Categorization training increases the perceptual separability of novel dimensions

Soto, Fabián A.; Ashby, F. Gregory

doi:10.1016/j.cognition.2015.02.006

Cited by 25 publications

(26 citation statements)

References 72 publications

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“…For example, face features are thought to be encoded through monotonic tuning functions (e.g., sigmoidal; see [5,45,43]). Using computational modeling and visual adaptation, it has been found that the effects of categorization on perception of face identities along the category-relevant dimension [59,60,61] can be best explained using a specific gain mechanism [45]. It is currently unknown exactly how the complex shape and object stimuli used in some studies are encoded, but encoding that is different from that of orientation might be at the heart of the results obtained with such dimensions.…”

Section: Re-interpreting Results In the Literaturementioning

confidence: 99%

Changes within neural population codes can be inferred from psychophysical threshold studies

Hays

Soto

2020

Preprint

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The use of population encoding models has come to dominate the study of human visual neuroscience, serving as a primary tool for making inferences about neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population responses via inverted encoding modeling. Recent research suggests that this approach may be prone to identifiability problems, with multiple mechanisms of encoding change producing similar changes in the estimated population responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. However, computational work aimed at determining to what extent different mechanisms can be differentiated using psychophysics is lacking. Here, we used simulation to explore exactly which of a number of changes in neural population codes could be differentiated from observed changes in psychophysical thresholds. Eight mechanisms of encoding change were under study, chosen because they have been proposed in the previous literature as mechanisms for improved task performance (e.g., due to attention or learning): specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. We simulated psychophysical thresholds as a function of both external noise (TvN curves) or stimulus value (TvS curves) for a number of variations of each one of the models. With the exception of specific gain and specific tuning, all studied mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to inverted encoding modeling, and provide strong constraints on inferences based on the latter. We use our results to provide recommendations for interested researchers and to re-interpret previous psychophysical data in terms of mechanisms of encoding change.

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Section: Re-interpreting Results In the Literaturementioning

confidence: 99%

Changes within neural population codes can be inferred from psychophysical threshold studies

Hays

Soto

2020

Preprint

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“…Their research suggested that successful discrimination depends on matching incoming patterns with those stored in memory. Furthermore, according to Soto and Ashby's () research on category learning, the process of storing odor activity patterns in memory is implicit. One strategy that has been shown to help such implicit learning involves training participants by exposing them to the odor mixtures of interest and providing immediate feedback on the correctness of their responses (Ell et al, ).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of omission testing as a method for identifying important odorants in a mixture

Weiß

Vickers

2018

Journal of Sensory Studies

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Our goal was to determine whether people could learn to discriminate between a five‐compound odor mixture with all compounds at the same perceived intensity and that same mixture with any one of the five compounds removed. We first selected, for each panelist separately, concentrations of each of four compounds that were equivalent in intensity to 5 ppm acetylpropionyl. We then constructed, for each participant, a mixture of the five compounds matched in intensity. Panelists then participated in 20 sessions consisting of a series of A‐not‐A tests with corrective feedback. Panelists, as a group, were able to discriminate between the full five‐compound mixture and mixtures with any one of the five compounds removed after 60 trials. However, low d′ values indicated that the mixtures were extremely difficult to discriminate. Only 60% of the panelists were able to discriminate between the complete mixture and the n‐1 mixtures even after 200 trials. Practical applications When compounds are at a similar perceived intensity, using omission testing, as a method for determining the important odorants in a mixture is very likely to indicate that compounds are not important, when, in fact, they are.

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“…Our package focuses heavily on analyses of the 2 × 2 design, which is easier to implement and can be applied more broadly than larger designs. Still, we expect that future releases of grtools will include analyses for larger designs, as well as better algorithms for the estimation of the Hessian in Wald tests (Soto et al, 2015), tests to compare the results of two groups (Soto and Ashby, 2015), and model-based analyses of response time data (Maddox and Ashby, 1996). …”

Section: Discussionmentioning

confidence: 99%

“…Violations of separability are common, and features of your task and design might produce such violations. Factors such as training in a categorization task can influence PS as well (Soto and Ashby, 2015). For these reasons, it is a good idea to include a group that would serve as a benchmark, if the design does not already involve more than one group.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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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

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Categorization training increases the perceptual separability of novel dimensions

Cited by 25 publications

References 72 publications

Changes within neural population codes can be inferred from psychophysical threshold studies

Changes within neural population codes can be inferred from psychophysical threshold studies

Evaluation of omission testing as a method for identifying important odorants in a mixture

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

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