Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content
This thesis investigates the human valence response to sequences of visual images. We f irst use crowd-sourcing and a novel nine-point psychometric scale to estimate human valence responses to individual images from the OASIS image set with high reliability (split-half Spearman rank-correlation ρ =...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156957 |
| _version_ | 1824457983943245824 |
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| author | Cucu, Theodor |
| author2 | DiCarlo, James J. |
| author_facet | DiCarlo, James J. Cucu, Theodor |
| author_sort | Cucu, Theodor |
| collection | MIT |
| description | This thesis investigates the human valence response to sequences of visual images. We f irst use crowd-sourcing and a novel nine-point psychometric scale to estimate human valence responses to individual images from the OASIS image set with high reliability (split-half Spearman rank-correlation ρ = 0.95). In a separate group of human participants, we then estimate valence responses following short, random sequences of those images (of length ≤ 10). Our key finding is that these sequence-contingent valence responses can be closely predicted by a simple linear combination of the estimated human valence responses to individual images (held-out ρ = 0.94). The combination weights are largest for the final image in the sequence; intuitively, this means the final image by itself can make predictions with high goodness-of-fit (ρ = 0.87). In summary, this research shows new evidence for a simple relationship between valence responses to individual images and valence responses to image sequences, with implications for future studies and practical applications in psychological assessment and beyond. |
| first_indexed | 2025-02-19T04:18:40Z |
| format | Thesis |
| id | mit-1721.1/156957 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2025-02-19T04:18:40Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1569572024-09-25T03:52:39Z Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content Cucu, Theodor DiCarlo, James J. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences This thesis investigates the human valence response to sequences of visual images. We f irst use crowd-sourcing and a novel nine-point psychometric scale to estimate human valence responses to individual images from the OASIS image set with high reliability (split-half Spearman rank-correlation ρ = 0.95). In a separate group of human participants, we then estimate valence responses following short, random sequences of those images (of length ≤ 10). Our key finding is that these sequence-contingent valence responses can be closely predicted by a simple linear combination of the estimated human valence responses to individual images (held-out ρ = 0.94). The combination weights are largest for the final image in the sequence; intuitively, this means the final image by itself can make predictions with high goodness-of-fit (ρ = 0.87). In summary, this research shows new evidence for a simple relationship between valence responses to individual images and valence responses to image sequences, with implications for future studies and practical applications in psychological assessment and beyond. M.Eng. 2024-09-24T18:22:54Z 2024-09-24T18:22:54Z 2024-05 2024-07-11T15:30:18.416Z Thesis https://hdl.handle.net/1721.1/156957 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Cucu, Theodor Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content |
| title | Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content |
| title_full | Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content |
| title_fullStr | Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content |
| title_full_unstemmed | Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content |
| title_short | Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content |
| title_sort | developing a psychometric tool to measure the emotional impact of visual content |
| url | https://hdl.handle.net/1721.1/156957 |
| work_keys_str_mv | AT cucutheodor developingapsychometrictooltomeasuretheemotionalimpactofvisualcontent |