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.

  • In this paper we explore the factors that make an answer valuable. Using human judgments of COVID-19-related frequently asked questions we found support for a dual-value model of question answering. The ratings of an answer’s value resolved into two principal components: an “informativeness” axis and an “interpersonal” axis. Informative answers tended to be expansive: the answers touch on question semantics but also include more distinct concepts. Interpersonal answers were characterized by recapitulation and tend to stay close to the question space. Ratings along these axes, in turn, were driven by key features of the semantic relationship between question and answer. These results highlight the existence of interpersonality as a challenge in question answering that is distinct from that of informing.

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. pnas.2203037119

  • In four studies we demonstrate that the public’s understanding of government budgetary expenditures is hampered by difficulty in representing large numerical magnitudes. Despite orders of magnitude difference between millions and billions, study partici- pants struggle with the budgetary magnitudes of government programs. When numerical values are rescaled as smaller magnitudes (in the thousands or lower), lay understanding improves, as indicated by greater sensitivity to numerical ratios and more accurate rank ordering of expenses. This improved sensitivity ultimately impacts funding choices and public perception of respective budgets, indicating the importance of numerical cognition for good citizenship.

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.

        • 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.

        • 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.

Working Papers

Communication in Teams

  • The focus of this project is on team communication. We employ tools from natural language processing to develop constructs that capture information spread, novel idea generation, and general group dynamics. We then relate individual level characteristics to communication patterns and to group-level outcomes.

Psychological Interventions to Reduce Workplace Fatalities (with Danny Oppenheimer)

  • The goal of this project is to map out psychological barriers to the use of personal protective equipment (PPE) and other workplace safety technology and protocols, and develop psychological interventions to overcoming those barriers.

Public and Scientist Perceptions of Problems in Science (with Stephanie Anglin, John Edlund, Chris Homan, and Andrew Baschnagel)

  • In this interdisciplinary team we are investigating public and scientist perceptions of issues related to scientific integrity such as bias, conflicts of interest, and ethical behavior.

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.

Lingua Technica (With Dakota Murray, Jeongki Lim, Andrew Shea)

      • 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.