Empirically-informed approaches to emotion often construe our emotions as modules: systems hardwired into our brains by evolution and purpose-built to generate certain coordinated patterns of expressive, physiological, behavioural and phenomenological responses. In ‘Against Modularity’ (2008), de Sousa argues that we shouldn’t think of our emotions in terms of a limited number of modules because this conflicts with our aspirations for a life of greater emotional richness. My aim in this paper is to defend de Sousa’s critique of modular emotion taxonomies (...) from some obvious rejoinders, and to develop his positive proposal as to how we might reconcile the evidence for emotional modularity with an attitude of disapproval towards rigid emotion taxonomies. (shrink)

The problem of variability concerns the fact that empirical data does not support the existence of a coordinated set of biological markers, either in the body or the brain, which correspond to our folk emotion categories; categories like anger, happiness, sadness, disgust and fear. Barrett (2006a, b, 2013, 2016, 2017a, b) employs this fact to argue (i) against the faculty psychology approach to emotion, e.g. emotions are the products of emotion-specific mechanisms, or “modules”, and (ii) for the view that emotions (...) are constructed from domain-general “core systems” with the aid of our folk concepts. The conjunction of (i) and (ii), she argues, heralds a paradigm shift in our understanding of emotion: emotions aren’t triggered but made. In this paper, I argue such a shift is premature for a faculty psychology framework can accommodate the neurobiological variability of emotion. This can be done by treating emotions as developmental modules: non-innate systems which behave like modules, but form as a product of ontogenetic development. (shrink)

The “puzzle” of emotional plasticity concerns making sense of two conflicting bodies of evidence: evidence that emotions often appear modular in key respects, and evidence that our emotions also often appear to transcend this modularity. In this paper, I argue a developmentalist approach to emotion, which builds on Karmiloff-Smith’s (1986, 1992, 1994, 2015) work on cognitive development, can help us dissolve this puzzle.

The relative efficiency of an action is a central criterion in action control and can be used to predict others’ behavior. Yet, it is unclear when the ability to predict on and reason about the efficiency of others’ actions develops. In three main and two followup studies, 3- to 6-year-old children (n = 242) were confronted with vignettes in which protagonists could take a short (efficient) path or a long path. Children predicted which path the protagonist would take and why (...) the protagonist would take a specific path. The 3-year-olds did not take efficiency into account when making decisions even when there was an explicit goal, the task was simplified and made more salient, and children were questioned after exposure to the agent’s action. Four years is a transition age for rational action prediction, and the 5-year-olds reasoned on the efficiency of actions before relying on them to predict others’ behavior. Results are discussed within a representational redescription account. (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)

In Section 1, I introduce three views that explain human cognitive development from different standpoints: Marcus’s neo-nativism, standard neuroconstructivism, and neo-neuroconstructivism. In Section 2, I assess Marcus’s attempt to reconcile nativism with developmental flexibility. In Section 3, I argue that in structurally reconfiguring nativism, Marcus ends up transforming it into an unrecognizable form, and I claim that his view could be accommodated within the more general framework provided by standard neuroconstructivism. In Section 4, I focus on recent empirical findings in (...) neuropsychology and cultural/social neuroscience, and propose a friendly revision to standard neuroconstructivism, thus developing the neo-neuroconstructivist view. I conclude the article in Section 5 by analysing the implications of the results discussed in Section 4 for both neo-nativism and standard neuroconstructivism. 1 Introduction2 Marcus’s Neo-nativism3 Is Marcus’s Neo-nativism Really a Form of Nativism?4 Neo-neuroconstructivism and Dynamic Enskillment5 Conclusion. (shrink)

In this article I have two primary goals. First, I present two recent views on the distinction between knowledge-that and knowledge-how (Stanley and Williamson, The Journal of Philosophy 98(8):411–444, 2001; Hetherington, Epistemology futures, 2006). I contend that neither of these provides conclusive arguments against the distinction. Second, I discuss studies from neuroscience and experimental psychology that relate to this distinction. Having examined these studies, I then defend a third view that explains certain relevant data from these studies by positing the (...) double dissociation of knowledge-that and knowledge-how and that is also able to do explanatory work elsewhere. (shrink)

This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that the (...) participants recursively model the mental states of other agents. Otherwise, an international negotiation may fail, even when it has potential for a win-win solution, and in a time-critical rescue mission, a software agent may depend on a teammate’s action that never materializes. First a survey is presented of past and current research on higher-order social cognition, from the various viewpoints of logic, artificial intelligence, and psychology. Do people actually reason about each other’s knowledge in the way proscribed by epistemic logic? And if not, how can logic and cognitive science productively work together to construct more realistic models of human reasoning about other minds? The paper ends with a delineation of possible avenues for future research, aiming to provide a better understanding of higher-order social reasoning. The methodology is based on a combination of experimental research, logic, computational cognitive models, and agent-based evolutionary models. (shrink)

