Abstract
In the 1940s, Gilbert Ryle argued that knowing how to do something is not just a matter of being well-regulated but also a matter of self-regulation. Ryle appears to have thought that know-how requires self-regulation in both a backward-looking and forward-looking sense, but both ideas run counter to ordinary intuitions about know-how. The basic idea behind self-regulation, undertaking trials and adjusting to feedback, is captured by the “law of effect.” Daniel Dennett has argued that the “law of effect will not go away”. After updating Dennett’s “Tower of Generate-and-Test” through a broad survey of the empirical literature on learning, I identify a Rylean intuition about intelligence and argue that self-regulation in a forward-looking sense is necessary for know-how.
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Notes
This distinguishes my project from that of Bäckström and Gustafsson, who seek to describe the role self-regulation plays in Ryle’s view (2017). Ryle was not himself interested in laying out necessary (or jointly sufficient) conditions for know-how. His goal was to identify the “logical space” or, more famously, “category” in which talk of know-how belongs. Talk of know-how belongs in a logical space where questions “about learning are applicable—where such questions make sense” (Bäckström & Gustafsson, 2017, 47).
I intend “mere regulation” to track something different from Ryle’s “well-regulated” (see fn. 5). It’s also worth noting that not all biologists use this terminology as I do (e.g. Bich et al., 2016). In this essay, I focus on self-regulation which involves changing one’s default response through learning. A more encompassing account would also incorporate, for example, the more minimal forms of self-regulation described by Peter Railton (2014), which arguably also factor into non-representational or enactivist models of cognition (Van Gelder, 1995; Di Paolo et al., 2017) and views of extended cognition (Clark, 1998).
It’s worth bearing in mind that Dennett considers creatures at successive levels of the Tower of Generate-and-Test to also be members of lower levels. Skinnerian creatures are also Darwinian creatures, subject to the forces of natural selection. I use the qualification “merely” to indicate creatures at their metaphorical ceiling (e.g. before I noted that birds and wasps aren’t merely Darwinian creatures because they are, at least, also Skinnerian).
I suspect this sort of phenomenon is another reason why Ryle associated habit with inflexibility without qualification. He had fairly fixed habits within stable performance circumstances in mind (and this seems to track what he has in mind with the phrase “well-regulated”).
There has also been a push to integrate reinforcement learning frameworks with research on the famous hippocampal map implicated in navigational tasks (see Stachenfeld et al., 2017), and attempts to unify these research programs have been accompanied by a deeper probing into what constitutes a cognitive map (Behrens et al., 2018). Notice that there are currently at least three senses of “cognitive map” at play in the Tolman-inspired literature (Tolman, 1948). In the neuro-computational literature on reinforcement learning, a cognitive map is broadly defined as a “representational template that enables an animal, through mental search, to find the best possible action at a particular state” (Dolan & Dayan, 2013, 313). In research on animal navigation, however, the term “cognitive map” refers either to the hippocampal map or, more generically, to any allocentric model encoding spatial relations between landmarks and goals within an open-field task environment (Geva-Sagiv, 2015; Wehner, 2020). Each of these senses of cognitive map has its roots in Tolman, but they are in principle distinct (see also, Behrens et al., 2018). Failing to keep these distinctions in mind may lead to confusion when approaching partially overlapping research programs. When researchers ask whether hymenopterans, especially desert ants and honeybees, navigate using a cognitive map similar to the famous hippocampal map found in rodents, the sense of cognitive map meaning allocentric model is typically at issue (Wehner, 2020). Research into hymenopteran navigation also focuses on a different kind of flexible choice. Suppose that a displaced ant recognizes a familiar landmark associated with a previously stored home-vector. If it has a cognitive map, it should be able to compute a direct route home based on its current spatial relationship to the landmark and the landmark-home vector stored in memory. In the literature on ant and bee navigation, the capacity to take novel shortcuts of this kind is taken to be the key issue for determining whether hymenopterans possess a cognitive map (Menzel et al., 2005; Cheeseman et al., 2014a). The persistent efforts to explain hymenopteran navigation in terms of egocentric path integration and route-following systems may serve as a potent counterpoint to models that explain “map-like behavior” in terms of more sophisticated representational capacities (Cheung et al., 2014; Hoinville & Wehner, 2018; but see, Cheeseman et al., 2014b; Webb, 2019).
One current limitation of the human origins literature worth noting here is that it tends to assume the common-sense categories of folk psychology and gives little attention to resources from cognitive science which might undermine those categories. In particular, the literature canvassed in this section has not been sufficiently integrated with the neuro-computational frameworks canvassed in the last two sections. Tomasello, for example, distinguishes the flexible behavior of apes from the inflexible behavior of other animals (Tomasello, 2014, 7–9), but he appears to be operating with the common-sense psychological distinction between habit and goal-directed behavior rather than a well-developed research program (Heyes, 2018; Sterelny, 2020, 780).
One might wonder whether the intuition that the second organism lacks know-how would hold if it has the relevant mind tools and brings them to bear cogently on the problem. This would mean that they can produce a propositionally structured model of the task environment and evaluate it by deploying normative language, but that they nevertheless fail to alter their default response within a trial or across multiple trials. This might sound, at first pass, like an incoherent state of affairs. If they understand the situation as I described it and learn that yellow berries are now poisonous, why wouldn’t they try something new? There are conceivable circumstances in which they won’t. One would be a case where an organism with human capacities nevertheless behaves instinctually—an informationally encapsulated module takes over. In other cases, it could be due to a failure of practical rationality or due to weakness of will.
It might, for example, pick out learning opportunities that are appropriate to the learner’s current abilities. Educational theory would be an important resource in fleshing this idea out, though I suspect, for considerations like Hawley’s, that any such metric should allow for somewhat vague boundaries.
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Acknowledgements
I would like to thank Bryce Huebner and Michael Kremer and the anonymous referees for their comments on the ideas developed in this paper.
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Elzinga, B. Know-how and why self-regulation will not go away. Synthese 201, 202 (2023). https://doi.org/10.1007/s11229-023-04168-5
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DOI: https://doi.org/10.1007/s11229-023-04168-5