How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

A central component of the cognitive revolution is a commitment to explaining behaviour by reference to internal representations of the world. This core aspect.

Nicholas Shea offers Varitel Semantics as a naturalistic account of mental content. I argue that the account secures determinate content only by appeal to pragmatic considerations, and so it fails to respect naturalism. But that is fine, because representational content is not, strictly speaking, necessary for explanation in cognitive science. Even in Shea’s own account, content serves only a variety of heuristic functions.

In this paper, we defend a novel, multidimensional account of representational unification, which we distinguish from integration. The dimensions of unity are simplicity, generality and scope, non-monstrosity, and systematization. In our account, unification is a graded property. The account is used to investigate the issue of how research traditions contribute to representational unification, focusing on embodied cognition in cognitive science. Embodied cognition contributes to unification even if it fails to offer a grand unification of cognitive science. (...) The study of this failure shows that unification, contrary to what defenders of mechanistic explanation claim, is an important mechanistic virtue of research traditions. (shrink)

What role does the concept of representation play in the contexts of experimentation and explanation in cognitive neurobiology? In this article, a distinction is drawn between minimal and substantive roles for representation. It is argued by appeal to a case study that representation currently plays a role in cognitive neurobiology somewhere in between minimal and substantive and that this is problematic given the ultimate explanatory goals of cognitive neurobiological research. It is suggested that what (...) is needed is for representation to instead play a more substantive role. (shrink)

The book is addressed to all readers interested in cognitive science, and especially in research combining a logical analysis with psychological, linguistic and neurobiological approaches. The publication is the result of a collaboration between the Department of Cognitive Science at University of Lodz and the Department of Cognitive Science at Lund University. It is intended to provide a comprehensive presentation of the key research issues undertaken in both Departments, including considerations on meaning, natural language (...) and reasoning, linguistic as well as numerical competence, the theory of decision making, modelling of conceptual representations, cognitive and game theoretic approach to social interactions. (shrink)

In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in (...) class='Hi'>cognitive sciences by providing unexplored connections between different theories; on the other hand it is aimed at sketching a computational characterization of the problem of concept representation in cognitively inspired artificial systems and in cognitive architectures. (shrink)

Nick Shea’s Representation in Cognitive Science commits him to representations in perceptual processing that are about probabilities. This commentary concerns how to adjudicate between this view and an alternative that locates the probabilities rather in the representational states’ associated “attitudes”. As background and motivation, evidence for probabilistic representations in perceptual processing is adduced, and it is shown how, on either conception, one can address a specific challenge Ned Block has raised to this evidence.

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) (...) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using (...) probing classifiers, a popular analysis technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

The past twenty years have seen an increase in the importance of the body in psychology, neuroscience, and philosophy of mind. This 'embodied' trend challenges the orthodox view in cognitive science in several ways: it downplays the traditional 'mind-as-computer' approach and emphasizes the role of interactions between the brain, body, and environment. In this article, I review recent work in the area of embodied cognitive science and explore the approaches each takes to the ideas of consciousness, (...) computation and representation. Finally, I look at the current relationship between orthodox cognitive science and the study of mental disorder, and consider the implications that the embodied trend could have for issues in psychopathology. (shrink)

Enactivist (Embodied, Embedded, etc.) approaches in cognitive science and philosophy of mind are sometimes, though not always, conjoined with an anti-representational commitment. A weaker anti-representational claim is that ascribing representational content to internal/sub-personal processes is not compulsory when giving psychological explanations. A stronger anti-representational claim is that the very idea of ascribing representational content to internal/sub-personal processes is a theoretical confusion. This paper criticises some of the arguments made by Hutto & Myin (2013, 2017) for the stronger anti-representational (...) claim and suggests that the default Enactivist view should be the weaker non-representational position. (shrink)

