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Knowing too much: Knowledge of energy content prevents liking change through flavour-nutrient associations

机译:知道太多:能量内容的知识可防止通过风味 - 营养协会改变

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Associations between flavours and the consequences of ingestion can lead to changes in flavour liking depending on nutrient content, an example of flavour-nutrient learning (FNL). Expectations about the consequences of ingestion can be modified by information at the point of ingestion, such as nutritional labelling. What is unknown is the extent to which these label-based expectations modify FNL. Since nutrient information can alter expectations about how filling a product would be, we hypothesised that labels predicting higher energy (HE) content would enhance satiety and so promote more rapid flavour learning. To test this, participants consumed either a lower energy (LE: 164kcal) or HE (330kcal) yoghurt breakfast on four separate days, either with no product label or with labels displaying either the actual energy content (Congruent label) or inaccurate energy (Incongruent label). Participants rated liking on all four days: on Days 1 and 4, they could also consume as much as they liked, but consumed a fixed amount (300g) on Days 2 and 3. Both liking and intake increased with exposure in the HE, and decreased in the LE, condition when unlabelled in line with FNL. In contrast, no significant changes were seen in either the Congruent or Incongruent label conditions. Contrary to predictions, these data suggest that FNL occurs when there is an absence of explicit expectations of actual nutrient content, with both accurate and inaccurate information on nutrient content disrupting learning.
机译:口味和摄入后果之间的关联可能导致风味喜欢的变化,这取决于营养含量,风味 - 营养学习(FNL)的一个例子。关于摄取后果的期望可以通过摄入的信息进行修改,例如营养标记。未知是什么,基于标签的期望修改FNL的程度。由于营养信息可以改变关于产品的期望,我们假设预测更高能量(HE)内容的标签会增强饱腹感,因此促进更快的风味学习。为了测试这一点,参与者在四个单独的日子中消耗了较低的能量(LE:164KCAL)或他(330KCAL)酸奶早餐,无论是没有产品标签或显示实际能量内容(全体标签)或不准确的能量的标签(INCONRUENT标签)。与会者在四天内获得的评价:在第1天和第4天,他们也可以像他们喜欢的那样消耗,但是在第2天和第3天消耗了固定金额(300克)。在他身上的暴露和摄入量增加了在LE中减少,在与FNL线上未标记时的条件。相比之下,在一致或不一致的标签条件下没有看到重大变化。与预测相反,这些数据表明,当没有明确对实际营养含量的绝明期望时,会发生FNL,具有准确和不准确的关于营养内容中丧失学习的信息。

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