dc.contributor.author | Creatore, Celestino | |
dc.contributor.author | Sabathiel, Silvester | |
dc.contributor.author | Solstad, Trygve | |
dc.date.accessioned | 2022-09-16T12:13:20Z | |
dc.date.available | 2022-09-16T12:13:20Z | |
dc.date.created | 2021-07-01T12:26:30Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0010-0277 | |
dc.identifier.uri | https://hdl.handle.net/11250/3018480 | |
dc.description.abstract | A system for approximate number discrimination has been shown to arise in at least two types of hierarchical neural network models—a generative Deep Belief Network (DBN) and a Hierarchical Convolutional Neural Network (HCNN) trained to classify natural objects. Here, we investigate whether the same two network architectures can learn to recognise exact numerosity. A clear difference in performance could be traced to the specificity of the unit responses that emerged in the last hidden layer of each network. In the DBN, the emergence of a layer of monotonic ‘summation units’ was sufficient to produce classification behaviour consistent with the behavioural signature of the approximate number system. In the HCNN, a layer of units uniquely tuned to the transition between particular numerosities effectively encoded a thermometer-like ‘numerosity code’ that ensured near-perfect classification accuracy. The results support the notion that parallel pattern-recognition mechanisms may give rise to exact and approximate number concepts, both of which may contribute to the learning of symbolic numbers and arithmetic. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Learning exact enumeration and approximate estimation in deep neural network models | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 215 | en_US |
dc.source.journal | Cognition | en_US |
dc.identifier.doi | 10.1016/j.cognition.2021.104815 | |
dc.identifier.cristin | 1919823 | |
dc.relation.project | Norges forskningsråd: 283441 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |