Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.

Abstract

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a “second-order” inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one’s own actions to metacognitive judgments. In addition, the model provides insight into why subjects’ metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains.

Confidence Predictions Affect Performance Confidence and Neural Preparation in Perceptual Decision Making.

“…Baranski & Petrusic, 1994;Boldt & Yeung, 2015). Despite these advancements, dominant theories of metacognition still struggle to explain how, and based on what information, confidence signals are formed (for recent accounts see Yeung & Summerfield, 2012;Boldt et al, 2017;Pouget et al, 2016;Fleming & Daw, 2017). In the present study we could extend previous accounts of metacognition in two important ways: First, our findings suggest that performance confidence is at least to some extent based on predicted confidence.…”

Temporal metacognition as the decoding of self-generated brain dynamics

“…This observation provides support to the notion that metacognition is a self-referential process (Block, 2005;Fleming & Daw, 2017) in turn questioning which mechanisms may support inference of self-generated dynamics (e.g., Kepecs et al, 2008).…”

“…Altogether, we interpret our results as supporting the availability of an internal state variable coding for duration, which sets up the goal of a state-dependent trajectory for time production. The metacognitive inferences would consist in reading out or decoding the internal state variables, consistent with a recent proposal (Fleming & Daw, 2017) and possibly relying on forward-inverse models of state-dependent computations in motor systems (Harris & Wolpert, 1998). We review and discuss the main evidence supporting this viewpoint along with the limitations of the current study.…”

Transcranial magnetic stimulation to visual cortex induces suboptimal introspection

“…It is also possible that other more complex models might have produced better fit by considering sources of contributions to the metacognitive signal that do not arise strictly from the feed-forward model architecture shared by both the aware and blind models. For example, it has been suggested that areas involved in motor planning or execution may also contribute to metacognitive computations (Fleming and Daw 2017; Fleming et al 2015). Unfortunately, our current study design precluded investigation of this possibility, as such non-sensory factors were not manipulated and therefore any parameter added to the models to capture such effects would be conflated with existing model parameters, making the models underconstrained.…”