Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by (...) class='Hi'>learning to predict features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference. (shrink)

Language and information processes are critical issues in scientific controversies regarding the qualities that epitomize humanness. Whereas some theorists claim human mental uniqueness with regard to language, others point to successes in teaching language skills to other animals. However, although these animals may learn names for things, they show little ability to utilize a complex framework of symbolic reference. In such a framework, words or other symbols refer not only to objects and concepts but also to sequential and hierarchical (...) relationships with other symbols. This process is essential to human mental operations, including language, mathematics, and music. In humans, these operations may have coevolved with the prefrontal area of the cerebral cortex, which is proportionately much larger in humans than in other animals and more intricately linked with other areas of the brain. Analysis of the structure and function of the prefrontal area suggests that it is centrally involved in the operation of higher‐order associative relationships involving the subordination of one set of associations to another. This alternate learning strategy apparently appeared at the cost of certain sensory, motor, or limbic abilities. The payoff was symbolic thinking. Humans thus are unique among species, not just for their highly developed language ability but for their odd style of thinking and learning. (shrink)

Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the (...) sense of “solving the symbol grounding problem.”. (shrink)

The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept in cognitive science and can be traced back more than a half century to Simon's early work on discrimination nets for simulating human expertise. The major difference between DNNs and the deep memory nets meant for explaining human cognition is that the latter (...) are symbolic networks meant to model the dynamics of human memory and learning. Cognition-inspired symbolic deep networks (SDNs) address several known issues with DNNs, including (1) learning efficiency, where a much larger number of training examples are required for DNNs than would be expected for a human; (2) catastrophic interference, where what is learned by a DNN gets unlearned when a new problem is presented; and (3) explainability, where there is no way to explain what is learned by a DNN. This paper explores whether SDNs can achieve similar classification accuracy performance to DNNs across several popular ML datasets and discusses the strengths and weaknesses of each approach. Simulations reveal that (1) SDNs provide similar accuracy to DNNs in most cases, (2) SDNs are far more efficient than DNNs, (3) SDNs are as robust as DNNs to irrelevant/noisy attributes in the data, and (4) SDNs are far more robust to catastrophic interference than DNNs. We conclude that SDNs offer a promising path toward human-level accuracy and efficiency in category learning. More generally, ML frameworks could stand to benefit from cognitively inspired approaches, borrowing more features and functionality from models meant to simulate and explain human learning. (shrink)

Deep Neural Networks (DNNs) are popular for classifying large noisy analogue data. However, DNNs suffer from several known issues, including explainability, efficiency, catastrophic interference, and a need for high‐end computational resources. Our simulations reveal that psychologically‐inspired symbolic deep networks (SDNs) achieve similar accuracy and robustness to noise as DNNs on common ML problem sets, while addressing these issues.

It is well-documented that mathematics achievement is an important predictor of many positive life outcomes like college graduation, career opportunities, salary, and even citizenship. As such, it is important for researchers and educators to help students succeed in mathematics. Although there are undoubtedly many factors that contribute to students' success in mathematics, much of the research and intervention development has focused on variations in instructional techniques. Indeed, even a cursory glance at many educational journals and granting agencies reveals that there (...) is a large amount of time, energy, and resources being spent on determining the best way to convey information through direct, declarative instruction. The proposed project is motivated by recent calls to expand the focus of research in mathematics education beyond direct, declarative instruction. The overarching goal of the presented experiment is to evaluate the efficacy of a novel mathematics intervention designed using principles taken from the literature on non-declarative learning. The intervention combines errorless learning and structured cue fading to help second grade students improve their understanding of symbolic magnitude. Results indicate that students who learned about symbolic magnitude using the novel intervention did better than students who were provided with extensive declarative support. These findings offer preliminary evidence in favor of using learning combination of errorless learning and cue fading techniques in the mathematics classroom. (shrink)

Cognitive theories based on a fixed feature set suffer from frame and symbol grounding problems. Flexible features and other empirically acquired constraints (e.g., analog-to-analog mappings) provide a framework for letting extrinsic relations influence symbol manipulation. By offering a biologically plausible basis for feature learning, nonorthogonal multiresolution analysis and dimensionality reduction, informed by functional constraints, may contribute to a solution to the symbol grounding problem.

