Topics in Cognitive Science最新文献

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

The dynamical hypothesis states that cognitive systems are dynamical systems. While dynamical systems play an important role in many cognitive phenomena, the dynamical hypothesis as stated applies to every system and so fails both to specify what makes cognitive systems distinct and to distinguish between proposals regarding the nature of cognitive systems. To avoid this problem, I distinguish several different types of dynamical systems, outlining four dimensions along which dynamical systems can vary: total-state versus partial-state, internal versus external, macroscopic versus microscopic, and systemic versus componential, and illustrate these with examples. I conclude with two illustrations of partial-state, internal, microscopic, componential dynamicism.

The Parallel Architecture is a conception of the organization of the mental representations involved in language and of the role of language in the mind as a whole. Its basic premise is that linguistic representations draw on three independent generative systems-phonological, syntactic, and semantic structures-plus a system of interface links by which they communicate with each other. In particular, words serve as partial interface links that govern the way they compose into novel sentences. It is shown that this architecture also provides a natural way to account for our ability to talk about what we see: semantic structure in language has to communicate via interface links with a level of spatial representation that encodes understanding of the physical world. It is suggested that such configurations of independent but linked representations are a widespread feature of cognition.

Kemmerer captured the drastic change in theories of word meaning representations, contrasting the view that word meaning representations are amodal and universal, with the view that they are grounded and language-specific. However, he does not address how language can be simultaneously grounded and language-specific. Here, we approach this question from the perspective of language acquisition and evolution. We argue that adding a new element-iconicity-is critically beneficial and offer the iconicity ring hypothesis, which explains how language-specific, secondary iconicity might emerge from biologically grounded and universally shared iconicity in the course of language acquisition and evolution.

Kemmerer argues that grounded cognition explains how language-specific semantic structures can influence nonlinguistic cognition. In this commentary, I argue that his proposal fails to fully consider the possibility that language itself can serve as a source of grounding. Our concepts are not merely shaped by a disembodied language system; they emerge in the context of linguistic experience and action. This inclusive approach to grounded cognition offers an expanded conception of the phenomena associated with linguistic relativity. I provide empirical and theoretical reasons to adopt this theoretical perspective.

Cross-linguistic differences in concepts have implications for all theories of concepts, not just for grounded ones. Failure to address these implications does not imply the belief that they do not exist. Instead, it reflects a division of labor between researchers who focus on general principles versus cultural variability. Furthermore, core principles of grounded cognition-empirical learning and situated conceptual processing-predict large cultural differences in conceptual systems. If asked, most grounded cognition researchers would anticipate and endorse these differences, as would most researchers from other perspectives. Finally, by incorporating ethnographic and linguistic analysis, grounded cognition researchers can examine how cultural differences manifest themselves in conceptual systems.

This commentary addresses the challenge of linking an individual-grounded theory of concepts to a phenomenon that assumes conceptual conventions at population level (linguistic relativity). We distinguish I-concepts (individual, interior, imagistic) from L-concepts (linguistic, labeled, local) and see that quite different causal processes are often conflated under the term "concepts." I argue that the Grounded Cognition Model (GCM) entails linguistic relativity only to the extent that it imports L-concepts into its scope, which it can hardly avoid doing given that practitioners require language to coordinate around their theory and findings. I conclude that what entails linguistic relativity is not the GCM but language itself.

In this commentary, we approach the topic of linguistic relativity from a predictive coding perspective. Discussing the role of "priors" in shaping perception, we argue that language creates an important set of priors for humans, which can affect how sensory information is processed and interpreted. Namely, languages create conventionalized conceptual systems for their speakers, mirroring and reinforcing what is behaviorally important in a society. As such, they create collective conceptual convergence on how to categorize the world and thus "streamline" what people rely on to guide their perception.