Cognition

The Triadic Mind: How Language Reveals the Limits of Human Cognition

Languages are the most complex symbolic systems humans have ever created. Yet children acquire them effortlessly, without formal…

Thinking modes

Floating in linguistic space.

You Don’t Need Words to Think

Brain studies show that language is not essential for the cognitive processes that underlie thought

machines will never understand language

I thought language was too complicated for machines to understand, until I got sick. a lifelong, meandering journey of parsing, learning, and fixing my broken body.

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...

Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...

We Should be Able to Change our Languages — Jimmy Miller

It's time we stopped treating languages as sacred fixed objects. We need now, more than ever, languages we can change.

The link between language and cognition is a red herring | Aeon Ideas

Humans alone have language, but many animals clearly have consciousness. Are parallels between the two a red herring?

Sense-making reconsidered: large language models and the blind spot of embodied cognition

Large Language Models (LLMs) demonstrate a kind of linguistic competence that theories of embodied and enactive cognition have long deemed impossible for systems lacking the meaningful perspective of a living being, i.e., the capacity for sense-making. Facing up to this unexpected technological development requires confronting what I propose to call the “AI dilemma”: either frontier LLMs are capable of sense-making despite lacking biological embodiment, or the kind of linguistic competence they exhibit does not necessarily require sense-making. In their chapter on cognition, Frank, Thompson, and Gleiser (2024) maintain that no AI system comes close to realizing relevance, a position that derives much of its motivation from past practical failures. However, frontier LLMs have effectively overcome Dreyfus’ commonsense knowledge problem, such that their dismissal as categorically mindless risks undermining Frank et al.’s central claim that human cognition is deeply intertwined with lived experience. I therefore argue in favor of the alternative side of the AI dilemma: human-level linguistic competence of LLMs should be recognized as a novel non‑biological form of sense‑making, based on a technologically‑mediated embodiment whose enabling properties are in need of further theoretical analysis. This reorientation invites enactive theory to clarify which aspects of sense-making may be universal and which aspects are specifically contingent on organic life, thereby advancing its conceptual framework in dialogue with contemporary AI.

The Philosophy of Language Models

ABSTRACT The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human‐like linguistic and cognitive capacities, whereas others ascribe it to shallow pattern matching. These disputes stem from deep‐seated philosophical disagreements about the nature of language and cognition. We provide an opinionated survey of these disagreements across core topics in the philosophy of mind and language, including syntactic competence, compositionality, linguistic meaning, representation, attitudes, reasoning, agency, and consciousness. We contend that progress on these issues requires not only clarity about background philosophical commitments but also, in many cases, close engagement with emerging empirical evidence.

Language Is a Poor Heuristic for Intelligence

With the emergence of LLM “AI”, everyone will have to learn what many disabled people have always understood

Large language mistake

The AI boom is based on a fundamental mistake.

Epistemological Fault Lines Between Human and Artificial Intelligence

Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between...

Deficient executive control in transformer attention

Abstract. Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language p

Bridging the biological and cultural models of cognition

Our minds are a choice