The taste of colours

29 citations

Abstract

A multitude of crossmodal correspondences have now been documented between taste (gustation) and visual features (such as hue). In the present study, new analytical methods are used to investigate taste-colour correspondences in a more fine-grained manner while also investigating potential underlying mechanisms. In Experiment 1, image processing analysis is used to evaluate whether searching online for visual images associated with specific taste words (e.g., bitter, sweet) generates outcomes with colour proportions similar to those that have been documented in the literature on taste–colour correspondences. Colour–taste matching tasks incorporating a much wider colour space than tested in previous studies, were assessed in Experiments 2 and 3. Experiments 3 and 4 assessed the extent to which the statistical regularities of the environment, as captured by food object categories, might help to explain the aforementioned correspondences and to what extent the correspondences are present in online content associated with specific tastes, respectively. Experiment 5 evaluated the role of statistical regularities in underpinning colour-taste correspondences related to the stage of ripening of fruit. Overall, the findings revealed consistent associations between specific colours and tastes, in a more nuanced manner than demonstrated in previous studies, while at the same time also showing that both food object categories and the stage of fruit ripening significantly influenced colour and taste perceptions. This, in turn, suggests that people might base these correspondences on both the foods present in their environments, as well as the natural changes that they undergo as they ripen. The results are discussed in light of the different accounts that have been suggested to explain colour-taste correspondences.

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Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.

1 day ago

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