Infatuation Rules
Infatuation Rules
Photo: Karolina Grabowska
Let us go back to the four core features of behaviour that characterise intelligence, namely generality, flexibility, goal-directedness and adaptivity.
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Read More »The notion of intelligence is typically, and justifiably, used in conjunction with the notions of cognition and rationality. The boundaries between the notions are often unclear. There is little agreement within and across fields about whether these notions capture different phenomena at all, intelligence being sometimes conflated with cognition, sometimes with rationality. The lack of terminological agreement is in itself unhelpful, as it may hinder cross-disciplinary exchange in an area of research that is by its very nature interdisciplinary. Regimenting terminology is thereby valuable and important. But I believe that there is more to the issue than mere terminology. These three notions, I hold, play distinctive explanatory roles in our sciences of cognition and behaviour. For when properly constrained, they capture specific sets of phenomena that pose different questions, call for different methods of investigation, and play different explanatory roles. In sum, in order to respect the epistemic distinctiveness desideratum, a characterisation of intelligence should be non-trivial, and such that it sets intelligence apart from the notions of cognition and rationality. Let us start with the notion of cognition. As Fridland (2015) points out, in the cognitive sciences cognition is applied quite broadly, encompassing most or all of the internal processes in the nervous systems of organisms that help to inform behaviour, including thereby sensation, perception, various forms of learning, memory, among many others. In mainstream cognitive science, these processes are typically taken to consist of computations being performed over internal representations (Godfrey-Smith 1996; Dennett 1996; Penn et al. 2008; Schulz 2010; Marcus 2020). Internal representations are states of a system that carry information about or stand in for external states—such as the presence of food or predators in the environment—and are used by the system or its subsystems in virtue of that, so as to guide appropriate behaviour. The conditions of adequacy of representations—that is, the conditions that determine whether a representation is true or false, satisfied or else—are representational contents. The content of a representation is what the representation is about, or, more intuitively, what it means. Often an additional requirement is added: only representational contents that depend on the history, interactions, and workings of the system itself are taken to be relevant to characterising cognition—representational contents that depend on the intentions and purposes of other systems are excluded (Adams and Aizawa 2008 phrase this requirement in terms of non-derived content, see also Rowlands 2009).Footnote 6 Alternative accounts deny that cognition need involve computations over representations (Varela et al. 1991; Hurley 1998; Noe 2004; Thompson 2007; Lyon 2017).Footnote 7 Such approaches see cognition as involving, minimally, processes of self-maintenance and self-organisation that are the bread and butter of life: maintaining the internal organisation and boundaries of the organism, preserving its unity in its dealings with its environment, and partly thanks to such dealings. Cognition, on this picture, is widespread in the biological realm, from bacteria to plants to mammals—albeit coming in many gradations, from less (bacteria) to more complex (humans). On this view, there are forms of cognition that may not involve computations and representations, while it remains a possibility that some forms of cognition do. It is well beyond the scope of this paper to settle this debate, which came to be known as the quest for ‘the mark of the cognitive’ (Adams and Aizawa 2008; Adams 2010; Rowlands 2009; Lyon 2017; Sims 2021). What should be rejected, I take, are overly demanding, narrower characterisations of cognition, which have been common especially in philosophy of mind. According to such views, cognition comprises only processes involving conceptual thinking, reasoning, planning, and other capacities that humans typically excel at (Fodor 1975; Adams 2010). This has led to what Hurley (1998) has dubbed the ‘sandwich picture’ of the mind. Perception and action are the two loaves of bread, between which lies the cognitive filling: the central executive processes that truly constitute cognition. Cognition, on this understanding, largely corresponds to intelligence (Fridland 2015). As Sims (2021) argues, such narrow views of cognition fail to capture the practices of cognitive science. Indeed, the domain of phenomena that cognitive scientists investigate includes capacities very unlike the ones delineated by such narrow views, such as non-flexible, automatic, and stereotypical behaviours (e.g. perceptual biases, behavioural routines); innate, non-learned capacities and behaviours (e.g. innate learning biases and conceptual structuresFootnote 8); domain-specific capacities and behaviours (e.g. cognitive modules and behavioural habitsFootnote 9); non-conceptual and subpersonal states and processes (Shea 2018); associative and reinforcement learning; in addition to the more complex capacities involved in reasoning, language, and planning that narrow views privilege (Lyon 2017).
