone & Steyvers, 2001a;Soto et al, 2017) and neuroimaging (Brants et al, 2016;Folstein et al, 2013) studies. Readout is usually considered to be taskspecific, as different tasks are solved optimally using different sources of information.…”

Section: What About Changes In Readout?mentioning

confidence: 99%

“…Acquired equivalence is observed as a decrease in discriminability along the category-irrelevant dimension after categorization training. Relatedly, categorization training increases the separability or invariance of the category-relevant dimension , which means that changes in an irrelevant dimension do not interfere with perception of the category-relevant dimension, according to a variety of tests from multidimensional signal detection theory Soto et al, 2017). Category-Relevant Dimension Category-Irrelevant Dimension Category Boundary Figure 1.…”

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confidence: 99%

“…An important piece of evidence in favor of it is that the directions in stimulus space that become perceptually differentiated after categorization can be arbitrarily chosen by the experimenter (Folstein et al, 2012b;Goldstone & Steyvers, 2001a). Dimension creation would also explain why the effects of categorization training can be measured using orthogonal tasks, both through psychophysics (e.g., Folstein et al, 2012b;Goldstone & Steyvers, 2001a;Soto et al, 2017) and neuroimaging (Brants et al, 2016;Folstein et al, 2013), and why in the latter case effects are observed in high-level visual cortex. An alternative interpretation, which has received less attention in the literature, is that categorization training produces alterations in already-existing visual representations, simply enhancing the pre-learning selectivity of populations of neurons that happened to provide useful information for the categorization task (Brants et al, 2016).…”

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confidence: 99%

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Adaptation aftereffects reveal how categorization training changes the encoding of face identity

Soto¹,

Escobar²,

Salan³

2020

Preprint

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Previous research suggests that learning to categorize faces along a novel dimension changes the perceptual representation of such dimension, increasing its discriminability, its invariance, and the information used to identify faces varying along the dimension. A common interpretation of these results is that categorization training promotes the creation of novel dimensions, rather than simply the enhancement of already-existing representations. Here, we trained a group of participants to categorize faces that varied along two morphing dimensions, one of them relevant to the categorization task and the other irrelevant to the task. An untrained group did not receive such categorization training. In three experiments, we used face adaptation aftereffects to explore how categorization training changes the encoding of face identities at the extremes of the category-relevant dimension, and whether such training produces encoding of the category-relevant dimension as a preferred direction in face space. The pattern of results suggests that categorization training enhances the already-existing norm-based coding of face identity, rather than creating novel category-relevant representations. We formalized this conclusion in a model that explains the most important results in our experiments and serves as a working hypothesis for future work in this area.

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“…Fitting a GRT-wIND model and testing for perceptual separability, perceptual independence, and decisional separability can be done in R with the grtools package (R Core Team, 2014;Soto, Zheng, Fonseca, & Ashby, 2016; a general overview of GRT, as well as the specifics of the GRT-wIND analysis, are covered in greater detail in the Appendix). The model was fit using the grt_wind_fit_parallel() function with 200 repetitions (i.e., the model is fit 200 times and the function returns the parameters of the model with the best fit).…”

Section: Resultsmentioning

confidence: 99%

Connecting Crossmodal Perception and Garner's Integrality: An Investigation into Crossmodal Correspondences using General Recognition Theory

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

Cited by 20 publications

References 46 publications

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Adaptation aftereffects reveal how categorization training changes the encoding of face identity

Connecting Crossmodal Perception and Garner's Integrality: An Investigation into Crossmodal Correspondences using General Recognition Theory

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