This paper is a tentative step towards a historical cognitive science, in the domain of memory and personal identity. I treat theoretical models of memory in history as specimens of the way cultural norms and artifacts can permeate ('proto')scientific views of inner processes. I apply this analysis to the topic of psychological control over one's own body, brain, and mind. Some metaphors and models for memory and mental representation signal the projection inside of external aids. Overtly at (...) least, medieval and Renaissance theorists agreed that such models had to allow for, or even guarantee, some conception of cognitive order and discipline. Individual memory traces had to be independent, not mixed up or interfering with others. The long tradition of improving or bypassing 'natural memory' by deliberately internalizing artefactual models was part of an arduous process of self-fashioning. Moral panic about confusion and mixture features centrally in the imposition of cognitive discipline in local memory traditions. (shrink)

This paper asks about the ways in which embodimentoriented cognitive science contributes to our understanding of phenomenal consciousness. It is first argued that central work in the field of embodied cognitive science does not solve the hard problem of consciousness head on. It is then argued that an embodied turn toward neurophenomenology makes no distinctive headway on the puzzle of consciousness; for neurophenomenology either concedes dualism in the face of the hard problem or represents only a (...) slight methodological variation on extant cognitive-scientific approaches to the easy problems of consciousness. The paper closes with the positive suggestion that embodied cognitive science supports a different approach to phenomenal consciousness, according to which the mind is massively representational, cognitive science has no use for the personal-level posits that tend to drive philosophical theorizing about consciousness and mind, and the hard problem is illusory. (shrink)

This paper deals with the Stoic concept of misprinted representation (φαντασία παρατυπωτική), which has received little attention compared to other concepts of Stoic epistemology and philosophy of mind. I aim at showing that a better understanding of this concept is important for grasping some elements of the Stoic account of mental representations that have been ignored or misunderstood in modern Stoic scholarship. First, by clarifying the status of the misprinted representation as a genuine representation, we can understand (...) what it means (and does not mean) to say, from the Stoic point of view, that the intentional object of a representation is the external object that caused it. Second, by understanding this issue, we obtain some resources to deal with the ambiguity of the preposition ἀπό in the definition of cognitive representation. Thus, the concept of misprinted representation proves to be important for appropriately understanding the Stoic concepts of representation and cognitive representation. (shrink)

Experiences of art involve exercise of ordinary cognitive and perceptual capacities but in unique ways. These two features of experiences of art imply the mutual importance of aesthetics and cognitive science. Cognitive science provides empirical and theoretical analysis of the relevant cognitive capacities. Aesthetics thus does well to incorporate cognitive scientific research. Aesthetics also offers philosophical analysis of the uniqueness of the experience of art. Thus, cognitive science does well to incorporate (...) the explanations of aesthetics. This paper explores this general framework of expansionism : a research strategy that suggests that the explanatory goals and resources of both aesthetics and cognitive science should expand to include those of the other. Two relations are considered. First, what is the relation between aesthetics and more traditional cognitive science? Second, what is the relation between aesthetics and new developments in cognitive science that de-emphasize mental representation and emphasize body and action? (shrink)

The sensorimotor theory of perceptual experience claims that perception is constituted by bodily interaction with the environment, drawing on practical knowledge of the systematic ways that sensory inputs are disposed to change as a result of movement. Despite the theory’s associations with enactivism, it is sometimes claimed that the appeal to ‘knowledge’ means that the theory is committed to giving an essential theoretical role to internal representation, and therefore to a form of orthodox cognitive science. This paper (...) defends the role ascribed to knowledge by the theory, but argues that this knowledge can and should be identified with bodily skill rather than representation. Making the further argument that the notion of ‘representation hunger’ can be replaced with ‘prima facie representation hunger’, it concludes that although the theory could optionally be developed scientifically in part by reference to internal representation, it makes a strong and natural fit with anti-representationalist embodied or enactive cognitive science. (shrink)

This is a comprehensive book on the philosophy of time. Leading philosophers discuss the metaphysics of time, our experience and representation of time, the role of time in ethics and action, and philosophical issues in the sciences of time, especially quantum mechanics and relativity theory.

