Implications and speculations

1. Implications and speculations Hongtao Zhang Mar 15, 2003

2. Research Goal • Understand human mind. • Difficulty • Minds are unobservable • Minds differ human from other animals. It is not comfortable to make research on it. • Benefits • Help us understand ourselves more • Help cure some diseases. • Help to write intelligent computer software.

3. What is mind? - 1 • Cognitive Science hopes to explain mind in terms of low-level neural events. • Measure electrical and chemical changes in the brain as it performs various tasks • Explain mind in terms of such things as synaptic dynamics and brain modularity • Authors attack this approach as ridiculous as predicting the weather based on the known behavior of gas molecules. • My different thoughts

4. What is mind? - 2 • Social-psychological science tries to explain mind in terms of social interactions. • Minds come from evaluating, comparing, and imitating one another, from experience and emulating the success behaviors of others. • My different thought. • How to explain inventions, such as integral, relativity theory. • Wolfram secludes himself for 10 years to write the book

5. Authors’ Assertions • Minds are social • Human intelligence results from social interaction • Culture and cognition are inseparable consequences of human sociality. Culture emerges as individuals become more similar through mutual social learning. • What is the relation between society and culture? • Particle swarms are a useful computational intelligence(soft computing) methodology.

6. Social learning Theory: Bandura • Arisen out of reinforcement theories of behaviorism • Human is different from other animals. Human can learn skills and behaviors by observations • Remnant of reinforcement theory: • people are more likely to imitate models whose behavior is rewarded • Why do so many criminals do the same crimes shortly after they are released from prisons?

7. Social learning theory: more • Two advantages: • Information • Punishments are more impressive than rewards • Motivation • Sources • Own experience • Other’s experiences (vicarious experience)

8. Formation of culture • Spread of influence When the influence reaches enough people, a culture is born

9. What is culture? • Kroeber, A.L., & Kluckhohn, C. (1952). Culture: A critical review of concepts and definitions • 161 variation of culture definition was listed. • " Culture consists of patterns, explicit and implicit, of and for behavior acquired and transmitted by symbols, constituting the distinctive achievements of human groups, including their embodiments in artifacts; the essential core of culture consists of traditional (i.e. historically derived and selected) ideas and especially their attached values; culture systems may, on the one hand, be considered as products of action, and on the other as conditioning elements of further action."

10. Culture’s influence on individuals • Individuals’ reasoning depends on their culture • Kaiping peng and richard Nisbett’s explanations on the different history of science in China and The West. • Different reasoning styles:

11. Reasoning styles in China and the west

12. Human behavior 1: Group Polarization • Risky shift Phenomenon • Old belief: Groups make more conservative choices that individuals. • Experiment result: Group decisions are more extreme than individual decisions. • The individuals really changed their views after group discussions. • Caution Shift phenomenon was found later. • Group Polarization: • Groups tend to exaggerate the opinions of the individuals. • Question: Do we need to change the jury system?

13. Explanation of Group Polarization • Persuasive argument • Individuals change their views because they are exposed to a greater number of arguments in favor of one position • Normative argument • In order to get other members’ recognition or approval, individuals tend to shift their view towards the extreme. • Particle swarm theoretical argument • Social learning and influence tends to make individuals to try more extreme positions to get optimized results.

14. Human behavior 2: Self-Esteem • People tend to seek behaviors and situations that help them value themselves positively and to avoid those that make them feel bad about who they are. • High self-esteem helps the individual deal with stress and other negative emotions and improves their confidence and persistence to achieve their goals • Low self-esteem makes the individual depressed and less confident and easy to give up their efforts.

15. Explanation of self-esteem • Self-esteem is a measure of how well the individuals are accepted by their social group. • Self-esteem can facilitate the maintenance of social groups • People do not have the need to maintain self-esteem itself; instead, they have the need to be included in the social group

16. Human behavior 3: Self-attribution and social illusion • Common belief: people have direct, immediate knowledge of our thoughts and feelings • People make attributions about themselves on the basis of the same kind of information they used to interpret the action of others (Daryl Bem) • Schachter and Singer’s “misattribution” paradigm. • What would happen if the subjects know the effect of those drugs? • Nisbett and Wilson’s self-report experiment • People are sometimes unable to report their own mental process because they are not aware of how they think.

17. Computer intelligence • Particle swarm computing. • Imitating human society. • Every particle can be considered as a person and particles interact with each other. • According to the social learning theory each particle is constantly watching the particles around it to see how they are doing and adjust its behavior accordingly. (people can learn by observation) • Each particle also has a memory of its behavior history. (people can learn from their own experiences)

18. Soft computing: Research Areas • Evolutionary algorithms and genetic programming • Neural science and neural network systems • Fuzzy set theory and fuzzy systems • Chaos theory and chaotic systems

19. Soft computing: attributes • Hard computing requires programs to be written; soft computing can evolve its own programs • Hard computing uses two-valued logic; soft computing can use multivalued or fuzzy logic • Hard computing is deterministic; soft computing incorporates stochasticity • Hard computing requires exact input data; soft computing can deal with ambiguous and noisy data • Hard computing is strictly sequential; soft computing allows parallel computations • Hard computing produces precise answers; soft computing can yield approximate answers