Skip to content

Assessing animal affect: an automated and self-initiated judgement bias task based on natural investigative behaviour

Research output: Contribution to journalArticle

  • Samantha Jones
  • Vikki Neville
  • Laura Higgs
  • Elizabeth S. Paul
  • Peter Dayan
  • Emma S. J. Robinson
  • Michael Mendl
Scientific methods for assessing animal affect, especially affective valence (positivity or negativity), allow us to evaluate animal welfare and the effectiveness of 3Rs Refinements designed to improve wellbeing. Judgement bias tasks measure valence; however, task-training may be lengthy and/or require significant time from researchers. Here we develop an automated and self-initiated judgement bias task for rats which capitalises on their natural investigative behaviour. Rats insert their noses into a food trough to start trials. They then hear a tone and learn either to stay for 2 s to receive a food reward or to withdraw promptly to avoid an air-puff. Which contingency applies is signalled by two different tones. Judgement bias is measured by responses to intermediate ambiguous tones. In two experiments we show that rats learn the task in fewer sessions than other automated variants, generalise responses across ambiguous tones as expected, self-initiate 4–5 trials/min, and can be tested repeatedly. Affect manipulations generate main effect trends in the predicted directions, although not localised to ambiguous tones, so further construct validation is required. We also find that tone-reinforcer pairings and reinforcement or non-reinforcement of ambiguous trials can affect responses to ambiguity. This translatable task should facilitate more widespread uptake of judgement bias testing.
Original languageEnglish
Number of pages12
JournalScientific Reports
Volume8
Issue number1
Early online date17 Aug 2018
DOIs
Publication statusPublished - 1 Dec 2018

Documents

Relations Get citation (various referencing formats)

ID: 11399492