Cumulative link function models for Experiment 2.

Cumulative link function models for Experiment 2.</p

The predictions of the three theories: A: Causal Models (CM); B: Fully fleshed out mental models FM); C: Initial mental models (IM).

The dashed lines with square markers are the predictions for the diagnostic conditionals (EC) and the full lines with circular markers are the predictions for causal conditionals (CE) when the consequent is present (C) and when it is absent (NC). y = 0 is where participants say there is no change, y = 1 is where participants say the likelihood goes up, y = -1 is where participants say the likelihood goes down, using the response procedure in Ali et al’s (2011) Experiment 2. Intermediate values are possible because of averaging over many items and participants.</p

Four mental model interpretations of the conditional.

<p>Four mental model interpretations of the conditional.</p

Discounting and Augmentation in Causal Conditional Reasoning: Causal Models or Shallow Encoding? - Fig 1

<p>A. Common effect structure with a noisy-OR integration rule. B. Common cause structure.</p

Discounting with the noisy-OR (A) and augmentation with the noisy-AND (B) integration functions varying the probability of alternative causes <i>W</i>_<i>a</i>.

<p>For noisy-OR, unless <i>W_a</i> = 1, <i>Pr</i>(<i>r</i>|<i>p</i>, <i>q</i>) < <i>Pr</i>(<i>r</i>|<i>q</i>) and discounting is predicted. For noisy-AND, unless <i>W_a</i> = 1 <i>or</i> 0, <i>Pr</i>(<i>r</i>|<i>p</i>, <i>q</i>) > <i>Pr</i>(<i>r</i>|<i>q</i>) and augmentation is predicted. In these graphs <i>Pr</i>(<i>p</i>) = <i>Pr</i>(<i>r</i>) = .2 and <i>Wp</i> = <i>Wr</i> = <i>Wpr</i> = .9.</p

Materials for the pre-tests used in Experiments 1 and 2.

<p>Participants were shown the display in A and invited to draw in arrows for causal relations and rate the strength of the relation (SR) on a 1 to 4 scale, as in, for example, B.</p

Cumulative link function models for Experiment 1.

<p>Cumulative link function models for Experiment 1.</p

The results of Experiments 1 and 2.

<p>Expt. 1 (EC-AND, CE-OR), means and 95% confidence intervals (CIs) based on Model 3 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167741#pone.0167741.t001" target="_blank">Table 1</a>. Expt. 2 (EC-OR, CE-AND), means and 95% CIs based on Model 4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167741#pone.0167741.t002" target="_blank">Table 2</a>.</p

Cross-Cultural Differences in Informal Argumentation: Norms, Inductive Biases and Evidentiality. Supplementary Material

<p>Cross-cultural difference in argumentation may be explained by the use of different, some norms derive from, presumably universal, mathematical laws. This inconsistency can be resolved, by considering that some norms of argumentation, like Bayes theorem, are mathematical functions. Systematic variation</p> <p>in the inputs may produce culture-dependent inductive biases although the function remains invariant. This hypothesis was tested by fitting a Bayesian model to data on informal argumentation from Turkish and English cultures, which linguistically mark evidence quality differently. The experiment varied evidential marking and informant reliability in argumentative dialogues and revealed cross-cultural differences for both independent variables. The Bayesian model fitted the data from both cultures well but there were differences in the parameters consistent with culture-specific inductive biases.</p> <p>These findings are related to current controversies over the universality of the norms of reasoning and the role of normative theories in the psychology of reasoning.</p