Natural statistics support a rational account of confidence biases

Metacognition of perceptual resolution across and around the visual field

Do people have accurate metacognition of non-uniformities in perceptual resolution across (i.e., eccentricity) and around (i.e., polar angle) the visual field? Despite its theoretical and practical importance, this question has not yet been empirically tested. This study investigated metacognition of perceptual resolution by guessing patterns during a degradation (i.e., loss of high spatial frequencies) localization task. Participants localized the degraded face among the nine faces that simultaneously appeared throughout the visual field: fovea (fixation at the center of the screen), parafovea (left, right, above, and below fixation at 4° eccentricity), and periphery (left, right, above, and below fixation at 10° eccentricity). We presumed that if participants had accurate metacognition, in the absence of a degraded face, they would exhibit compensatory guessing patterns based on counterfactual reasoning ("The degraded face must have been presented at locations with lower perceptual resolution, because if it were presented at locations with higher perceptual resolution, I would have easily detected it."), meaning that we would expect more guess responses for locations with lower perceptual resolution. In two experiments, we observed guessing patterns that suggest that people can monitor non-uniformities in perceptual resolution across, but not around, the visual field during tasks, indicating partial in-the-moment metacognition. Additionally, we found that global explicit knowledge of perceptual resolution is not sufficient to guide in-the-moment metacognition during tasks, which suggests a dissociation between local and global metacognition.

Reflexivity gradient-Consciousness knowing itself

Some consider phenomenal consciousness to be the great achievement of the evolution of life on earth, but the real achievement is much more than mere phenomenality. The real achievement is that consciousness has woken up within us and has recognized itself, that within us humans, consciousness knows that it is conscious. This short review explores the reflexivity of consciousness from the perspective of consciousness itself-a non-conceptual nondual awareness, whose main property is its non-representational reflexivity. In light of this nondual reflexivity, different types of reflexivity proposed by current theories can be seen as a gradation of relational or transitive distances between consciousness as the knower and consciousness as the known, from fully representational and dual, through various forms of qualified monism, to fully non-representational and nondual.

Pharmacological Elevation of Catecholamine Levels Improves Perceptual Decisions, But Not Metacognitive Insight

Perceptual decisions are often accompanied by a feeling of decision confidence. Where the parietal cortex is known for its crucial role in shaping such perceptual decisions, metacognitive evaluations are thought to additionally rely on the (pre)frontal cortex. Because of this supposed neural differentiation between these processes, perceptual and metacognitive decisions may be divergently affected by changes in internal (e.g., attention, arousal) and external (e.g., task and environmental demands) factors. Although intriguing, causal evidence for this hypothesis remains scarce. Here, we investigated the causal effect of two neuromodulatory systems on behavioral and neural measures of perceptual and metacognitive decision-making. Specifically, we pharmacologically elevated levels of catecholamines (with atomoxetine) and acetylcholine (with donepezil) in healthy adult human participants performing a visual discrimination task in which we gauged decision confidence, while electroencephalography was measured. Where cholinergic effects were not robust, catecholaminergic enhancement improved perceptual sensitivity, while at the same time leaving metacognitive sensitivity unaffected. Neurally, catecholaminergic elevation did not affect sensory representations of task-relevant visual stimuli but instead enhanced well-known decision signals measured over the centroparietal cortex, reflecting the accumulation of sensory evidence over time. Crucially, catecholaminergic enhancement concurrently impoverished neural markers measured over the frontal cortex linked to the formation of metacognitive evaluations. Enhanced catecholaminergic neuromodulation thus improves perceptual but not metacognitive decision-making.

Human-like dissociations between confidence and accuracy in convolutional neural networks

Prior research has shown that manipulating stimulus energy by changing both stimulus contrast and variability results in confidence-accuracy dissociations in humans. Specifically, even when performance is matched, higher stimulus energy leads to higher confidence. The most common explanation for this effect is the positive evidence heuristic where confidence neglects evidence that disconfirms the choice. However, an alternative explanation is the signal-and-variance-increase hypothesis, according to which these dissociations arise from low-level changes in the separation and variance of perceptual representations. Because artificial neural networks lack built-in confidence heuristics, they can serve as a test for the necessity of confidence heuristics in explaining confidence-accuracy dissociations. Therefore, we tested whether confidence-accuracy dissociations induced by stimulus energy manipulations emerge naturally in convolutional neural networks (CNNs). We found that, across three different energy manipulations, CNNs produced confidence-accuracy dissociations similar to those found in humans. This effect was present for a range of CNN architectures from shallow 4-layer networks to very deep ones, such as VGG-19 and ResNet -50 pretrained on ImageNet. Further, we traced back the reason for the confidence-accuracy dissociations in all CNNs to the same signal-and-variance increase that has been proposed for humans: higher stimulus energy increased the separation and variance of the CNNs' internal representations leading to higher confidence even for matched accuracy. These findings cast doubt on the necessity of the positive evidence heuristic to explain human confidence and establish CNNs as promising models for adjudicating between low-level, stimulus-driven and high-level, cognitive explanations of human behavior.

A confidence framing effect: Flexible use of evidence in metacognitive monitoring

Human behavior is flexibly regulated by specific goals of cognitive tasks. One notable example is goal-directed modulation of metacognitive behavior, where logically equivalent decision-making problems can yield different patterns of introspective confidence depending on the frame in which they are presented. While this observation highlights the important heuristic nature of metacognitive monitoring, computational mechanisms underlying this phenomenon remain elusive. We confirmed the confidence framing effect in two-alternative dot-number discrimination and in previously published preference-choice data, demonstrating distinctive confidence patterns between "choose more" or "choose less" frames. Formal model comparisons revealed a simple confidence heuristic behind this phenomenon, which assigns greater weight to chosen than unchosen stimulus evidence. This computation appears to be based on internal evidence constituted under specific task demands rather than physical stimulus intensity itself, a view justified in terms of ecological rationality. These results shed light on the adaptive nature of human decision-making and metacognitive monitoring.

Metacognition and Confidence: A Review and Synthesis

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

Toward 'Computational-Rationality' Approaches to Arbitrating Models of Cognition: A Case Study Using Perceptual Metacognition

Perceptual confidence results from a metacognitive process which evaluates how likely our percepts are to be correct. Many competing models of perceptual metacognition enjoy strong empirical support. Arbitrating these models traditionally proceeds via researchers conducting experiments and then fitting several models to the data collected. However, such a process often includes conditions or paradigms that may not best arbitrate competing models: Many models make similar predictions under typical experimental conditions. Consequently, many experiments are needed, collectively (sub-optimally) sampling the space of conditions to compare models. Here, instead, we introduce a variant of optimal experimental design which we call a computational-rationality approach to generative models of cognition, using perceptual metacognition as a case study. Instead of designing experiments and post-hoc specifying models, we began with comprehensive model comparison among four competing generative models for perceptual metacognition, drawn from literature. By simulating a simple experiment under each model, we identified conditions where these models made maximally diverging predictions for confidence. We then presented these conditions to human observers, and compared the models' capacity to predict choices and confidence. Results revealed two surprising findings: (1) two models previously reported to differently predict confidence to different degrees, with one predicting better than the other, appeared to predict confidence in a direction opposite to previous findings; and (2) two other models previously reported to equivalently predict confidence showed stark differences in the conditions tested here. Although preliminary with regards to which model is actually 'correct' for perceptual metacognition, our findings reveal the promise of this computational-rationality approach to maximizing experimental utility in model arbitration while minimizing the number of experiments necessary to reveal the winning model, both for perceptual metacognition and in other domains.