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Hajo Greif (2022). Likeness-Making and the Evolution of Cognition. Biology & Philosophy 37 (2022): Article 1 (online). Open Access. https://doi.org/10.1007/s10539-021-09830-1.

Abstract: Paleontological evidence suggests that human artefacts with intentional markings might have originated already in the Lower Paleolithic, up to 500.000 years ago and well before the advent of ‘behavioural modernity’. These markings apparently did not serve instrumental, tool-like functions, nor do they appear to be forms of figurative art. Instead, they display abstract geometric patterns that potentially testify to an emerging ability of symbol use. In a variation on Ian Hacking’s speculative account of the possible role of “likeness-making” in the evolution of human cognition and language, this essay explores the central role that the embodied processes of making and the collective practices of using such artefacts might have played in early human cognitive evolution. Two paradigmatic findings of Lower Paleolithic artefacts are discussed as tentative evidence of likenesses acting as material scaffolds in the emergence of symbolic reference-making. They might provide the link between basic abilities of mimesis and imitation and the development of modern language and thought.

Acknowledgements: The (already temporally distant) foundations for this work were laid in my Austrian Science Fund (FWF) grant J3448-G15. A first version of this paper was presented at the 3rd International Avant-Conference 2017 in Lublin, Poland. I thank the organiser, Marcin Trybulec, for encouragement back then, and Peter Gärdenfors, Anton Killin, Kim Sterelny and Jörg Wernecke for helpful comments and practical suggestions later on.

Hajo Greif (2021). Adaptation and its Analogues: Biological Categories for Biosemantics. Studies in History and Philosophy of Science 90 (2021): 298–307. Open Access. https://doi.org/10.1016/j.shpsa.2021.

Abstract: “Teleosemantic” or “biosemantic” theories form a strong naturalistic programme in the philosophy of mind and language. They seek to explain the nature of mind and language by recourse to a natural history of “proper functions” as selected-for effects of language- and thought-producing mechanisms. However, they remain vague with respect to the nature of the proposed analogy between selected-for effects on the biological level and phenomena that are not strictly biological, such as reproducible linguistic and cultural forms. This essay critically explores various interpretations of this analogy. It suggests that these interpretations can be explicated by contrasting adaptationist with pluralist readings of the evolutionary concept of adaptation. Among the possible interpretations of the relations between biological adaptations and their analogues in language and culture, the two most relevant are a linear, hierarchical, signalling-based model that takes its cues from the evolution of co-operation and joint intentionality and a mutualistic, pluralist model that takes its cues from mimesis and symbolism in the evolution of human communication. Arguing for the merits of the mutualistic model, the present analysis indicates a path towards an evolutionary pluralist version of biosemantics that will align with theories of cognition as being environmentally “scaffolded”. Language and other cultural forms are partly independent reproducible structures that acquire proper functions of their own while being integrated with organism-based cognitive traits in co-evolutionary fashion.

Acknowledegements: This article started as an improvised contribution to the ‘The Future of Teleosemantics’ conference in Bielefeld, Germany, in 2018. I thank the organisers, especially Peter Schulte, for providing the opportunity to present my first ideas – which matured with the help of the critical comments from the participants, especially Ruth Millikan, and two anonymous reviewers.

Hajo Greif (2021): “Exploring minds: Modes of modelling and simulation in Artificial Intelligence”. Perspectives on Science 29, 4: 409–435. Special Issue on Exploratory Models and Exploratory Modelling in Science, Guest Editors: Axel Gelfert, Grant Fisher, Friedrich Steinle. doi:10.1162/posc_a_00377.

A self-archived manuscript version (not identical with the published version) is available free of charge at PhilSci Archive.

Abstract: The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI.
According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI).
In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them.