Given the fundamental role that concepts play in theories of cognition, philosophers and cognitive scientists have a common interest in concepts. Nonetheless, there is a great deal of controversy regarding what kinds of things concepts are, how they are structured, and how they are acquired. This chapter offers a detailed high-level overview and critical evaluation of the main theories of concepts and their motivations. Taking into account the various challenges that each theory faces, the chapter also presents a novel (...) approach to concepts that is organized around two ideas. The first is a pluralistic view of differing types of conceptual structure. The second is a model that treats concepts as atomic representations that are linked to various types of conceptual structures. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the (...) classical problem of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

This paper outlines the case for a future research program that uses the tools of experimental cognitive science to investigate questions that traditionally fall under the remit of the philosophy of historiography. The central idea is this – the epistemic profile of historians’ representations of the past is largely an empirical matter, determined in no small part by the cognitive processes that produce these representations. However, as the philosophy of historiography is not presently equipped to investigate such (...) cognitive questions, legitimate concerns about evidential quality go largely overlooked. The case of mental state representation provides an excellent illustration of this. Representations of past mental states – the thoughts and fears and knowledge and desires of past agents – play much the same evidential role in historiography as in everyday life, serving in the causal explanation of agents’ behaviors and supporting normative evaluation of those behaviors. However, we have good reason to suspect that the theory of mind processes that support these representations may be more susceptible to error when deployed in the context of historiography than under everyday conditions. This raises worries about the quality of evidence that theory of mind can provide historiography, worries which require experimental cognitive science to properly address. (shrink)

Intellectualism is the claim that practical knowledge or ‘know-how’ is a kind of propositional knowledge. The debate over Intellectualism has appealed to two different kinds of evidence, semantic and scientific. This paper concerns the relationship between Intellectualist arguments based on truth-conditional semantics of practical knowledge ascriptions, and anti-Intellectualist arguments based on cognitive science and propositional representation. The first half of the paper argues that the anti-Intellectualist argument from cognitive science rests on a naturalistic approach to (...) metaphysics: its proponents assume that findings from cognitive science provide evidence about the nature of mental states. We demonstrate that this fact has been overlooked in the ensuing debate, resulting in inconsistency and confusion. Defenders of the semantic approach to Intellectualism engage with the argument from cognitive science in a way that implicitly endorses this naturalistic metaphysics, and even rely on it to claim that cognitive science support Intellectualism. In the course of their arguments, however, they also reject that scientific findings can have metaphysical import. We argue that this situation is preventing productive debate about Intellectualism, which would benefit from both sides being more transparent about their metaphilosophical assumptions. (shrink)

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a (...) variety of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence. (shrink)

Recent research on infant and animal imitation and on mirror neuron systems has
brought imitation back in focus in psychology and cognitive science. This topic has
always been important for philosophical hermeneutics as well, focusing on theory and
method of understanding. Unfortunately, relations between the scientific and the
hermeneutic approaches to imitation and understanding have scarcely been investigated,
to the loss of both disciplines. In contrast to the cognitive scientific emphasis on
sharing and convergence of representations, the hermeneutic analysis emphasizes the
indeterminacy and openness (...) of action understanding due to preunderstanding, action
configuration, and the processual nature of understanding. This article discusses empirical
evidence in support of these aspects and concludes that hermeneutics can
contribute to the scientific investigation of imitation and understanding. Since, conversely,
some grounding—and constraining—aspects of hermeneutics may be derived
from cognitive science, both should be integrated in a multilevel explanation of
imitation and understanding. This holds also for explanations that are largely based on
mirror neuron systems, since these appear to be sensitive to developmental and
experiential factors, too. (shrink)

That the state-of-affairs of cognitive science is not good is brought into figural salience in "What happened to cognitive science?" (Núñez et al., 2019). We extend their objective description of 'what's wrong' to a prescription of 'how to correct'. Cognitive science, in its quest to elucidate 'how we know', embraces a long list of subjects, while ignoring Mathematics (Fig. 1a, Núñez et al., 2019). Mathematics is known for making the unknown to be known (cf. (...) solving for unknowns). This acknowledgement naturally raises the question: does mathematical knowing inform knowing in general? Here we show that drawing parallels to mathematical knowing can facilitate the advancement of cognitive science (Lawvere, 1994). (shrink)