This paper contrasts two influential theoretical accounts of language change and evolution – Iterated Learning and Social Coordination. The contrast is based on an experiment that compares drawings produced with Garrod et al’s ‘pictionary’ task with those produced in an Iterated Learning version of the same task. The main finding is that Iterated Learning does not lead to the systematic simplification and increased symbolicity of graphical signs produced in the standard interactive version of the task. A second (...) finding is that Iterated Learning leads to less conceptual and structural alignment between participants than observed for those in the interactive condition. The paper concludes with a comparison of the two accounts in relation to how each promotes signs that are effi cient, systematic and learnable. (shrink)

This paper contrasts two influential theoretical accounts of language change and evolution – Iterated Learning and Social Coordination. The contrast is based on an experiment that compares drawings produced with Garrod et al’s ‘pictionary’ task with those produced in an Iterated Learning version of the same task. The main finding is that Iterated Learning does not lead to the systematic simplification and increased symbolicity of graphical signs produced in the standard interactive version of the task. A second (...) finding is that Iterated Learning leads to less conceptual and structural alignment between participants than observed for those in the interactive condition. The paper concludes with a comparison of the two accounts in relation to how each promotes signs that are effi cient, systematic and learnable. (shrink)

methods to improve reinforcement learning are identi ed and discussed in some detail Each demonstrates to some extent the advantages of combining RL and symbolic meth ods These methods point to the potentials and the chal lenges of this line of research..

This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network (...) being trained on the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation. (shrink)

To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that (...) shed light on the working of the model.. (shrink)

Perruchet & Vinter claim that with the additional capacity to determine whether two arbitrary stimuli are the same or different, their association-based PARSER model is sufficient to account for learning transfer. This claim overstates the generalization capacity of perceptual versus nonperceptual (symbolic) relational processes. An example shows why some types of learning transfer also require the capacity to bind arbitrary representations to nonperceptual relational symbols.

Supporters of eliminative connectionism have argued for a pattern association-based explanation of language learning and language processing. They deny that explicit rules and symbolic representations play any role in language processing and cognition in general. Their argument is based to a large extent on two artificial neural network (ANN) models that are claimed to be able to learn the past tenses of English verbs (Rumelhart & McClelland, 1986, Parallel distributed processing, Vol. 2, Cambridge, MA: MIT Press; MacWhinney & (...) Leinbach, 1991, Cognition, 40, 121-157). In this article we critically review Rumelhart and McClelland's as well as MacWhinney and Leinbach's ANN models and conclude that they do not succeed in the assigned task of learning the past tenses of English verbs. In order to answer their challenge to the symbolic processing approach, we present our symbolic pattern associator (SPA)-a general-purpose pattern associator that can learn to associate arbitrary discrete patterns. We carried out several experiments with the SPA using the same set of verbs that was used in MacWhinney and Leinbach's simulation with more realistic training and testing procedures. The SPA outperformed the connectionist models by a wide margin in the accuracy of learning, and successful inductive generalizations to unseen verbs. Our SPA has very natural and psychologically realistic explanations to many psychological effects such as U-shaped learning curve, and is much closer to human subjects in predicting past tense of the pseudo-verbs. In contrast to ANNs, whose internal representations are entirely opaque, the SPA can represent the acquired knowledge in the form of production rules that allow for further higher-level processing and integration, resulting in linguistically realistic associative templates for irregular verbs and production rules for regular verbs. In the light of these findings, we conclude that eliminative connectionists' vision of cognition as simple pattern association and pattern recognition without symbolic representation is inadequate. Pattern association as such does not imply rule-less or cue-based models of language acquisition or of human learning in general. (shrink)

Ten essays by noted scholars address the subjects of educational policy, methods, ideology, and more, with stress upon the rhetoric of contemporary teaching and learning.

