Group performance often benefits when members divide the group’s task into separate components and each member specializes their role so as to accomplish only one of the components. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms that allow for its emergence. For example, maximization of expected utility often does not predict how individuals divide labor in one particular way rather than another. We have developed an iterative two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game as a human experimental task and simulated a heuristic that is able to explain the observed behavior. Our results show that a priori salience of different divisions of labor is insufficient to account for their prevalence in interacting dyads. Instead, dyads frequently achieve efficient division of labor through simple but feedback-sensitive heuristics that systematically decrease the amount of overlap across the members’ roles.
The division of labor phenomenon has been observed with respect to both manual and cognitive labor, but there is no clear understanding of the intra- and inter-individual mechanisms allowing for its emergence, especially when there are multiple divisions possible and communication is limited. Situations fitting this description include individuals splitting a geographical region for resource harvesting without explicit negotiation, or a couple tacitly negotiating the hour of the day for each to shower so that there is sufficient hot water for both. We studied this phenomenon by means of an iterative two-person game in which different divisions are possible, but no explicit communication is allowed. Our results suggest that biases toward a priori divisions are revealed and consolidated through the dyadic interaction.
A.Two psychological experiments in nodeGame to determine some factors that influence the division of cognitive labor.
B. Computational models to solve the tasks posed in the experiments described in result (A).
C. Article in an internationally indexed journal that presents the problem of the division of cognitive labor, the task we use to explore the problem, and the results obtained in (A), the simulation of the computational model obtained in the result (B), and the contrast between experimental data and computational simulation.
D.Article in an internationally indexed journal in which the relevance of the two experiments is discussed in a more abstract context about language, especially in the discussions about semantic externalism.
Conduct two experimental tests to study the division of cognitive and linguistic labor, using computational models that explain the observed behavior.
Dissemination of the results through articles in indexed journals and presentations at international conferences.
|Effective start/end date||6/25/18 → 10/10/19|