Erdin Mujezinović, Vsevolod Kapatsinski, Ruben van de Vijver
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So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error-driven learning.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.13450","citationCount":"0","resultStr":"{\"title\":\"One Cue's Loss Is Another Cue's Gain—Learning Morphophonology Through Unlearning\",\"authors\":\"Erdin Mujezinović, Vsevolod Kapatsinski, Ruben van de Vijver\",\"doi\":\"10.1111/cogs.13450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. 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引用次数: 0
摘要
一个词往往表达许多不同的形态功能。一个词的哪个部分对整体意义的哪个部分起作用并不总是很清楚,这就提出了如何学习这些功能的问题。虽然语言学研究默认线索和结果的共同出现足以帮助学习这些功能(Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012),但错误驱动学习表明偶然性而非连续性才是关键(Nixon, 2020; Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010)。在错误驱动学习中,如果线索能预测出某种结果,它们就会获得关联强度,而如果预测不到结果,它们就会失去关联强度。这种关联强度的降低被称为 "非学习"。到目前为止,还不清楚这种 "解除学习 "是否会对线索与结果之间的关联产生影响。为了测试这种解除学习的后果,我们在人工语言学习实验中向参与者教授了形态音素模式。在一个区块中,两个词形结果的线索--大写和小写--出现在相同的词形中。在另一个区块中,在一组不同的词形中只出现了这两种结果中的一种。我们想知道,如果被试没有学习到这一线索与结果之间的关联,而这一线索又不能单独预测结果,那么被试是否能将缺失的线索与缺失的结果联系起来。我们的研究结果表明,如果可以取消学习,那么与无法取消学习的情况相比,参与者能更好地学习到缺失线索对缺失结果的预测作用。如果未学习的线索更加突出,这种效果会更强。这表明,即使没有提供缺失结果的替代线索,学习者也会进行解除学习,这突出表明学习者会同时考虑积极和消极的证据--正如一般错误驱动学习领域所预测的那样。
One Cue's Loss Is Another Cue's Gain—Learning Morphophonology Through Unlearning
A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error-driven learning.