Dan Bang

Supra-personal cognitive control and metacognition.

Highlights • We propose a ‘dual systems’ framework for thinking about metacognition.• System 1 metacognition is for ‘intra-personal’ cognitive control.• System 2 metacognition is for ‘supra-personal’ cognitive control.• The latter allows agents to share metacognitive representations.• This sharing creates benefits for the group and facilitates cumulative culture.

Sociocultural patterning of neural activity during self-reflection

Western cultures encourage self-construals independent of social contexts, whereas East Asian cultures foster interdependent self-construals that rely on how others perceive the self. How are culturally specific self-construals mediated by the human brain? Using functional magnetic resonance imaging, we monitored neural responses from adults in East Asian (Chinese) and Western (Danish) cultural contexts during judgments of social, mental and physical attributes of themselves and public figures to assess cultural influences on self-referential processing of personal attributes in different dimensions. We found that judgments of self vs a public figure elicited greater activation in the medial prefrontal cortex (mPFC) in Danish than in Chinese participants regardless of attribute dimensions for judgments. However, self-judgments of social attributes induced greater activity in the temporoparietal junction (TPJ) in Chinese than in Danish participants. Moreover, the group difference in TPJ activity was mediated by a measure of a cultural value (i.e. interdependence of self-construal). Our findings suggest that individuals in different sociocultural contexts may learn and/or adopt distinct strategies for self-reflection by changing the weight of the mPFC and TPJ in the social brain network.

What failure in collective decision-making tells us about metacognition

Condorcet (1785) proposed that a majority vote drawn from individual, independent and fallible (but not totally uninformed) opinions provides near-perfect accuracy if the number of voters is adequately large. Research in social psychology has since then repeatedly demonstrated that collectives can and do fail more often than expected by Condorcet. Since human collective decisions often follow from exchange of opinions, these failures provide an exquisite opportunity to understand human communication of metacognitive confidence. This question can be addressed by recasting collective decision-making as an information-integration problem similar to multisensory (cross-modal) perception. Previous research in systems neuroscience shows that one brain can integrate information from multiple senses nearly optimally. Inverting the question, we ask: under what conditions can two brains integrate information about one sensory modality optimally? We review recent work that has taken this approach and report discoveries about the quantitative limits of collective perceptual decision-making, and the role of the mode of communication and feedback in collective decision-making. We propose that shared metacognitive confidence conveys the strength of an individuals opinion and its reliability inseparably. We further suggest that a functional role of shared metacognition is to provide substitute signals in situations where outcome is necessary for learning but unavailable or impossible to establish.

Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making

Highlights • We tested whether a confidence heuristic could replace interaction in a collective perceptual decision-making task.• For individuals of nearly equal reliability, the confidence heuristic is just as accurate as interaction.• For individuals with different reliabilities, the confidence heuristic is less accurate than interaction.• Interacting individuals use the credibility of each other’s confidence estimates to guide their joint decisions.• Interacting individuals face a problem of how to map ‘internal’ variables onto ‘external’ (shareable) variables.

Together, Slowly but Surely: The Role of Social Interaction and Feedback on the Build-Up of Benefit in Collective Decision-Making.

That objective reference is necessary for formation of reliable beliefs about the external world is almost axiomatic. However, Condorcet (1785) suggested that purely subjective information—if shared and combined via social interaction—is enough for accurate understanding of the external world. We asked if social interaction and objective reference contribute differently to the formation and build-up of collective perceptual beliefs. In three experiments, dyads made individual and collective perceptual decisions in a two-interval, forced-choice, visual search task. In Experiment 1, participants negotiated their collective decisions with each other verbally and received feedback about accuracy at the end of each trial. In Experiment 2, feedback was not given. In Experiment 3, communication was not allowed but feedback was provided. Social interaction (Experiments 1 and 2 vs. 3) resulted in a significant collective benefit in perceptual decisions. When feedback was not available a collective benefit was not initially obtained but emerged through practice to the extent that in the second half of the experiments, collective benefits obtained with (Experiment 1) and without (Experiment 2) feedback were robust and statistically indistinguishable. Taken together, this work demonstrates that social interaction was necessary for build-up of reliable collaborative benefit, whereas objective reference only accelerated the process but—given enough opportunity for practice—was not necessary for building up successful cooperation.

