Congratulations to the Awardees of the Division 10 Early Career Research Micro-Grants!

Division 10 is pleased to announce the recipients of the Division 10 Early Career Research Micro Grants competition for 2024-2025. Congrats to these stellar researchers who received funding this round!

Here are this year’s awardees and descriptions of their projects. The Division funded 4 PhD students and 2 early career scholars. Of the 6 awardees, we funded 4 domestic and 2 international applicants.

(As a reminder, the timeline for the 2025-2026 grant applications has shifted to align with the Division 10 conference that takes place every March. The next round of applications will be due in November 2025 with a January 2026 start date.)

Gianmarco Biancalani, University of Haifa

The Life Stories of Older Gay Men in Italy and Their Response to a Tele-Drama-Based Intervention: A Qualitative Study

This qualitative research project explores the life stories of older gay men in Italy and examine their response to a group tele-drama intervention tailored to their psychosocial needs. Narrative interviews with older gay men and semi-structured interviews with volunteers who work with this population are conducted, triangulating the two datasets. Based on these findings and relevant literature, a group tele-drama intervention is created. The video-recorded sessions are analyzed using the 6-Key model of drama therapy to examine how participants portray their narratives through the drama work. Post-intervention, participants undergo Client Change Interviews to examine their response to the intervention.

 Hansika KapoorMonk Prayogshala

Can Creating Misinformation Counter its Spread?

A significant chunk of misinformation research has focused on its identification and discernment, but little work has been done on how misinformation is created. According to the self-generation effect, information is better remembered (and consequently understood) when one generates it instead of simply consuming it. Thus, we aim to study the impact of the creation of misinformation on its discernment, weaving together novel areas of dark creativity and misinformation research. In a controlled setting, can generating misinformation serve as a strategy for psychological inoculation?

Clin Lai, The Pennsylvania State University

Creativity Partner: Comparing human and generative AI as collaborator in creative tasks

Creative endeavors thrive on collaboration with others. With the rise in the use of generative AI in creative processes, it remains unknown if ideas generated with AI tend to be more or less creative than ideas generated when working with another human. This study will compare human-human and human-ChatGPT collaborations on the Alternative Uses Task (AUT) and a short story writing task to assess creativity differences. Through this analysis, I aim to identify the strengths and limitations of each partnership, determining when AI is a beneficial collaborative tool and when human interaction remain essential.

Simone LuchiniThe Pennsylvania State University

Preserving Human Diversity in the AI Era: How Human-AI Collaboration Impacts Cultural Identity in Literary Creativity

This project will evaluate modern AI assistants’ effectiveness at facilitating short story writing for English Literature students. By collecting a racially diverse sample of experienced writers, we will investigate whether AI tools equally benefit all artists, or whether AI shows evidence of racial bias in creative contexts. Furthermore, we will evaluate the perception of these stories in a diverse audience, testing whether stories written in collaboration with AI are appreciated differently than those written by humans alone.

Stephanie Miller, University of Vienna & Rebekah Rodriguez-Boerwinkle, UNC Greensboro

From the Gallery to the Lab: Phenomenal Art-Experience Across (Digital) Contexts

Recent years have seen a dramatic shift towards digital arts-engagement, but little is known about how these contexts compare to genuine-gallery settings, especially regarding viewers’ felt-experiences. With this project, we aim to assess experience in various settings, first through a direct comparison of the gallery and lab, via a novel measure for capturing emotional-phenomenal experiences of meetings with visual art (Miller et al., in prep). In a second study, we will further these comparisons by considering intermediate, digital contexts, including OGAR, an emerging virtual-gallery tool designed for research use (Rodriguez-Boerwinkle et al., 2023), and immersive virtual reality.

Edward A Vessel, The City College of New York, CUNY

Using personalized deep neural networks to model the effect of category learning on internal representations of art

How do people mentally represent visual art, and how those representations relate to the potential impact of art? A person’s response to artwork is fundamentally personal: how an art object impacts a viewer depends on that person’s detailed, internal model of the visual world, which in turn depends on their personal biography of visual experience. This study combines a category learning task with machine learning to create “personalized” deep neural network models that capture aspects of individual observers’ unique internal representations of visual artworks. These models will then enable measurement of how internal representations of art relate to their impact.

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