Hunter Gentry

We use language to communicate our thoughts. But is language merely the expression of thoughts, which are themselves produced by other, nonlinguistic parts of our minds? Or does language play a more transformative role in human cognition, allowing us to have thoughts that we otherwise could (or would) not have? Recent developments in artificial intelligence (AI) and cognitive science have reinvigorated this old question. We argue that language may hold the key to the emergence of both more general AI systems and central aspects of human intelligence. We highlight two related properties of language that make it such a powerful tool for developing domain-general abilities. First, language offers compact representations that make it easier to represent and reason about many abstract concepts (e.g., exact numerosity). Second, these compressed representations are the iterated output of collective minds. In learning a language, we learn a treasure trove of culturally evolved abstractions. Taken together, these properties mean that a sufficiently powerful learning system exposed to language—whether biological or artificial—learns a compressed model of the world, reverse engineering many of the conceptual and causal structures that support human (and human-like) thought.

What is the nature of semantic memory? Philosophers and cognitive scientists have long held that semantic memory stores invariant knowledge structures to be retrieved as such. In this paper, I argue that this conception of semantic memory is likely false. In particular, I argue that if episodic and semantic memory share causal mechanisms, and episodic memory is (re)constructive, then semantic memory is likely constructive too. I review evidence that suggests that episodic and semantic memory are subserved by a domain-general system that supports representing and navigating relations among various kinds of stimuli, including space, time, events, and semantic relations. I then review the supposed hallmark properties of constructivism in episodic memory and show that they appear in semantic memory as well. To increase the inductive support for my proposal, I show how the view predicts some of the evidence others have marshaled in favor of a constructivist semantic memory system. Finally, I close by providing a proof of concept for the view on offer, the semantic pointer architecture.

We use language to communicate our thoughts. But is language merely the expression of thoughts, which are themselves produced by other, nonlinguistic parts of our minds? Or does language play a more transformative role in human cognition, allowing us to have thoughts that we otherwise could (or would) not have? Recent developments in artificial intelligence and cognitive science have reinvigorated this old question. Could language hold the key to the emergence of both artificial intelligence and important aspects of human intelligence? The four contributions in this symposium address this question by drawing on behavioral and neural evidence from people, and the remarkable recent developments in AI which appear to show that artificial neural networks trained on language come to have an astonishing range of abilities. Despite the diversity of the speakers' perspectives, the four contributions paint a coherent (if complex) picture: The abilities of large language models (LLMs) serve as an existence proof of just how much can in principle be learned from language. LLMs also act as a stress test of cognitive theories. The evidence of neural dissociation between linguistic and conceptual processing points to the multiple realizability of human-like cognition. Finally, there is an acknowledged need for systematic research on how the successes and failures of LLMs inform our understanding of human cognition.

A central task of developmental psychology and philosophy of science is to show how humans learn radically new concepts. Famously, Fodor has argued that such learning is impossible if concepts have definitional structure and all learning is hypothesis testing. We present several learning processes that can generate novel concepts. They yield transformations of the fundamental feature space, generating new similarity structures which can underlie conceptual change. This framework provides a tractable, empiricist-friendly account that unifies and shores up various strands of the neo-Quinean approach to conceptual development.

In this article, we explore various arguments against the traditional distinction between episodic and semantic memory based on the metaphysical phenomenon of transitional gradation. Transitional gradation occurs when two candidate kinds A and B grade into one another along a continuum according to their characteristic properties. We review two kinds of arguments—from the gradual semanticization of episodic memories as they are consolidated, and from the composition of episodic memories during storage and recall from semantic memories—that predict the proliferation of such transitional forms. We further explain why the distinction cannot be saved from the challenges of transitional gradation by appealing to distinct underlying memory structures and applying our perspective to the impasse over research into ‘episodic-like’ memory in non-human animals. On the whole, we recommend replacing the distinction with a dynamic life cycle of memory in which a variety of transitional forms will proliferate, and illustrate the utility of this perspective by tying together recent trends in animal episodic memory research and recommending productive future directions.

Some embodied theories of concepts state that concepts are represented in a sensorimotor manner, typically via simulation in sensorimotor cortices. Fred Adams (2010) has advanced an empirical argument against embodied concepts reasoning as follows. If concepts are embodied, then patients with certain sensorimotor impairments should perform worse on categorization tasks involving those concepts. Adams cites a study with Moebius Syndrome patients that shows typical categorization performance in face-based emotion recognition. Adams concludes that their typical performance shows that embodiment is false. Moebius patients must draw on amodal (non-embodied) emotion concepts. In this paper, I review face-based emotion recognition studies with Moebius patients yielding conflicting results and diagnose these conflicts as a difference in experimental design. When emotion labels are provided, patients have typical performance, but when labels are not provided patients are severely deficient. I then show how an embodied, psychological constructionist view of emotions predicts and explains these performance differences. The upshot is that embodied theories of concepts are vindicated.

Patient RB has a peculiar memory impairment wherein he experiences his memories in rich contextual detail, but claims to not own them. His memories do not feel as if they happened to him. In this paper, I provide an explanatory model of RB’s phenomenology, the self-attentional model. I draw upon recent work in neuroscience on self-attentional processing and global workspace models of conscious recollection to show that RB has a self-attentional deficit that inhibits self-bias processes in broadcasting the contents of episodic memories to the global workspace. Typically, self-related contents enjoy a higher salience level than other-related contents. Elimination of bias toward self-related contents diminishes the salience of those contents to the level of other-related contents. Because the typical high salience of self-related content is necessary for the feeling of ownership, RB lacks the feeling of ownership. I also discuss potential applications of the self-attentional model to other psychopathological cases.

Philosophers and cognitive scientists alike have recently been interested in whether cognition extends beyond the boundaries of skin and skull and into the environment. However, the extended cognition hypothesis has suffered many objections over the past few decades. In this paper, I explore the option of control extending beyond the human boundary. My aim is to convince the reader of three things: (i) that control can be implemented in artifacts, (ii) that humans and artifacts can form extended control systems, and (iii) that perhaps extended control ought to be preferred over extended cognition. Using the objections to extended cognition as constraints on my own extended theorizing and the example of autofocus systems in cameras, I decompose and localize the components of an autofocus system that realize the central properties of control from a plausible theory of control in the literature. I then provide criteria according to which control can be extended in a system. Finally, I consider how this theory of extended control ought to be preferred to theories of extended cognition.