Rapid learning and unlearning of predicted sensory delays in self-generated touch
- Karolinska Institutet, Sweden
Figures
Figure 1
Experimental Hypotheses, Procedures and Psychophysical Analysis.
(a) (Left) When the internal model is tuned to 0 ms as in natural situations, the probability distribution for the occurrence of touch on the left index finger (approximated as a normal distribution …
Figure 2 with 1 supplement
Results from the somatosensory attenuation experiments.
(a) Mean PSE (± s.e.m.) for each condition. Only the important planned comparisons are displayed (*p<0.05, **p<0.01, ***p<0.001, n.s. not significant). (b) Scatterplot of the attenuation shifts in …
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Figure 2—source data 1
Mean PSE (± s.e.m.) for each condition.
- https://doi.org/10.7554/eLife.42888.005
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Figure 2—source data 2
Attenuation shifts in immediate touch (unlearning) and delayed touch (learning).
- https://doi.org/10.7554/eLife.42888.006
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Figure 2—source data 3
Model parameters for the group fits.
- https://doi.org/10.7554/eLife.42888.007
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Figure 2—source data 4
Mean PSE (± s.e.m.) as a function of exposure trials.
- https://doi.org/10.7554/eLife.42888.008
Figure 2—figure supplement 1
Individual Fits of all participants in Experiment 1.
The figure shows the fits of the 30 participants per condition. The horizontal gray dashed line indicates the 50% point of psychometric functions, and the vertical gray dashed line indicates the …
Figure 3
Methods and Results from the tickling experiment.
(a) Participants were instructed to move the stylus of a robot with their right hand (master robot). The stylus was free to move along a sinusoidal path within a 3D-printed mold, thus constraining …
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Figure 3—source data 1
Median and interquartile range (IQR) for the frequency of a 150 ms delayed touch being perceived as more ticklish than a 0 ms touch, per condition.
- https://doi.org/10.7554/eLife.42888.010
Tables
Author response table 1
Experiment 1:
https://doi.org/10.7554/eLife.42888.012| PSE comparisons | Original p – value | Corrected p-value |
|---|---|---|
| [0, 0] vs. baseline | p < 0.001 (0.0000676947) | p < 0.001 (0.0002369315) |
| [0, 100] vs. baseline | p = 0.9032264724 | p = 0.9032264724 |
| [0, 0] vs. [0, 100] | p < 0.001 (0.0000004712) | p < 0.001 (0.0000032987) |
| [0, 0] vs. [100, 0] | p = 0.0107374617 | p = 0.0187905579 |
| [100, 0] vs. baseline | p = 0.0701934955 | p = 0.0818924114 |
| [0, 100] vs. [100, 100] | p = 0.0026044238 | p = 0.0060769889 |
| [100, 100] vs. baseline | p = 0.0570803400 | p = 0.0799124760 |
Author response table 2
Experiment 2:
https://doi.org/10.7554/eLife.42888.013| PSE comparisons for [100 ms, 0 ms] | Original p – value | Corrected p-value |
|---|---|---|
| 0 vs. 50 trials | p = 0.1815470400 | p = 0.1815470400 |
| 0 vs. 200 trials | p = 0.0282742723 | p = 0.0424114084 |
| 0 vs. 500 trials | p = 0.0135470857 | p = 0.0406412571 |
| PSE comparisons for [100 ms, 100 ms] | Original p – value | Corrected p-value |
| 0 vs. 50 trials | p = 0.0098760710 | p = 0.0136271106 |
| 0 vs. 200 trials | p = 0.0136271106 | p = 0.0136271106 |
| 0 vs. 500 trials | p = 0.0044865222 | p = 0.0134595666 |
Author response table 3
| Comparisons | Estimate | Inferior CI | Superior CI |
|---|---|---|---|
| [0 ms, 0 ms] versus baseline | -0.124 | -0.158 | -0.093 |
| [0 ms, 100 ms] versus baseline | 0.020 | -0.008 | 0.057 |
| [0 ms, 0 ms] versus [0 ms, 100 ms] | -0.144 | -0.185 | -0.110 |
| [0 ms, 0 ms] versus [100 ms, 0 ms] | -0.066 | -0.104 | -0.027 |
| [0 ms, 100 ms] versus [100 ms, 100 ms] | 0.087 | 0.051 | 0.125 |
| [100 ms, 0 ms] versus baseline | -0.058 | -0.093 | -0.025 |
| [100 ms, 100 ms] versus baseline | -0.067 | -0.098 | -0.032 |
Additional files
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Transparent reporting form
- https://doi.org/10.7554/eLife.42888.011
