Guy Dove’s book is about the puzzle of how our minds can represent and process abstract concepts. Abstract concepts have posed a problem for the influential view of the embodied mind and grounded c...

Predictive Processing (PP) is an increasingly influential neurocognitive-computational framework. PP research has so far focused predominantly on lower level perceptual, motor, and various psychological phenomena. But PP seems to face a “scale-up challenge”: How can it be extended to conceptual thought, language, and other higher cognitive competencies? Compositionality, arguably a central feature of conceptual thought, cannot easily be accounted for in PP because it is not couched in terms of classical symbol processing. I argue, using the example of language, that (...) there is no strong reason to think that PP cannot be scaled up to higher cognition. I suggest that the tacitly assumed common-sense conception of language as Generative Grammar (“folk linguistics”) and its notion of composition leads to the scale-up concerns. Fodor’s Language of Thought Hypothesis (LOTH) plays the role of a cognitive computational paradigm for folk linguistics. Therefore, we do not take LOTH as facing problems with higher cognition, at least with regard to compositionality. But PP can plausibly play the role of a cognitive-computational paradigm for an alternative conception of language, namely Construction Grammar. If Construction Grammar is a plausible alternative to folk linguistics, then PP is not in a worse position than LOTH. (shrink)

How are new Bayesian hypotheses generated within the framework of predictive processing? This explanatory framework purports to provide a unified, systematic explanation of cognition by appealing to Bayes rule and hierarchical Bayesian machinery alone. Given that the generation of new hypotheses is fundamental to Bayesian inference, the predictive processing framework faces an important challenge in this regard. By examining several cognitive‐level and neurobiological architecture‐inspired models of hypothesis generation, we argue that there is an essential difference between the two types of (...) models. Cognitive‐level models do not specify how they can be implemented in brains and include structures and assumptions that are external to the predictive processing framework. By contrast, neurobiological architecture‐inspired models, which aim to better resemble brain processes, fail to explain important capacities of cognition, such as categorization and few‐shot learning. The “scaling‐up” challenge for proponents of predictive processing is to explain the relationship between these two types of models using only the theoretical and conceptual machinery of Bayesian inference. (shrink)