Tullis, J. G. & Goldstone, R. L. (2023). Peer Discussions Improve Student Learning. In C. E. Overson, C. M. Hakala, L. L. Kordonowy, & V. A. Benassi (Eds.), In their own words: What scholars and teachers want you to know about why and how to apply the science of learning in your academic setting (pp. 465-473). Society for the Teaching of Psychology.
In our research, we examined whether and why peer instruction benefits learning in undergraduate and graduate psychology classes. Students’ answers following discussion are typically more accurate than their answers before discussion. However, one concern with using peer instruction is that the more knowledgeable partner just tells the less knowledgeable student what the correct answer is. This kind of direct transmission of knowledge from the more knowledgeable student to the less knowledgeable student may involve shallow learning and not produce long-lasting learning benefits. Alternatively, peer instruction may prompt students to actively engage with each other to test ideas and yield a new understanding that neither student possessed prior to their interaction. We tested whether peer instruction encourages knowledge transmission or knowledge generation by assessing students’ answers and their confidence in their answers before and after discussion. More specifically, we analyzed whether students just choose the answer of the more confident partner during discussion or whether the discussion between the partners generates novel information.
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Trench, M., Tavernini, L. M., & Goldstone, R. L. (2023). Promoting spontaneous analogical transfer by idealizing target representations. Memory & Cognition, https://doi.org/10.3758/s13421-023-01411-9.
Recent results demonstrate that inducing an abstract representation of target analogs at retrieval time aids access to analogous situations with mismatching surface features (i.e., the late abstraction principle). A limitation of current implementations of this principle is that they either require the external provision of target-specific information or demand very high intellectual effort. Experiment 1 demonstrated that constructing an idealized situation model of a target problem increases the rate of correct solutions compared with constructing either concrete simulations or no simulations. Experiment 2 confirmed that these results were based on an advantage for accessing the base analog, and not merely an advantage of idealized simulations for understanding the target problem in its own terms. This target idealization strategy has broader applicability than prior interventions based on the late abstraction principle because it can be achieved by a greater proportion of participants and without the need to receive target-specific information. We present a computational model, SampComp, that predicts successful retrieval of a stored situation to understand a target based on the overlap of a random, but potentially biased, sample of features from each. SampComp is able to account for the relative benefits of base and target idealization, and their interaction.
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Galesic, M., Barkoczi, D., Berdahl, A., Biro, D., Giannoccaro, I., Goldstone, R., Gonzalez, C., Kandler, A., Kao, A., Kendal, R., Kline, M., Lee, E., Massari, G. F., Mesoudi, A., Olsson, H., Pescetelli, N., Sloman, S., Smaldino, P. E., & Stein, D. L. (2023). Beyond collective intelligence: Collective adaptation. Journal of the Royal Society Interface, 20, 20220736. https://doi.org/10.1098/rsif.2022.0736.
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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Goldstone, R. L., Andrade-Lotero, E., Hawkins, R. D., & Roberts, M. E. (2023). The emergence of specialized roles Within groups. Topics in Cognitive Science, DOI: 10.1111/tops.12644.
Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms—“Group Binary Search,” “Battles of the Exes,” and “Find the Unicorn”—that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework.
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