The lack of conceptual analysis within cognitive science results in multiple models of the same phenomena. However, these models incorporate assumptions that contradict basic structural features of the domain they are describing. This is particularly true about the domain of mathematical cognition. In this paper we argue that foundational theoretic aspects of psychological models for language and arithmetic should be clarified before postulating such models. We propose a means to clarify these foundational concepts by analyzing the distinctions between metric and (...) linguistic compositionality, which we use to assess current models of mathematical cognition. Our proposal is consistent with the scientific methodology that determines that careful conceptual analysis should precede theoretical descriptions of data. 2012 APA, all rights reserved). (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self‐sourced/endogenous and social sources, respectively. The current study investigated whether humans integrate the statistics (i.e., mean and variance) of endogenous perceptual and social information in a Bayes optimal way while estimating numerosities. Our results (...) demonstrated adjustment of initial estimations toward group means only when group estimations were more reliable (or “certain”), compared to participants’ endogenous metric uncertainty. Our results support Bayes optimal social conformity while also pointing to an implicit form of metacognition. (shrink)