Incorporating Social Presence in the Design of the Anthropomorphic Interface of Recommendation Agents: Insights from an fMRI Study
Why this work is in the frame
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Bibliographic record
Abstract
Recommendation agents (RAs) are regularly used in online environments to give consumers advice on products. Since social components of human(like RAs (humanoid avatars) are important components in their adoption and use, this study focuses on how the design of the anthropomorphic interface of RAs in terms of social demographics, namely ethnicity and gender, can enhance the RA’s social presence to facilitate their adoption. Since social presence has been shown in the literature to predict the adoption and use of RAs, we examine whether match or mismatch in terms of the anthropomorphic RA’s ethnicity and gender can enhance the user’s social interaction with an RA. To overcome concerns of social desirability bias and political correctness when users assess the social presence of RAs that vary in their ethnicity and gender, we conducted a functional Magnetic Resonance Imaging (fMRI) study to complement a traditional behavioral experiment. Our goal was to explain prior behavioral findings that showed that ethnicity (as opposed to gender) match is associated with higher social presence, particularly among women. Specifically, brain activity was captured in an fMRI scanner while users who varied on their ethnicity and gender to either match or mismatch the ethnicity and gender of four RAs evaluated each of the RAs on their social presence. Besides contributing to the neuroscience literature by identifying the brain activations that relate to social presence, the fMRI results shed light on the nature of social presence and explain earlier behavioral findings by showing gender differences in the neural correlates of social presence in terms of ethnicity and gender match and mismatch. Implications on designing anthropomorphic interfaces to embody social demographics to enhance social presence are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it