Reverse engineering of metacognition

Abstract

The human ability to introspect on thoughts, perceptions or actions – metacognitive ability – has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive framework and model of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of type 1 decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of type 1 decision values. Successful recovery of model parameters is demonstrated and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of type 1 performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets.

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

“…For instance, it is possible that estimates of confidence bias may be inherently less noisy than estimates of metacognitive sensitivity and efficiency. Although Meta-d' measures of metacognitive performance are widely adopted and currently represent the state-of-the-art in the field 41,42,57 , alternative approaches to modelling/quantifying metacognitive abilities [128][129][130] are emerging which may be applied in future research to further characterise relationships between metacognition and psychopathology.…”

GGSDT: A unified signal detection framework for confidence data analysis

“… This provides a strong rationale for treating β as a metacognitive efficiency measure being controlled for objective decision accuracy. The intuition behind this is that metacognitive lapse (putative operation of β) causes greater information loss in the situation of higher objective accuracy (when S1 and S2 distributions are less overlapped), through which the influence of objective accuracy is implicitly taken into account in the β estimate (for more discussion about objective accuracy contamination in metacognitive measurement, see Fleming & Lau, 2014; Guggenmos, 2022).…”

“…In other words, the GGSDT is supposed to be a measurement model, being agnostic of specific processes behind varying levels of metacognitive performance. To pinpoint and draw specific conclusions regarding the mechanisms of particular metacognitive behavior, a process model approach (e.g., Fleming & Daw, 2017; Guggenmos, 2022; Shekhar & Rahnev, 2021; Webb et al, 2022) would be required on top of the current measurement framework.…”