Boyce-Jacino, C.M., Chapman, G.B., and DeDeo, S. (Under review) Common Ground in Question-Asking: Evidence for a dual-value model of information exchange.
Boyce-Jacino, C.M., and Harrington, N. (2022) Toward the Optimization of Team Formation: A Review of Computational Strategies to Aid Decision Making. Defense Technical Information Center (DTIC) No. AD1179137. RN 2022-08.
In this comprehensive review we offers a multi-disciplinary review of the current computational approaches to the team formation problem. We organize the review around two questions: (1) What are the common decision types, computational approaches, and optimization constraints in the literature? (2) How can theoretical contributions from psychology advance computational methods of team formation to make them both psychologically relevant and applicable to real world problems?
Boyce-Jacino, C.M., Peters, E., Galvani, A., and Chapman, G.B. (2022) Large numbers cause magnitude neglect: The case of government expenditures. PNAS. 19(28), e2203037119. https://doi.org/10.1073/ pnas.2203037119
Boyce-Jacino, C.M. & DeDeo (2022). Cooperation, Interaction, Search: Computational approaches to the psychology of asking and answering questions. In M. Dehghani, & R. Boyd (Eds.) The Atlas of Language Analysis in Psychology. New York, NY: Guilford Press. https://psyarxiv.com/5mgn2/
In this chapter, we argue that question-asking should be studied as a process of interactive search between social agents. We show how new tools in machine learning, combined with large-scale data sets on question-asking ``in the wild'', can give us new insights into how inquiry works beyond the confines of the laboratory. We introduce technical aspects of these tools, new mathematical frameworks that can use them to gain insights into psychology of asking and answering questions, an examples of their application. We draw attention to the accompanying challenges, including the difficulty of using metadata as dependent variables, and determining the construct validity of the tools themselves.
Boyce-Jacino, C.M. & DeDeo, S. (2020). Opacity, Obscurity, and the Geometry of Question-Asking. Cognition. https://doi.org/10.1016/j.cognition.2019.104071
In this paper we establish a novel data science driven approach to question-asking that allowed us to show the dominance of two orthogonal dimensions along which questions can be made difficult to answer: the rarity of the answer (obscurity) and the directness of the question (opacity). Our methods allowed us to quantify not only the qualitative aspects of answering difficult questions, but also the ways in which latent cues within a question relate to its answer.
Exegesis (with John Miller)
In this paper, we develop an algorithm to quantify the alignment of a word with a psychological concept (e.g. morality, intentional). We demonstrate that using this algorithm, we can identify key words in a passage and replace them with words more or less aligned with a concept. Experimentally, we show that this text manipulation changes participant perceptions.
Eye of the Beholder: Attribution of Intentions in Unethical Behavior (with Gretchen Chapman)
Here, we present an account of unethical behavior which elucidates the conditions under which agents are dishonest and defines the existence of distinct types of dishonest behavior. We evaluate our key theoretical predictions in an experiment and show that the uncertainty of the decision context strongly affects behavior: when uncertainty is low, agents either cheat maximally or not at all, and when it is high, they cheat incrementally.
This work leverages computational techniques from natural language processing and network science to develop a tool to explore and visualize text generated by design workshops.