File:Developing-Bayesian-adaptive-methods-for-estimating-sensitivity-thresholds-(d′)-in-Yes-No-and-Video4.ogv
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[edit]DescriptionDeveloping-Bayesian-adaptive-methods-for-estimating-sensitivity-thresholds-(d′)-in-Yes-No-and-Video4.ogv |
English: The qFC method applied in forced-choice detection. The movie demonstrates the quick FC method applied in a two-interval forced-choice detection task. The simulated observer demonstrates a response bias that favors the first interval (60% Interval 1 vs. 40% Interval 2 for the null stimulus). The upper panels present the true and estimated psychometric functions (left), the trial sequence (middle), and the pre-trial calculation of the stimulus selection algorithm (right). In addition to the %Correct psychometric functions presented for each interval, we also present the unwrapped psychometric function (inset), which describes probability of reporting Interval 2 as a function of relative contrast in the two intervals. In the presented trial sequence, the signal contrast selected for each trial is represented by the dot's position on the ordinate, and the trial's response (Interval 1 or Interval 2) is represented by the dot's color (black, blue). The correctness of responses in the trial sequence can be inferred from dot location and color as follows: correct responses are marked by blue dots above and black dots below the 0 position of the abcissa's relative contrast scale; black dots above and blue dots below mark incorrect responses. The 1-D marginal pdfs for the sensitivity and decision parameters are presented in the lower panels. The stimulus search is calculated over the one-dimensional space of relative signal contrast. Therefore, unlike previous FC methods, the method selects both the signal contrast and the interval in which it's presented. This strategy is needed to estimate the decision criterion (interval bias). |
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Source | Movie 4 from Lesmes L, Lu Z, Baek J, Tran N, Dosher B, Albright T (2015). "Developing Bayesian adaptive methods for estimating sensitivity thresholds (d′) in Yes-No and forced-choice tasks". Frontiers in Psychology. DOI:10.3389/fpsyg.2015.01070. PMC: 4523789. | ||
Author | Lesmes L, Lu Z, Baek J, Tran N, Dosher B, Albright T | ||
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current | 03:45, 23 August 2015 | 20 s, 1,254 × 882 (1.52 MB) | Open Access Media Importer Bot (talk | contribs) | Automatically uploaded media file from Open Access source. Please report problems or suggestions here. |
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Author | Lesmes L, Lu Z, Baek J, Tran N, Dosher B, Albright T |
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Usage terms | http://creativecommons.org/licenses/by/4.0/ |
Image title | The qFC method applied in forced-choice detection. The movie demonstrates the quick FC method applied in a two-interval forced-choice detection task. The simulated observer demonstrates a response bias that favors the first interval (60% Interval 1 vs. 40% Interval 2 for the null stimulus). The upper panels present the true and estimated psychometric functions (left), the trial sequence (middle), and the pre-trial calculation of the stimulus selection algorithm (right). In addition to the %Correct psychometric functions presented for each interval, we also present the unwrapped psychometric function (inset), which describes probability of reporting Interval 2 as a function of relative contrast in the two intervals. In the presented trial sequence, the signal contrast selected for each trial is represented by the dot's position on the ordinate, and the trial's response (Interval 1 or Interval 2) is represented by the dot's color (black, blue). The correctness of responses in the trial sequence can be inferred from dot location and color as follows: correct responses are marked by blue dots above and black dots below the 0 position of the abcissa's relative contrast scale; black dots above and blue dots below mark incorrect responses. The 1-D marginal pdfs for the sensitivity and decision parameters are presented in the lower panels. The stimulus search is calculated over the one-dimensional space of relative signal contrast. Therefore, unlike previous FC methods, the method selects both the signal contrast and the interval in which it's presented. This strategy is needed to estimate the decision criterion (interval bias). |
Software used | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
Date and time of digitizing | 2015-08-04 |