{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/156957"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/156957","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Cucu, Theodor"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content"]}]}],"canonical_facts":{"dc:contributor.advisor":["DiCarlo, James J."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences"],"dc:creator":["Cucu, Theodor"],"dc:date.accessioned":["2024-09-24T18:22:54Z"],"dc:date.available":["2024-09-24T18:22:54Z"],"dc:date.issued":["2024-05"],"dc:description.abstract":["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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/156957"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","Copyright retained by author(s)"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:title":["Developing a Psychometric Tool to Measure the Emotional Impact of Visual Content"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Computation and Cognition"]},"updated_at":"2026-07-22T22:21:09Z"}