Ian Hacking instigated a revolution in 20th century philosophy of science by putting experiments (“interventions”) at the top of a philosophical agenda that historically had focused nearly exclusively on representations (“theories”). In this paper, I focus on a set of conceptual tools Hacking (1992) put forward to understand how laboratory sciences become stable and to explain what such stability meant for the prospects of unity of science and kind discovery in experimental science. I first use Hacking’s tools (...) to understand sources of instability and disunity in rodent behavioral neuroscience. I then use them to understand recent grass-roots collaborative open science initiatives aimed at establishing stability in this research area and tease out some implications for unity of science and kind creation and discovery in cognitive neuroscience. -/- . (shrink)

Interdisciplinary understanding requires integration of insights from different perspectives, yet it appears questionable whether disciplinary experts are well prepared for this. Indeed, psychological and cognitive scientific studies suggest that expertise can be disadvantageous because experts are often more biased than non-experts, for example, or fixed on certain approaches, and less flexible in novel situations or situations outside their domain of expertise. An explanation is that experts’ conscious and unconscious cognition and behavior depend upon their learning and acquisition of a (...) set of mental representations or knowledge structures. Compared to beginners in a field, experts have assembled a much larger set of representations that are also more complex, facilitating fast and adequate perception in responding to relevant situations. This article argues how metacognition should be employed in order to mitigate such disadvantages of expertise: By metacognitively monitoring and regulating their own cognitive processes and representations, experts can prepare themselves for interdisciplinary understanding. Interdisciplinary collaboration is further facilitated by team metacognition about the team, tasks, process, goals, and representations developed in the team. Drawing attention to the need for metacognition, the article explains how philosophical reflection on the assumptions involved in different disciplinary perspectives must also be considered in a process complementary to metacognition and not completely overlapping with it. (Disciplinary assumptions are here understood as determining and constraining how the complex mental representations of experts are chunked and structured.) The article concludes with a brief reflection on how the process of Reflective Equilibrium should be added to the processes of metacognition and philosophical reflection in order for experts involved in interdisciplinary collaboration to reach a justifiable and coherent form of interdisciplinary integration. An Appendix of “Prompts or Questions for Metacognition” that can elicit metacognitive knowledge, monitoring, or regulation in individuals or teams is included at the end of the article. (shrink)

Distributed Cognition and Integrational Linguistics have much in common. Both approaches see communicative activity and intelligent behaviour in general as strongly con- text-dependent and action-oriented, and brains as permeated by history. But there is some ten- sion between the two frameworks on three important issues. The majority of theorists of distributed cognition want to maintain some notions of mental representation and computa- tion, and to seek generalizations and patterns in the various ways in which creatures like us couple with (...) technologies, media, and other agents; many also want to offer explanations at subpersonal levels which may undercut the autonomy of personal-level accounts. In contrast, dominant views in integrational linguistics reject all invocation of representation, resist the explanatory search for similarity across contexts and moments, and see linguistics as a lay dis- cipline which should not offer explanations in terms alien to ordinary agents. On each of these issues, I argue that integrationists could move closer to the distributed cognition frame- work without losing the most important aspects of their view: integrationist criticisms of mainstream or classical theories can be respected while alliances with revised cognitivist views about representation, context, and explanation are developed. Ó 2004 Elsevier Ltd. All rights reserved. (shrink)

Recent research on infant and animal imitation and on mirror neuron systems has brought imitation back in focus in psychology and cognitive science. This topic has always been important for philosophical hermeneutics as well, focusing on theory and method of understanding. Unfortunately, relations between the scientific and the hermeneutic approaches to imitation and understanding have scarcely been investigated, to the loss of both disciplines. In contrast to the cognitive scientific emphasis on sharing and convergence of representations, the (...) hermeneutic analysis emphasizes the indeterminacy and openness of action understanding due to preunderstanding, action configuration, and the processual nature of understanding. This article discusses empirical evidence in support of these aspects and concludes that hermeneutics can contribute to the scientific investigation of imitation and understanding. Since, conversely, some grounding—and constraining—aspects of hermeneutics may be derived from cognitive science, both should be integrated in a multilevel explanation of imitation and understanding. This holds also for explanations that are largely based on mirror neuron systems, since these appear to be sensitive to developmental and experiential factors, too. (shrink)

