<p><b>AB</b> The log-likelihood differences between the models, using the Difference model as a baseline. Note the small error bars, representing two standard-errors, given by running the algorithm 10 times, and each time using 1000 samples to estimate the model evidence (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004519#pcbi.1004519.e040" target="_blank">Eq (25)</a>). <b>CD</b> The posterior probability of the models, assuming a uniform prior. Left column, one response. Right column, two responses.</p
Difference in summed AIC (black) and BIC (brown) between the free-exponent model and other models fr...
<p>The figure shows the results for varying values of the autocorrelation parameter (<i>α</i>) where...
<p>We tested a class of alternative models of decision making which differ with respect to predictio...
<p><b>AB</b> Subjects are assumed to use each model with some probability. The coloured regions repr...
<p>(<b>A</b>) Posterior probability and (<b>B</b>) Bayes factors for the three models we compared, s...
A multi-level model allows the possibility of marginalization across levels in different ways, yield...
<p><b>a</b>: Each column represents a subject, divided by test group (all datasets include a Gaussia...
<p>Posterior probabilities against the posterior differences between <i>μ</i><sub>1</sub> and <i>μ</...
<p>The 13 MMN models were compared by their posterior probability given the trial-wise MMN amplitude...
<p>p<sub>C</sub>, prior common-source probability; σ<sub>P</sub>, standard deviation of the spatial ...
Columns show different versions of the task. Rows show model fits for (A) the distance model (unscal...
<p>Train (a) and test (b) log likelihood of the negative control data for the two proposed models, a...
<p>Each panel shows the marginal posterior distributions over a single parameter for each subject an...
<p>(top) Posterior model probability (see color bar) for each subject. For an exact description of e...
<p>(top) Posterior model probability (see color bar) for each subject. For the exact description of ...
Difference in summed AIC (black) and BIC (brown) between the free-exponent model and other models fr...
<p>The figure shows the results for varying values of the autocorrelation parameter (<i>α</i>) where...
<p>We tested a class of alternative models of decision making which differ with respect to predictio...
<p><b>AB</b> Subjects are assumed to use each model with some probability. The coloured regions repr...
<p>(<b>A</b>) Posterior probability and (<b>B</b>) Bayes factors for the three models we compared, s...
A multi-level model allows the possibility of marginalization across levels in different ways, yield...
<p><b>a</b>: Each column represents a subject, divided by test group (all datasets include a Gaussia...
<p>Posterior probabilities against the posterior differences between <i>μ</i><sub>1</sub> and <i>μ</...
<p>The 13 MMN models were compared by their posterior probability given the trial-wise MMN amplitude...
<p>p<sub>C</sub>, prior common-source probability; σ<sub>P</sub>, standard deviation of the spatial ...
Columns show different versions of the task. Rows show model fits for (A) the distance model (unscal...
<p>Train (a) and test (b) log likelihood of the negative control data for the two proposed models, a...
<p>Each panel shows the marginal posterior distributions over a single parameter for each subject an...
<p>(top) Posterior model probability (see color bar) for each subject. For an exact description of e...
<p>(top) Posterior model probability (see color bar) for each subject. For the exact description of ...
Difference in summed AIC (black) and BIC (brown) between the free-exponent model and other models fr...
<p>The figure shows the results for varying values of the autocorrelation parameter (<i>α</i>) where...
<p>We tested a class of alternative models of decision making which differ with respect to predictio...