AI
Large language models might engage in associative thinking, researchers argue
A new paper challenges prior claims about whether LLMs can think, proposing instead that they may rely on pattern-matching rather than rational reasoning.
Researchers Joseph Gottlieb and colleagues have published a new analysis of whether large language models genuinely "think" in any meaningful sense. Rather than settling the debate, their work opens up a new possibility: that LLMs, if they engage in thinking at all, do so through arational and associative processes rather than rational ones.
Challenging existing arguments#
The paper critiques a recent argument by Daniel Stoljar and Zhihe Vincent Zhang that uses rationality as a litmus test for thinking. Gottlieb's team contends this approach is fundamentally flawed—not because LLMs necessarily think, but because the reasoning behind the claim doesn't hold up under scrutiny. By dismantling that argument, the researchers reveal space for an alternative view.
A new theoretical possibility#
Instead of rational deliberation, the authors propose that LLMs might operate through purely associative cognition—essentially sophisticated pattern recognition and contextual connection-making without the step-by-step logical reasoning humans typically associate with thought. Under this framing, an LLM's responses emerge from learned associations in its training data rather than from reasoned inference.
The implications#
This reframing shifts how we should think about LLM capabilities and limitations. If LLMs do "think," it would represent a fundamentally different mode of cognition than human reasoning, one rooted in statistical relationships rather than logical rules. The finding invites further philosophical and empirical investigation into what we mean by thinking itself.