Advocates of dynamic systems have suggested that higher mental processes are based on continuous representations. In order to evaluate this claim, we first define the concept of representation, and rigorously distinguish between discrete representations and continuous representations. We also explore two important bases of representational content. Then, we present seven arguments that discrete representations are necessary for any system that must discriminate between two or more states. It follows that higher mental processes require discrete representations. We also argue that (...) discrete representations are more influenced by conceptual role than continuous representations. We end by arguing that the presence of discrete representations in cognitive systems entails that computationalism (i.e., the view that the mind is a computational device) is true, and that cognitive science should embrace representational pluralism. (shrink)

Naturalistic theories of representation seek to specify the conditions that must be met for an entity to represent another entity. Although these approaches have been relatively successful in certain areas, such as communication theory or genetics, many doubt that they can be employed to naturalize complex cognitive representations. In this essay I identify some of the difficulties for developing a teleosemantic theory of cognitive representations and provide a strategy for accommodating them: to look into models of signaling (...) in evolutionary game theory. I show how these models can be used to formulate teleosemantics and expand it in new directions. (shrink)

The concepts expressed by social role terms such as artist and scientist are unique in that they seem to allow two independent criteria for categorization, one of which is inherently normative. This study presents and tests an account of the content and structure of the normative dimension of these “dual character concepts.” Experiment 1 suggests that the normative dimension of a social role concept represents the commitment to fulfill the idealized basic function associated with the role. Background information can affect (...) which basic function is associated with each social role. However, Experiment 2 indicates that the normative dimension always represents the relevant commitment as an end in itself. We argue that social role concepts represent the commitments to basic functions because that information is crucial to predict the future social roles and role-dependent behavior of others. (shrink)

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that Marr’s levels of (...) explanation cannot explain such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (shrink)

Many debates in philosophy focus on whether folk or scientific psychological notions pick out cognitive natural kinds. Examples include memory, emotions and concepts. A potentially interesting type of kind is: kinds of mental representations (as opposed, for example, to kinds of psychological faculties). In this chapter we outline a proposal for a theory of representational kinds in cognitive science. We argue that the explanatory role of representational kinds in scientific theories, in conjunction with a mainstream approach to (...) explanation in cognitive science, suggest that representational kinds are multi-level. This is to say that representational kinds’ properties cluster at different levels of explanation and allow for intra- and inter-level projections. (shrink)

The paper is dedicated to particular cases of interaction and mutual impact of philosophy and cognitive science. Thus, philosophical preconditions in the middle of the 20th century shaped the newly born cognitive science as mainly based on conceptual and propositional representations and syntactical inference. Further developments towards neural networks and statistical representations did not change the prejudice much: many still believe that network models must be complemented with some extra tools that would account for proper human (...) cognitive traits. I address some real implemented connectionist models that show how ‘new associationism ’ of the neural network approach may not only surpass Humean limitations, but, as well, realistically explain abstraction, inference and prediction. Then I stay on Predictive Processing theories in a little more detail to demonstrate that sophisticated statistical tools applied to a biologically realist ontology may not only provide solutions to scientific problems or integrate different cognitive paradigms but propose some philosophical insights either. To conclude, I touch on a certain parallelism of Predictive Processing and philosophical inferentialism as presented by Robert Brandom. (shrink)

The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various (...) psychological theories. Our analysis suggests that all theorists accept the idea that cognitive processing involves internal information-carrying states that mediate cognitive processing. These mediating states are a superordinate category of representations. We discuss five properties that can be added to mediating states and examine their importance in various cognitive models. Finally, three methodological lessons are drawn from our analysis and discussion. (shrink)

In this paper, an account of theoretical integration in cognitive (neuro)science from the mechanistic perspective is defended. It is argued that mechanistic patterns of integration can be better understood in terms of constraints on representations of mechanisms, not just on the space of possible mechanisms, as previous accounts of integration had it. This way, integration can be analyzed in more detail with the help of constraintsatisfaction account of coherence between scientific representations. In particular, the account has resources to (...) talk of idealizations and research heuristics employed by researchers to combine separate results and theoretical frameworks. The account is subsequently applied to an example of successful integration in the research on hippocampus and memory, and to a failure of integration in the research on mirror neurons as purportedly explanatory of sexual orientation. (shrink)

