Abstract
Category formation lies at the heart of a number of higher-order behaviors, including language. We assessed the ability of human adults to learn, from distributional information alone, categories embedded in a sequence of input stimuli using a serial reaction time task. Artificial grammars generated corpora of input strings containing a predetermined and constrained set of sequential statistics. After training, learners were presented with novel input strings, some of which contained violations of the category membership defined by distributional context. Category induction was assessed by comparing performance on novel and familiar strings. Results indicate that learners develop increasing sensitivity to the category structure present in the input, and become sensitive to fine-grained differences in the pre- and post-element contexts that define category membership. Results suggest that distributional analysis plays a significant role in the development of visuomotor categories, and may play a similar role in the induction of linguistic form-class categories.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 98-112 |
| Number of pages | 15 |
| Journal | Journal of Memory and Language |
| Volume | 62 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2010 |
| Externally published | Yes |
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Keywords
- Artificial grammar
- Category induction
- Distributional analysis
- Serial reaction time
- Statistical learning
Cite this
Category induction via distributional analysis : Evidence from a serial reaction time task. / Hunt, Ruskin H; Aslin, Richard N.
In: Journal of Memory and Language, Vol. 62, No. 2, 01.02.2010, p. 98-112.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Category induction via distributional analysis
T2 - Evidence from a serial reaction time task
AU - Hunt, Ruskin H
AU - Aslin, Richard N.
PY - 2010/2/1
Y1 - 2010/2/1
N2 - Category formation lies at the heart of a number of higher-order behaviors, including language. We assessed the ability of human adults to learn, from distributional information alone, categories embedded in a sequence of input stimuli using a serial reaction time task. Artificial grammars generated corpora of input strings containing a predetermined and constrained set of sequential statistics. After training, learners were presented with novel input strings, some of which contained violations of the category membership defined by distributional context. Category induction was assessed by comparing performance on novel and familiar strings. Results indicate that learners develop increasing sensitivity to the category structure present in the input, and become sensitive to fine-grained differences in the pre- and post-element contexts that define category membership. Results suggest that distributional analysis plays a significant role in the development of visuomotor categories, and may play a similar role in the induction of linguistic form-class categories.
AB - Category formation lies at the heart of a number of higher-order behaviors, including language. We assessed the ability of human adults to learn, from distributional information alone, categories embedded in a sequence of input stimuli using a serial reaction time task. Artificial grammars generated corpora of input strings containing a predetermined and constrained set of sequential statistics. After training, learners were presented with novel input strings, some of which contained violations of the category membership defined by distributional context. Category induction was assessed by comparing performance on novel and familiar strings. Results indicate that learners develop increasing sensitivity to the category structure present in the input, and become sensitive to fine-grained differences in the pre- and post-element contexts that define category membership. Results suggest that distributional analysis plays a significant role in the development of visuomotor categories, and may play a similar role in the induction of linguistic form-class categories.
KW - Artificial grammar
KW - Category induction
KW - Distributional analysis
KW - Serial reaction time
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=73649143839&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73649143839&partnerID=8YFLogxK
U2 - 10.1016/j.jml.2009.10.002
DO - 10.1016/j.jml.2009.10.002
M3 - Article
AN - SCOPUS:73649143839
VL - 62
SP - 98
EP - 112
JO - Journal of Memory and Language
JF - Journal of Memory and Language
SN - 0749-596X
IS - 2
ER -