Theories of consciousness are solutions in need of problems

Doerig et al. point out a number of shortcomings with existing theories of consciousness and argue they should be systematically constrained by empirical data. In this commentary I suggest a further constraint - the potential functions of (the contents of) consciousness. One such candidate function in humans is the social sharing of reportable mental states. The social function of consciousness provides a general framework within which to understand the evolution and neurobiology of conscious awareness

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

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. (PsycINFO Database Recor

The Dunning-Kruger effect revisited

The Dunning–Kruger effect describes a tendency for incompetent individuals to overestimate their ability. The effect has both seeped into popular imagination and been the subject of scientific critique. Jansen et al. combine computational modelling with a large-scale replication of the original findings to shed new light on the drivers of the Dunning–Kruger effect

Confirmation bias is adaptive when coupled with efficient metacognition

Biases in the consideration of evidence can reduce the chances of consensus between people with different viewpoints. While such altered information processing typically leads to detrimental performance in laboratory tasks, the ubiquitous nature of confirmation bias makes it unlikely that selective information processing is universally harmful. Here, we suggest that confirmation bias is adaptive to the extent that agents have good metacognition, allowing them to downweight contradictory information when correct but still able to seek new information when they realize they are wrong. Using simulation-based modelling, we explore how the adaptiveness of holding a confirmation bias depends on such metacognitive insight. We find that the behavioural consequences of selective information processing are systematically affected by agents' introspective abilities. Strikingly, we find that selective information processing can even improve decision-making when compared with unbiased evidence accumulation, as long as it is accompanied by good metacognition. These results further suggest that interventions which boost people's metacognition might be efficient in alleviating the negative effects of selective information processing on issues such as political polarization. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'

Efficient search termination without task experience

As a general rule, if it is easy to detect a target in a visual scene, it is also easy to detect its absence. To account for this, models of visual search explain search termination as resulting either from counterfactual reasoning over second-order representations of search efficiency, automatic extraction of ensemble statistics of a display, or heuristic adjustment of a search termination strategy based on previous trials. Traditional few-subjects/many-trials lab-based experiments render it impossible to disentangle the unique contribution of these different processes to absence pop-out - the immediate recognition that a feature is missing from a display. In 2 preregistered large-scale online experiments (N1 = 1187; N2 = 887) we show that search termination times are already aligned with target identification times in the very first trials of the experiment before any experience with target presence. Exploratory analysis reveals that explicit metacognitive knowledge about search efficiency is not necessary for efficient search termination. We conclude that for basic stimulus properties, efficient inference about absence is independent of task experience and of explicit metacognitive knowledge about visual search

Subjective signal strength distinguishes reality from imagination

Humans are voracious imaginers, with internal simulations supporting memory, planning and decision-making. Because the neural mechanisms supporting imagery overlap with those supporting perception, a foundational question is how reality and imagination are kept apart. One possibility is that the intention to imagine is used to identify and discount self-generated signals during imagery. Alternatively, because internally generated signals are generally weaker, sensory strength is used to index reality. Traditional psychology experiments struggle to investigate this issue as subjects can rapidly learn that real stimuli are in play. Here, we combined one-trial-per-participant psychophysics with computational modelling and neuroimaging to show that imagined and perceived signals are in fact intermixed, with judgments of reality being determined by whether this intermixed signal is strong enough to cross a reality threshold. A consequence of this account is that when virtual or imagined signals are strong enough, they become subjectively indistinguishable from reality

Spontaneous attribution of false beliefs in adults examined using a signal detection approach

Understanding other people have beliefs different from ours or different from reality is critical to social interaction. Previous studies suggest that healthy adults possess an implicit mentalising system, but alternative explanations for data from reaction time false belief tasks have also been given. In this study, we combined signal detection theory (SDT) with a false belief task. As application of SDT allows us to separate perceptual sensitivity from criteria, we are able to investigate how another person’s beliefs change the participant’s perception of near-threshold stimuli. Participants (n = 55) watched four different videos in which an actor saw (or did not see) a Gabor cube hidden (or not hidden) behind an occluder. At the end of each video, the occluder vanished revealing a cube either with or without Gabor pattern, and participants needed to report whether they saw the Gabor pattern or not. A pre-registered analysis with classical statistics weakly suggests an effect of the actor’s belief on participant’s perceptions. An exploratory Bayesian analysis supports the idea that when the actor believed the cube was present, participants made slower and more liberal judgements. Although these data are not definitive, these current results indicate the value of new measures for understanding implicit false belief processing

Erratum to: Stage 1 registered report: metacognitive asymmetries in visual perception and Stage 2 registered report: metacognitive asymmetries in visual perception.

[This corrects the article DOI: 10.1093/nc/niab005.][This corrects the article DOI: 10.1093/nc/niab025.]

Metacognitive asymmetries in visual perception

Representing the absence of objects is psychologically demanding. People are slower, less confident and show lower metacognitive sensitivity (the alignment between subjective confidence and objective accuracy) when reporting the absence compared with presence of visual stimuli. However, what counts as a stimulus absence remains only loosely defined. In this Registered Report, we ask whether such processing asymmetries extend beyond the absence of whole objects to absences defined by stimulus features or expectation violations. Our pre-registered prediction was that differences in the processing of presence and absence reflect a default mode of reasoning: we assume an absence unless evidence is available to the contrary. We predicted asymmetries in response time, confidence, and metacognitive sensitivity in discriminating between stimulus categories that vary in the presence or absence of a distinguishing feature, or in their compliance with an expected default state. Using six pairs of stimuli in six experiments, we find evidence that the absence of local and global stimulus features gives rise to slower, less confident responses, similar to absences of entire stimuli. Contrary to our hypothesis, however, the presence or absence of a local feature has no effect on metacognitive sensitivity. Our results weigh against a proposal of a link between the detection metacognitive asymmetry and default reasoning, and are instead consistent with a low-level visual origin of metacognitive asymmetries for presence and absence

HMeta-d: hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings

Metacognition refers to the ability to reflect on and monitor one's cognitive processes, such as perception, memory and decision-making. Metacognition is often assessed in the lab by whether an observer's confidence ratings are predictive of objective success, but simple correlations between performance and confidence are susceptible to undesirable influences such as response biases. Recently, an alternative approach to measuring metacognition has been developed (Maniscalco and Lau 2012) that characterizes metacognitive sensitivity (meta-d') by assuming a generative model of confidence within the framework of signal detection theory. However, current estimation routines require an abundance of confidence rating data to recover robust parameters, and only provide point estimates of meta-d'. In contrast, hierarchical Bayesian estimation methods provide opportunities to enhance statistical power, incorporate uncertainty in group-level parameter estimates and avoid edge-correction confounds. Here I introduce such a method for estimating metacognitive efficiency (meta-d'/d') from confidence ratings and demonstrate its application for assessing group differences. A tutorial is provided on both the meta-d' model and the preparation of behavioural data for model fitting. Through numerical simulations I show that a hierarchical approach outperforms alternative fitting methods in situations where limited data are available, such as when quantifying metacognition in patient populations. In addition, the model may be flexibly expanded to estimate parameters encoding other influences on metacognitive efficiency. MATLAB software and documentation for implementing hierarchical meta-d' estimation (HMeta-d) can be downloaded at https://github.com/smfleming/HMeta-d