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Dobromir Rahnev, Ji Won Bang, Medha Shekhar; The influence of low-level stimulus characteristics on metacognitive efficiency. Journal of Vision 2018;18(10):1048. doi: 10.1167/18.10.1048.
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Metacognition is the ability to employ confidence ratings in order to predict the accuracy of one's decisions. Despite years of research, it is still unclear how visual metacognitive efficiency can be manipulated. In particular, it is typically assumed that the low-level stimulus characteristics have no impact on the metacognitive efficiency. However, we show that a hierarchical model of confidence generation makes a counterintuitive prediction: Higher sensory noise should increase metacognitive efficiency. The reason is that sensory noise has a large negative influence on the decision (where it is the only corrupting influence) but a smaller negative influence on the confidence judgment (where it is one of two corrupting influences; the other one being metacognitive noise). To test this prediction, we used a perceptual learning paradigm to decrease the amount of sensory noise. In Experiment 1, seven days of training led to a significant decrease in noise as well as a corresponding decrease in metacognitive efficiency. Experiment 2 showed the same effect in a brief 97-trial learning for each of two different tasks. Finally, in Experiment 3, we combined increasingly dissimilar stimulus contrasts to create conditions with higher sensory noise and observed a corresponding increase in metacognitive efficiency. Our findings demonstrate the existence of a robust positive relationship between sensory noise and metacognitive efficiency. These results could not be captured by a standard model in which decision and confidence judgments are made based on the same underlying information. Thus, our study provides a novel way to directly manipulate metacognitive efficiency via the low-level stimulus characteristics and suggests the existence of metacognitive noise that corrupts confidence but not the perceptual decision.
Meeting abstract presented at VSS 2018
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