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This paper introduces LAPITHS, a new analytical framework designed to challenge the behavioralist tendency of equating transformer model outputs with genuine cognitive abilities or human-like computation. By applying quantitative assessments like the Minimal Cognitive Grid, the authors argue that current claims about unified models of cognition lack theoretical and empirical justification. The work offers a critical perspective on interpreting AI performance, urging researchers to distinguish between statistical mimicry and actual structural constraints associated with cognition.
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Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances

arXiv2 min read

arXiv:2604.27927v2 Announce Type: replace Abstract: We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use it to show that several major claims advanced by models such as CENTAUR, proposed as an artificial Unified Model of Cognition, are not theoretically or empirically justified. LAPITHS provides a principled reference point for counteracting the current behaviouristic tendency in AI research to interpret the human level performances of transformer based language models as evidence of human like underlying computation and, by extension, as signs of cognitive abilities. The novelty of LAPITHS lies in making explicit the arguments grounded in two quantitative assessments: (i) the Minimal Cognitive Grid, a theoretically motivated method for estimating the cognitive plausibility of artificial systems, and (ii) a behavioural comparison showing that results similar to those reported for CENTAUR like models can be reproduced by other systems that do not satisfy the structural constraints typically associated with cognitive plausibility, and whose outputs do not provide independent explanatory insight into human cognition.

Abstract:We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use it to show that several major claims advanced by models such as CENTAUR, proposed as an artificial Unified Model of Cognition, are not theoretically or empirically justified. LAPITHS provides a principled reference point for counteracting the current behaviouristic tendency in AI research to interpret the human level performances of transformer based language models as evidence of human like underlying computation and, by extension, as signs of cognitive abilities. The novelty of LAPITHS lies in making explicit the arguments grounded in two quantitative assessments: (i) the Minimal Cognitive Grid, a theoretically motivated method for estimating the cognitive plausibility of artificial systems, and (ii) a behavioural comparison showing that results similar to those reported for CENTAUR like models can be reproduced by other systems that do not satisfy the structural constraints typically associated with cognitive plausibility, and whose outputs do not provide independent explanatory insight into human cognition.

Artificial Intelligence (cs.AI)

arXiv:2604.27927 [cs.AI]

(or arXiv:2604.27927v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.27927 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 258 words

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