Citizen Science as a New Tool in Dog Cognition Research

Abstract

Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition.com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.

Fig 1. Experimental set up for all experiments requiring dogs to make a choice between one of two hiding locations.

Participants were instructed to place treats or cups (represented by cylinders) 1.2 meters apart while standing 1.8 meters from their dog. Three post-it notes (represented by grey squares) were placed on the ground to aid in live coding of a dog’s choice.

Fig 2. Mean performance (+/- SEM) in each task in which dogs faced a two way choice with chance being 50%.

In the memory exercises (Mem Vs. Point and Mem Vs. Smell) success was scored when a dog made a choice consistent with relying on their memory (i.e. not a gesture or olfactory cues). Light grey bars represent Beta and dark grey representing Live datasets. *<0.05 binomial probability.

Fig 3. Factor loadings from exploratory factor analyses of the (A) Beta, and (B) Live datasets.

Both datasets were best described by four factor models consisting of factors related to gesture comprehension, memory, and cunning, with a fourth factor that varied between the Beta an Live datasets. The order of factors varied between datasets but the following factors resemble one another between analyses (Live PA1 & Beta PA2; Live PA2 & Beta PA3; Live PA3 & Beta PA4. The remaining factors (Live PA4 & Beta PA1) had no clear analog in the other dataset.