Affective forecasting in an orangutan: predicting the hedonic outcome of novel juice mixes

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Abstract

Affective forecasting is an ability that allows the prediction of the hedonic outcome of never-before experienced situations, by mentally recombining elements of prior experiences into possible scenarios, and pre-experiencing what these might feel like. It has been hypothesised that this ability is uniquely human. For example, given prior experience with the ingredients, but in the absence of direct experience with the mixture, only humans are said to be able to predict that lemonade tastes better with sugar than without it. Non-human animals, on the other hand, are claimed to be confined to predicting—exclusively and inflexibly—the outcome of previously experienced situations. Relying on gustatory stimuli, we devised a non-verbal method for assessing affective forecasting and tested comparatively one Sumatran orangutan and ten human participants. Administered as binary choices, the test required the participants to mentally construct novel juice blends from familiar ingredients and to make hedonic predictions concerning the ensuing mixes. The orangutan’s performance was within the range of that shown by the humans. Both species made consistent choices that reflected independently measured taste preferences for the stimuli. Statistical models fitted to the data confirmed the predictive accuracy of such a relationship. The orangutan, just like humans, thus seems to have been able to make hedonic predictions concerning never-before experienced events.

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Subject classification (UKÄ) – MANDATORY

  • Psychology (excluding Applied Psychology)
  • Other Biological Topics
  • Other Humanities

Keywords

  • affective forecasting, episodic memory, decision-making, humans, orangutans, animal planning
Original languageEnglish
Pages (from-to)1081-1092
Number of pages12
JournalAnimal Cognition
Volume19
Issue number6
Early online date2016 Aug 11
Publication statusPublished - 2016 Nov
Publication categoryResearch
Peer-reviewedYes

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