TY - GEN
T1 - Exploring the use of overhypotheses by children and capuchin monkeys
AU - Felsche, Elisa
AU - Stevens, Patience
AU - Völter, Christoph
AU - Buchsbaum, Daphna
AU - Seed, Amanda
N1 - Funding Information:
We thank Justine Biado, Kiah Caneira and Kay Otsubo. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. [639072]). We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [funding reference number 2016-05552]
Funding Information:
We thank Justine Biado, Kiah Caneira and Kay Otsubo. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [639072]). We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [funding reference number 2016-05552]
Publisher Copyright:
© Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019.All rights reserved.
PY - 2019
Y1 - 2019
N2 - The use of abstract higher-level knowledge (overhypotheses) allows humans to learn quickly from sparse data, and make predictions in new situations. Previous research has suggested that humans may be the only species capable of abstract knowledge formation, but this remains controversial, and there is also mixed evidence for when this ability emerges over human development. Kemp et al. (2007) proposed a computational model of overhypothesis formation from sparse data. We provide the first direct test of this model: an ecologically valid paradigm for testing two species, capuchin monkeys (Sapajus spp.) and 4-5-year-old human children. We compared performance to predictions made by models with and without the capacity to learn overhypotheses. Children's choices were consistent with the overhypothesis model predictions, whereas monkeys performed at chance level.
AB - The use of abstract higher-level knowledge (overhypotheses) allows humans to learn quickly from sparse data, and make predictions in new situations. Previous research has suggested that humans may be the only species capable of abstract knowledge formation, but this remains controversial, and there is also mixed evidence for when this ability emerges over human development. Kemp et al. (2007) proposed a computational model of overhypothesis formation from sparse data. We provide the first direct test of this model: an ecologically valid paradigm for testing two species, capuchin monkeys (Sapajus spp.) and 4-5-year-old human children. We compared performance to predictions made by models with and without the capacity to learn overhypotheses. Children's choices were consistent with the overhypothesis model predictions, whereas monkeys performed at chance level.
KW - abstraction
KW - animal cognition
KW - cognitive development
KW - computational modeling
KW - generalization
KW - Overhypotheses
UR - http://www.scopus.com/inward/record.url?scp=85139435495&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85139435495
T3 - Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
SP - 1731
EP - 1737
BT - Proceedings of the 41st Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
T2 - 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
Y2 - 24 July 2019 through 27 July 2019
ER -