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) (...) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior. (shrink)

This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to (...) deal with sequential decision tasks. Experiments and analyses in various ways are reported that shed light on the advantages of the model. (shrink)

In the article, we propose to frame organizational learning as inquiry into and resolving tensions arising from the performance of different commitments to work and its organizing. We expand learning as participation with its focus upon identity and membership to the development of work and the experiences and knowledge of its participants. The proposal is inspired by pragmatist philosophy both through its emphasis on learning as ascribing meaning to experience and its sociological version, symbolic interactionism with (...) its emphasis on work and its organizing in social arenas/worlds. We show that a pragmatist learning theory, founded on principles of non-dualistic, non-hierarchy, and a non-linear view on relations, thinking and practice aligns well with the proposed refurbished learning approach via complexity theory in this special issue. Further, we propose that Dewey’s pragmatism adds an important feature to a refurbished theory of learning in organizations by emphasizing the tensional and uncertain aspects of situations as contextual wholes that for a pragmatist learning theory constitute the analytical point of departure. (shrink)

This book is a selection of papers from a conference which took place at the University of Keele in July 1982. The conference was an extraordinarily enjoyable one, and we would like to take this opportunity of thanking all participants for helping to make it so. The conference was intended to allow scholars working on different aspects of symbolic behaviour to compare findings, to look for common ground, and to identify differences between the various areas. We hope that it (...) was successful in these aims: the assiduous reader may judge for himself. Several themes emerged during the course of the conference. Some of these were: 1. There is a distinction to be made between those symbol systems which attempt, more or less directly, to represent a state of affairs in the world (e. g. language, drawing, map and navigational skill) and those in which the representational function is complemented, if not overshadowed, by properties of the symbol system itself, and the systematic inter-relations that symbols can have to one another (e. g. music, mathematics). The distinction is not absolute, for the nature of all symbolic skills is, in part, a function of the structure of the symbolic system employed. Nonetheless, this distinction helps us to understand some common acquisition difficulties, such as that experienced in mathematics, where mental manipulation of symbols can go awry if a child assumes too close a correspondence between mathematical symbols and the world they represent. 2. (shrink)

Symbolic arithmetic is fundamental to science, technology and economics, but its acquisition by children typically requires years of effort, instruction and drill1,2. When adults perform mental arithmetic, they activate nonsymbolic, approximate number representations3,4, and their performance suffers if this nonsymbolic system is impaired5. Nonsymbolic number representations also allow adults, children, and even infants to add or subtract pairs of dot arrays and to compare the resulting sum or difference to a third array, provided that only approximate accuracy is required6–10. (...) Here we report that young children, who have mastered verbal counting and are on the threshold of arithmetic instruction, can build on their nonsymbolic number system to perform symbolic addition and subtraction11–15. Children across a broad socio-economic spectrum solved symbolic problems involving approximate addition or subtraction of large numbers, both in a laboratory test and in a school setting. Aspects of symbolic arithmetic therefore lie within the reach of children who have learned no algorithms for manipulating numerical symbols. Our findings help to delimit the sources of children’s difficulties learning symbolic arithmetic, and they suggest ways to enhance children’s engagement with formal mathematics. We presented children with approximate symbolic arithmetic problems in a format that parallels previous tests of non-symbolic arithmetic in preschool children8,9. In the first experiment, five- to six-year-old children were given problems such as ‘‘If you had twenty-four stickers and I gave you twenty-seven more, would you have more or less than thirty-five stickers?’’. Children performed well above chance (65.0%, t1952.77, P 5 0.012) without resorting to guessing or comparison strategies that could serve as alternatives to arithmetic. Children who have been taught no symbolic arithmetic therefore have some ability to perform symbolic addition problems. The children’s performance nevertheless fell short of performance on non-symbolic arithmetic tasks using equivalent addition problems with numbers presented as arrays of dots and with the addition operation conveyed by successive motions of the dots into a box (71.3% correct, F1,345 4.26, P 5 0.047)8.. (shrink)