It is not rare in philosophy and psychology to see theorists fall into dichotomous thinking about mental phenomena. On one side of the dichotomy there are processes that I will label “unintelligent.” These processes are thought to be unconscious, implicit, automatic, unintentional, involuntary, procedural, and non-cognitive. On the other side, there are “intelligent” processes that are conscious, explicit, controlled, intentional, voluntary, declarative, and cognitive. Often, if a process or behavior is characterized by one of the features from either of the (...) above lists, the process or behavior is classified as falling under the category to which the feature belongs. For example, if a process is implicit this is usually considered sufficient for classifying it as “unintelligent” and for assuming that the remaining features that fall under the “unintelligent” grouping will apply to it as well. Accordingly, if a process or behavior is automatic, philosophers often consider it to be unintelligent. It is my goal in this paper to challenge the conceptual slip from “automatic” to “unintelligent”. I will argue that there are a whole range of properties highlighted by the existing psychological literature that make automaticity a much more complex phenomenon than is usually appreciated. I will then go on to discuss two further important relationships between automatic processes and controlled processes that arise when we think about automatic processes in the context of skilled behavior. These interactions should add to our resistance to classifying automaticity as unintelligent or mindless. In Sect. 1, I present a few representative cases of philosophers classifying automatic processes and behaviors as mindless or unintelligent. In Sect. 2, I review trends in the psychology of automaticity in order highlight a complex set of features that are characteristic, though not definitive, of automatic processes and behaviors. In Sect. 3, I argue that at least some automatic processes are likely cognitively penetrable. In Sect. 4, I argue that the structure of skilled automatic processes is shaped diachronically by practice, training and learning. Taken together, these considerations should dislodge the temptation to equate “automatic” with “unintelligent”. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

Müller argues that general cognitive skills and linguistic skills are not necessarily independent. However, cross-linguistic evidence from an inherited specific language disorder affecting productive rules suggests significant degrees of modularity, innateness, and universality of language. Confident claims about the overall nature of such a complex system still await more interdisciplinary research.

Müller misconstrues autonomy to mean strict locality of brain function, something quite different from the functional autonomy that linguists claim. Similarly, he misperceives the interaction of learned and innate components hypothesized in current generative models. Evidence from sign languages, Creole languages, and neurological studies of rare forms of aphasia also argues against his conclusions.

This bold and brilliant book asks the ultimate question of the life sciences: How did the human mind acquire its incomparable power? In seeking the answer, Merlin Donald traces the evolution of human culture and cognition from primitive apes to the era of artificial intelligence, and presents an original theory of how the human mind evolved from its presymbolic form. In the emergence of modern human culture, Donald proposes, there were three radical transitions. During the first, our bipedal but still (...) apelike ancestors acquired "mimetic" skill—the ability to represent knowledge through voluntary motor acts—which made Homo erectus successful for over a million years. The second transition—to "mythic" culture —coincided with the development of spoken language. Speech allowed the large-brained Homo sapiens to evolve a complex preliterate culture that survives in many parts of the world today. In the third transition, when humans constructed elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematics, human biological memory became an inadequate vehicle for storing and processing our collective knowledge. The modern mind is thus a hybrid structure built from vestiges of earlier biological stages as well as new external symbolic memory devices that have radically altered its organization. According to Donald, we are symbol-using creatures, more complex than any that went before us, and we may have not yet witnessed the final modular arrangement of the human mind. (shrink)

This paper considers the extent to which the earliest stages of learning about systems of inscription requires not just individual mental effort, but effort that is distributed across a wide physical and intellectual environment. It is particularly concerned with how children under the age of three learn about notational systems, including writing, and examines parallels with the evolution of written systems. It considers the position that children gain knowledge incrementally over the early months and years of life, supported by a (...) commonly accepted view that learning is a mental activity in which knowledge accumulates hierarchically in the minds of individual children, starting with smallest parts of the system. The paper presents evidence to the contrary, suggesting that the early learning of inscriptional systems is associated not just with individual minds, but with social and cultural cognition that is dispersed across minds, bodies, tools, and material environments. It presents data and evidence from a small study of the sign-making of children under the age of three that indicates that children of this age already use notations purposefully in the construction of signs that are intentional, multimodal, and unbounded, and that already have features associated with conventional systems of inscription. In their early sign-making they use certain underlying principles of symbolic reference associated with conventional systems, including the use of ‘generic’ structures derived from social, bodily, and material experience. Central to this process are networks of interactions between co-participants, tools, materials, and the physical environment. (shrink)

This paper takes a fresh look at the nativism–empiricism debate, presenting and defending a nativist perspective on the mind. Empiricism is often taken to be the default view both in philosophy and in cognitive science. This paper argues, on the contrary, that there should be no presumption in favor of empiricism (or nativism), but that the existing evidence suggests that nativism is the most promising framework for the scientific study of the mind. Our case on behalf of nativism has four (...) parts. (1) We characterize nativism’s core commitments relative to the contemporary debate between empiricists and nativists, (2) we present the positive case for nativism in terms of two central nativist arguments (the poverty of the stimulus argument and the argument from animals), (3) we respond to a number of influential objections to nativist theories, and (4) we explain the nativist approach to the conceptual system. (shrink)

