University of Sussex
Browse

File(s) not publicly available

Understanding Individual Differences in Acquired Flavour Liking in Humans

journal contribution
posted on 2023-06-07, 18:06 authored by Martin YeomansMartin Yeomans
The majority of human food likes and dislikes are learned, and there are multiple learning models which explain how flavour liking may be acquired. Two models based on flavour-based learning have attracted considerable attention as potential explanations for acquisition of flavour liking. The first model is based on associations between a novel flavour and an existing liked or disliked flavour (flavour-flavour learning: FFL) and the second based on associations between the flavour and an effect of ingestion (flavour-consequence learning: FCL). However, experimental studies of acquired flavour liking based on these models have had mixed outcomes, with as many studies unable to find changes in liking post-training as there are studies reporting positive findings. This brief review discusses the extent to which the apparent inconsistency in the literature may reflect individual differences in evaluation of the training flavour in FFL or the consequence in FCL. The conclusion is that an understanding of these individual differences can explain many apparent inconsistencies in the flavour-learning literature. These findings also suggest that differences in sensitivity to these types of learning may explain individual differences in sensitivity to hedonically-driven overeating.

History

Publication status

  • Published

Journal

Chemosensory Perception

ISSN

1936-5802

Publisher

Springer Verlag

Issue

1

Volume

3

Page range

34-41

Department affiliated with

  • Psychology Publications

Notes

Short invited review arising from a keynote conference talk

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC