10 / 31 Outline

1. 10 / 31 Outline • Perception workshop groups • Signal detection theory • Scheduling meetings

2. Detection experiment • Question • How sensitive is an observer to a sensory stimulus; for example, light?

3. Detection experiment • Question • How sensitive is an observer to (for example) light? • Classic experiment • Yes/No task

4. Detection experiment • Question • How sensitive is an observer to (for example) light? • Classic experiment • Yes/No task • Measure threshold intensity needed to have 50% hits

14. Threshold

17. Threshold

18. Jane Nancy

19. Summary of results • Thresholds • Jane = 20 • Nancy = 25

20. Summary of results • Thresholds • Jane = 20 • Nancy = 25 • False alarm rates • Jane = 51% • Nancy = 18.7%

21. Look at one intensity level • I = 25

24. Jane’s Hit Rate P(H) = .84

25. Nancy’s Hit Rate P(H) = .5

26. Look at one intensity level • I = 25 • Jane • Hit rate: P(H) = .84

27. Look at one intensity level • I = 25 • Jane • Hit rate: P(H) = .84 • False alarm rate: P(FA) = .51

28. Look at one intensity level • I = 25 • Jane • Hit rate: P(H) = .84 • False alarm rate: P(FA) = .51 • Nancy • Hit rate: P(H) = .5

29. Look at one intensity level • I = 25 • Jane • Hit rate: P(H) = .84 • False alarm rate: P(FA) = .51 • Nancy • Hit rate: P(H) = .5 • False alarm rate: P(FA) = .187

30. Signal detection theory terms • Hits - p(H) • Proportion of “yes” responses when signal is present

31. Signal detection theory terms • Hits - p(H) • Proportion of “yes” responses when signal is present • Misses - p(M) • Proportion of “no” responses when signal is present

32. Signal detection theory terms • Hits - p(H) • Proportion of “yes” responses when signal is present • Misses - p(M) • Proportion of “no” responses when signal is present • False alarms - p(FA) • Proportion of “yes” responses when signal is not present

33. Signal detection theory terms • Hits - p(H) • Proportion of “yes” responses when signal is present • Misses - p(M) • Proportion of “no” responses when signal is present • False alarms - p(FA) • Proportion of “yes” responses when signal is not present • Correct rejections - p(CR) • Proportion of “no” responses when signal is not present

34. Relationships between terms • P(H) + P(M) = 1

35. Relationships between terms • P(H) + P(M) = 1 • P(FA) + P(CR) = 1

36. Relationships between terms • P(H) + P(M) = 1 • P(FA) + P(CR) = 1 • Only need to specify P(H) and P(FA)

37. Extreme detection strategies • Most liberal (always say yes)

38. Extreme detection strategies • Most liberal (always say yes) • P(H) = 1, P(FA) = 1

39. Extreme detection strategies • Most liberal (always say yes) • P(H) = 1, P(FA) = 1 • Most conservative (always say no)

40. Extreme detection strategies • Most liberal (always say yes) • P(H) = 1, P(FA) = 1 • Most conservative (always say no) • P(H) = 0, P(FA) = 0

41. Signal Detection Theory

42. Signal Detection Theory • Assume an internal measure of signal strength.

43. Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell

44. Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell • X is corrupted by noise

45. Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell • X is corrupted by noise • E.g. random variations in firing rate

46. Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell • X is corrupted by noise • E.g. random variations in firing rate • When signal is not present, X = X0 + N

48. Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell • X is corrupted by noise • E.g. random variations in firing rate • When signal is not present, X = X0 + N • When signal is present, X = XS + N

50. o Firing rate when signal is present o Firing rate when signal is not present