A great number of investigations suggest that cognition involves both linguistic and cartographic representations. These researches have motivated a pluralist conception of cognition; also, they have been used to clarify how maps differ from linguistic representations. However, the computational processes underlying the interphase between both kinds of representations deserve further attention. In this paper, I argue that, despite their differences, cartographic representations coexist and interact with linguistic representations in interesting ways.

The question of where, between theory and experiment, computer simulations (CSs) locate on the methodological map is one of the central questions in the epistemology of simulation (cf. Saam Journal for General Philosophy of Science, 48, 293–309, 2017). The two extremes on the map have them either be a kind of experiment in their own right (e.g. Barberousse et al. Synthese, 169, 557–574, 2009; Morgan 2002, 2003, Journal of Economic Methodology, 12(2), 317–329, 2005; Morrison Philosophical Studies, 143, 33–57, 2009; Morrison (...) 2015; Massimi and Bhimji Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 51, 71–81, 2015; Parker Synthese, 169, 483–496, 2009) or just an argument executed with the aid of a computer (e.g. Beisbart European Journal for Philosophy of Science, 2, 395–434, 2012; Beisbart and Norton International Studies in the Philosophy of Science, 26, 403–422, 2012). There exist multiple versions of the first kind of position, whereas the latter is rather unified. I will argue that, while many claims about the ‘experimental’ status of CSs seem unjustified, there is a variant of the first position that seems preferable. In particular I will argue that while CSs respect the logic of (deductively valid) arguments, they neither agree with their pragmatics nor their epistemology. I will then lay out in what sense CSs can fruitfully be seen as experiments, and what features set them apart from traditional experiments nonetheless. I conclude that they should be seen as surrogate experiments, i.e. experiments executed consciously on the wrong kind of system, but with an exploitable connection to the system of interest. Finally, I contrast my view with that of Beisbart (European Journal for Philosophy of Science, 8, 171–204, 2018), according to which CSs are surrogates for experiments, arguing that this introduces an arbitrary split between CSs and other kinds of simulations. (shrink)

By pointing to deep philosophical confusions endemic to cognitive science, Wittgenstein might seem an enemy of computational approaches. We agree that while Wittgenstein would reject the classicist’s symbols and rules approach, his observations align well with connectionist or neural network approaches. While many connectionisms that dominated the later twentieth century could fall prey to criticisms of biological, pedagogical, and linguistic implausibility, current connectionist approaches can resolve those problems in a Wittgenstein-friendly manner. We present the basics of a Vector Symbolic Architecture (...) formalism, inspired by Smolensky, and indicate how high-dimensional vectors can operate in a context-sensitive and object-independent manner in biologically plausible time scales, reflecting Wittgenstein’s notions of language-games and family resemblance; we show how “soft” symbols for such a formalism can be formed with plausible learning cycles using Sparse Distributed Memory, resolving disputes surrounding Wittgenstein’s private language argument; and show how connectionist networks can extrapolate meaningful patterns to solve problems, providing “ways to go on” without explicit rules, which indicates linguistic plausibility. Connectionism thus provides a systematicity and productivity that is more than a mere implementation of a classical approach, and provides Wittgenstein-friendly and Wittgenstein-illuminating models of mind and language for cognitive science. (shrink)

This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general. The puzzle identified here touches on a larger debate in Artificial Intelligence related to epistemic opacity but specifically focuses on computational applications of human level linguistic abilities or properties and a special difficulty with relation to these. Thus, the resulting issue is both general and unique. A partial solution is suggested.

