<p><b>A</b>) Learning the mean of a perturbation during the first perturbation block for a typical subject. Solid lines denote exponential fits. <b>B</b>) Learning the mean of a perturbation during the first perturbation block across subjects (n = 8, n = 4, and n = 4 for the standard deviations, of 0°, 4° and 12°, respectively). Thick lines are average (±SD) across subjects considering bins of 5 trials. Thin lines are exponential fits. Grey dashed lines indicate the absolute average of the imposed perturbation (30°). <b>C</b>) Learning the mean of a perturbation considering all blocks for each variance condition. Thick lines denote medians across subjects and trials in a trial window of 5 trials. Shaded area is 95% confidence region (boots...
<p>Top row describes the distribution of target width over the course of trials. In the first experi...
Average fluctuations in Φ and surprisal on trial time split into trials with negative (purple, coef....
What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our ...
<p><b>A</b>) Baseline and generalization of the mean (±SEM) of a perturbation for a typical subject ...
<p><b>A</b> displays the variance (group mean and standard error of the mean, SEM) calculated in the...
<p><i>A:</i> Group mean residual error against mean perturbation size. Error bars are SEM between su...
<p>All experimental trials are represented in the line graph (bins 1–50), where each point represent...
<p>(<b>A</b>) <b>Average Trajectories.</b> We show the average empirical trajectories across subject...
<p>Endpoint variance (Eq. 28) is plotted as a function of the learning rates in the Extent and Direc...
A) average surprisal over trial time. B) average surprisal over trial time, centered around the firs...
<p>Mean timing coefficients of variation and fit of power law for the first 24 learning trials at th...
<p><i>Very top:</i> Experimental distributions for Short Uniform (red) and Long Uniform (green) bloc...
<p>Each circle represents the slope of the mean residual error (<a href="http://www.plosone.org/arti...
The solid line is the mean of 500 particles, and dashed lines show ± the standard deviation. The red...
<p>Between-groups, the Learners in comparison to the Non-Learners did not demonstrate a difference i...
<p>Top row describes the distribution of target width over the course of trials. In the first experi...
Average fluctuations in Φ and surprisal on trial time split into trials with negative (purple, coef....
What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our ...
<p><b>A</b>) Baseline and generalization of the mean (±SEM) of a perturbation for a typical subject ...
<p><b>A</b> displays the variance (group mean and standard error of the mean, SEM) calculated in the...
<p><i>A:</i> Group mean residual error against mean perturbation size. Error bars are SEM between su...
<p>All experimental trials are represented in the line graph (bins 1–50), where each point represent...
<p>(<b>A</b>) <b>Average Trajectories.</b> We show the average empirical trajectories across subject...
<p>Endpoint variance (Eq. 28) is plotted as a function of the learning rates in the Extent and Direc...
A) average surprisal over trial time. B) average surprisal over trial time, centered around the firs...
<p>Mean timing coefficients of variation and fit of power law for the first 24 learning trials at th...
<p><i>Very top:</i> Experimental distributions for Short Uniform (red) and Long Uniform (green) bloc...
<p>Each circle represents the slope of the mean residual error (<a href="http://www.plosone.org/arti...
The solid line is the mean of 500 particles, and dashed lines show ± the standard deviation. The red...
<p>Between-groups, the Learners in comparison to the Non-Learners did not demonstrate a difference i...
<p>Top row describes the distribution of target width over the course of trials. In the first experi...
Average fluctuations in Φ and surprisal on trial time split into trials with negative (purple, coef....
What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our ...