The effects of recursive communication dynamics on belief updating

Action-based confidence sharing and collective decision making

Joint action research explores how multiple humans can coordinate their movements to achieve common goals. When there is uncertainty about the joint goal, individuals need to integrate their perceptual information of the environment to collaboratively determine their new goal. To ensure that a group reaches a consensus about the goal, collective decision making among the individuals is required. Collective decision making can be facilitated by nonverbal expressions of opinions and associated confidence levels. Here, we show that confidence sharing in groups of 2, 3, and 4 individuals can be studied using their trajectories when jointly moving toward one of several options. We found that both opinions and confidence levels can be distinguished in individual movement trajectories, and found that movement features can predict an individual's influence. Our results suggest that movement trajectories are a valid way to study confidence sharing in human collective decision making.

Person-to-person opinion dynamics: An empirical study using an online game

A model needs to make verifiable predictions to have any scientific value. In opinion dynamics, the study of how individuals exchange opinions with one another, there are many theoretical models which attempt to model opinion exchange, one of which is the Martins model, which differs from other models by using a parameter that is easier to control for in an experiment. In this paper, we have designed an experiment to verify the Martins model and contribute to the experimental design in opinion dynamic with our novel method.

A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams

Algorithmic agents, popularly known as bots, have been accused of spreading misinformation online and supporting fringe views. Collectives are vulnerable to hidden-profile environments, where task-relevant information is unevenly distributed across individuals. To do well in this task, information aggregation must equally weigh minority and majority views against simple but inefficient majority-based decisions. In an experimental design, human volunteers working in teams of 10 were asked to solve a hidden-profile prediction task. We trained a variational auto-encoder (VAE) to learn people’s hidden information distribution by observing how people’s judgments correlated over time. A bot was designed to sample responses from the VAE latent embedding to selectively support opinions proportionally to their under-representation in the team. We show that the presence of a single bot (representing 10% of team members) can significantly increase the polarization between minority and majority opinions by making minority opinions less prone to social influence. Although the effects on hybrid team performance were small, the bot presence significantly influenced opinion dynamics and individual accuracy. These findings show that self-supervized machine learning techniques can be used to design algorithms that can sway opinion dynamics and group outcomes.

A Brief Taxonomy of Hybrid Intelligence

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

Confidence, advice seeking and changes of mind in decision making

Humans and other animals rely on social learning strategies to guide their behaviour, especially when the task is difficult and individual learning might be costly or ineffective. Recent models of individual and group decision-making suggest that subjective confidence judgments are a prime candidate in guiding the way people seek and integrate information from social sources. The present study investigates the way people choose and use advice as a function of the confidence in their decisions, using a perceptual decision task to carefully control the quality of participants' decisions and the advice provided. The results show that reported confidence guides the search for new information in accordance with probabilistic normative models. Moreover, large inter-individual differences were found, which strongly correlated with more traditional measures of metacognition. However, the extent to which participants used the advice they received deviated from what would be expected under a Bayesian update of confidence, and instead was characterised by heuristic-like strategies of categorically ignoring vs. accepting advice provided, again with substantial individual differences apparent in the relative dominance of these strategies.

Distinct signatures of subjective confidence and objective accuracy in speech prosody

Whether speech prosody truly and naturally reflects a speaker's subjective confidence, rather than other dimensions such as objective accuracy, is unclear. Here, using a new approach combing psychophysics with acoustic analysis and automatic classification of verbal reports, we tease apart the contributions of sensory evidence, accuracy, and subjective confidence to speech prosody. We find that subjective confidence and objective accuracy are distinctly reflected in the loudness, duration and intonation of verbal reports. Strikingly, we show that a speaker's accuracy is encoded in speech prosody beyond their own metacognitive awareness, and that it can be automatically decoded from this information alone with performances up to 60%. These findings demonstrate that confidence and accuracy have separable prosodic signatures that are manifested with different timings, and on different acoustic dimensions. Thus, both subjective mental states of confidence, and objective states related to competence, can be directly inferred from this natural behavior.

The effects of recursive communication dynamics on belief updating

Many social interactions are characterized by dynamic interplay, such that individuals exert reciprocal influence over each other's behaviours and beliefs. The present study investigated how the dynamics of reciprocal influence affect individual beliefs in a social context, over and above the information communicated in an interaction. To this end, we developed a simple social decision-making paradigm in which two people are asked to make perceptual judgments while receiving information about each other's decisions. In a Static condition, information about the partner only conveyed their initial, independent judgment. However, in a Dynamic condition, each individual saw the evolving belief of their partner as they learnt about and responded to the individual's own judgment. The results indicated that in both conditions, the majority of confidence adjustments were characterized by an abrupt change followed by smaller adjustments around an equilibrium, and that participants' confidence was used to arbitrate conflict (although deviating from Bayesian norm). Crucially, recursive interaction had systematic effects on belief change relative to the static baseline, magnifying confidence change when partners agreed and reducing confidence change when they disagreed. These findings indicate that during dynamic interactions-often a characteristic of real-life and online social contexts-information is collectively transformed rather than acted upon by individuals in isolation. Consequently, the output of social events is not only influenced by what the dyad knows but also by predictable recursive and self-reinforcing dynamics.