(1) This is Part 2 of the semantic theory I call TM. In Part 1, I developed TM as a theory in the analytic philosophy of language, in lexical semantics, and in the sociology of relating occasions of statement production and comprehension to formal and informal lexicographic conclusions about statements and lexical items – roughly, as showing how synchronic semantics is a sociological derivative of diachronic, person-relative acts of linguistic behavior. I included descriptions of new cognitive psychology experimental paradigms (...) which would allow us to precisely measure the two constituents of semantics – meaning and reference – both at the level of individual speech acts and at the level of societal convergences, i.e. at both the token and type levels. -/- (2) In the Introduction, I recapitulate the arguments of Part 1. The Introduction also develops some analytic philosophical and lexical semantics themes not discussed in Part 1. -/- (3) After the Introduction, I present neural TM (nTM) as a theory of the neural mechanisms and processes which give rise to these person/occasion-relative acts of linguistic behavior. I develop nTM at three levels, the first two of which describe linguistic/semantic functions independently of their cortical locations. At the first level, I describe individual word-to-word and word-to-object connections. At the second level, I describe the corresponding structuralist networks of which they are the individual components. At this level, I introduce some key linguistic concepts of TM – its graded meaning, reference, and generalization sets, and the types of statements which express various levels of word-to-word and word-to-object relationships among lexical items which, because of the constraints they impose on the use of those lexical items in statements we produce and comprehend, are concepts. This constitutes the second structural level of nTM. -/- (4) At the third level, I associate the non-localized structures of the previous levels with cortically located neural structures and with the fasciculi that connect them. I distinguish neural areas in which primary (phonetic) and secondary (orthographic) lexicons are stored in long-term memory. I also describe the embodied concepts which co-exist in the anterior temporal lobes with the images they lexicalize. These concepts are often said to name physical objects and their features, although what they in fact name are kinds of physical objects and features. I describe how conceptual constraints and referential constraints interact to channel our intentions to say how things are into statements which are semantically well-formed, and which consequently successfully communicate information. -/- (5) Following this presentation of nTM, I examine five prominent neural semantic theories. I point out what is wrong with each of them as far as their explanations of semantics are concerned, and I also indicate how nTM can replace the “semantic cores” of those theories. -/- (6) The two basic mistakes made by neuroscience semantic theories, as I will explain, are (i) that all but one of them regard semantics as a matter of the association of words with perceptual images, and of generalizations from those associations; and (ii) that they all rely on an unspecified set of neural structures which purportedly encode the meaning of concepts in abstraction from their phonological and orthographic forms. nTM maintains, in contrast, that there are no abstract neural representations of semantic content. Neural constraints on our linguistic behavior, especially on our ascriptive and co-ascriptive use of words, express the semantic constraints on those words which make them concepts. That is the semantic content of words. -/- (7) I next consider several results from neuroscience experimental data which have been given one interpretation by one or another of the standard neurosemantic theories, but to which nTM gives a different interpretation. I include several predictions which I have found neither confirmed nor disconfirmed in the experimental neuroscience literature. -/- (8) After a concluding section in which I summarize the major changes to neurosemantic theory introduced by TM, and the analytic philosophy of language and lexical semantics contexts within which TM is situated, there follows an appendix in which I discuss neural net AI, and make some recommendations for implementing nTM in silicon. (shrink)

I will present two different applications - Dual PECCS and the TCL reasoning framework - addressing some crucial aspects of commonsense reasoning (namely: dealing with typicality effects and with the problem of commonsense compositionality) in a way that is integrated or compliant with different cognitive architectures. In doing so I will show how such aspects are better dealt with at different levels of representation and will discuss the adopted solution to integrate such representational layers.

