Christian Michel

The debate about the nature of the representational format of concepts seems to have reached an impasse. The debate faces two fundamental problems. Firstly, amodalists (i.e., those who argue that concepts are represented by amodal symbols) and modalists (i.e., those who see concepts as involving crucially representations including sensorimotor information) claim that the same empirical evidence is compatible with their views. Secondly, there is no shared understanding of what a modal or amodal format amounts to. Both camps recognize that the two formats play essential roles in higher cognition, leading to an increasing number of hybrid proposals. In this paper, I argue that the existence of those fundamental problems should make us suspicious about a modal/amodal dichotomy. Also, I suggest that hybrid approaches, as they currently stand, do not provide suitable solutions to the impasse. Instead, we should overcome the dichotomy and treat the modal/amodal distinction as a graded phenomenon. I illustrate this hypothesis with an example of a cognitive-computational model of concepts based on the Predictive Processing framework.

Most discussions frame the Liar Paradox as a formal logical-linguistic puzzle. Attempts to resolve the paradox have focused very little so far on aspects of cognitive psychology and processing, because semantic and cognitive-psychological issues are generally assumed to be disjunct. I provide a motivation and carry out a cognitive-computational treatment of the liar paradox based on a cognitive-computational model of language and conceptual knowledge within the Predictive Processing framework. I suggest that the paradox arises as a failure of synchronization between two ways of generating the liar situation in two different PP sub-models, one corresponding to language processing and the other to the processing of meaning and world-knowledge. In this way, I put forward the claim that the liar sentence is meaningless but has an air of meaningfulness. I address the possible objection that the proposal violates the Principle of Unrestricted Compositionality, which purportedly regulates the conceptual competence of thinkers.

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.

Most discussions frame the Liar Paradox as a formal logical-linguistic puzzle. Attempts to resolve the paradox have focused very little so far on aspects of cognitive psychology and processing, because semantic and cognitive-psychological issues are generally assumed to be disjunct. I provide a motivation and carry out a cognitive-computational treatment of the liar paradox based on a model of language and conceptual knowledge within thePredictive Processing(PP) framework. I suggest that the paradox arises as a failure of synchronization between two ways of generating the liar situation in two different (idealized) PP sub-models, one corresponding to language processing and the other to the processing of meaning and world-knowledge. In this way, I put forward the claim that the liar sentence is meaningless but has an air of meaningfulness. I address the possible objection that the proposal violates thePrinciple of Unrestricted Compositionality, which purportedly regulates the conceptual competence of thinkers.

Autistic individuals are commonly said – and also consider themselves – to be excessively literalist, in the sense that they tend to prefer literal interpretations of words and utterances. This literalist bias seems to be fairly specific to autism and still lacks a convincing explanation. In this paper we explore a novel hypothesis that has the potential to account for the literalist bias in autism. We argue that literalism results from an atypical functioning of the predictive system: specifically, an atypical balance between predictions and error signals in language processing may make individuals more uncertain about their own predictions. Such uncertainty is then often resolved by resorting to the safest interpretation, that is, the literal one. We start by reviewing existing explanations of other autistic traits that appeal to predictive processing. We then apply these insights to language, by showing that predictions play a key role in everyday comprehension and that a low level of confidence in one’s own predictions is likely to escalate comprehension difficulties. Finally, we take a deeper look at non-literal uses of language by discussing the case of metaphors, to illustrate how a predictive processing account offers a promising explanation of the literalist bias in autism.

The United Nations Educational, Scientific and Cultural Organization recently declared transhumance pastoralism as an Intangible Cultural Heritage of Humanity. The notion of heritage seeks to recognize the culture behind the seasonal grazing movements along herding routes, between distant and dissimilar ecosystems. The pastoral families move with their herds from pasturelands used during the winter (winter-lands) to areas pastured during the summer (summer-lands). Whereas this is a key step towards the recognition of the cultural dimension associated to this ancient practice, a relevant feature of transhumance pastoralism is its strong linkage with environmental dynamics. This activity developed in a spatiotemporal and co-evolutionary trajectory, which gave rise to a pastoral territory. A territory is the union or linkage of a meaning with a specific place, that is, the space that is appropriated and valued, both symbolically and instrumentally, by human groups. Hence, the pastoral territory represents the socio-ecological system that integrates a pastoral-based community with the natural environment that it inhabits. We propose a co-evolutionary approach to analyzing some key attributes of transhumant pastoralism, which modulate the socio-ecological interdependence. Based on a study case from Northwest Patagonia, Argentina, we identified and characterized seven attributes: (I) mobility, (II) connectivity, (III) temporal synchrony, (IV) local interdependence, (V) local ecological knowledge, (VI) adaptive capacity legacy, and (VII) mixture of land tenure. We discuss these features as examples that represent keystone socio-ecological attributes for the recognition of transhumant pastoral territories as a biocultural heritage.

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.

In Löhr and Michel (2022), we proposed a neurocognitive model within the predictive processing paradigm for acceptability intuitions about copredication sentences. We also addressed “predicate order effects,” the phenomenon that the acceptability of copredication sentences can vary with the order in which the predicates occur. We discussed Murphy's interpretation of order preferences based on a hierarchy of semantic complexity and tried to motivate that other interpretations are worthwhile pursuing. In a reply letter, Murphy (2022) takes issue with our approach, putting forward several purported methodological and conceptual issues with our paper. In this reply, we first clarify where exactly we agree and disagree. Second, we focus on a crucial equivocation and some misrepresentations of our proposal by Murphy.

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.

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 associated with the sign. Second, they provide resources to understand how the semiological principle is compatible with a dynamic and flexible notion of "meaning".

Predictive processing, an increasingly popular paradigm in cognitive sciences, has focused primarily on giving accounts of perception, motor control and a host of psychological phenomena, including consciousness. But higher cognitive processes, like conceptual thought, language, and logic, have received only limited attention to date and PP still stands disconnected from a huge body of research in those areas. In this thesis, I aim to address this gap and I attempt to go some way towards developing and defending a cognitive-computational approach to higher cognition within the predictive processing paradigm. To test its explanatory potential, I apply it to a range of linguistic and conceptual phenomena. I proceed in three steps. First, I lay out an account of concepts and suggest how concepts are represented, how they can be context-sensitively processed, and how the apparent diversity of formats arise. Secondly, I propose how paradigmatic higher cognitive competencies, like language and logical reasoning, could fit into the PP picture. Thirdly, I apply the PP account of concepts and language to a range of linguistic-conceptual phenomena as test cases, namely: metaphor, the semantic paradox (specifically the Liar Paradox) and copredication. Finally, I discuss some challenges and objections to the PP framework as applied to higher cognition and in general.