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 1Introduction - why cognitive science? 2Cognitive modelling 3Cognitive modelling 4Cognitive modelling 5Cognitive modelling 6The development of concepts 7Learning word meanings 8Ambiguous words 9Compositionality 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. Cognitive Modelling Heavy baby Heavy Baby Learns by training over and over Distributed 3.2.3.7.2.4.6.2.3.5.8.4.6.2.3.6.4.2.2 Distributed 3.7.3.4.6.5.9.4.6.6.5.2.2 NODES: nodes = 4 inputs = 6 outputs = 2 output nodes are 1-4 CONNECTIONS: 1-4 from i1 – i6

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. Gavagai Learning word meanings

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 ClintonCharlie impeaches a shoe Clinton is impeached by CongressA shoe is impeached…

20. Congress impeaches Clinton NP1V 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