Data from one subject, S1, for a 3-way MLCM experiment in which the element spacing, element size and inter-element jitter of dot patterns were systematically varied. Pairs of stimuli were presented with the 3 attributes varied independently, and the subject judged which of the pair appeared more regular.
A data frame with 4140 observations on the following 7 variables.
a numeric vector taking values 0/1 depending on whether the subject chose the first or second stimulus as more regular.
a numeric vector indicating which of 3 levels of average element spacing for the first stimulus.
a numeric vector indicating which of 3 levels of average element spacing for the second stimulus.
a numeric vector indicating which of 3 levels of element size for the first stimulus.
a numeric vector indicating which of 3 levels of element size for the second stimulus.
a numeric vector indicating which of 5 levels of element jitter for the first stimulus.
a numeric vector indicating which of 5 levels of element jitter for the second stimulus.
The authors describe the data sets as follows. Each participant completed 4140 experimental trials, as shown in the 4140 rows: Column 1: response value. 0: participant chose stimulus 1 of a pair as more regular. 1: participant chose stimulus 2 as more regular. Column 2: element spacing level (1–3) for stimulus 1. Column 3: element spacing level (1–3) for stimulus 2. Column 4: element size level (1–3) for stimulus 1. Column 5: element size level (1–3) for stimulus 2. Column 6: element jitter level (1–5) for stimulus 1. Column 7: element jitter level (1–5) for stimulus 2. Note, the trial order was randomized during the experiment.
Additional data files for the other 5 participants in the study can be found in csv format files at doi: 10.1371/journal.pcbi.1008802. With respect to the original file, the current data set was modified to include column names.
A fuller analysis of this data set can be found in the examples at
Sun H.-C., St-Amand D., Baker C. L. Jr, Kingdom F. A. A. (2021), Visual perception of texture regularity: Conjoint measurements and a wavelet response-distribution model. PLoS Computational Biology 17(10), e1008802. doi: 10.1371/journal.pcbi.1008802.
Knoblauch K., Maloney L. T. (2012) Modeling Psychophysical Data in R, Springer Science \& Business Media, doi: 10.1007/978-1-4614-4475-6.
data(Texture) # additive model fit Texture.mlcm <- mlcm(Texture) summary(Texture.mlcm) plot(Texture.mlcm, type = "b")
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