Integrative and naturalistic philosophy of mind can both learn from and contribute to the contemporary cognitive sciences of dreaming. Two related phenomena concerning self-representation in dreams demonstrate the need to bring disparate fields together. In most dreams, the protagonist or dream self who experiences and actively participates in dream events is or represents the dreamer: but in an intriguing minority of cases, self-representation in dreams is displaced, disrupted, or even absent. Working from dream reports in established databanks, (...) we examine two key forms of polymorphism of self-representation: dreams in which I take an external visuospatial perspective on myself, and those in which I take someone else's perspective on events. In remembering my past experiences or imagining future or possible experiences when awake, I sometimes see myself from an external or 'observer' perspective. By relating the issue of perspective in dreams to established research traditions in the study of memory and imagery, and noting the flexibility of perspective in dreams, we identify new lines of enquiry. In other dreams, the dreamer does not appear to figure at all, and the first person perspective on dream events is occupied by someone else, some other person or character. We call these puzzling cases 'vicarious dreams' and assess some potential ways to make sense of them. Questions about self-representation and perspectives in dreams are intriguing in their own right and pose empirical and conceptual problems about the nature of self-representation with implications beyond the case of dreaming. (shrink)

This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. -/- Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is (...) really that way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. -/- There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale (...) of observation. My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead (...) to unification are analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models. (shrink)

This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is really (...) that way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

This brief commentary has three goals. The first is to argue that ‘‘framework debate’’ in cognitive science is unresolvable. The idea that one theory or framework can singly account for the vast complexity and variety of cognitive processes seems unlikely if not impossible. The second goal is a consequence of this: We should consider how the various theories on offer work together in diverse contexts of investigation. A final goal is to supply a brief review for readers (...) who are compelled by these points to explore existing literature on the topic. Despite this literature, pluralism has garnered very little attention from broader cognitive science. We end by briefly considering what it might mean for theoretical cognitive science. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted (...) behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

The notions of information, representation and enaction entertain historical and complex relations with cognition. Historical relations because representational structures belong to the central hypothesis of cognitive sciences. Complex relations because cognitive sciences apply the notion of representation to animals, humans and robots, and also because the enactive approach tends to disregard the GOFAI type of representations. In this wide horizon of relations, we propose to look at a systemic approach that could bring up a common denominator (...) for information and representations in the build up of cognition, and also keep a link with the enactive approach. Our purpose is to show that systems submitted to constraints can generate meaningful information to maintain their natures, and consequently build up meaningful representations that have some compatibility with the enactive approach. Such a systemic approach to the notion of meaningful information could then make available a link between enaction and meaningful representations. The first part of the presentation is about reminding that cognition does not exist per se, but is related to the system that builds it. We look at cognition as constituted by dynamic meaningful representations built up by systems that have constraints to satisfy in their environments. Cognition is considered here at the level of the system that builds it and uses it in order to maintain its nature in its environment. Such a systemic approach fits with evolution. Organisms build representations to cope with survival constraints (frogs build representations of moving black dots in order to satisfy food constraints). Humans build representations and cognition to satisfy constraints that are conscious and unconscious. Artificial systems can use representations and cognition to run activities related to constraints implemented by the designers or coming from the environment (a goal to reach being considered as a constraint to satisfy). In the second part of the presentation we define what are a meaningful information and a representation for a system submitted to a constraint in its environment, and we link these to the enactive approach. We define a meaningful information (a meaning) as an information generated by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is precisely that connection. The meaning belongs to the interactions that link the system to its environment. The function of the meaning is to participate to the determination of an action that will be implemented in order to satisfy the constraint. (Menant, 2003). The satisfaction of the constraint goes with maintaining the nature of the system in its environment. A Meaning Generator System (MGS) is defined correspondingly. It is a building block for higher level systems. We present some characteristics of the MGS (groundings of a meaning, domain of efficiency and transfer of meanings, networking of meanings, evolutionary usage). The MGS approach is close to a simplified version of the Peircean triadic theory of signs (Menant, 2003, 2005 ). We define the representation of an item for a system as being the dynamic set of meaningful information corresponding to the item for the system in its environments (an elementary representation being made of a single meaningful information). These representations link the system to its environment by their meaningful components related to the nature of the system. These representations are different from the GOFAI ones. The possibilities for linking these notions of meaning and representation with the enactive approach come from the structure of the MGS: the need for an action is the cause of the meaning generation by and for the system. The action on the environment is for the system to maintain its nature (its identity). The MGS links together the generation of meaningful representations, the nature of the system, and the interactions with the environment. This can be considered as close to enacting a world by meaning generation (Di Paolo and all 2007), and to the enactive concept of sense making (De Jaegher, Di Paolo 2007). We propose that basing the definition of a representation on the notion of meaningful information generated by a system submitted to a constraint can open a way for making the notion of representation compatible with the enactive approach. In the third part of the presentation, we consider some cases of meaningful information and representations for organisms and for robots. Regarding organisms, the MGS can be used in an evolutionary context by looking at the evolution of the systems and of the constraints. Purpose is to modelize the generation of meanings and of representations in order to make available a tool usable for different levels of evolution, as evolution has a place in cognitive sciences (Proust, 2007). Constraints for basic life are survival constraints (individual and species). Group life constraints are also to be considered. Reaching the level of humans in evolution brings in new constraints that cannot be clearly identified as they have to take into account human consciousness which is today a mystery (the “hard problem”). On an evolutionary standpoint, human constraints come in addition to the ones existing for non human organisms. We can make some hypothesis on the nature of human constraints (Maslow pyramid based constraints, anxiety limitation…). For robots, the MGS is initially based on the design of the robot. The meaning generated within a robot is initially derived from the constraints implemented by the designer and from the environment. But some non calculable or non predictable evolutions of the robot can introduce meanings that look proper to the robot. This last point can be linked to the notion of autonomy in robots. In such examples, the dynamic management of meanings thru the MGSs in their environments keeps the link with the enactive approach. We finish the presentation by summarising the points addressed and by proposing several continuations. (shrink)

