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There’s no ball without noise: cats’ prediction of an object from noise


We used an expectancy violation procedure to ask whether cats could use a causal rule to infer the presence of an unseen object on hearing the noise it made inside a container and predict its appearance when the container was turned over. We presented cats with either an object dropping out of an opaque container or no object dropping out (turning-over phase) after producing either a rattling sound by shaking the container with the object inside, or no sound (shaking phase). The cats were then allowed to freely explore the experimental environment (exploration phase). The relation between the sound and the object matched with physical laws in half of the trials (congruent condition) and mismatched in the other half (incongruent condition). Inferring the presence of an unseen object from the noise was predicted to result in longer looking time in the incongruent condition. The prediction was supported by the cats’ behavior during the turning-over phase. The results suggest that cats used a causal-logical understanding of auditory stimuli to predict the appearance of invisible objects. The ecology of cats’ natural hunting style may favor the ability for inference on the basis of sounds.

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This study was financially supported by the Grant-in-aide for Scientific Research No. 25240020 to Kazuo Fujita from the Japan Society for the Promotion of Science (JSPS). The authors acknowledge with thanks all owners and cats who volunteered in this study. The authors also wish to thank James R. Anderson for editing the article.

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Correspondence to Saho Takagi.

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This study adhered to the ethical guidelines of Kyoto University, and was approved by the Animal Experiments Committee of the Graduate School of Letters, Kyoto University.

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The authors declare no conflicts of interest.

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Takagi, S., Arahori, M., Chijiiwa, H. et al. There’s no ball without noise: cats’ prediction of an object from noise. Anim Cogn 19, 1043–1047 (2016).

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  • Cats
  • Felis catus
  • Inference
  • Causal-logical understanding
  • Expectancy violation method