The proponents of embodied cognition often try to present their research program as the next step in the evolution of standard cognitive science. The domain of standard cognitive science is fairly clearly circumscribed (perception, memory, attention, language, problem solving, learning). Its ontological commitments, that is, its commitments to various theoretical entities, are overt: cognition involves algorithmic processes upon symbolic representations. As a research program, embodied cognition exhibits much greater latitude in subject matter, ontological commitment, and methodology than does standard cognitive (...) science. The proponents of embodied cognition to explain the aspects of human cognition are using the importance of embodied interaction with the environment, which is a dynamic relation. The cause of disagreement between these two approaches is regarding the role assumed by the notion of representation. The discussion about the contrast between embodied cognition and standard cognitive science is incomplete without Gibson’s ecological theory of perception and connectionist account of cognition. I will briefly contrast these important theories with computational view of cognition, highlighting the debate over role of representation. Embodied cognition has incorporated rather extensively a variety of insights emerging from research both in ecological psychology and in connectionism. The way I have followed to contrast embodied cognition with standard cognitive science, involves concentration on those several themes that appear to be prominent in the body of work that is often seen as illustrative of embodied cognition. This strategy has the advantage of postponing hard questions about “the” subject matter, ontological commitments, and methods of embodied cognition until more is understood about the particular interests and goals that embodied cognition theorists often pursue. This approach might show embodied cognition to be poorly unified, suggesting that the embodied cognition label should be abandoned in favor of several labels that reflect more accurately the distinct projects that have been clumped together under a single title. Alternatively, it might show that, in fact, there are some overarching commitments that bring tighter unity to the various bodies of work within embodied cognition that seem thematically only loosely related. The contrast between these two approaches is highlighted not only the basis of a priori argument but major experiments have been mentioned, to show the weight of the assumptions of both the contrasting approaches. (shrink)

An ongoing philosophical discussion concerns how various types of mental states fall within broad representational genera—for example, whether perceptual states are “iconic” or “sentential,” “analog” or “digital,” and so on. Here, I examine the grounds for making much more specific claims about how mental states are structured from constituent parts. For example, the state I am in when I perceive the shape of a mountain ridge may have as constituent parts my representations of the shapes of each peak and saddle (...) of the ridge. More specific structural claims of this sort are a guide to how mental states fall within broader representational kinds. Moreover, these claims have significant implications of their own about semantic, functional, and epistemic features of our mental lives. But what are the conditions on a mental state's having one type of constituent structure rather than another? Drawing on explanatory strategies in vision science, I argue that, other things being equal, the constituent structure of a mental state determines what I call its distributional properties—namely, how mental states of that type can, cannot, or must co‐occur with other mental states in a given system. Distributional properties depend critically on and are informative about the underlying structures of mental states, they abstract in important ways from aspects of how mental states are processed, and they can yield significant insights into the variegation of psychological capacities. (shrink)

Many contemporary epistemologists take rational inference to be a conscious action performed by the thinker (Boghossian 2014; 2018; Valaris 2014; Malmgren 2018). It is tempting to think that rational evaluability requires responsibility, which in turn requires conscious action. In that case, unconscious cognition involves merely associative or otherwise arational processing. This paper argues instead for deep rationalism: unconscious inference often exhibits the same rational status and richly structured logical character as conscious inference. The central case study is rationalization, in which (...) people shift their attitudes in logically structured, reason-responsive ways in response to evidence of their own incompetence or immorality. These attitude shifts are irrational in a way that reflects on the thinker. Thus rationally evaluable inference extends downward into the unconscious. Many take the sole aim of belief to be truth (Velleman 2000) or knowledge (Williamson 2000), but the prevalence of rationalization suggests that belief updating often aims instead at preserving our positive conceptions of ourselves—that is, belief updating is part of a psychological immune system (Gilbert 2006; Mandelbaum 2019). This paper argues that the psychological immune system comprises a suite of distinct cognitive mechanisms, some (ir)rational and some arational, which are united by a common function of avoiding the maladaptive predomination of negative affect and maintaining stable motivation. Other aspects of the psychological immune system include (i) a domain-general positive bias in evaluative attitudes and (ii) “terror management,” i.e., the systematic strengthening of meaning-conferring beliefs to avoid death anxiety. The multiplicity of processes underlying the psychological immune system point toward an irrational but adaptive function of cognition to keep us motivated in a world rife with negativity and death. (shrink)

