Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks
Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold – the signal intensity corresponding to a pre-defined sensitivity level ( d' = 1)-- in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimul...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-08-01
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| Series: | Frontiers in Psychology |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.01070/full |
| _version_ | 1811323709576708096 |
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| author | Luis Andres Lesmes Luis Andres Lesmes Luis Andres Lesmes Zhong-Lin eLu Jongsoo eBaek Nina eTran Barbara Anne Dosher Thomas D Albright |
| author_facet | Luis Andres Lesmes Luis Andres Lesmes Luis Andres Lesmes Zhong-Lin eLu Jongsoo eBaek Nina eTran Barbara Anne Dosher Thomas D Albright |
| author_sort | Luis Andres Lesmes |
| collection | DOAJ |
| description | Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold – the signal intensity corresponding to a pre-defined sensitivity level ( d' = 1)-- in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks --(1) simple YN detection, (2) cued YN detection, which cues the observer’s response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) forced-choice detection -- the quick YN and quick FC methods yield sensitivity thresholds that are independent of the task’s decision structure (YN or FC) and/or the observer’s subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d.=.10-.15 decimal log units), but more trials are needed for forced-choice thresholds. When the same subjects were tested across tasks of simple, cued, rated, and forced-choice detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli, and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods. |
| first_indexed | 2024-04-13T14:00:12Z |
| format | Article |
| id | doaj.art-a20a0135f4bf4278a0ff261764701ebc |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-13T14:00:12Z |
| publishDate | 2015-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-a20a0135f4bf4278a0ff261764701ebc2022-12-22T02:44:04ZengFrontiers Media S.A.Frontiers in Psychology1664-10782015-08-01610.3389/fpsyg.2015.01070110168Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasksLuis Andres Lesmes0Luis Andres Lesmes1Luis Andres Lesmes2Zhong-Lin eLu3Jongsoo eBaek4Nina eTran5Barbara Anne Dosher6Thomas D Albright7Adaptive Sensory Technology, LLCUniversity of California, San DiegoMassachusetts Eye and Ear InfirmaryThe Ohio State UniversityThe Ohio State UniversityThe Ohio State UniversityUniversity of California, IrvineUniversity of California, San DiegoMotivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold – the signal intensity corresponding to a pre-defined sensitivity level ( d' = 1)-- in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks --(1) simple YN detection, (2) cued YN detection, which cues the observer’s response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) forced-choice detection -- the quick YN and quick FC methods yield sensitivity thresholds that are independent of the task’s decision structure (YN or FC) and/or the observer’s subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d.=.10-.15 decimal log units), but more trials are needed for forced-choice thresholds. When the same subjects were tested across tasks of simple, cued, rated, and forced-choice detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli, and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.01070/fullSignal detectionCuingratingforced-choiceAdaptive psychophysicsYes-No |
| spellingShingle | Luis Andres Lesmes Luis Andres Lesmes Luis Andres Lesmes Zhong-Lin eLu Jongsoo eBaek Nina eTran Barbara Anne Dosher Thomas D Albright Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks Frontiers in Psychology Signal detection Cuing rating forced-choice Adaptive psychophysics Yes-No |
| title | Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks |
| title_full | Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks |
| title_fullStr | Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks |
| title_full_unstemmed | Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks |
| title_short | Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in Yes-No and Forced-Choice tasks |
| title_sort | developing bayesian adaptive methods for estimating sensitivity thresholds d in yes no and forced choice tasks |
| topic | Signal detection Cuing rating forced-choice Adaptive psychophysics Yes-No |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.01070/full |
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