Learning artificial number symbols with ordinal and magnitude information
The question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2023-06-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.220840 |
| _version_ | 1797809671414743040 |
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| author | Hanna Weiers Matthew Inglis Camilla Gilmore |
| author_facet | Hanna Weiers Matthew Inglis Camilla Gilmore |
| author_sort | Hanna Weiers |
| collection | DOAJ |
| description | The question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process. |
| first_indexed | 2024-03-13T06:56:11Z |
| format | Article |
| id | doaj.art-b7aff53a7a7b4271bdb476fe9b616b03 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-03-13T06:56:11Z |
| publishDate | 2023-06-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-b7aff53a7a7b4271bdb476fe9b616b032023-06-07T07:27:27ZengThe Royal SocietyRoyal Society Open Science2054-57032023-06-0110610.1098/rsos.220840Learning artificial number symbols with ordinal and magnitude informationHanna Weiers0Matthew Inglis1Camilla Gilmore2Centre for Mathematical Cognition, Loughborough University, Loughborough LE11 3TU, UKCentre for Mathematical Cognition, Loughborough University, Loughborough LE11 3TU, UKCentre for Mathematical Cognition, Loughborough University, Loughborough LE11 3TU, UKThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.https://royalsocietypublishing.org/doi/10.1098/rsos.220840symbol-grounding problemartificial symbol learningmagnitude vs ordinalitysymbolic comparisonorder judgementcross-modal comparison |
| spellingShingle | Hanna Weiers Matthew Inglis Camilla Gilmore Learning artificial number symbols with ordinal and magnitude information Royal Society Open Science symbol-grounding problem artificial symbol learning magnitude vs ordinality symbolic comparison order judgement cross-modal comparison |
| title | Learning artificial number symbols with ordinal and magnitude information |
| title_full | Learning artificial number symbols with ordinal and magnitude information |
| title_fullStr | Learning artificial number symbols with ordinal and magnitude information |
| title_full_unstemmed | Learning artificial number symbols with ordinal and magnitude information |
| title_short | Learning artificial number symbols with ordinal and magnitude information |
| title_sort | learning artificial number symbols with ordinal and magnitude information |
| topic | symbol-grounding problem artificial symbol learning magnitude vs ordinality symbolic comparison order judgement cross-modal comparison |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.220840 |
| work_keys_str_mv | AT hannaweiers learningartificialnumbersymbolswithordinalandmagnitudeinformation AT matthewinglis learningartificialnumbersymbolswithordinalandmagnitudeinformation AT camillagilmore learningartificialnumbersymbolswithordinalandmagnitudeinformation |