The impact of feedback on perceptual decision making and metacognition: Reduction in bias but no change in sensitivity

Haddara, Nadia; Rahnev, Dobromir

doi:10.31234/osf.io/p8zyw

Cited by 7 publications

(18 citation statements)

References 29 publications

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“…To test the prediction of the lognormal meta noise model, we selected the data from three tasks made available in the Confidence Database (Rahnev et al, 2020). Tasks 1 and 2 are from a dataset named "Haddara_2020" (Haddara & Rahnev, 2020b), which includes 443 subjects. Task 3 is from a dataset named "Rouault_2018_Expt1" (Rouault et al, 2018), which includes 498 subjects.…”

Section: Dataset Selectionmentioning

confidence: 99%

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Examining the robustness of the relationship between metacognitive efficiency and metacognitive bias

Xue¹

2021

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It is now widely appreciated that confidence ratings are corrupted by metacognitive noise. We recently demonstrated that the level of metacognitive noise increases as the evidence for a decision becomes stronger. This effect leads to the prediction that metacognitive sensitivity and metacognitive bias are confounded such that increasing one’s confidence should result in higher estimated metacognitive sensitivity. In order to test this predicted relationship, we developed a new method to simulate a change in confidence by removing the confidence criteria separating either the highest or the lowest confidence ratings. The method enables us to manipulate a subject’s metacognitive bias after data have already been collected. We applied this manipulation to the data from three tasks from the Confidence Database (N > 400 in each) and found that simulating a bias towards higher confidence indeed led to higher estimates of metacognitive sensitivity. These results provide support for the notion that metacognitive noise increases with decision evidence, and point to an important confound between metacognitive sensitivity and metacognitive bias.

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Section: Dataset Selectionmentioning

confidence: 99%

“…Details about Tasks 1 and 2 can be found in the original paper (Haddara & Rahnev, 2020a). Briefly, subjects indicated whether the letters X or O (Task 1) or the colors red or blue (Task 2) were presented more frequently in a 7 x 7 grid.…”

Section: Experimental Designsmentioning

confidence: 99%

Examining the robustness of the relationship between metacognitive efficiency and metacognitive bias

Xue¹

2021

Preprint

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“…Therefore, we performed all analyses on the tasks from these two datasets. The first dataset, originally reported in Haddara & Rahnev (2020), consists of two separate tasks, considered here as Task 1 and Task 2. The second dataset, originally reported as Experiment 1 in Rouault, Seow, Gillan, & Fleming (2018)…”

Section: Dataset Selectionmentioning

confidence: 99%

“…Complete details about Tasks 1 and 2 are available in the original paper (Haddara & Rahnev, 2020). Briefly, subjects indicated whether the letter X or O (Task 1) or the color red or blue (Task 2) occurred more frequently in a 7 x 7 grid.…”

Section: Experimental Designsmentioning

confidence: 99%

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Response bias reflects individual differences in sensory encoding

Rahnev¹

2020

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Humans exhibit substantial biases in their decision making even in simple 2-choice tasks but the origin of these biases remains unclear. We hypothesized that one source of bias could be individual differences in sensory encoding. Specifically, if one stimulus category gives rise to an internal evidence distribution with higher variability, then responses should optimally be biased against that stimulus category. Therefore, response bias may reflect a previously unappreciated subject-to-subject difference in the shape of the internal evidence distributions. We tested this possibility by analyzing data from three different 2-choice tasks (N = 443, 443, 498). We found that, for all three tasks, response bias moved in the direction of the optimal criterion determined by each subject’s idiosyncratic internal evidence variability. These results demonstrate that seemingly random variations in response bias can be driven by individual differences in sensory encoding and are thus partly explained by normative strategies.

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