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Strößner, Corina; Schurz, Gerhard – Cognitive Science, 2020
The modifier effect refers to the fact that the perceived likelihood of a property in a noun category is diminished if the noun is modified. For example, "Pigs live on farms" is rated as more likely than "Dirty pigs live on farms." The modifier effect has been demonstrated in many studies, but the underlying cognitive…
Descriptors: Abstract Reasoning, Pragmatics, Nouns, Form Classes (Languages)
Mayrhofer, Ralf; Waldmann, Michael R. – Cognitive Science, 2016
Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when…
Descriptors: Causal Models, Bayesian Statistics, Inferences, Probability
Bonnefon, Jean-Francois; Sloman, Steven A. – Cognitive Science, 2013
The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘"if 'p' then 'q'’" statements where the realization of "p" or "q" or…
Descriptors: Logical Thinking, Inferences, Influences, Probability
Pearl, Judea – Cognitive Science, 2013
Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the "possible worlds" account of counterfactuals, this "structural" model enjoys the advantages of representational economy,…
Descriptors: Causal Models, Cognitive Science, Sentences, Inferences
Fenton, Norman; Neil, Martin; Lagnado, David A. – Cognitive Science, 2013
A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs…
Descriptors: Networks, Bayesian Statistics, Persuasive Discourse, Models