Concept representation is still an open problem in the field of ontology engineering and, more generally, of knowledge representation. In particular, the issue of representing “non classical” concepts, i.e. concepts that cannot be defined in terms of necessary and sufficient conditions, remains unresolved. In this paper we review empirical evidence from cognitive psychology, according to which concept representation is not a unitary phenomenon. On this basis, we sketch some proposals for concept representation, taking into account (...) suggestions from psychological research. In particular, it seems that human beings employ both prototype-based and exemplar-based representations in order to represent non classical concepts. We suggest that a similar, hybrid prototype-exemplar based approach could also prove useful in the field of knowledge representation technology. Finally, we propose conceptual spaces as a suitable framework for developing some aspects of this proposal. (shrink)

This special issue on what some regard as a crisis of replicability in cognitive science (i.e. the observation that a worryingly large proportion of experimental results across a number of areas cannot be reliably replicated) is informed by three recent developments. -/- First, philosophers of mind and cognitive science rely increasingly on empirical research, mainly in the psychological sciences, to back up their claims. This trend has been noticeable since the 1960s (see Knobe, 2015). This development (...) has allowed philosophers to draw on a wider range of relevant resources, but it also makes them vulnerable to relying on claims that may not survive further scrutiny. If we have reasons to believe that a large proportion of findings in the psychological sciences cannot be reliably replicated, this would be a problem for philosophers who use such findings in their work. Second, philosophers are increasingly designing and carrying out their own experiments to back up claims, or to test claims earlier made from the armchair, for example, on the perceived permissibility of diverting trolleys or on the nature of free will. This growing field of experimental philosophy has diversified the intellectual field in philosophy, but may also be vulnerable to issues of replicability that philosophers did not face before. -/- Third, the recent evidence of apparently widespread non-replicability in the social sciences (and other fields) has forced philosophers of science to grapple with long standing questions from their field from a new perspective. To what extent does replicability matter for theory construction? How do the notions of replicability and scientific progress interact? How can normative insights from philosophy of science be used in order to improve scientific practice? -/- It is with these three developments in mind—the increasing importance of empirically-informed philosophy, of experimental philosophy, and of philosophy of science around replicability—that this special issue has been conceived. (shrink)