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Cognitive Science

Cognitive Science. Introduction. Overview. Aims and learning outcomes Assessment Programme Cognitive science is interdisciplinary Cognitive science uses formal models Beware This strategy might not succeed (!) Fashion can influence the perception of research. Aims.

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Cognitive Science

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  1. Cognitive Science Introduction

  2. Overview • Aims and learning outcomes • Assessment • Programme • Cognitive science is interdisciplinary • Cognitive science uses formal models Beware • This strategy might not succeed (!) • Fashion can influence the perception of research

  3. Aims • To introduce interdisciplinary approaches to the study of higher cognitive processes • To familiarise you with computational and other formal modelling • To illustrate the application of modelling to cognitive processes

  4. Learning outcomes • To gain direct experience of computational and other formal modelling techniques. • To integrate material across areas within psychology and across traditional subject disciplines. • To compare and critically evaluate formal techniques in relation to empirical findings. • To tackle key theoretical problems in cognitive science, particularly problems linked by the theme of common sense reasoning.

  5. Assessment • Two hour examination in June, which counts for two thirds of the mark. • Three pieces of coursework (counting for 4%, 4%, and 25% respectively of the course mark) • Coursework assesses the first and, to a lesser degree, the third learning objectives. • The exam will assess learning objectives two, three and four.

  6. Coursework AW 1 - Connectionist modelling 1 (4%) AW 2 - Connectionist modelling 2 (4%) Modelling project (25%)

  7. Programme 1 Introduction - why cognitive science? 2 Cognitive modelling 3 Cognitive modelling 4 Cognitive modelling 5 Cognitive modelling 6 The development of concepts 7 Learning word meanings 8 Ambiguous words 9 Compositionality and word meaning 10 Common-sense reasoning

  8. Cognitive Modelling Project – construct a model of adjective-noun combination red apple fake gun heavy baby / heavy elephant

  9. <weight feature> Cognitive Modelling Learns by training over and over Heavy baby NODES: nodes = 4 inputs = 6 outputs = 2 output nodes are 1-4 CONNECTIONS: 1-4 from i1 – i6 Distributed 3 .7 .3 .4 .6 .5 .9 .4 .6 .6 .5 .2 .2 Baby Heavy Distributed 3 .2 .3 .7 .2 .4 .6 .2 .3 .5 .8 .4 .6 .2 .3 .6 .4 .2 .2

  10. The development of concepts What do we mean concept? Why is concept learning tricky to understand? Connectionist nets as a simple model of concept learning Some features of natural concept learning that make the picture less simple e.g. Role of existing background knowledge

  11. Learning word meanings Gavagai

  12. Ambiguity and vagueness Complex links between words and concepts Bank Newspaper To paint

  13. Combining concepts Compositionality is key to language red apple, red brick, red mist Watergate, blood gate, Stargate

  14. Commonsense reasoning Which information is relevant to drawing a conclusion? Which facts are affected by an event? • Yale shooting problem • Property inheritance Tweety is a bird. So, Tweety can fly?

  15. A little history – the Cognitive Revolution Skinner (1957) Children learn words (language) through operant conditioning - stimulus controls response Chomsky's (1959) review of Verbal Behavior (link on course web pages) "Dutch" - what stimulus? proliferate "stimuli” but role of attention etc.  mind 'Creativity' of language  compositionality

  16. Technical concepts of Skinner's behaviorism (stimulus, reinforcement, operant etc.) were used non-technically in "Verbal Behavior“ Eg. the artist is reinforced by the effects his work may have on others … but the artist's (often) not there when these effects occur. It's not like reinforcement in a Skinner box.

  17. "I now believe that mind is something more than a four letter Anglo-Saxon word - human minds exist and it is our job as psychologists to study them." Miller (1962) in American Psychologist, 17, p. 761 Nb Piaget, even Freud, were always cognitively oriented

  18. Chomsky (1957; 1965)Transformational Generative Grammar Account for syntactic facts (linguistics) e.g. active and passive have same meaning Judge facts using 'intuitions' (psychology)  the resulting grammars are related to something people know (linguistic competence)

  19. A small transformational generative grammar S  NP, VP NP  determiner, noun VP  verb, NP determiner: {the, a} noun: {boy, dog} verb: {eat, kick, bite, occur} Passive transformation (simplified): NP1, V, NP2  NP2, BE, V, EN, by, NP1 Captures the fact that selection restrictions match Congress impeaches Clinton Charlie impeaches a shoe Clinton is impeached by Congress A shoe is impeached…

  20. Congress impeaches Clinton NP1 V NP2 Rule NP1, V, NP2  NP2, BE, V, EN, by, NP1 Clinton is impeached by Congress

  21. More history – early machine translation Weaver (1949) memorandum Georgetown (1954-66) 250 words & 6 rules at start Alpac Commission (1966) speed? cost? quality? Meteo (1977) English  French Use existing materials (style sheets) Translators involved

  22. Fashion and the life cycle of (some) AI projects Oblivion, fading  Rebirth  Excitement  Claims  More excitement  Wild claims  Unmet expectations  Fading, oblivion.

  23. Cognitive science now "higher" cognitive functions; processes & representations Interdisciplinary Psychology, linguistics, philosophy, computer science, brain sciences, anthropology, …. Use formal / explicit models Computational metaphor strong v. weak

  24. The original question "Can machines think?“ I believe to be too meaningless to deserve discussion. Alan Turing www.warwick.ac.uk/~psrex/cogsci.html

  25. The end

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