This paper is an attempt at exploring the possibility of reconciling the two interpretations of biolinguistics which have been recently projected by Koster(Biolinguistics 3(1):61–92, 2009). The two interpretations—trivial and nontrivial—can be roughly construed as non-internalist and internalist conceptions of biolinguistics respectively. The internalist approach boils down to a conception of language where language as a mental grammar in the form of I-language grows and functions like a biological organ. On the other hand, under such a construal consistent with Koster’s (Biolinguistics (...) 3(1):61-92, 2009), the non-internalist version does not necessarily have to be externalist in nature; rather it is a matter of mutual reinforcement of biology and culture under the rubric of a co-evolutionary dynamics. Here it will be argued that the apparent dichotomy between these two conceptions of biolinguistics can perhaps be resolved if we have a richer synthesis that accounts for both internalism and non-internalism. (shrink)

In this response, we consider four main issues arising from the commentaries to the target article. These include further details of the theory of interactive specialization, the relationship between neuroconstructivism and selectionism, the implications of neuroconstructivism for the notion of representation, and the role of genetics in theories of development. We conclude by stressing the importance of multidisciplinary approaches in the future study of cognitive development and by identifying the directions in which neuroconstructivism can expand in the Twenty-first Century.

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...) the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds. (shrink)

This paper advocates explicitness about the type of entity to be considered as content- bearing in connectionist systems; it makes a positive proposal about how vehicles of content should be individuated; and it deploys that proposal to argue in favour of representation in connectionist systems. The proposal is that the vehicles of content in some connectionist systems are clusters in the state space of a hidden layer. Attributing content to such vehicles is required to vindicate the standard explanation for some (...) classificatory networks’ ability to generalise to novel samples their correct classification of the samples on which they were trained. (shrink)

Many philosophers and cognitive scientists claim that our everyday or "folk" understanding of mental states constitutes a theory of mind. That theory is widely called "folk psychology" (sometimes "commonsense" psychology). The terms in which folk psychology is couched are the familiar ones of "belief" and "desire", "hunger", "pain" and so forth. According to many theorists, folk psychology plays a central role in our capacity to predict and explain the behavior of ourselves and others. However, the nature and status of folk (...) psychology remains controversial. (shrink)

The broad issue in this paper is the relationship between cognitive psychology and neuroscience. That issue arises particularly sharply for cognitive neurospsychology, some of whose practitioners claim a methodological autonomy for their discipline. They hold that behavioural data from neuropsychological impairments are sufficient to justify assumptions about the underlying modular structure of human cognitive architecture, as well as to make inferences about its various components. But this claim to methodological autonomy can be challenged on both philosophical and empirical grounds. A (...) priori considerations about (cognitive) multiple realisability challenge the thesis on philosophical grounds, and neuroscientific findings from developmental disorders substantiate that challenge empirically. The conclusion is that behavioural evidence alone is inadequate for scientific progress since appearances of modularity can be thoroughly deceptive, obscuring both the dynamic processes of neural development and the endstate network architecture of real cognitive systems. (shrink)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)

Our results support the idea that adults have access to the principles and parameters of Universal Grammar (UG), contrary to Epstein et al.'s misrepresentation of our work as involvingpartial access toUG. For both LI and L2 acquisition, functional projections appear to develop in a gradual fashion, but in L2 acquisition there ispartial transferin that the lowest projection (VP) is transferred from the speaker's LI.

We agree with Müller's epigenetic view of evolution and ontogeny and applaud his multilevel perspective. With him, we stress the importance in ontogeny of progressive specialisation rather than prewired structures. However, we argue that he slips from “speech” to “language” and that, in seeking homologies, these two levels need to be kept separate in the analysis of evolution and ontogeny.

Evidence is presented from a polyglot savant to suggest that double dissociations between linguistic and nonverbal abilities are more important than Müller's target article implies. It is also argued that the special nature of syntax makes its assimilation to other aspects of language or to nonhuman communication systems radically implausible.

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three (...) mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment. (shrink)

It has been speculated, by Chomsky and others, that our capacity for scientific understanding is not only enabled but also limited by a biologically endowed science forming faculty . I look at two sorts of consideration for the SFF thesis and find both wanting. Firstly, it has been claimed that a problem‐mystery distinction militates for the SFF thesis. I suggest that the distinction can be coherently drawn for cases, but that the purported‘evidence’for even a fairly lose general demarcation of problems (...) and mysteries is not best explained by a SFF. Secondly, I consider in detail a range of cognitive considerations for the SFF thesis and contend that it is at best moot whether science can be so construed as to make it feasible that it is a faculty competence. (shrink)

Any advanced theory of physics contains modules defined as essential components that are themselves theories with different domains of application. Different kinds of modules can be distinguished according to the way in which they fit in the symbolic and interpretive apparatus of a theory. The number and kind of the modules of a given theory vary as the theory evolves in time. The relative stability of modules and the variability of their insertion in other theories play a vital role in (...) the application, comparison, construction, and communication of theories. Modularity conveys some global unity to physics through the sharing of modules by diverse theories. This alternative to rigid hierarchies and holistic totalities permits a dynamical, plastic, and symbiotic approach to physical theory. (shrink)