Sources of Metacognitive Inefficiency

Abstract

Confidence judgments are typically less informative about one's accuracy than they could be; a phenomenon we call metacognitive inefficiency. We review the existence of different sources of metacognitive inefficiency and classify them into four categories based on whether the corruption is due to: (i) systematic or nonsystematic influences, and (ii) the input to or the computation of the metacognitive system. Critically, the existence of different sources of metacognitive inefficiency provides an alternative explanation for behavioral findings typically interpreted as evidence for domain-specific (and against domain-general) metacognitive systems. We argue that, contrary to the dominant assumption in the field, metacognitive failures are not monolithic and suggest that understanding the sources of metacognitive inefficiency should be a primary goal of the science of metacognition.

Keywords: confidence; metacognition; metacognitive inefficiency; metacognitive noise; perceptual decision making.

Figure 1, Key Figure.. Categorizing the sources of metacognitive inefficiency.

(A) Metacognitive inefficiency may arise from either systematic or non-systematic sources, as well as from failures in either input or computation. (B) Combining these two dimensions results in four categories of metacognitive inefficiency. Non-systematic sources of metacognitive inefficiency (Categories 1 and 2) lead to random perturbations that cannot be used to predict confidence on a trial-by-trial basis, whereas systematic sources of metacognitive inefficiency (Categories 3 and 4) lead to predictable perturbations that can be used to predict confidence on a trial-by-trial basis. Input failures (Categories 1 and 3) affect the signal for confidence such that confidence ratings are bound to be less informative regardless of the computation, whereas computational failures (Categories 2 and 4) are due to the metacognitive system arriving at less informative confidence ratings despite working with signals that allow for more informative confidence ratings to be made. Several categories of failures can co-exist within a single task. Metacognitive inefficiency is currently most often viewed as input failure (Categories 1 and 3) but in virtually all cases the empirical findings could also be explained as computational failure instead (Categories 2 and 4). Similarly, many models include non-systematic sources of noise (Categories 1 and 2) but it is currently unclear whether confidence judgments are corrupted by truly random noise or by unmodeled systematic sources of metacognitive inefficiency (Categories 3 and 4).

Figure 2.. Interpreting metacognitive accuracy correlations between different tasks.

It is typically assumed that the correlation between the metacognitive scores on two different tasks can be used to infer whether metacognition is domain general (claimed in cases of positive correlation) or domain specific (claimed in cases of no correlation) [,,–85,8,13,14,16,17,32,81,82]. However, a markedly different interpretation of such findings is possible. According to this interpretation, metacognition is a priori assumed to be domain general. A lack of correlation between two different tasks is then taken as evidence that the two tasks are dominated by different sources of metacognitive inefficiency. For example, the upper panel depicts a situation where two different sources of noise completely determine the extent of metacognitive inefficiency in Tasks 1 and 2, and therefore the correlation in the metacognitive score between these tasks is zero. On the other hand, if the contribution of each source is comparable (bottom panel), then a positive correlation is observed. Thus, in this alternative interpretation, metacognition is simply assumed to be domain general and the strength of correlation between the metacognitive accuracy on two different tasks is taken as evidence regarding whether different sources of metacognitive noise dominate the two tasks.