<p><b>(A)</b> Log-group Bayes factors (smaller is better) relative to the simplest models (Model 1) based on BIC for the nine models tested (<i>left part</i>) and histogram of best-performing models per participant (<i>right part</i>). The models belonged to three families. In the first family, models were based on various combinations of weighting parameters for the first three statistical moments (i.e., EV, Var, and Skw). The second family comprised two rank-dependent utility models in which probabilities were weighted according to non-linear weighting functions (Prelec-I and Prelec-II). In the third family, models were based on the homeostatic principle of minimizing p<sub>starve</sub> in addition to combinations of weighting parameters ...
<p>(a) Bayes factors for the two-variable models (black) and for the three-variable models (violet...
Research that seeks to compare two predictive models requires a thorough statistical approach to dra...
<p>The relative measure of model performance, i.e. the per-bin log-likelihood Δ<i>p</i> (see <a href...
<p>(<i>A</i>) Bayesian Information Criterion scores for each model (a low score is better). Models b...
<p>We tested a class of alternative models of decision making which differ with respect to predictio...
BF10 = Bayes Factor (where 10 refers to the alternative hypothesis, H1, relative to the null hypothe...
Bayes provides a better fit, but both models have large deviations from the data. Left and middle co...
<p><b>a</b>: Each column represents a subject, divided by test group (all datasets include a Gaussia...
<p>The table shows the log likelihoods (A, B) and the Akaike information criterion (D) comparing thr...
<p>Comparison of an additional model including frame-specific parameters: Relative log-group Bayes f...
<p>The upper panel shows a typical simulated dataset under each of three scenarios (see text), but w...
<p>The observed λ (mean; 95% HPD) were contrasted with values expected under the hypotheses of no ph...
<p>Lower DIC is better, and higher Bayes factor is better, so Cue Mixing has the best score in both ...
<p><b>A</b>: Bayesian Information Criterion (), summed across participants (<i>N</i> = 25) for the a...
(A) Comparison of the Bayesian information criterion (BIC) relative to the baseline model. Negative ...
<p>(a) Bayes factors for the two-variable models (black) and for the three-variable models (violet...
Research that seeks to compare two predictive models requires a thorough statistical approach to dra...
<p>The relative measure of model performance, i.e. the per-bin log-likelihood Δ<i>p</i> (see <a href...
<p>(<i>A</i>) Bayesian Information Criterion scores for each model (a low score is better). Models b...
<p>We tested a class of alternative models of decision making which differ with respect to predictio...
BF10 = Bayes Factor (where 10 refers to the alternative hypothesis, H1, relative to the null hypothe...
Bayes provides a better fit, but both models have large deviations from the data. Left and middle co...
<p><b>a</b>: Each column represents a subject, divided by test group (all datasets include a Gaussia...
<p>The table shows the log likelihoods (A, B) and the Akaike information criterion (D) comparing thr...
<p>Comparison of an additional model including frame-specific parameters: Relative log-group Bayes f...
<p>The upper panel shows a typical simulated dataset under each of three scenarios (see text), but w...
<p>The observed λ (mean; 95% HPD) were contrasted with values expected under the hypotheses of no ph...
<p>Lower DIC is better, and higher Bayes factor is better, so Cue Mixing has the best score in both ...
<p><b>A</b>: Bayesian Information Criterion (), summed across participants (<i>N</i> = 25) for the a...
(A) Comparison of the Bayesian information criterion (BIC) relative to the baseline model. Negative ...
<p>(a) Bayes factors for the two-variable models (black) and for the three-variable models (violet...
Research that seeks to compare two predictive models requires a thorough statistical approach to dra...
<p>The relative measure of model performance, i.e. the per-bin log-likelihood Δ<i>p</i> (see <a href...