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| Implicit Relational Assessment Procedure and Q Methodology: Measurements of Behavioral History and Its Effect on Choice Behavior |
| Sunday, May 25, 2025 |
| 12:00 PM–12:20 PM |
| Marriott Marquis, M4 Level, Independence D |
| Area: VBC |
| Instruction Level: Intermediate |
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| Implicit Relational Assessment Procedure and Q Methodology: Measurements of Behavioral History and Its Effect on Choice Behavior |
| Domain: Basic Research |
| RITA OLLA (University of Nevada, Reno), Ramona Houmanfar (University of Nevada, Reno), Elisabetta Cherchi (New York University Abu Dhabi), Diane Montgomery (Oklahoma State University) |
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| Abstract: Behavioral principles suggest that an individual's interaction history and contextual factors influence choice behavior. This study employed two assessment tools to measure participants' interaction histories in work-related preferences: 1) Implicit Relational Assessment Procedure (IRAP) which provided measurements of immediate relational verbal responses under time pressure, 2) Q Methodology which provided the analysis of verbal responses without time constraints. Two studies, each with 22 participants, followed a two-phase experimental design. Initially, participants completed the IRAP and Q assessments. One week later, they engaged in a simulated data entry task which provided a choice option between human and AI assistance in the context of different accuracy levels. Researchers used the IRAP and Q results with the manipulated accuracy levels to estimate a discrete choice model via panel data analysis. All individuals’ choices across participants were examined. The findings revealed that significant differences in partner accuracy levels influenced choice behavior. Furthermore, specific IRAP and Q Methodology results effectively predicted preferences for human or AI assistance. This research introduces a preliminary methodology for measuring verbally expressed interaction history as predictors of choice preferences. Moreover, it may contribute to the development of models that enhance understanding of human-AI interactions, and decision-making processes in collaborative work environments. |
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