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10 / 31 Outline. Perception workshop groups Signal detection theory Scheduling meetings. Detection experiment. Question How sensitive is an observer to a sensory stimulus; for example, light?. Detection experiment. Question How sensitive is an observer to (for example) light?
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10 / 31 Outline • Perception workshop groups • Signal detection theory • Scheduling meetings
Detection experiment • Question • How sensitive is an observer to a sensory stimulus; for example, light?
Detection experiment • Question • How sensitive is an observer to (for example) light? • Classic experiment • Yes/No task
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
Jane Nancy
Summary of results • Thresholds • Jane = 20 • Nancy = 25
Summary of results • Thresholds • Jane = 20 • Nancy = 25 • False alarm rates • Jane = 51% • Nancy = 18.7%
Look at one intensity level • I = 25
Jane’s Hit Rate P(H) = .84
Nancy’s Hit Rate P(H) = .5
Look at one intensity level • I = 25 • Jane • Hit rate: P(H) = .84
Look at one intensity level • I = 25 • Jane • Hit rate: P(H) = .84 • False alarm rate: P(FA) = .51
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
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
Signal detection theory terms • Hits - p(H) • Proportion of “yes” responses when signal is present
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
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
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
Relationships between terms • P(H) + P(M) = 1
Relationships between terms • P(H) + P(M) = 1 • P(FA) + P(CR) = 1
Relationships between terms • P(H) + P(M) = 1 • P(FA) + P(CR) = 1 • Only need to specify P(H) and P(FA)
Extreme detection strategies • Most liberal (always say yes)
Extreme detection strategies • Most liberal (always say yes) • P(H) = 1, P(FA) = 1
Extreme detection strategies • Most liberal (always say yes) • P(H) = 1, P(FA) = 1 • Most conservative (always say no)
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
Signal Detection Theory • Assume an internal measure of signal strength.
Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell
Signal Detection Theory • Assume an internal measure of signal strength (X). • E.g. firing rate of ganglion cell • X is corrupted by noise
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
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
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
o Firing rate when signal is present o Firing rate when signal is not present