A rebuttal of two common deflationary stances against LLM cognition (2025)

Abstract: Large language models (LLMs) are arguably the most predictive models of human cognition available. Despite their impressive human-alignment, LLMs are often labeled as "*just* next-token predictors” that purportedly fall short of genuine cognition. We argue that these deflationary claims need further justification. Drawing on prominent cognitive and artificial intelligence research, we critically evaluate two forms of “Justaism” that dismiss LLM cognition by labeling LLMs as “just” simplistic entities without specifying or substantiating the critical capacities these models supposedly lack. Our analysis highlights the need for a more measured discussion of LLM cognition, to better inform future research and the development of artificial intelligence.

## introduction over 70 years ago, alan turing posed a question that has since captivated computer scientists, cognitive scientists, and philosophers alike: "can machines think?" @xcite . with the recent proliferation of increasingly capable artificial intelligence systems (e.g., @xcite )-namely, large language models (llms)-variants of this question have made their way far beyond the confines of academic departments. although llms have been shown to be predictive of human representations and behavior across a broad range of tasks @xcite @xcite , a number of critics maintain that llms cannot be said to possess genuine cognition because they are "just...": "next-token predictors", "function approximators", or "stochastic parrots", and thus lack some essential capacity necessary for "thought", "reasoning", or "understanding" (henceforth, "cognition"). unfortunately, such deflationary claims often fail to state what exactly this capacity is and have been given the pejorative label "justaism" (pronounced "just-a-ism") due to the confident self-evidence with which they are wielded @xcite . such views on the reality of llm cognition have implications for people's willingness to use them as scientific tools @xcite , and trust such systems in everyday contexts @xcite . in what follows, we discuss two flavors of justaism-anti-simple-objectives ("it's just a nexttoken predictor") and anti-anthropomorphic ("it's just a machine")-and provide a critical analysis of these positions based on cognitive and artificial intelligence research. we conclude our analysis by putting forth three guiding principles to help clarify the status of llm cognition. although we refer to the flavors' prototypical forms, we also provide specific justaic examples in a companion webpage (github.com/zak-hussain/againstjustaism). the page includes examples found in the literature and public scientific discourse on llm cognition, as well as (secondary) references and responses to justaic stances. for instance, it includes anti-simple-objectives claims that llms are just "complex", "stochastic", "statistical auto-complete" tools. these kinds of claims are more frequent and often more explicit than their anti-anthropomorphic counterparts, which argue that attributing ostensibly or "profound[ly]" human cognitive capacities to llms is not only tempting but also erroneous, and may have "serious [negative] consequences". the former can also reinforce the latter: a justaic argument might, for instance, claim that because llms are clearly "just [next-]token predictors", the best explanation for the mistaken belief that llms possess cognition is naive anthropomorphism. thus, the two forms of justaism can sometimes appear in concert. before proceeding, we clarify the scope of our work. first, our focus is not on whether llms exhibit human (or human-like) cognition, but on stances opposing the notion of llm cognition in general. we follow a broader conception of cogni-tion that allows for the possibility of ascribing cognition to non-human animals @xcite (see also, section 2.2) and other information process systems @xcite . we do, nevertheless, draw on research comparing humans and llms, because evidence that llms exhibit humanlike cognition is, by extension, also evidence that llms exhibit cognition in general. second, we do not base our argument on any specific definition of cognition, nor do we develop one. instead, we critique justaic stances against llm cognition on other grounds, such as internal consistency (section 2.1) or risk of bias (section 2.2). third, although we focus on two forms of justaism, other substantial perspectives critical of llm cognition exist and deserve consideration. these views differ fundamentally from justaism and hence are not the target of our critique. for instance, some empirical research highlights specific llm cognitive deficits (e.g., @xcite @xcite . rather than denying llm cognition outright, such work is better understood as qualifying the extent of cognitive abilities in llms. other research presents substantive theoretical arguments against llm cognition, for example, by distinguishing form (syntax) from meaning (semantics) (e.g., @xcite . we view these efforts as making important definitional and conceptual progress on cognition, and they are thus not the target of our critique. we hope that our work can contribute to a better understanding of the arguments in favor of and against llm cognition, ultimately shaping how we evaluate and compare artificial and biological intelligence. perhaps the most common form of justaism, which we dub anti-simple-objectives justaism, takes issue with how llms are pre-trained (see our companion webpage, github.com/zak-hussain/againstjustaism, for examples). the assertion is that because the llm pre-training objective is simply to predict the masked or next token, llms cannot be doing something as complex or substantial as cognition. assuming proponents of this view believe that humans possess cognition, anti-simple-objectives justaism can be questioned by making the following facetious analogy to humans and other creatures shaped by evolution: we humans are "just nextchild producers", stumbling forward in pursuit of the all-encompassing base objective of inclusive fitness maximization. the point here is not to argue that humans