Abstract
Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between basic and higher cognition. In this paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear.
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Notes
As Hutto and Myin (2013) argue, some enactivists endorse the need of action-oriented representations in their framework, such as Clark (2016). In this paper, however, we will construe ‘enactivism’ in Hutto and Myin’s sense of radical enactivism, i.e., enactivism that rejects the need of any kind of representational content in order to explain basic cognition. For a more fine-grained distinction about varieties of enactivism, see Ward et al. (2017).
Consequently, the non-representationalism of ecological and enactive approaches is an epistemological thesis—they claim that representations are not needed to explain all cognition. This is a weaker claim than the ontological one that says that mental representations do not exist. This is clear, for instance, in Chemero’s (2009) work, but not as clear in the arguments against mental representations put forth by radical enactivists (Hutto and Myin 2013, 2017).
Others have used the names ‘ecological-enactive’ (Kiverstein and Rietveld 2018; Segundo-Ortin 2020; van den Herik 2018, 2020) and ‘enactive-ecological’ (Carvalho and Rolla 2020) in order to refer to the same core ideas as we do here. However, our conception of the EE approach does not rely specifically on those authors’ characterizations, but on the one we present above.
Throughout this text, we say that models ‘scaffold’ performances of scientific cognition (in model-based science, that is) to emphasize that this kind of performance depends on and is modified by artifacts, which is different from taking models to be external devices that extend internal capacities. We do so in order to differentiate our view from the extended mind hypothesis applied to modeling (the latter view is developed by Kuorikoski and Ylikoski 2015), as we discuss in 3.1.
This mirrors the issue of what Hutto and Myin (2013) call “conservative accounts of enactive cognition” (CEC). CEC proponents admit that cognition necessarily involves action, but remain committed to representationalism, albeit of a non-classical kind, one according to which cognition requires action-oriented representations. The problem is that, according to Hutto and Myin (2013), CEC is not pragmatist or radical enough, because mental representations cannot be fully naturalized.
Cartesian dualism ontologically divides the world in mental and physical parts. Mental is conceived as an internal category of phenomena, encapsulated in the thinking subject. Nowadays, the ontological aspect of Cartesian dualism is generally discharged in favor of physicalism—but traditional cognitive sciences still conceive of cognitive phenomena as internal phenomena encapsulated in subjects (or in their brains). The division between res cogitans and res extensa is thereby replaced by the division between cognitive processing and physical phenomena (Aston 2019).
Perceiving one’s surroundings through tools characterizes the Homo sapiens as a species. From to the first stone tools to more complex artifacts used to understand astronomic events (probably in order to assist in harvest), the human capacity to know is always materially mediated (Chakrabarty 2019). Insofar as human knowledge takes place in virtue of the human capacity to act in the world through artifacts, Homo faber might be a more appropriate name than Homo sapiens (Ihde and Malafouris 2019). Malafouris (2013, 2019) illustrated the point with the classic example of the cane used by a blind person: a substantial part of her experience of reality is mediated by the cane, analogously to the way that a sighted person may use a glass. The cane thus becomes a constitutive part of the experience of being in the world. Brain, body and cane are coupled, and it is this coupling that constitutes the person’s cognitive access to her environment. Therefore, the cyborg status that Clark ascribes to the human species has a more radical meaning according to MET (Chakrabarty 2019; A Clark 2003; Ihde and Malafouris 2019).
From the point of view of the neurodynamics involved, MET offers a distinctive view on how material engagements shape our cognitive activity. Whereas a cognitivist would claim that processes that involve artifacts cause (over large time scales) more complex brain activity that leaves material traces as mere epiphenomena, MET takes material engagement as transformative of cognition. According to MET, agents (not brains alone, but embodied, situated brains) are engaged in activities with tools which are materialized in many ways, and these engagements in turn may reorganize neural activity and deeply affect the agent’s sensorimotor abilities. Over sufficiently large time scales, couplings of that kind allow for new kinds of engagements and the development of new artifacts. Thus, the engagements with different technologies causes the reuse of brain areas that were developed for other tasks (Jones 2018)—possibly the ones related to basic cognition.
Even though models are a special kind of artifacts (and material engagements happens at basic levels of cognition), this does not mean that modern day science could be developed in the same way in the remote past. Consider the epistemic and pragmatic consequences of using computers and data, which is spread across basically every scientific areas today, from physics to social sciences (Vallverdú i Segura 2009). For instance, in mathematics, the proofs of many theorems are no longer elaborated on pen and paper, instead they are done by computers that are capable of performing more calculations in less time than a person (for some caveats on this issue, see Casacuberta and Vallverdú 2014). Similarly, astronomy, cosmology and physics require long and complex calculations that could only be accomplished by computers. In social sciences and in biology, large data sets are used to base conclusions about populations, which, without computers, would be impractical or highly time consuming in virtue of the amount of data and the complexity of the statistical analyses involved. So, if the material constitution of technology deeply influences our cognitive complexity, then certain scientific endeavors would be unfathomable for people of different ages. This means that even if a scribe from the Babylonian empire somehow were to learn present-day astronomy, he would be incapable to perform successfully in that area by using only clay elements, due to the lack of computers needed to manipulate a huge amount of data.
We thank an anonymous reviewer for this suggestion.
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Acknowledgements
We would like to thank two anonymous reviewers whose comments were crucial for improving the quality of this manuscript. We would also like to thank the audience of the group Epistemology and Philosophy of Science in Quarantine for important remarks on a previous version of this paper. Finally, we thank Henrique Barbosa da Costa for helpful discussions on analytical tractability.
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Rolla, G., Novaes, F. Ecological-enactive scientific cognition: modeling and material engagement. Phenom Cogn Sci 21, 625–643 (2022). https://doi.org/10.1007/s11097-020-09713-y
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DOI: https://doi.org/10.1007/s11097-020-09713-y