Overlapping and unique neural circuits are activated during perceptual decision making and confidence

Abstract

The period of making a perceptual decision is often followed by a period of rating confidence where one evaluates the likely accuracy of the initial decision. However, it remains unclear whether the same or different neural circuits are engaged during periods of perceptual decision making and confidence report. To address this question, we conducted two functional MRI experiments in which we dissociated the periods related to perceptual decision making and confidence report by either separating their respective regressors or asking for confidence ratings only in the second half of the experiment. We found that perceptual decision making and confidence reports gave rise to activations in large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions with the results being remarkably consistent across the two experiments. Further, the confidence report period activated a number of unique regions, whereas only early sensory areas were activated for the decision period across the two experiments. We discuss the possible reasons for this overlap and explore their implications about theories of perceptual decision making and visual metacognition.

Human perceptual and metacognitive decision-making rely on distinct brain networks

“…Instead, it supports the notion that confidence generation and perceptual sensitivity are supported by relatively independent mechanisms [ 54 – 56 ]. In detail, the sensory representation necessary for the perceptual readout would not be the absolute source for metacognitive estimation, being the latter the result of an accumulation process, which integrates further information after, or even while the perceptual choice is made [ 16 , 57 ]. This mechanism would admit conditions in which alterations of sensory representation produce divergences between perceptual and metacognitive outcomes [ 7 , 58 ], and seems to be suggested by secondary evidence showing a bias in confidence generation following V5/MT+-to-V1/V2 stimulation (see Text B in S1 File ), leading the subjects to nonspecific overestimation of certainty, without altering their metacognitive efficiency (Text C in S1 File ).…”

Quantifying the contribution of subject and group factors in brain activation

“…However, it could be that rather than a binary distinction, there is more of a continuum of analysis types. For example, examining the differential activations of two different tasks (Yeon et al, 2020) may show slightly higher idiosyncrasy levels than examining the activations of a single task in isolation (as in the current analyses). Similarly, analyses that compare internal states (e.g., aroused vs. unaroused, excited vs. bored) (Rosenberg et al, 2020) or the effects of brain stimulation (Chen et al, 2013;Rafiei et al, 2021) may show yet greater levels of idiosyncrasy despite our provisional classification of such analyses as "task-based.…”

Visual metacognition: Measures, models, and neural correlates.

“…For example, an early study used a metacontrast masking paradigm that produced a difference in subjective visibility judgments for conditions with matched performance (Lau & Passingham, 2006). Other studies have compared brain activations in conditions where participants did or did not rate their confidence (Fleming et al, 2012; Morales et al, 2018; Yeon et al, 2020), employed decoded neurofeedback to induce a change in confidence (Cortese et al, 2016), or compared confidence in the accuracy of one’s own versus another agent’s choice (Pereira et al, 2020). Another approach has been to go beyond trial-by-trial confidence and instead focus on the formation of global judgments about one's perceptual ability (Rouault & Fleming, 2020), an understudied topic that provides a distinct perspective on visual metacognition.…”

“…This research has implicated a number of mostly frontal, parietal, and cingulate areas as integral to visual metacognition (Baird et al, 2013; Fleck et al, 2006; Fleming et al, 2012; Lau & Passingham, 2006; Morales et al, 2018; Yeon et al, 2020). Importantly, it has also shown the critical role of the network communications between these regions such as the observed increase in functional connectivity between frontal and visual areas associated when engaging in metacognitive evaluation (Fleming et al, 2012).…”

“…Although studies correlating metacognitive ability with anatomical measures have been influential, they can only reveal individual anatomical structures. However, there is accumulating evidence that metacognition emerges via network interactions and not simply from the function of single regions (Yeon et al, 2020). Revealing the networks underlying visual metacognition has largely relied on neuroimaging research.…”