Probability matching

Slides for the PSYC3211 probability matching assessment

Transcript

1. Cognitive Science PSYC3211
2. Probability matching
3. What is probability matching?? Lets play a game….
25. Part two… Guess the next ten colours…
27. A B Probability matching Probability maximising
28. 30% 70%
29. Why should you care? • It’s a well-established finding • There are still open questions • It’s your main assignment…
30. Overview • Brief literature review • James & Koehler (2011) • Assignment overview • Form groups, get started
31. Why do people probability match??
32. Experiments are boring! • “It’s easy to win on the left; the real skill comes in winning on the right.” – Participant • “One could win on the right key by betting on it very often but... this would be no more a test of skill than deer-hunting with a machine gun” – Jacqueline Goodnow Goodnow (1955)
33. People search for patterns
34. Pattern search • Participants that reported complex patterns were more likely to probability match. • Participants were more likely to probability match when the task was described as problem solving rather than gambling.
35. Pattern detection • Participants that used the irrational probability matching strategy were more likely to detect useful patterns when they were present.
36. People solve the wrong problem
37. Thinking fast and slow • Matching: fast, intuitive • Maximising: slow, deliberative
38. Linda the feminist bank- teller • “Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student she was deeply concerned with issues of discrimination and social justice and also participated in antinuclear demonstrations."
39. Cognitive ability • Participants that used a probability matching strategy tended to have lower SAT scores. • No differences were observed in the number of maths and stats courses that participants had taken.
40. Cognitive load • Participants completed a probability matching task either with or without a concurrent working memory task. • Participants that completed both tasks were less likely to probability match.
41. Cognitive load
42. Children
43. Strategy availability • “Consider these two strategies that could be used in a 10 roll game: • (a) you could predict green for all 10 throws, or • (b) you could predict green for 7 throws and red for 3 throws. • Which strategy do you think will win more money?”
44. No hint
45. Hint
46. Cognitive Reflection Test • “A bat and a ball cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost?” • “If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?” • “In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake?” Fredrick, 2005
48. Can people overcome probability matching?
49. Incentives and training • Participants were told that there was no system which would make it possible to get all correct answers. • Financial incentives (£40), regular feedback, and extensive training (1800 trials) each decreased probability matching.
50. Feedback
51. Group decision-making
52. Lab report… James & Koehler (2011)
53. Koehler & James (2011) • Probability matching occurs because it is more available. But why? • People generate expectations for whole sequences instead of focusing on individual outcomes. • Can this be manipulated?
54. Experiment 1
55. Experiment 2
56. Experiment 3 • Global focus: “In 10 rolls of the dice, how many times would you expect each outcome?” • Local focus: “On any individual roll of the die, which colour is more likely to be rolled?”
57. What would you do next?
58. Three steps 1. Conduct an experiment in groups 2. Class presentation in groups (5%) 3. Write an individual report (35%)
59. Timeline • Today: Get into groups and start discussing ideas • Week 5: Have some data collected. Start thinking about analysis • Week 8: 15 minute group presentation • Week 10: Individual report due
60. Individual report • Due: April 23rd, Week 10 • Weighting: 35 % • Word count: 1750 words
61. Report structure • Abstract • Introduction • Methods • Results • Discussion • References
62. Keep it simple!
63. Things to keep in mind? • What are the theoretical implications? • How strong do you think the effect will be? • How can you make it stronger? • What analyses will you use?
64. Project checklist q Rationale q Design q Independent variable q Dependent variable q Predictions q Statistical analyses
65. Form groups, get started