We propose a cognitive‐psychological model of linguistic intuitions about copredication statements. In copredication statements, like “The book is heavy and informative,” the nominal denotes two ontologically distinct entities at the same time. This has been considered a problem for standard truth‐conditional semantics. In this paper, we discuss two questions that have so far received less attention: What kinds of word representations and cognitive mechanisms are responsible for judgments about the felicitousness of copredication statements? Relatedly, why can similar copredication statements have different degrees of felicitousness? We first propose a cognitive‐computational model of copredication within the predictive processing framework. We then suggest that certain asymmetries in felicitousness judgments can be modeled in terms of a set of expectations that are influenced by higher‐order priors associated with discourse context and world knowledge.
We seem to learn and use concepts in a variety of heterogenous “formats”, including exemplars, prototypes, and theories. Different strategies have been proposed to account for this diversity. Hybridists consider instances in different formats to be instances of a single concept. Pluralists think that each instance in a different format is a different concept. Eliminativists deny that the different instances in different formats pertain to a scientifically fruitful kind and recommend eliminating the notion of a “concept” entirely. In recent years, hybridism has received the most attention and support. However, we are still lacking a cognitive-computational model for concept representation and processing that would underpin hybridism. The aim of this paper is to advance the understanding of concepts by grounding hybridism in a neuroscientific model within the Predictive Processing framework. In the suggested view, the different formats are not distinct parts of a concept but arise from different ways of processing a functionally unified representational structure.
According to predictive processing, an increasingly influential paradigm in cognitive science, the function of the brain is to minimize the prediction error of its sensory input. Conceptual engineering is the practice of assessing and changing concepts or word meanings. We contribute to both strands of research by proposing the first cognitive account of conceptual engineering, using the predictive processing framework. Our model reveals a new kind of implementation problem as prediction errors are only minimized if enough agents embrace conceptual changes. This problem can be overcome by emphasizing the importance of social norms and conceptual pluralism.
The aim of this commentary is to underpin Duffley's notion of a stable mental content that corresponds to the literal word meaning with a computationally plausible cognitive theory. Our approach is to investigate what these stable contents could be according to the so-called Predictive Processing architecture. We argue that recent advances in cognitive science can make at least two contributions to the debate. First, they can provide some underpinning of Duffley's ideas of a stable linguistic meaning
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