should actually be thought of in such a way but to highlight a common error with this kind of deflationary thinking-the error of assuming that simple base objectives necessarily produce simple systems. of course, there are important differences between next-token prediction and inclusive fitness maximization. for instance, the ancestral environment from which we evolved was potentially richer than the online text corpora used to train llms. combined with a sufficiently complex nervous system and other distinguishing factors (e.g., resource competition), biological evolution may lead to the development of instrumental objectives that are more conducive to cognition than next-token prediction. however, even if it were the case that these distinguishing factors were pivotal to the development of instrumental objectives in humans, it is nevertheless plausible that cognition-enabling instrumental objectives could be acquired via other means during next-token-prediction-based pre-training. in fact, empirical evidence suggests that llms are already employing such instrumental strategies in order to achieve high performance on the base objective (through a process known as mesaoptimization, @xcite . there is also reason to expect that these instrumental objectives are similar to those of humans (and thus potentially cognition-enabling). after all, the llm pre-training distribution was generated (mainly) by humans, who would have had various (instrumental) motives driving their text production. an llm that learns to model these human objectives and incorporate them into its prediction could thus improve its performance on the training distribution by better capturing the data generating process @xcite . there is also empirical precedence for this sort of convergence, with research in representational alignment demonstrating that predicting human-generated text can lead to increased alignment between llms and human brains @xcite . relatedly, llm (instrumental) objectives need not be especially complex to be on par with those of human beings. after all, many foundational the-ories of human cognition posit relatively simple objectives as fundamental components, with prominent examples including predictive brain theories (e.g., bayesian brain, predictive coding, active inference, @xcite . notably, these objectives may not be so different from next-token prediction, which raises a similar question to the evolutionary analogy that opened this section: if simple predictive objectives are generally considered insufficient for the development of cognition, might it be that humans similarly lack genuine cognition? finally, it is important to qualify that most contemporary llms not only are (pre-)trained with next-token prediction but also go through several stages of fine-tuning. these often include reinforcement-learning from (subjective) human feedback @xcite and (objective) rulebased rewards @xcite , which are targeted at improving the model's helpfulness. as such, it is now often factually incorrect to claim that llms are only trained to predict the next token, although it is still true that the vast majority of data and compute goes into such pre-training (see, e.g., @xcite . ultimately, the extent to which next-token prediction enables or precludes cognition is a question that requires further theoretical and empirical research. nevertheless, we hope that the above arguments demonstrate that it is by no means selfevident that an llm is devoid of cognition. ## conclusion: toward a more measured discussion in support of a more measured discussion of llm cognition, we would like to advance three guiding principles: (i) modesty regarding human cognition (and our understanding of it), (ii) consistency for future work comparing humans and llms, and (iii) a focus on empirical benchmarks. regarding modesty, we would reiterate that human history is littered with delusions of human exceptionalism @xcite . this is despite our limited understanding of the mechanisms underlying cognition. thus, although we fully support cautioning against the dangers of (naive) anthropomorphism, we see the need for a backstop against the opposite tendency: viewing human cognition as too special to also be ascribed to llms. regarding consistency, we would reiterate the need for consistent goalposts: are we applying the same standards to llms as we would to humans? for instance, if we wish to reduce llm cognition to its pre-training objective (i.e., nexttoken prediction), we must show why the same reductionism should not apply to humans as well. similarly, when llms commit errors that appear so elementary to us as to discredit llm cognition, it is important to recall the host of fallacies and illusions to which humans are susceptible and which, consequently, we may not so easily identify or view as significant. these considerations not only help guard against certain biases (e.g., algorithmic aversion), but they can also provide a new perspective on human cognition by helping identify aspects of cognition that are, in fact, uniquely human. for instance, it has been argued that (current) llms probably lack sentience, consciousness, or self-awareness (chalmers, 2023)-capacities that are thus unique to humans and other animals. finally, we are sympathetic to @xcite 's view (among others, e.g., niv, 2021) that discussions of cognition should focus on observables. as @xcite note, axiomatic rejections of llm cognition can lead to positions that have no empirically testable implications. not only does this run contrary to good scientific practice, but it can also lead to investigations of llm cognition that lack practical relevance. after all, it is predominantly the behavior of a system that impacts the world. consequently, we believe in the need for clear and consistent empirical benchmarks (e.g., chollet, 2019) that allow for direct evaluations of the cognitive capacities of humans and llms. ultimately, the jury is still out on the existence and extent of llm cognition. we hope that these principles can help researchers move beyond justaic reasoning towards a deeper, more measured understanding of the cognitive capacities of llms.