This article reconsiders the early hominid ‘‘lithic niche’’ by examining the social implications of stone artifact making. I reject the idea that making tools for use is an adequate explanation of the elaborate artifact forms of the Lower Palaeolithic, or a sufficient cause for long-term trends in hominid technology. I then advance an alternative mechanism founded on the claim that competency in making stone artifacts requires extended learning, and that excellence in artifact making is attained only by highly skilled (...) individuals who have been taught and practiced for extensive periods. Consequently both competency and expertise in knapping comes at a high learning cost for both the individual learner and the social group to which they belong. Those high intrinsic costs of learning created contexts in which groups selected cost-reducing forms of social learning and teaching, and in which specialization could develop. Artifacts and their manufacturing processes probably acquired functions as social signals—as honest signals of valuable capacities. The magnification of these signals, through competition between knappers and through inspiring later craftspeople, may account for a substantial amount of the accumulated elaboration visible in the archaeological record. Consequently lithic artifacts operated as material symbols from an early time in hominid evolution. (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be (...) grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

Self-relevance has been demonstrated to impair instrumental learning. Compared to unfamiliar symbols associated with a friend, analogous stimuli linked with the self are learned more slowly. What is not yet understood, however, is whether this effect extends beyond arbitrary stimuli to material with intrinsically meaningful properties. Take, for example, stimulus valence an established moderator of self-bias. Does the desirability of to-be-learned material influence self-learning? Here, in conjunction with computational modelling (i.e. Reinforcement Learning Drift Diffusion Model analysis), a (...) probabilistic selection task was used to establish if and how stimulus valence (i.e. desirable/undesirable posters) impacts the acquisition of knowledge relating to object-ownership (i.e. owned-by-self vs. owned-by-friend). Several interesting results were observed. First, undesirable posters were learned more rapidly for self compared to friend, an effect that was reversed for desirable posters. Second, learning rates were accompanied by associated differences in reward sensitivity toward desirable and undesirable choice selections as a function of ownership. Third, decisional caution was greater for self-relevant (vs. friend relevant) responses. Collectively, these findings inform understanding of self-function and how valence and stimulus relevance mutually influence probabilistic learning. (shrink)

In typical statistical learning studies, researchers define sequences in terms of the probability of the next item in the sequence given the current item (or items), and they show that high probability sequences are treated as more familiar than low probability sequences. Existing accounts of these phenomena all assume that participants represent statistical regularities more or less as they are defined by the experimenters—as sequential probabilities of symbols in a string. Here we offer an alternative, or possibly supplementary, hypothesis. (...) Specifically, rather than identifying or labeling individual stimuli discretely in order to predict the next item in a sequence, we need only assume that the participant is able to represent the stimuli as evincing particular similarity relations to one another, with sequences represented as trajectories through this similarity space. We present experiments in which this hypothesis makes sharply different predictions from hypotheses based on the assumption that sequences are learned over discrete, labeled stimuli. We also present a series of simulation models that encode stimuli as positions in a continuous two‐dimensional space, and predict the next location from the current location. Although no model captures all of the data presented here, the results of three critical experiments are more consistent with the view that participants represent trajectories through similarity space rather than sequences of discrete labels under particular conditions. (shrink)

Humans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? -/- The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This (...) book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics. By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capabilities. -/- The book will be invaluable reading for academic researchers, graduate students, and senior undergraduates in computer science, artificial intelligence, machine learning, cognitive science and engineering. It will also be of interest to computational logicians, and professional specialists on applications of cognitive, hybrid and artificial intelligence systems. (shrink)

The paper develops a positive account of the representational capacity of cognitive systems: simple, associationist learning mechanisms and an architecture that supports bootstrapping are sufficient conditions for symbol tool use. In terms of the debates within the philosophy of mind, this paper offers a plausibility account of representation externalism, an alternative to the reductive, computational/representational models of intentionality that still play a leading role in the field. Although the central theme here is representation, methodologically this view complements embodied, enactivist (...) approaches to studying cognition. (shrink)

