Theories of emotion have often maintained artificial boundaries: for instance, that cognition and emotion are separable, and that an emotion concept is separable from the emotional events that comprise its category (e.g. “fear” is distinct from instances of fear). Over the past several years, research has dissolved these artificial boundaries, suggesting instead that conceptual construction is a domain-general process—a process by which the brain makes meaning of the world. The brain constructs emotion concepts, but also cognitions and perceptions, all in (...) the service of guiding action. In this view, concepts are multimodal constructions, dynamically prepared from a set of highly variable instances. This approach obviates old questions (e.g. how does cognition regulate emotion?) but generates new ones (e.g. how does a brain learn emotion concepts?). In this paper, we review this constructionist, predictive coding account of emotion, considering its implications for health and well-being, culture and development. (shrink)

Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the (...) notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call “resource-rational analysis.”. (shrink)

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as ahierarchy, ranging from the concrete to the abstract; however, we argue that (...) there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged,diversity.Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into:modality-specificrepresentations, instantiated on perceptual similarity;multimodalrepresentations, instantiated primarily on the discovery of spatiotemporal contiguity; andcategoricalrepresentations, instantiated primarily on social interaction. These elements serve as the building blocks ofcomplex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e.,predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology. (shrink)

This paper argues that the cognitive neuroscientific use of ordinary mental terms to report research results and draw implications can contribute to public confusion and misunderstanding regarding neuroscience results. This concern is raised at a time when cognitive neuroscientists are increasingly required by funding agencies to link their research to specific results of public benefit, and when neuroethicists have called for greater attention to public communication of neuroscience. The paper identifies an ethical dimension to the problem and presses for greater (...) sensitivity and responsibility among neuroscientists regarding their use of such terms. (shrink)

Cognitive Science, Volume 46, Issue 8, August 2022.

This entry provides an overview of theories of concepts that is organized around five philosophical issues: (1) the ontology of concepts, (2) the structure of concepts, (3) empiricism and nativism about concepts, (4) concepts and natural language, and (5) concepts and conceptual analysis.