Using serial dependence to predict confidence across observers and cognitive domains

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 re-analyzed. 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. Intriguingly, these serial dependence effects also generalized across correct and incorrect trials, indicating that serial dependence in confidence generation is 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 vs. specificity of metacognition.