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Read More »A broader understanding of cognition is thereby to be preferred. Importantly for my purposes, a broad notion of cognition, motivated by the foregoing considerations, allows us to carve up smaller domains of cognitive phenomena that have distinctive features. Intelligence, I hold, is one such domain. Therefore, cognition is best understood broadly, capturing capacities that are common in phylogeny and ontogeny. I will here remain neutral on which specific view of the mark of the cognitive is to be preferred among the broad ones.Footnote 10 In either case, cognition is a process-based notion: it refers to capacities of biological and artificial systems that involve certain kinds of processes, be them computational processes over representations, or processes of self-maintenance and self-organisation. Cognition is thereby an undemanding notion, which might be applicable to plants, fungi, bacteria, should ongoing empirical research provide compelling evidence that they undergo the appropriate kinds of processes picked out by the preferred mark of the cognitive. In consequence, the notion of cognition includes those systems to which a liberal view of intelligence may want to ascribe low levels of intelligence. This indicates that using the notion of intelligence in this liberal way has little epistemic value, insofar as claiming that such systems have low levels of intelligence does not add much of epistemic and pragmatic value over and beyond the claim that they are cognitive. For this reason, I prefer the more conservative view of intelligence mentioned in the previous section. If the proponent of the liberal view should introduce additional distinctions to capture the relevant quantitative and qualitative differences between what, by their lights, would all be instances of intelligent phenomena, then the liberal view would amount to little else than a terminological variant of the conservative view. Importantly, the conservative view does not exclude by fiat the possibility that plants, fungi, bacteria, and other similar systems might be intelligent. It just requires them to meet more demanding standards—manifesting general, flexible, goal-directed, adaptive behaviour often enough—than the ones to be met to count as cognitive. Whether such systems meet those standards or not is of course an empirical question. Rationality, in contrast, captures a much narrower domain of phenomena. As Kacelnik (2006) points out, there is considerable variation over how to characterise rationality across different fields—all of them narrower than the characterisations of cognition (and intelligence). In philosophy and psychology, he argues, rationality is a process-based notion involving deliberation and reasoning in the formation of beliefs and belief-like states. In other fields, such as economics and evolutionary biology, rationality is, on the contrary, purely or mostly an outcome-based notion, focused on the extent to which systems’ behaviours respect or approximate optimality constraints fixed by a normative model, typically involving reward or fitness maximisation (Kacelnik 2006). Even in its process-based characterisation, the normativity involved in judgements of optimality is central to the notion of rationality, for which metacognitive processes of error detection and confidence estimation may be required (Hurley and Nudds 2006a; Stanovich 2012). In addition, the normativity of rationality may come from many different sources, several of which tied to anthropocentric economic, social, and moral values (Pepperberg 2006). My contention is that intelligence lies somewhere in between cognition and rationality. Some intelligent systems are cognitive systems, and some intelligent systems—those that respect normative and/or optimality constraints and involve additional error-sensitive processes—are rational. I remain neutral on whether all intelligent systems are cognitive. While the claim seems plausible, a definite answer depends on the open question of the mark of the cognitive. Should we take processes of self-maintenance and self-organisation to be characteristic of the cognitive, there might be computational-representational systems that are general, flexible, adaptive, and goal-directed but that lack those processes—they would thereby be intelligent, but not cognitive. On the other hand, should we take the presence of computations over representations as central to cognition, then it becomes more likely that all intelligent systems are also cognitive, since the core features that characterise intelligence arguably require such processes (see Sect. 5).
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Read More »Rational systems, in their turn, may not be all located within the set of intelligent systems, at least if we accept pointillist rationality, that is, narrow domain-specific rationality: think of DeepBlue or AlphaGo and their rational choices of winning moves in chess and Go. In consequence, while there are some intelligent systems that are rational, there are (pointillist) rational systems that are not intelligent.Footnote 11
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