The goal of inquiry in this essay is to ascertain to what extent the Principle of Compositionality – the thesis that the meaning of a complex expression is determined by the meaning of its parts and its mode of composition – can be justifiably imposed as a constraint on semantic theories, and thereby provide information about what meanings are. Apart from the introduction and the concluding chapter the thesis is divided into five chapters addressing different questions pertaining to the overarching (...) goal of inquiry. Chapter Two is an attempt to determine whether the Principle of Compositionality is a trivial principle. It is argued that this is not the case in the context of providing the semantics of natural languages since it is an open question whether the best theories that respect available syntactic and semantic data are compositional. Chapter Three and Chapter Four ask whether there are reasons to think that the Principle of Compositionality is true. It is argued that the three most commonly cited reasons for thinking that correct semantic theories must be compositional – Linguistic Creativity, Productivity and Systematicity – in fact only give us reasons to think that the meaning of complex expressions depend on the meanings of their parts and their modes of composition, but not that the meaning of complex expressions depend ONLY on those factors. Chapter Five asks whether there are any reasons to think that the Principle of Compositionality is false. It is argued that all the phenomena that have been suggested entail non-compositionality are in fact compatible with some versions of compositionality. Even if this conclusion is reached in this chapter, the conclusion of the previous two chapters entails that we are not justified in imposing the Principle of Compositionality as an adequacy constraint on Semantic theories. Chapter Six explores the ways in which information about what meanings are can be derived by imposing constraints on semantic theories. It is argued that the distinction emphasized in Chapter Three and Chapter Four between depending on and depending ONLY on is of critical importance. It is demonstrated that the informative potential of the Principle of Compositionality goes far beyond that of the principle we are in fact justified in imposing on semantic theories. So not only is it the case that we cannot learn anything about meanings from the justified imposition of the Principle of Compositionality – since we cannot justifiably impose it as a constraint on semantic theories – what we could have learned from it had we been justified in imposing it is very different from what we can learn from the principle that we are justified in imposing. (shrink)

Some thoughts just come to mind together. This is usually thought to happen because they are connected by associations, which the mind follows. Such an explanation assumes that there is a particular kind of simple psychological process responsible. This view has encountered criticism recently. In response, this paper aims to characterize a general understanding of associative simplicity, which might support the distinction between associative processing and alternatives. I argue that there are two kinds of simplicity that are treated as characteristic (...) of association, and as a result three possible versions of associative processing. This provides a framework that informs our understanding of association as a current and historical concept, including how various specific versions in different parts of psychology relate to one another. This framework can also guide debates over normative evaluations of actions produced by processes thought to be associative. (shrink)

Proponents of the hard problem of consciousness argue that the zombie and inverted spectrum thought experiments demonstrate that consciousness cannot be physical. They present scenarios designed to demonstrate that it is conceivable that a physical replica of someone can have radically different or no conscious experiences, that such an experience-less replica is possible and therefore that materialism is false. I will argue that once one understands the limitations that the physics of this world puts on cognitive systems, zombies and the (...) inverted spectrum are not conceivable. (shrink)

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...) aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed. (shrink)

This article surveys the philosophical literature on theoretical linguistics. The focus of the paper is centred around the major debates in the philosophy of linguistics, past and present, with specific relation to how they connect to the philosophy of science. Specific issues such as scientific realism in linguistics, the scientific status of grammars, the methodological underpinnings of formal semantics, and the integration of linguistics into the larger cognitive sciences form the crux of the discussion.

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...) targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

As Macnamara (1978) once asked, how can we talk about what we see? We report on a study manipulating realistic dynamic scenes and sentences aiming to understand the interaction between linguistic and visual representations in real-world situations. Specifically, we monitored participants’ eye movements as they watched video clips of everyday scenes while listening to sentences describing these scenes. We manipulated two main variables. The first was the semantic class of the verb in the sentence and the second was the action/motion (...) of the agent in the unfolding event. The sentences employed two verb classes—causatives (e.g., break) and perception/psychological (e.g., notice)—which impose different constraints on the nouns that serve as their grammatical complements. The scenes depicted events in which agents either moved towards a target object (always the referent of the verb-complement noun), away from it, or remained neutral performing a given activity (such as cooking). Scenes and sentences were synchronized such that the verb onset corresponded to the first video frame of the agent motion towards or away from the object. Results show effects of agent motion but weak verb-semantic restrictions: causatives draw more attention to potential referents of their grammatical complements than perception verbs only when the agent moves towards the target object. Crucially, we found no anticipatory verb-driven eye movements toward the target object, contrary to studies using non-naturalistic and static scenes. We propose a model in which linguistic and visual computations in real-world situations occur largely independent of each other during the early moments of perceptual input, but rapidly interact at a central, conceptual system using a common, propositional code. Implications for language use in real world contexts are discussed. (shrink)

