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Animal welfare science focuses on assessing and maximizing animals' quality of life. In the research context, improving the welfare of laboratory animals is a high priority. To this end, objective measures of animal welfare are greatly needed. One way to evaluate welfare is to consider an animal's affective state, which can be assessed indirectly by observing its influence on cognitive processes, such as those underlying the interpretation of ambiguous signals (or judgment bias). A negative affective state leads to an expectation of negative outcomes and thus a negative bias in the interpretation of ambiguous signals (a pessimistic outlook). In contrast, a positive affective state leads to an expectation of positive outcomes and a positive bias in the interpretation of ambiguous signals (an optimistic outlook). A recent report in PLoS One (9, e107794; 2014) describes the use of a portable, automated apparatus to train and test the judgment bias of dogs. “This research is exciting because it measures positive and negative emotional states in dogs objectively and non-invasively. It offers researchers and dog owners an insight into the outlook of dogs and how that changes,” said Melissa Starling (University of Sydney, Australia), who led the research, in a press release. “Finding out as accurately as possible whether a particular dog is optimistic or pessimistic is particularly helpful in the context of working and service dogs and has important implications for animal welfare.”

Dogs were taught to differentiate between two different sounds (two octaves apart), one associated with access to a small volume of lactose-free milk (a reward) and the other not associated with a reward. Once they learned to discriminate between the two notes, they were presented with ambiguous tones and their responses were measured. Some dogs responded to the ambiguous tones, even those that were more similar to the unrewarded sound than to the sound associated with milk. Other dogs did not respond to the ambiguous tones. The results indicate that judgment bias exists in dogs, differs between dogs and can be measured objectively as a readout of affective state.

Starling explained some applications of her work. “This research could help working dog trainers select dogs best suited to working roles. [...] A pessimistic dog that avoids risks would be better as a guide dog while an optimistic, persistent dog would be more suited to detecting drugs or explosives. [...] This research has the potential to completely remodel how animal welfare is assessed. If we know how optimistic or pessimistic an animal usually is, it's possible to track changes in that optimism that will indicate when it is in a more positive or negative emotional state than usual. [...] It could be used to monitor their welfare in any environment, to assess how effective enrichment activities might be in improving welfare, and pinpoint exactly what a dog finds emotionally distressing.”