Equality bias impairs collective decision-making across cultures

Significance When making decisions together, we tend to give everyone an equal chance to voice their opinion. To make the best decisions, however, each opinion must be scaled according to its reliability. Using behavioral experiments and computational modelling, we tested (in Denmark, Iran, and China) the extent to which people follow this latter, normative strategy. We found that people show a strong equality bias: they weight each other’s opinion equally regardless of differences in their reliability, even when this strategy was at odds with explicit feedback or monetary incentives. We tend to think that everyone deserves an equal say in a debate. This seemingly innocuous assumption can be damaging when we make decisions together as part of a group. To make optimal decisions, group members should weight their differing opinions according to how competent they are relative to one another; whenever they differ in competence, an equal weighting is suboptimal. Here, we asked how people deal with individual differences in competence in the context of a collective perceptual decision-making task. We developed a metric for estimating how participants weight their partner’s opinion relative to their own and compared this weighting to an optimal benchmark. Replicated across three countries (Denmark, Iran, and China), we show that participants assigned nearly equal weights to each other’s opinions regardless of true differences in their competence—even when informed by explicit feedback about their competence gap or under monetary incentives to maximize collective accuracy. This equality bias, whereby people behave as if they are as good or as bad as their partner, is particularly costly for a group when a competence gap separates its members.

Doubly Bayesian analysis of confidence in perceptual decision-making

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people’s confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.

Confidence matching in group decision-making

Most important decisions in our society are made by groups, from cabinets and commissions to boards and juries. When disagreement arises, opinions expressed with higher confidence tend to carry more weight1,2. Although an individual’s degree of confidence often reflects the probability that their opinion is correct3,4, it can also vary with task-irrelevant psychological, social, cultural and demographic factors5–9. Therefore, to combine their opinions optimally, group members must adapt to each other’s individual biases and express their confidence according to a common metric10–12. However, solving this communication problem is computationally difficult. Here we show that pairs of individuals making group decisions meet this challenge by using a heuristic strategy that we call ‘confidence matching’: they match their communicated confidence so that certainty and uncertainty is stated in approximately equal measure by each party. Combining the behavioural data with computational modelling, we show that this strategy is effective when group members have similar levels of expertise, and that it is robust when group members have no insight into their relative levels of expertise. Confidence matching is, however, sub-optimal and can cause miscommunication about who is more likely to be correct. This herding behaviour is one reason why groups can fail to make good decisions10–12.

Learning to make collective decisions: the impact of confidence escalation.

Little is known about how people learn to take into account others’ opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants’ confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members’ confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions.

Is voice a marker for Autism spectrum disorder? A systematic review and meta-analysis.

Individuals with Autism Spectrum Disorder (ASD) tend to show distinctive, atypical acoustic patterns of speech. These behaviors affect social interactions and social development and could represent a non‐invasive marker for ASD. We systematically reviewed the literature quantifying acoustic patterns in ASD. Search terms were: (prosody OR intonation OR inflection OR intensity OR pitch OR fundamental frequency OR speech rate OR voice quality OR acoustic) AND (autis* OR Asperger). Results were filtered to include only: empirical studies quantifying acoustic features of vocal production in ASD, with a sample size >2, and the inclusion of a neurotypical comparison group and/or correlations between acoustic measures and severity of clinical features. We identified 34 articles, including 30 univariate studies and 15 multivariate machine‐learning studies. We performed meta‐analyses of the univariate studies, identifying significant differences in mean pitch and pitch range between individuals with ASD and comparison participants (Cohens d of 0.4–0.5 and discriminatory accuracy of about 61–64%). The multivariate studies reported higher accuracies than the univariate studies (63–96%). However, the methods used and the acoustic features investigated were too diverse for performing meta‐analysis. We conclude that multivariate studies of acoustic patterns are a promising but yet unsystematic avenue for establishing ASD markers. We outline three recommendations for future studies: open data, open methods, and theory‐driven research. Autism Res 2017, 10: 384–407.