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  1. CHAPTER 15 Human Learning and Cognition: Learning about Causes

  2. CONDITIONING AND CAUSATION • Causality detection is vital to behavioral adaptation in humans. • Can associative learning principles in animals teach us something about how people detect causes? • Recently, methods and principles of operant and Pavlovian conditioning in animals have been applied to causality detection in humans.

  3. CONDITIONING AND CAUSATION • How plausible is this connection? • In text, we have argued that associative learning is “causality detection.” • Animals appear to be learning about causes of important events in world. • In Pavlovian conditioning, causes are environmental stimuli. • In operant conditioning, causes are organism’s own actions.

  4. CONDITIONING AND CAUSATION • How can we evaluate this account? • One can directly study human causal judgment using methods inspired by animal learning research.

  5. David Hume and Causality • Familiarity with David Hume’s ideas is helpful in order to comprehend causality detection. • More than any other thinker, this 18th century Scottish philosopher has shaped our understanding of causality detection.

  6. David Hume and Causality • Causation is psychological impression. • Succession of experiences is given; but, impression of connection goes beyond sensory evidence. • For example, lightning regularly precedes thunder. • We associate one with the other; but, we do not sense their interconnection.

  7. David Hume and Causality • Three conditions are crucial to forming causal impressions: • 1. Cause and effect must be contiguous in space and time. • 2. Cause must occur prior to effect. • 3. There must be a constant connection between cause and effect.

  8. David Hume and Causality • Three other conditions better define and sharpen causal attributions: • 4. Same cause produces same effect, same effect comes from same cause. • 5. When several different events produce same effect, it must be due to something that events share. • 6. Any difference between effects of similar events must arise because events differ from one another.

  9. David Hume and Causality • Mechanical model of causal perception: • Completely non-conscious processes lead us to automatically associate consistently contiguous experiences. • This learning is too important to leave to rational thought.

  10. David Hume and Causality • Hume’s principles suggest that a single pairing of cause and effect will not forge a firm causal association. • A causal impression rises to its greatest strength by degrees as a function of number of cause-effect pairings. • Consistency of relation affects ultimate strength of causal association; any inconsistency weakens connection.

  11. David Hume and Causality • Also relevant to strength of causality judgment is superiority of one possible cause above rival candidates. • If several events often precede a given event and if one does so more reliably than others, then it will be singled out as the cause.

  12. David Hume and Causality • Hume’s ideas are similar to Rescorla-Wagner model of associative learning: • Is completely mechanical and phrased as a simple mathematical equation. • Posits gradual growth of associations to an asymptotic level. • Expects unpaired events to lower strength of associative learning. • Involves competition among causes.

  13. David Hume and Causality • Hume hypothesized operation of same associative principles in nonhuman animals. • This testified to the breadth of these associative principles, plus their operation in absence of language or logic.

  14. EMPIRICAL INVESTIGATIONS OF HUMAN CAUSALITY DETECTION • Just what is known about causality detection beyond its suspected operation in animal conditioning? • We next explore human research directed at causality detection in controlled laboratory settings.

  15. EMPIRICAL INVESTIGATIONS OF HUMAN CAUSALITY DETECTION • Contingency • Temporal contiguity • Cue competition

  16. EMPIRICAL INVESTIGATIONS OF HUMAN CAUSALITY DETECTION • Contingency • Temporal contiguity • Cue competition

  17. Contingency • Statistical relation between events. • Computed from 2 X 2 table.

  18. Contingency

  19. Contingency • General formula: a/(a+b)-c/(c+d) • Specific experimental investigation: • P(Light|Tap) and P(Light|No Tap)

  20. P(Light|Tap) and P(Light|No Tap)

  21. P(Light|Tap) - P(Light|No Tap)

  22. Contingency • Data suggest that people can quite keenly detect prevailing response-outcome contingency: positive or negative or zero. • Under proper experimental conditions, people can with great accuracy and little bias report positive, negative, and noncontingent response-outcome relations.

  23. Contingency Learning Curves • Do people show negatively accelerated learning curves in contingency judgment tasks? • Yes.

  24. Contingency Learning Curves

  25. Contingency • Effects of differential conditional probability and increased training are exactly what would be expected if people’s causality and contingency judgments were based on an associative learning process like that captured in Rescorla-Wagner model. • Conditioned inhibition also occurs.

  26. EMPIRICAL INVESTIGATIONS OF HUMAN CAUSALITY DETECTION • Contingency • Temporal contiguity • Cue competition

  27. Thomas Procedure With Humans

  28. Thomas Procedure With Humans • Results are like those with rats. • Contiguity promotes operant responding. • Contiguity also leads to more positive causal ratings, despite no actual contingency. • Both results also hold with negative contingency.

  29. EMPIRICAL INVESTIGATIONS OF HUMAN CAUSALITY DETECTION • Contingency • Temporal contiguity • Cue competition

  30. Cue competition • Relative validity effect • Blocking

  31. Cue competition • Relative validity effect • Blocking

  32. Relative validity effect • Group C: AX+ versus BX- • Group U: AX+/- versus BX+/- • More control by X in Group U despite equal association of X with + and - • True of rats, rabbits, and pigeons in conditioning experiments • True of human causal attributions

  33. Cue competition • Relative validity effect • Blocking

  34. Blocking • AX+ alone • A+, then AX+ • Responding to X is greater in first case for animals in conditioning experiments • And, for humans in causal judgment experiments

  35. LEARNING AND COGNITION: A THEORETICAL PERSPECTIVE • Studies on human causality detection are consistent with Hume’s theoretical approach plus literature on Pavlovian and operant conditioning in animals. • Concordances are not peculiar to findings discussed in Chapter 15. • Also extends to overshadowing, configural conditioning, and occasion setting.

  36. LEARNING AND COGNITION: A THEORETICAL PERSPECTIVE • An additional concordance is that Rescorla-Wagner model is also a promising theory of causality detection in human beings. • So, basic mechanisms of association formation may have just as much to say about causality detection in human beings as they do about conditioning in animals.

  37. LEARNING AND COGNITION: A THEORETICAL PERSPECTIVE • This is as it should be if fundamental mechanisms of learning and behavior are truly general. • Hume’s belief in generality of associative learning and its centrality to mind and behavior is thus supported by work we have reviewed in our course and textbook.