Nancy Salay

Hubert Dreyfus has argued recently that the frame problem, discussion of which has fallen out of favour in the AI community, is still a deal breaker for the majority of AI projects, despite the fact that the logical version of it has been solved. (Shanahan 1997, Thielscher 1998). Dreyfus thinks that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, instead, a thoroughly Heideggerian model of cognition, in particular one that does not appeal to representations. I argue that Dreyfus is too hasty in his condemnation of all representational views; the argument he provides licenses only a rejection of disembodied models of cognition. In casting his net too broadly, Dreyfus circumscribes the cognitive playing field so closely that one is left wondering how his Heideggerian alternative could ever provide a foundation explanatorily robust enough for a theory of cognition. As a consequence, he dilutes the force of his legitimate conclusion, that disembodied cognitive models will not work, and this conclusion needs to be heard. By disentangling the ideas of embodiment and representation, at least with respect to Dreyfus‘ frame problem argument, the real locus of the general polemic between traditional computational/representational cognitive science and the more recent embodied approaches is revealed. From this, I hope that productive debate will ensue.

We present a simple example that disproves the universality principle. Unlike previous counter-examples to computational universality, it does not rely on extraneous phenomena, such as the availability of input variables that are time varying, computational complexity that changes with time or order of execution, physical variables that interact with each other, uncertain deadlines, or mathematical conditions among the variables that must be obeyed throughout the computation. In the most basic case of the new example, all that is used is a single pre-existing global variable whose value is modified by the computation itself. In addition, our example offers a new dimension for separating the computable from the uncomputable, while illustrating the power of parallelism in computation.

The current orthodoxy in cognitive science, what I describe as a commitment to deep representationalism, faces intractable problems. If we take these objections seriously, and I will argue that we should, there are two possible responses: 1. We are mistaken that representation is the locus of our cognitive capacities — we manage to be the successful cognitive agents in some other, non-representational, way; or, 2. Our representational capacities do give us critical cognitive advantages, but they are not fundamental to us qua human beings. As Andy Clark has convincingly argued, antirepresentationalism, option one, is explanatorily weak. Consequently, I will argue, we need to take the second option seriously. In the first half of the paper I rehearse the problems with the current representational view and in the second half of the paper I defend and give a positive sketch of a two-systems view of cognition – a non-representational perceptual system coupled with a representational language-dependent one – and look at some consequences of the view.

The computational metaphor for mind is still the central guiding idea in cognitive science despite many insightful and well-founded rejections of it. There is good reason for its staying power: when we are at our cognitive best, we reason about our world with our concepts. But the challengers are right, I argue, in insisting that no reductive account of that capacity is forthcoming. Here I describe an externalist account that grounds representations in organism-level engagement with its environment, not in its neural activity.

Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this interaction, can learn responses to increasingly abstract properties, including representational ones. This representational capacity, arguably the mark of intelligence, then, is not available to current MLS’s

In the recent twenty-year retrospective issue of Animal Behavior and Cognition, Povinelli and Henley (2020) argue that a host of comparative studies into “complex cognition” suffer, fatally, from a theoretical confusion. To rectify the problem, they issue the following challenge: alongside specifications of the higher-order capacity to be tested, provide hypotheses of the mechanism(s) necessary to implement it. They spearhead this effort with a discussion of how the Relational Reinterpretation Hypothesis (RRH) provides just such an account. In the first part of the paper, I argue that RRH is neither necessary nor sufficient to explain the second-order behavior in question. In part two, I describe an alternative hypothesis, externalism, that does sufficiently account for it. Further, it opens new avenues of comparative research.

The “problem of intentionality” from the vantage point of a representational understanding of mind is explaining what thoughts and beliefs are and how they guide behaviour. From an anti-representationalist perspective, on the other hand, on which cognition itself is taken to be a kind of action, intentionality is a capacity to engage in behaviour that is meaningfully directed toward or about some situation. That these are not in fact competing insights is obscured by the representational/anti-representational framing of the debate. This paper begins the work of shifting the conversation in two ways: (1) by arguing that it is the commitment to internal representations, not the acknowledgement of a role for representation per se, that is problematic; and, (2) by describing an alternative, externalist, representational approach that draws on extended, embodied, enactive insights

The paper develops a positive account of the representational capacity of cognitive systems: simple, associationist learning mechanisms and an architecture that supports bootstrapping are sufficient conditions for symbol tool use. In terms of the debates within the philosophy of mind, this paper offers a plausibility account of representation externalism, an alternative to the reductive, computational/representational models of intentionality that still play a leading role in the field. Although the central theme here is representation, methodologically this view complements embodied, enactivist approaches to studying cognition.

One of the central debates in cognitive science is the dispute over the role of representation in cognition: on computational/representational accounts, representations are theoretically central; on dynamic systems approaches in which cognition is investigated as a particular sort of physical process, representations play either no role, or, at best, a derivative one. But these two perspectives lead to a deeply unsatisfying theoretical divide: accounts situated in the representational camp are plagued by the inscrutable problem of intentionality, while those hedging towards anti-representationalism seem incapable of saying anything theoretically interesting about high-level cognition. This unhelpful polarization is due in part, at least, to a muddy debate; while some take representationalism to be a commitment to the necessity of conceptual representations for cognition, representations the having of which require certain conceptual capacities, others do not. Recently, there has been a surge of work on non-conceptual representation. This article aims to add to this movement by suggesting a particular cognitive mechanism for non-conceptual representations, one that plays a pivotal role in making conceptual representations possible. One of the central consequences of this new view of representation is the possibility of a non-question-begging naturalistic account of intentionality

In their article entitled “Ethical Particularism and Patterns”, Frank Jackson, Philip Pettit, and Michael Smith (JPS henceforth) argue that moral particularism is a cognitively implausible theory since it appears to entail the view that one might have a skill that is not grounded in an ability to recognise and represent natural patterns in the world. This charge echoes the complaints of computational theorists of cognition against their embodied cognition counterparts, namely that, theories of cognition that eschew talk of mental representation are implausible qua theories of cognition. In both debates, the cognitive role of generalisation is central to the discussion; however, contrary to the received wisdom, I want to suggest that the dispute is not between generalisation or mental representation on the one hand and no generalisation or mental representation on the other, but rather between what I will call global and local generalisation. Using the dialogue between JPS and Dancy (our paradigm particularist) to frame this discussion, I show that by replacing Dancy's connectionist model for particularist reasoning with a case-based one, we not only vindicate his response to JPS, but we also gain insight into how it is the global/local distinction rather than the generalisation/no generalisation distinction that divides the two views.