Núñez et al (2019) argue (1) that the field of Cognitive Science has failed, in that it has not arrived at a cohesive theory, and (2) that this is contrary to the intentions of the founders. Their survey of publication and citation patterns bears out the lack of a cohesive theory and also provides corroboration for (3) the concern that the field is becoming unbalanced, with psychology overweighted (Gentner, 2010). I will argue against points (1) and (2), but agree with (...) point (3). My central claim is that cognitive science was never meant to have one unified theoretical framework, nor should it have. (shrink)

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of (...) artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them. (shrink)

The idea that cognition makes use of one or more “languages of thought” remains central to much cognitive-scientific and philosophical theorizing. And yet, virtually no attention has been paid to the question of how a language of thought might evolve in the first place. In this article, I take some steps towards addressing this issue. With the aid of the so-called Sender–Receiver framework, I elucidate a family of distinctions and processes which enable us to see how languages of thought might (...) evolve via a series of small, incremental changes. While much work doubtlessly remains to be done in this area, I hope to at least show that there need be nothing mysterious about the existence of languages of thought on evolutionary grounds. (shrink)

Humans’ capacity for so-called symbolic cognition is often invoked by evolutionary theorists, and in particular archaeologists, when attempting to explain human cognitive and behavioral uniqueness. But what is meant by “symbolic cognition” is often left underspecified. In this article, I identify and discuss three different ways in which the notion of symbolic cognition might be construed, each of them quite distinct. Getting clear on the nature of symbolic cognition is a necessary first step in determining what symbolic cognition might plausibly (...) explain. (shrink)

Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it (...) offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest. (shrink)

Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical tests that comparative (...) psychologists use to determine whether a behavior was generated by a cognitive or a non-cognitive process. Cognition should be understood as the natural kind of psychological process that non-accidentally exhibits the properties assessed by these tests (as well as others we have not yet discovered). Finally, I review two plausible neural accounts of cognition's underlying mechanisms—one based in localization of function to particular brain regions and another based in the more recent distributed networks approach to neuroscience—which would explain why these properties non-accidentally cluster. While this notion of cognition may be useful for a number of debates, I here focus on its application to a recent crisis over the distinction between cognition and association in comparative psychology. (shrink)

A fundamental task for any prospective cognitive architecture is information control: routing information to the relevant mechanisms to support a variety of tasks. Jerry Fodor has argued that the Massive Modularity Hypothesis cannot account for flexible information control due to its architectural commitments and its reliance on heuristic information processing. I argue instead that the real trouble lies in its commitment to nativism—recent massive modularity models, despite incorporating mechanisms for learning and self-organization, still cannot learn to control information flexibly enough. (...) After defending this position, I close the paper by discussing wider implications for control and cognitive architecture. (shrink)

This paper refines a controversial proposal: that core systems belong to a perceptual kind, marked out by the format of its representational outputs. Following Susan Carey, this proposal has been understood in terms of core representations having an iconic format, like certain paradigmatically perceptual outputs. I argue that they don’t, but suggest that the proposal may be better formulated in terms of a broader analogue format type. Formulated in this way, the proposal accommodates the existence of genuine icons in perception, (...) and avoids otherwise troubling objections. (shrink)

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the (...) form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection. (shrink)

One major fault line in foundational theories of cognition is between the so-called “representational” and “non-representational” theories. Is it possible to formulate an intermediate approach for a foundational theory of cognition by defining a conception of representation that may bridge the fault line? Such an account of representation, as well as an account of correspondence semantics, is offered here. The account extends previously developed agent-based pragmatic theories of semantic information, where meaning of an information state is defined by its interface (...) role, to a theory that accommodates a notion of representation and correspondence semantics. It is argued that the account can be used to develop an intermediate approach to cognition, by showing that the major sources of tension between “representational” and “non-representational” theories may be eased. (shrink)

