Using serial dependence to predict confidence across observers and cognitive domains

Abstract

Our perceptual system appears hardwired to exploit regularities of input features across space and time in seemingly stable environments. This can lead to serial dependence effects whereby recent perceptual representations bias current perception. Serial dependence has also been demonstrated for more abstract representations, such as perceptual confidence. Here, we ask whether temporal patterns in the generation of confidence judgments across trials generalize across observers and different cognitive domains. Data from the Confidence Database across perceptual, memory, and cognitive paradigms was reanalyzed. Machine learning classifiers were used to predict the confidence on the current trial based on the history of confidence judgments on the previous trials. Cross-observer and cross-domain decoding results showed that a model trained to predict confidence in the perceptual domain generalized across observers to predict confidence across the different cognitive domains. The recent history of confidence was the most critical factor. The history of accuracy or Type 1 reaction time alone, or in combination with confidence, did not improve the prediction of the current confidence. We also observed that confidence predictions generalized across correct and incorrect trials, indicating that serial dependence effects in confidence generation are uncoupled to metacognition (i.e., how we evaluate the precision of our own behavior). We discuss the ramifications of these findings for the ongoing debate on domain-generality versus domain-specificity of metacognition.