Concepts such as “symbolism” and “symbolic cognition” often remain unspecified in discussions the symbolic capacities of earlier hominins. In this paper, I use conceptual tools from phenomenology to reflect on the origins of early symbolic cognition. In particular, I discuss the possible early use of pointing gestures around the time of the earliest known stone tool industries. I argue that unlike more basic social acts such as expression, gaze following, and attention-getters, which are used by extant non-human (...) great apes, communicative pointing involves key elements that are characteristic of symbolic cognition. In particular, it involves “third order intentionality” as well as “shared practice horizons”: shared frameworks of understanding which are required for the interpretation of communicative acts whose meaning is not codified indexically or iconically in the signaling behavior. In the final part, I briefly review some indications for the use of pointing gestures around the time of the Lomekwian and Oldowan industries, as a way to sustain cooperation and possibly learning by instruction. It is suggested that pointing is more complex than is standardly acknowledged, and that it may have been an important communicative act for Early Stone Age hominins in transitioning to more fully symbolic speech capacities. (shrink)

This article focuses on how young children acquire concepts for exact, cardinal numbers. I believe that exact numbers are a conceptual structure that was invented by people, and that most children acquire gradually, over a period of months or years during early childhood. This article reviews studies that explore children’s number knowledge at various points during this acquisition process. Most of these studies were done in my own lab, and assume the theoretical framework proposed by Carey. In this framework, the (...) counting list and the counting routine form a placeholder structure. Over time, the placeholder structure is gradually filled in with meaning to become a conceptual structure that allows the child to represent exact numbers A number system is a socially shared, structured set of symbols that pose a learning challenge for children. But once children have acquired a number system, it allows them to represent information that they had no way of representing before. (shrink)

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a (...) novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture. (shrink)

A new theory about the origins of consciousness that finds learning to be the driving force in the evolutionary transition to basic consciousness. What marked the evolutionary transition from organisms that lacked consciousness to those with consciousness—to minimal subjective experiencing, or, as Aristotle described it, “the sensitive soul”? In this book, Simona Ginsburg and Eva Jablonka propose a new theory about the origin of consciousness that finds learning to be the driving force in the transition to basic consciousness. (...) Using a methodology similar to that used by scientists when they identified the transition from non-life to life, Ginsburg and Jablonka suggest a set of criteria, identify a marker for the transition to minimal consciousness, and explore the far-reaching biological, psychological, and philosophical implications. After presenting the historical, neurobiological, and philosophical foundations of their analysis, Ginsburg and Jablonka propose that the evolutionary marker of basic or minimal consciousness is a complex form of associative learning, which they term unlimited associative learning (UAL). UAL enables an organism to ascribe motivational value to a novel, compound, non-reflex-inducing stimulus or action, and use it as the basis for future learning. Associative learning, Ginsburg and Jablonka argue, drove the Cambrian explosion and its massive diversification of organisms. Finally, Ginsburg and Jablonka propose symbolic language as a similar type of marker for the evolutionary transition to human rationality—to Aristotle's “rational soul.”. (shrink)

top-down approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward lowlevel skill learning, where procedural knowledge develops rst and declarative knowledge develops from it. Clarionwhich follows this approach is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a mine eld navigation task. A match between the model and human data is observed in several comparisons.

How can imitation lead to free musical expression? This article explores the role of auditory imitation in jazz. Even though many renowned jazz musicians have assessed the method of imitating recorded music, no systematic study has hitherto explored how the method prepares for aural jazz improvisation. The article uses Berliner's assumption that learning jazz by aural imitation is “just like” learning a mother tongue. The article studies three potential stages in the method, comparing them to the imitative, rhythmic, (...) multimodal, and protosymbolic behavior of infant perception (building on the works of Stern, Trevarthen, and Merleau-Ponty). The demonstrations of the aural imitation method draw on pedagogic experiences accumulated since 1979 at the Jazz Program at the Norwegian University of Science and Technology. By analyzing structures of behavior suggested by the method, the article indicates key traits that render aural jazz improvisation possible, such as a fundamental sense of rhythm, formation of symbolic behavior, joint musical attention, and the facility to “hear via the other.” In conclusion, we critically address a frequent theoretical model describing musical improvisation as a synthesis of discrete elements or building blocks. (shrink)