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Estimation of Separable Representation in Psychophysical Experiments

Authors: Bernasconi, M.; Choirat, C.; SERI, RAFFAELLO;

Estimation of Separable Representation in Psychophysical Experiments

Abstract

Studying how individuals compare two given quantitative stimuli, say d1 and d2, is a fundamental problem. One very common way to address it is through ratio magnitude estimation, that is to ask individuals not to give values to d1 and d2 but rather to give their estimates of the ratio p = d1/d2 . Several psychophysical theories (the most known being Stevens’ power-law) claim that this ratio cannot be known directly and that there are cognitive distortions on the apprehension of the different quantities. These theories result in the so-called separable representations (which include Stevens’ model). In this paper, we propose a general statistical framework that allows for testing in a rigorous way whether the separable representation theory is grounded or not. We conclude in favour of it, but strongly reject Stevens’ model. As a byproduct, we provide estimates of the psychometric functions of interest.

Countries
Italy, United States, Italy
Keywords

Psychophysical experiments; Steven’s model; Separable representation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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