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Sujin Kim, David Alais; Attraction to the recent past in aesthetic judgments: a positive serial dependency for ratings of artwork. Journal of Vision 2018;18(10):1312. doi: 10.1167/18.10.1312.
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© ARVO (1962-2015); The Authors (2016-present)
Recent work shows that current visual perception can be systematically biased towards recently viewed stimuli. This is known as positive serial dependency and has been shown for various low-level visual features (e.g., orientation) and facial attractiveness. In the current study, we tested whether serial dependency is present in aesthetic judgments of artworks. A set of 100 paintings was collected through online archives (portraits and paintings containing people were excluded). For each of 17 participants, 40 paintings were randomly selected from the set and each was rated 20 times in a random order, with the constraint that the same painting was never presented consecutively. Paintings were presented for 1 s followed by a slide bar that was used to rate attractiveness. We measured the serial dependency effect for each observer by classifying their response on every trial into one of two categories depending on whether the current painting was preceded by a more attractive or less attractive painting (determined by the average of the 20 ratings for each painting). Results showed that the current painting earned significantly higher aesthetic ratings when participants viewed more attractive painting on the previous trial, compared to when they viewed a less attractive one. Significant positive dependencies were also found for 2-back and 3-back analyses. Our data show that positive serial dependencies in perception are not limited to low-level stimuli: high-level judgements such as aesthetics are also assimilated towards the recent past. This finding shows that the aesthetic experience of artworks is influenced by the order in which they are viewed and has meaningful implications for real-world visual environments such as art exhibitions and museums.
Meeting abstract presented at VSS 2018
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