_role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...) existing models of conscious-_. (shrink)

Maud Boyer Arnaud Destrebecqz Axel Cleeremans.

Comparing the relative sensitivity of direct and indirect measures of learning is proposed as the best way to provide evidence for unconscious learning when both conceptual and operative definitions of awareness are lacking. This approach was first proposed by Reingold & Merikle (1988) in the context of subliminal perception. In this paper, we apply it to a choice reaction time task in which the material is generated based on a probabilistic finite-state grammar (Cleeremans, 1993). We show (1) that participants progressively (...) learn about the statistical structure of the stimulus material over training with the choice reaction time task, and (2) that they can use some of this knowledge to predict the location of the next stimulus in a subsequent “generation” task. However, detailed partial correlational analyses of the correspondence between performance during the reaction time task and the statistical structure of the training material showed that large effects remained even when controlling for explicit knowledge as assessed by the generation task. Hence we conclude (1) that at least some of the knowledge expressed through reaction time performance can not be characterized as conscious, and (2) that even when associations are found at a global level of analysis, dissociations can still be obtained when more detailed analyses are conducted. Finally, we also show that participants are limited in the depth of the contingencies they can learn about, and that these limitations are shared by the Simple Recurrent Network model of Cleeremans & McClelland (1991). (shrink)

Complete URL to this document: http://srsc.ulb.ac.be/axcWWW/93-Principles.html.

High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...) BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. (shrink)

Scholars studying the origins and evolution of language are also interested in the general issue of the evolution of cognition. Language is not an isolated capability of the individual, but has intrinsic relationships with many other behavioral, cognitive, and social abilities. By understanding the mechanisms underlying the evolution of linguistic abilities, it is possible to understand the evolution of cognitive abilities. Cognitivism, one of the current approaches in psychology and cognitive science, proposes that symbol systems capture mental phenomena, and attributes (...) cognitive validity to them. Therefore, in the same way that language is considered the prototype of cognitive abilities, a symbol system has become the prototype for studying language and cognitive systems. Symbol systems are advantageous as they are easily studied through computer simulation (a computer program is a symbol system itself), and this is why language is often studied using computational models. (shrink)

Many studies of language, whether in philosophy, linguistics, or psychology, have focused on highly developed human languages. In their highly developed forms, such as are employed in scientific discourse, languages have a unique set of properties that have been the focus of much attention. For example, descriptive sentences in a language have the property of being "true" or "false," and words of a language have senses and referents. Sentences in a language are structured in accord with complex syntactic rules. Theorists (...) focusing on language are naturally led to ask questions such as what constitutes the meanings of words and sentences and how are the principles of syntax encoded in the heads of language users. While there is an important function for inquiries into the highly developed forms of these cultural products (Abrahamsen, 1987), such a focus can be quite misleading when we want to explain how these products have arisen or the human capacity to use language. The problem is that focusing on its most developed forms makes linguistic ability seem to be a _sui generis_ phenomenon, not related to, and hence not explicable in terms of other cognitive capacities. Chomsky's (1980) postulation of a specific language module equipped with specialized resources needed to process language and possessed only by hum ans is not a surprising result. (shrink)

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current (...) thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. (shrink)

Arguments for the autonomy of psychology or other higher-level sciences have often taken the form of denying the possibility of reduction. The form of reduction most proponents and critics of the autonomy of psychology have in mind is theory reduction. Mechanistic explanations provide a different perspective. Mechanistic explanations are reductionist insofar as they appeal to lower-level entities—the component parts of a mechanism and their operations— to explain a phenomenon. However, unlike theory reductions, mechanistic explanations also recognize the fundamental role of (...) organization in enabling mechanisms to engage their environments as units (as well as the role of yet higher-level structures in constraining such engagement). Especially when organization is non-linear, it can enable mechanisms to generate phenomena that are quite surprising given the operations of the components taken in isolation. Such organization must be discovered—it cannot simply be derived from knowledge of lower-level parts and their operations. Moreover, the organized environments in which mechanisms operate must also be discovered. It is typically the higher-level disciplines that have the tools for discovering the organization within and between mechanisms. Although these inquiries are constrained by the knowledge of the parts and operations constituting the mechanism, they make their own autonomous contribution to understanding how a mechanism actually behaves. Thus, mechanistic explanations provide a strong sense of autonomy for higher levels of organization and the inquiries addressing them even while recognizing the distinctive contributions of reductionistic research investigating the operations of the lower level components. (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)

Studies based on the Contemporary Theory of Metaphor (Lakoff & Johnson, 1980, 1999) usually identify conceptual metaphors by analysing linguistic expressions and creating a post hoc interpretation of the findings. This method has been questioned for a variety of reasons, including its circularity (Müller, 2008), lack of falsifiability (Vervaeke & Kennedy, 1996, 2004), and lack of predictive power (Ritchie, 2003). It has been argued that CTM requires additional constraints to improve its applicability for empirical research (Gibbs, 2011; Ritchie, 2003). This (...) paper sets out to propose additional methodological structure to CTM, a theory of conceptual metaphor in which much of abstract thought is generated by metaphorical mapping from embodied experience (Ruiz de Mendoza Ibáñez & Pérez Hernández, 2011). Introducing Objectification Theory defined by Szwedek (2002, 2007, 2011) ameliorates a number of methodological issues in CTM. First, the embodiment claim of CTM in its current form cannot be empirically proven incorrect (Vervaeke & Kennedy, 2004) as any mapping within it is possible (although only some actually happen). Objectification introduces pre-metaphorical structure of the kind suggested by Glucksberg (2001), constraining source and target domain selection, predicting which mappings are more likely to happen. Second, while many claim that metaphors trace back to a literal concept based on embodied physical experience (Gibbs, Costa Lima, & Francozo, 2004), it is unclear what criteria are used to define „physical”. Metaphorical domains are often described using the terms „abstract” and „concrete”, Objectification proposes objective criteria for deciding whether a concept is experientially grounded. Finally, Objectification provides grounds for introducing a hierarchical framework for metaphor typology, preventing post-hoc addition of metaphor types if and when suitable for the explanation of a phenomenon; thus increasing the consistency of the CTM framework, both internally and with other cognitive science disciplines. This thesis focuses on providing evidence for Objectification Theory and identifying its applications in metaphor and gesture research. (shrink)

