Individual exploration and selective social learning: balancing exploration-exploitation trade-offs in collective foraging

Interaction among participants in a collective intelligence experiment: an emotional approach

Introduction

The construct of collective intelligence assumes that groups have a better capacity than individuals to deal with complex, poorly defined problems. The digital domain allows us to analyze this premise under circumstances different from those in the physical environment: we can gather an elevated number of participants and generate a large quantity of data.

Methods

This study adopted an emotional perspective to analyze the interactions among 794 adolescents dealing with a sexting case on an online interaction platform designed to generate group answers resulting from a certain degree of achieved consensus.

Results

Our results show that emotional responses evolve over time in several phases of interaction. From the onset, the emotional dimension predicts how individual responses will evolve, particularly in the final consensus phase.

Discussion

Responses gradually become more emotionally complex; participants tend to identify themselves with the victim in the test case while increasingly rejecting the aggressors.

Visual social information use in collective foraging

Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.

Reduced effects of social feedback on learning in Turner syndrome

Turner syndrome is a genetic condition caused by a complete or partial loss of one of the X chromosomes. Previous studies indicate that Turner syndrome is associated with challenges in social skills, but the underlying mechanisms remain largely unexplored. A possible mechanism is a reduced social influence on learning. The current study examined the impact of social and non-social feedback on learning in women with Turner syndrome (n = 35) and a sex- and age-matched control group (n = 37). Participants were instructed to earn points by repeatedly choosing between two stimuli with unequal probabilities of resulting in a reward. Mastering the task therefore required participants to learn through feedback which of the two stimuli was more likely to be rewarded. Data were analyzed using computational modeling and analyses of choice behavior. Social feedback led to a more explorative choice behavior in the control group, resulting in reduced learning compared to non-social feedback. No effects of social feedback on learning were found in Turner syndrome. The current study thus indicates that women with Turner syndrome may be less sensitive to social influences on reinforcement learning, than the general population.

Evolutionary emergence of collective intelligence in large groups of students

The emergence of collective intelligence has been studied in much greater detail in small groups than in larger ones. Nevertheless, in groups of several hundreds or thousands of members, it is well-known that the social environment exerts a considerable influence on individual behavior. A few recent papers have dealt with some aspects of large group situations, but have not provided an in-depth analysis of the role of interactions among the members of a group in the creation of ideas, as well as the group's overall performance. In this study, we report an experiment where a large set of individuals, i.e., 789 high-school students, cooperated online in real time to solve two different examinations on a specifically designed platform (Thinkhub). Our goal of this paper 6 to describe the specific mechanisms of idea creation we were able to observe and to measure the group's performance as a whole. When we deal with communication networks featuring a large number of interacting entities, it seems natural to model the set as a complex system by resorting to the tools of statistical mechanics. Our experiment shows how an interaction in small groups that increase in size over several phases, leading to a final phase where the students are confronted with the most popular answers of the previous phases, is capable of producing high-quality answers to all examination questions, whereby the last phase plays a crucial role. Our experiment likewise shows that a group's performance in such a task progresses in a linear manner in parallel with the size of the group. Finally, we show that the controlled interaction and dynamics foreseen in the system can reduce the spread of "fake news" within the group.