1. 1. The Matching Law The purest form of Behavioral Science
2. 2. You have a term paper due in two weeks and.. You realize that the paper is due tomorrow
3. 3. The study In a nutshell Rate of Reinforcement Strength of Reinforcement Schedule External Stimulus Behavior B1 B1 + B2 R1 R1 + R2 B Behavior R - Reinforcement The Strict Matching Further studies revealed explicitly deviant behavior log(B1/B2) = s log(R1/R2) + log b Basics of Matching Law : https://www.youtube.com/watch?v=rst7dIQ4hL8
4. 4. 3-Point and 2-Point scales
5. 5. The Past, Present and Future of Hot-Hand Research Moving away from a fixation on sports
6. 6. From a fixation of sports to an exploration of mechanism: The past, present and future of hot hand research By Adam Alter and Daniel Oppenheimer The London Blitz (1940-41):
7. 7. Contents of the paper Literature review on the hot hand fallacy over the past 2 decades Focus of research is criticism of GVT rather than exploring general implications of the hot hand fallacy for human cognition and probabilistic reasoning Domain-general mechanistic approaches to understand the hot hand fallacy Suggestions for future hot hand research Hot hand fallacy: is the fallacious belief that a person who has experienced success with a random event has a greater chance of further success in additional attempts. Gamblers fallacy: is the mistaken belief that, if something happens more frequently than normal during some period, it will happen less frequently in the future
8. 8. The Culprits? Gilovich, Vallone and Tversky (GVT) showed that peoples sometimes perceive patterns where they do not exist They illustrated it using basketball(streak shooting) where there is a strong perception of patterns GVT showed streaks were no more prevalent than if shots were truly independent of each other In order to prove the existence of the hot hand, a lot of research has attacked GVTs work Arguments against GVTs statistical analyses: 1. Use of a simple binomial model 2. nature of data set used 3. inability to reflect what constitutes a hot hand in the observers mind Some also believe that hot hand exists in roulette, where each event is independent, implying the belief exists in domain where streakiness is statistically impossible
9. 9. Mechanisms underpinning the hot hand fallacy 1. Moldoveanue and Langer: People approach probabilistic phenomena with prior assumptions 2. McDonald and Newell: People pay little attention to the cause of the streak when outcomes alternate frequently 3. Boynton and Ayton & Fischer: People express greater confidence in their subsequent expectations after a series of correct guesses 4. Choi, Oppenheimer and Monin: Peoples prediction following a streak are consistent with the outcome that benefits them 5. Fedotova and Oppenheimer: People believe a random process will generate a favorable outcome when previous outcomes where similar
10. 10. All is not Lost (Burns,2004) In spite of it being a fallacy, people benefit from streaks as a cue when deciding which player should receive the ball in basketball Better players are more likely to shoot consecutive scoring shots, so in a way passing becomes efficient Positive reinforcement of the hot hand fallacy The issue with this adaptive approach is that people use the approximation even in situations where base rates of success are available or obvious(eg: roulette)
11. 11. Further Scope Explore the nature of naiive cognitive models that drive peoples probabilistic reasoning Uncover reliable differences in probabilistic reasoning under conditions that elicit heuristic and systematic processing Three types of studies to determine how people perform in each domain relative to the others: 1. Explanation studies 2. Prediction studies 3. Generation studies
12. 12. Perceived Hotness and its effects on behavior In NBA basketball
13. 13. MARCO BELINELLI IS HOT
14. 14. BELINELLI THE PLAYER
15. 15. The Game that was. Top scorer for the Spurs, Bellineli helped keep the scores safe, San Antonio Lost eventually 102 96
16. 16. Deeper look into the game The NYTIMES website gives significantly abundant detail play-by- play to gauge the sequence of events in a game
17. 17. STRUCTURE OF THE RESEARCH SHOT ATTEMPTED (Hit or Miss) Repeating a shot following a HIT or MISS Shot distance following a HIT or MISS Same player hit rate following a HIT or MISS Player Substitution following a HIT or MISS Runs of 2 HITS or MISSES The DISTANCE factor
18. 18. The Distance Factor How far is too far?
19. 19. Distance as a governing factor for shot-difficulty
20. 20. Repeating a shot following a Hit or Miss Some data from the paper and personal findings
21. 21. What the paper says. Odds of taking a shot after a HIT 40% greater than after a MISS Breslow-Day statistic accounts for player-class/ player level by looking into the odds ratios across players (player identity) Longer the first shot, greater the odds of taking the next Not only are the odds of taking the next shot higher, the odds of that shot being a longer one are higher as well 0.197 Overall rate of same player consecutive shots 0.171 0.223 Consecutive shots after a HIT! Consecutive shots after a MISS Breslow Day
22. 22. Shot Distance following a HIT or MISS Adding in the Lebron James Study
23. 23. What the paper says. Regardless of outcome, shots from longer range were generally followed by shots from closer range and vice-versa Longer the first shot(HIT), greater the odds of the player going for a longer shot First shot Successful long range HIT has a stronger effect on the subsequent shot than a short range HIT Effect Two Level Covariance Analysis
24. 24. Lebron James Study A look into LeBron James attempts throughout a regular league game, shows some glimpses of this hypothesis
25. 25. Same player hit rate following a HIT or MISS Interestingly opposite
26. 26. What the paper says. 0.518 0.427 Probability of hit after HIT Probability of hit after MISS The Free-throw comparison
27. 27. WHAT THE PAPER DOES NOT SAY.. Things to consider in the course of the paper that could have been accounted for
28. 28. SHOT SELECTION THE PROBLEM
29. 29. SHOT SELECTION THE SUGGESTED SOLUTION
30. 30. QUESTIONS ?