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for (...) many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence. (shrink)

In this paper, I review the objections against the claim that brains are computers, or, to be precise, information-processing mechanisms. By showing that practically all the popular objections are either based on uncharitable interpretation of the claim, or simply wrong, I argue that the claim is likely to be true, relevant to contemporary cognitive (neuro)science, and non-trivial.

Neurological syndromes in which consciousness seems to malfunction, such as temporal lobe epilepsy, visual scotomas, Charles Bonnet syndrome, and synesthesia offer valuable clues about the normal functions of consciousness and ‘qualia’. An investigation into these syndromes reveals, we argue, that qualia are different from other brain states in that they possess three functional characteristics, which we state in the form of ‘three laws of qualia’. First, they are irrevocable: I cannot simply decide to start seeing the sunset as green, or (...) feel pain as if it were an itch; second, qualia do not always produce the same behaviour: given a set of qualia, we can choose from a potentially infinite set of possible behaviours to execute; and third, qualia endure in short-term memory, as opposed to non-conscious brain states involved in the on-line guidance of behaviour in real time. We suggest that qualia have evolved these and other attributes because of their role in facilitating non-automatic, decision-based action. We also suggest that the apparent epistemic barrier to knowing what qualia another person is experiencing can be overcome by using a ‘bridge’ of neurons; and we offer a hypothesis about the relation between qualia and one's sense of self. (shrink)

Cognitive sciences are grouped together according to their substantial disciplines such as neurobiology, psychology, linguistics and many others. From more than ten years a search has been undertaken for the best fitting cognitive research method, that does not influence the scientific output. More recently attention has been turned to systems which might operate through their dynamic aspect, called the dynamic approach. Their great advantage is to see the old linguistic axioms such as connectivity in a new way.

One of the great successes of the application of generalized quantifiers to natural language has been the ability to formulate robust semantic universals. When such a universal is attested, the question arises as to the source of the universal. In this paper, we explore the hypothesis that many semantic universals arise because expressions satisfying the universal are easier to learn than those that do not. While the idea that learnability explains universals is not new, explicit accounts of learning that can (...) make good on this hypothesis are few and far between. We propose a model of learning — back-propagation through a recurrent neural network — which can make good on this promise. In particular, we discuss the universals of monotonicity, quantity, and conservativity and perform computational experiments of training such a network to learn to verify quantifiers. Our results are able to explain monotonicity and quantity quite well. We suggest that conservativity may have a different source than the other universals. (shrink)

This project provides a detailed examination and critique of current philosophical, linguistic, and cognitive accounts of first language acquisition. In particular, I focus on the concept of "innate" and how it is embraced, marginally utilized, or abandoned altogether in efforts to describe the way that a child comes to be a competent user of a language. A central question that naturally falls out of this general inquiry is therefore what exactly is supposed to be "innate," according to various theories? Philosophically, (...) the theory of innate ideas put forth to explain human learning has existed for centuries and hence, this thesis as it relates to language is discussed. The revival of nativism by linguists like Chomsky is thus a central theme of the first chapter. Universal Grammar and the various arguments for it are closely scrutinized, and I close this chapter with what I take to be the commitments of linguistic nativism, how its proponents conceive of "innate," and several possible objections to the arguments they put forth. Just as the theory of innate ideas has had its contesters throughout the history of philosophy, so too have linguists and cognitive scientists rejected Universal Grammar and other forms of linguistic nativism. Thus, the second chapter presents several of these alternative explanations of language acquisition. Namely, I divide the chapter into three sections, Usage-Based Linguistics, Emergentism, and Sociolinguistic Acquisition, as it is my suggestion that most of the anti-UG attacks are levied from one of these three fields. In discussing the details of each, two distinctions become of particular concern: first, a large part of the differing conceptions of "innate" seem to hinge on what is meant by "learning" and "acquiring," and therefore second, a fine line between UBL and Emergentism can be drawn, a relationship that is otherwise conflated in the literature. Because chapter two involves a brief account of the way in which connectionist simulations are often utilized to model or represent language acquisition, particularly from an Emergentist perspective, chapter three begins by examining this feature of Emergentism in more detail. Due to its explanatory power, ability to be effectively modeled, and the evidentiary support found in neuroscience, Emergentism would appear to be the most tenable position to maintain regarding language acquisition. This possibility seems further strengthened when we take into account the neuroscientific data often used to bolster anti-nativist claims. Nevertheless, reflecting on the overarching concern of the project, regarding what is really meant by "innate," it is shown that this attack on nativism might stand on shakier ground than was originally assumed. Finally, based on these considerations, a case is made for an intermediary position, a theory of "Minimally Innate Ideas.". (shrink)

In this article we will argue that given certain conditions for the evolution of bi- ological controllers, these will necessarily evolve in the direction of incorporating consciousness capabilities. We will also see what are the necessary mechanics for the provision of these capabilities and extrapolate this vision to the world of artifi- cial systems postulating seven design principles for conscious systems. This article was published in the journal Neural Networks special issue on brain and conscious- ness.