Investigating the impact of background noise on collaborative decision-making using an individual-weighted voting model

The development and evaluation of hearing rehabilitation strategies would greatly benefit from quantification of theoretical constructs related to communication success. Motivated by this, we present a model-based approach to analyze information exchange in a collaborative general knowledge decision-making task. Through a combination of experiments and simulations, we investigate how this model can be used to quantify the exchange of information between interlocutors. Experiments were conducted with ten triads (N = 30) to examine the impact of loud background noise on decision-making in collaborating triads. The group discussions took place in two different levels of background noise, 48dB and 78dB. An existing model of joint decision-making was extended to fit cases where decisions are made individually after engaging in a collaborative discussion. A maximum likelihood estimator for the model was derived and validated in terms of parameter recovery and sensitivity to participant response bias and was used to quantify the relative influence of group members on each other's post-discussion decisions, formalized as a set of model weights. Four statistics were used to summarize the results: overall weight change, self-weighting, weight equality, and weight similarity. Background noise was found to significantly alter how participants influenced each other's decisions, but the direction of change remained unclear. These findings demonstrate how group members' influence on each other's decisions can be quantified and suggest that loud background noise can have a tangible impact on how group decisions are formed.