(Georgia Institute of Technology, 2021-07-22)
Shekhar, Medha
Visual metacognition depends on regions within the prefrontal cortex. Two areas in particular have been repeatedly implicated: the dorsolateral prefrontal cortex (DLPFC) and the anterior prefrontal cortex (aPFC). However, it is still unclear what the function of each of these areas is and how they differ from each other. To establish the specific roles of DLPFC and aPFC in metacognition, we employed online transcranial magnetic stimulation (TMS) to causally interfere with their functioning during confidence generation. Human subjects from both sexes performed a perceptual decision-making task and provided confidence ratings. We found a clear dissociation between the two areas: DLPFC TMS lowered confidence ratings, whereas aPFC TMS increased metacognitive ability but only for the second half of the experimental blocks. These results support a functional architecture where DLPFC reads out the strength of the sensory evidence and relays it to aPFC, which makes the confidence judgement by potentially incorporating additional, non-perceptual information. Indeed, simulations from a model that incorporates these putative DLPFC and aPFC functions reproduced our behavioral results. These findings establish DLPFC and aPFC as distinct nodes in a metacognitive network and suggest specific contributions from each of these regions to confidence generation.
(Georgia Institute of Technology, 2021-07-20)
Shekhar, Medha
Humans have the metacognitive ability to assess the likelihood of their decisions being correct via estimates of confidence. Several theories have attempted to model the computational mechanisms that generate confidence. Yet, due to little work directly comparing these models using the same data, there is no consensus among these theories. Here, we compare twelve popular process models by fitting them to large datasets from two experiments in which participants completed a perceptual task with confidence ratings. Quantitative comparisons, validated by model recovery analysis, selected the best fitting model as one that postulates a single system for generating both choice and confidence judgments, where confidence is additionally corrupted by signal-dependent noise. These results contradict dual processing theories – according to which confidence and choice arise from coupled or independent systems. Model evidence from these data also failed to support popular notions that confidence is derived from post-decisional evidence, strictly decision-congruent evidence, or posterior probability computations. Further, we explored the link between model performance and the model’s ability to predict different qualitative patterns in the data, in order to determine the reasons why some models fail. These analyses showed that the models that consistently perform the worst fail to capture individual variations in either primary task performance or metacognitive ability. Together, these analyses establish a general framework for model evaluation that also provides qualitative insights into the successes and failures of these models. Most importantly, these results begin to reveal the nature of metacognitive computations.