Jerry Fodor has claimed to have a solution to the traditional problem of what comes first, thought or language. Compositionality, he says, will give us the answer, for at least one must be compositional, and if only one of them is, that is the one that has underived semantic content. He argues that natural languages are not compositional, and therefore that the content of language is derived from the content of thought. I will argue that the idea that language is (...) not compositional conflicts with his productivity and systematicity arguments for the existence of a language of thought. I will also show that Fodor’s solution to the problem fails, as his main argument is circular. Finally, I suggest that Fodor’s argument against the compositionality of language is not decisive, and that we can still attribute at least some degree of compositionality to language. (shrink)

Je soutiens qu’accepter les explications réalistes intentionnelles du comportement cognitif conduit inévitablement à endosser l’hypothèse du langage de la pensée, et que cette position théorique est, par conséquent, largement répandue chez les philosophes de l’esprit. Au cours de la discussion, je propose un exposé succinct et précis de cette hypothèse et j’analyse une série d’exemples représentatifs de l’argumentation pro-LOT. Après avoir examiné deux cas de résistance à ce type de raisonnement, je conclus en montrant que le soutien accordé à la (...) LOT est empiriquement substantiel, en dépit de la flexibilité et du caractère incomplet de cette hypothèse. (shrink)

Tracing the leading role of emotions in the evolution of the mind, a philosopher and a psychologist pair up to reveal how thought and culture owe less to our faculty for reason than to our capacity to feel. Many accounts of the human mind concentrate on the brain’s computational power. Yet, in evolutionary terms, rational cognition emerged only the day before yesterday. For nearly 200 million years before humans developed a capacity to reason, the emotional centers of the brain were (...) hard at work. If we want to properly understand the evolution of the mind, we must explore this more primal capability that we share with other animals: the power to feel. Emotions saturate every thought and perception with the weight of feelings. The Emotional Mind reveals that many of the distinctive behaviors and social structures of our species are best discerned through the lens of emotions. Even the roots of so much that makes us uniquely human—art, mythology, religion—can be traced to feelings of caring, longing, fear, loneliness, awe, rage, lust, playfulness, and more. From prehistoric cave art to the songs of Hank Williams, Stephen T. Asma and Rami Gabriel explore how the evolution of the emotional mind stimulated our species’ cultural expression in all its rich variety. Bringing together insights and data from philosophy, biology, anthropology, neuroscience, and psychology, The Emotional Mind offers a new paradigm for understanding what it is that makes us so unique. (shrink)

The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of folk (...) psychology, and from much of the epistemology literature. But it is principled and empirically grounded, and shows good prospects for yielding the desired epistemological verdicts. The paper articulates and elaborates the theory, drawing out some of its consequences. Toward the end, the fleshed-out theory is applied to two important case studies: hallucination and cognitive penetration of perception. (shrink)

While it may be a datum of common sense that perceptual experiences can justify beliefs, there is no clear consensus about how they can do so. According to what I call “inferentialism,” perceptual experiences can justify beliefs because perceptual experiences have propositional contents and thus can serve as reasons for belief. A critical commitment of inferentialism is that justification requires the obtaining of a nonarbitrary or nonaccidental semantic relation between justifier and justified, a requirement that I call semantic appropriateness (SA). (...) By contrast, reliabilists reject SA and argue that perceptual experiences can justify beliefs because perceptual experiences are part of a reliable belief‐forming process. In this paper, I explore whether a commitment to SA inevitably leads to a commitment to inferentialism. This exploration is largely motivated by doubts over whether perceptual experiences have propositional contents. If those doubts prove to be well‐founded, then it seems either that perceptual experiences cannot justify beliefs or that some form of reliabilism is true. I argue that although we should take the doubts seriously, there is a way to make sense of SA that does not require inferentialism. (shrink)