This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit

/pdf/a-survey-of-artificial-cognitive-systems-implications-for-1aa3j829s0.pdf

A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents

Of course, words aren’t magic. Neither are sextants, compasses, maps, slide rules and all the other paraphenelia which have accreted around the basic biological brains of homo sapiens. In the case of these other tools and props, however, it is transparently clear that they function so as to either carry out or to facilitate computational operations important to various human projects. The slide rule transforms complex mathematical problems (ones that would baffle or tax the unaided subject) into simple tasks of perceptual recognition. The map provides geographical information in a format well-suited to aid complex planning and strategic military operations. The compass gathers and displays a kind of information that (most) unaided human subjects do not seem to command. These various tools and props thus act to generate information, or to store it, or to transform it, or some combination of the three. In so doing, they impact our individual and collective problemsolving capacities in much the same dramatic ways as various software packages impact the performance of a simple pc.

/pdf/magic-words-how-language-augments-human-computation-4l312wo6ii.pdf

Magic Words: How Language Augments Human Computation

Rethinking Eliminative Connectionism

Previous behavioral and functional neuroimaging data indicate that certain aspects of phonological processing may not be acquired spontaneously, but are modulated by learning an alphabetic written language, that is, learning to read and write. It appears that learning an alphabetic written language modifies the auditory-verbal (spoken) language processing competence in a nontrivial way. We have previously suggested, based on behavioral and functional neuroimaging data, that auditory-verbal and written language interact not only during certain language tasks, but that learning and developing alphabetic written language capacities significantly modulates the spoken language system. Specifically, the acquisition of alphabetic orthographic knowledge has a modulatory influence on sublexical phonological processing and the awareness of sublexical phonological structure. We have suggested that developing an orthographic representation system for an alphabetic written language, and integrating a phoneme-grapheme correspondence with an existing infrastructure for auditory-verbal language processing, will result in a modified language network. Specifically, we suggest that the parallel interactive processing characteristics of the underlying language-processing brain network differ in literate and illiterate subjects. Therefore, the pattern of interactions between the regions of a suitably defined large-scale functional-anatomical network for language processing will differ between literate and illiterate subjects during certain language tasks. In order to investigate this hypothesis further, we analyzed the observed covariance structure in a PET data set from a simple auditory-verbal repetition paradigm in literate and illiterate subjects, with a network approach based on structural equation modeling (SEM). Based on a simple network model for language processing, the results of the present network analysis indicate that the network interactions during word and pseudoword repetition in the illiterate group differ, while there were no significant differences in the literate group. The differences between the two tasks in the illiterate group may reflect differences in attentional modulation of the language network, executive aspects of verbal working memory and the articulatory organization of verbal output. There were no significant differences between the literate and illiterate group during word repetition. In contrast, the network interactions differed between the literate and illiterate group during pseudoword repetition. In addition to differences similar to those observed in the illiterate group between word and pseudoword repetition, there were differences related to the interactions of the phonological loop between the groups. In particular, these differences related to the interaction between Broca's area and the inferior parietal cortex as well as the posterior-midinsula bridge between Wernicke's and Broca's area. In conclusion, the results of this network analysis are consistent with our previously presented results and support the hypothesis that learning to read and write during childhood influences the functional architecture of the adult human brain. In particular, the basic auditory-verbal language network in the human brain is modified as a consequence of acquiring orthographic language skills.

/pdf/language-processing-modulated-by-literacy-a-network-analysis-2kmssdgyhx.pdf

Language Processing Modulated by Literacy: A Network Analysis of Verbal Repetition in Literate and Illiterate Subjects

1. Introduction 2. The historical perspective 3. Approaching 'language change' 4. The Darwinian approach 5. Generalising Darwinism 6. Towards an evolutionary theory of language 7. What does all this imply for the study of language change? 8. How to live with feet, if one happens to be a morph-meme 9. The prosodic evolution of English word forms or the great trochaic conspiracy 10. Conclusion.

Selfish Sounds and Linguistic Evolution: A Darwinian Approach to Language Change

Connectionism: Debates on Psychological Explanation

PART I. 1. Causal Relevance. a. Introduction: C. & G. Macdonald. b. Can the mind change the world?: N. Block (MIT). c. How to be psychologically relevant: C. & G. Macdonald. d. Reply: N. Block. 2. Explanatory Relevance. a. Introduction: C. & G. Macdonald. b. Does meaning matter?: F. Dretske (Stanford). c. Explanatory exclusion and the problem of mental causation: J.Kim (Brown University). d. Reply: F. Dretske. PART II. 3. Anti-Individualism and Psychological Explanation. a. Introduction: C. & G. Macdonald. b. Individualism and psychology: T. Burge (University of California). c. A modal argument for narrow content: J. Fodor (Rutgers University). d. Reply: T. Burge. 4. Biopsychology. a. Introduction: C. & G. Macdonald. b. Biosemantics: R. Millikan (University of Connecticut). c. Excerpt from 'A Study of Concepts': C. Peacocke (University of Oxford). d. Reply: R. Millikan. Part III. 5. Subdoxastic Explanation I: Connectionism and Classical Architecture. a. Introduction: C. & G. Macdonald. b. On the proper treatment of connectionism: P. Smolensky. c. Connectionism and cognitive architecture: J. Fodor and Z. Pylyshyn. d. Connectionism, constituency, and the language of thought: P. Smolensky. e. Connectionsim and the problem of systematicity: J. Fodor and B. McLaughlin. f. Reply: P. Smolensky 6. Subdoxastic Explanation II: Connectionism and Eliminativism. a. Introduction: C. & G. Macdonald. b. Connectionism, Eliminativism, and the future of folk psychology: W. Ramsey, S. Stich, and J. Garon. c. Connectionist minds: A. Clark (University of Wisconsin, Milwaukee). d. Reply: S. Stich and T. Warfield. Part IV. 7. Tacit Knowledge, the Unconscious, and Psychological Explanation. a. Introduction: C. & G. Macdonald. b. Tacit knowledge and subdoxastic states: M. Davies (University of Oxford). c. Consciousness, explanatory inversion, and cognitive science: J. Searle (University of California). d. Reply: M. Davies. 8. Psychoanalytic Explanation. a. Introduction: C. & G. Macdonald. b. On Freud: J. Hopkins. c. Self-deception and the nature of mind: M. Johnston (University of Princeton).

Philosophy of psychology

Causal relevance and explanatory exclusion

/pdf/explanation-in-historiography-440jhdr6hn.pdf

Explanation in Historiography

Mental causation and non-reductive monism

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