Nonlinear relation of the comparison distance effect and the Weber fraction
Created by Attila Krajcsi at 2019-01-25 10:15:55, updated at 2019-02-16 10:13:36
Short description By using simulations, the work investigates whether the slope of the distance effect of a comparison task is linearly related to the Weber fraction. It founds that the relation is an inverted j-shaped function. It means that depending on the specific parameters, increasing distance effect slope might mean either better, worse or equivalent Weber fraction.
Reference Chesney, D. (2018). Numerical distance effect size is a poor metric of approximate number system acuity. Attention, Perception, & Psychophysics, 80(5), 1057–1063. https://doi.org/10.3758/s13414-018-1515-x
Authors Dana Chesney
Comment by Attila Krajcsi (2019-01-25 10:42:58, updated at 2019-02-16 10:13:36)
I liked this work a lot: The idea is simple, but the result is non-intuitive, and its consequences are essential. The results were so non-intuitive for me that first I didn’t want to believe it, so I made a similar script in Python to check the simulation, and I found the very same results. I think everyone working with distance effect as an index for individual differences and calculating correlations (especially in meta-analyses) should consider this result in their work, because without these considerations one might miss the real phenomena or can get a biased one.
Disclaimer: I was one of the reviewers for this paper.
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