Andrea Martin

We commend Sanbonmatsu et al. (2025) for centring metatheory and metamethod as remedies to “difficult research problems”. However, we wish to depart from their conceptualisation of what models are and what role they play in psychological theorising. Under computationalism, models are not a container for observations through being fit to neurobehavioural data and should not be held to the standard of providing us with numerical predictions. Computational cognitive models can only play their role of mediating from theory to data and back, if and only if we buttress their special status against confusions with other model types, such as with inferential statistical models of the data. This is a realistic goal for our fields. Ultimately, theory crisis or no, blurring the boundary between model, theory, and data in our metatheoretical calculus, results in many of the problems all authors have pointed out.

Contemporary artificial intelligence models owe much of their success and discontents to connectionism, a framework in cognitive science that has been (and continues to be) highly influential. Herein, we analyze artificial neural networks: (a) when used as scientific instruments of study and (b) when functioning as emergent arbiters of the zeitgeist in the cognitive, computational, and neural sciences. Building on our previous work with respect to analogizing between artificial neural networks and cognition, brains, or behavior (Guest & Martin, 2023), we use metatheoretical analysis techniques (Guest, 2024), including formal logic, to characterize two distinct tendencies within connectionism that we dub classical and modern, with divergent properties, for example, goals, mechanisms, and scientific questions. We also demonstrate how we, as a field, often fail to follow important lines of argument to their end—this results in a paradoxical praxis. By engaging more deeply with (meta)theory surrounding artificial neural networks, our field can obviate the cycle of artificial intelligence winters and summers, which need not be inevitable.

Contemporary AI models owe much of their success and discontents to connectionism, a framework in cognitive science that has been (and continues to be) highly influential. Herein, we analyze artificial neural networks (ANNs): a) when used as scientific instruments of study; and b) when functioning as emergent arbiters of the zeitgeist in the cognitive, computational, and neural sciences. Building on our previous work with respect to analogizing between ANNs and cognition, brains, or behaviour (Guest & Martin, 2023), we use metatheoretical analysis techniques (Guest, 2024), including formal logic, to characterise two distinct tendencies within connectionism that we dub classical and modern, with divergent properties, e.g. goals, mechanisms, scientific questions. We also demonstrate how we, as a field, often fail to follow important lines of argument to their end --- this results in a paradoxical praxis. By engaging more deeply with (meta)theory surrounding ANNs, our field can obviate the cycle of AI winters and summers, which need not be inevitable.

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference.

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

In order to understand cognition, we often recruit analogies as building blocks of theories to aid us in this quest. One such attempt, originating in folklore and alchemy, is the homunculus: a miniature human who resides in the skull and performs cognition. Perhaps surprisingly, this appears indistinguishable from the implicit proposal of many neurocognitive theories, including that of the 'cognitive map,' which proposes a representational substrate for episodic memories and navigational capacities. In such 'small cakes' cases, neurocognitive representations are assumed to be meaningful and about the world, though it is wholly unclear who is reading them, how they are interpreted, and how they come to mean what they do. We analyze the 'small cakes' problem in neurocognitive theories (including, but not limited to, the cognitive map) and find that such an approach a) causes infinite regress in the explanatory chain, requiring a human-in-the-loop to resolve, and b) results in a computationally inert account of representation, providing neither a function nor a mechanism. We caution against a 'small cakes' theoretical practice across computational cognitive modelling, neuroscience, and artificial intelligence, wherein the scientist inserts their (or other humans') cognition into models because otherwise the models neither perform as advertised, nor mean what they are purported to, without said 'cake insertion.' We argue that the solution is to tease apart explanandum and explanans for a given scientific investigation, with an eye towards avoiding van Rooij's (formal) or Ryle's (informal) infinite regresses.

En replaçant les travaux de Philippe Walter dans les cadres historique et théorique des analyses bachelardiennes et durandiennes sur l’imaginaire, Blanca Solares met en évidence les apports spécifiques de ses thèses, tant du point de vue méthodologique que du point de vue idéologique. Cette entreprise de reconstruction des présupposés conceptuels de Philippe Walter permet de souligner l’importance d’une réévaluation en profondeur d’un imaginaire médiéval trop souvent compris à l’aune d’une conception simplificatrice de la christianisation, tout en montrant la nécessité d’une approche nuancée et renouvelée des sources traditionnelles. By putting the work of Philippe Walter in the historical and theoretical contexts of the Bachelard and Durand’s analysis on the imaginary, Blanca Solares highlights the specific contributions of his theses, both from methodological and ideological points of view. This reconstruction effort on the conceptual presuppositions of Philippe Walter serves to accentuate the importance of a thorough revaluation of a medieval imaginary too often understood, at the beginning of a simplified design to the Christianization, while showing the need for a nuanced and renewed approach to traditional sources. Al situar las investigaciones de Philippe Walter dentro del marco histórico-teórico de los análisis de Bachelard y Durand sobre el imaginario, Blanca Solares evidencia sus aportes específicos, tanto metodológicos como ideológicos. Esta labor de reconstrucción de los fundamentos conceptuales de Philippe Walter muestra cuánto importa cambiar el enfoque al imaginario medieval empobrecido por unas aproximaciones a la cristianización demasiado simplificadoras y justifica plenamente una aproximación renovada y fina a las fuentes tradicionales.

I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue’s reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.