Human behavior is fundamentally generative: People create pictures, write stories, compose music, and engage in conversation. Traditional approaches in psychology and cognitive science have not focused on this open-endedness, instead favoring more constrained task settings that admit a limited set of outcomes. Although those approaches have been fruitful, new approaches might be needed to develop a unified understanding of the generative, open-ended behaviors that are so emblematic of human cognition. This article demonstrates the value of generative behaviors as targets for cognitive modeling by providing rich behavioral data that reveal how multiple cognitive processes coordinate. Drawing production serves as a case study illustrating this approach, showing how perception, memory, social inference, and motor control coordinate flexibly on the basis of communicative context. Recent advances in generative artificial intelligence offer both new tools for modeling open-ended human behavior and new comparative targets for understanding similarities and differences between human and machine intelligence. However, applying these tools effectively might require new experimental paradigms, larger data sets, and careful consideration of what mechanistic correspondence between models and human cognition is necessary for scientific progress. Embracing the open-ended nature of human thought and behavior poses methodological challenges but offers a promising path toward understanding the most distinctive aspects of human intelligence.

Abstract Background STEM minority participation programs have been widely implemented in higher education with the goal of diversifying the global STEM workforce. Informed by research highlighting the potential of targeted exploration of STEM roles and reflection on the self in relation to STEM (identity exploration), this work examines how engagement in a government funded STEM minority participation program shaped these processes in current students and program alumni. Results Epistemic network analysis (ENA) was used to visualize conceptual connections between identity themes that emerged from interviews with present and past program participants. Network models were developed for current students and alumni for cross-group comparisons. Differences were found in how participants at different stages of their careers enact and describe their identity exploration processes. Summative network models highlighted how students discussed action-taking (sometimes through participation in STEM minority program initiatives) as they explored less-certain possible future STEM roles, while alumni integrated more diverse and holistic facets of their identities when conceptualizing their futures. To close the interpretive loop, a qualitative interpretation of interview discourse was used to give context to network patterns. Conclusions Results highlight the differences between novices' and professionals' conceptualizations of their future selves and illustrate how minoritized individuals describe their long-term patterns of identity exploration related to STEM majors and careers. Implications for future STEM identity research and practice, including higher education programming as a tool to support students' STEM identity exploration processes, are discussed.

Funding of research is critically important because it affects the flow of new, doctorally qualified scientists into the workforce. This paper provides new insights into how survey data can be combined with administrative records to examine the ways in which funding affects workforce decisions. We show that NSF supports more graduate students per dollar spent than other federal agencies. Not surprisingly, NIH heavily supports biology, health, and psychology PhDs, while NSF heavily supports PhDs in engineering, the physical sciences, mathematics, and computer science.