Cited time in webofscience Cited time in scopus

Full metadata record

DC Field Value Language
dc.contributor.advisor 전현애 - Jungtak Park - 2020-08-06T06:16:20Z - 2020-08-06T06:16:20Z - 2020 -
dc.identifier.uri en_US
dc.identifier.uri -
dc.description statistical learning, mathematical modeling, executive functions -
dc.description.abstract Statistical learning (SL) is an essential learning mechanism which enables humans to extract probabilistic regularities from the world. Even though previous studies have examined quality of SL, they overlooked quality of learning and efficiency of learning in SL. Moreover, the ultimate quality of learning with training, that is, the potential of learning has been known to be dissociated with the efficiency of learning. Therefore, in the present study, we elucidated the potential as well as the efficiency of SL separately and investigated which processes of executive functions mainly exerted on them. Specifically, we quantified the efficiency and the potential of SL through mathematical modeling, using participants’ performances in alternating serial reaction time (ASRT) task and correlating them with individuals’ executive functions such as set shifting, updating, and inhibition. In results, low efficiency of SL was closely related to good inhibitory function whereas potential of SL was not associated with any of the executive functions. Our results, via a novel approach of mathematical modeling, shed lights on the overarching role of inhibition in the efficiency of SL. -
dc.description.statementofresponsibility open -
dc.description.tableofcontents I. Introduction 1
II. Methods
2.1 Participants 6
2.2 Procedure 6
2.3 Neuropsychological tests
2.3.1 Word fluency test (category and letter) 7
2.3.2 Counting span test (forward and backward) 7
2.3.3 Corsi-block test (forward and backward) 8
2.3.4 Wisconsin card sorting test (WCST) 8
2.3.5 Stroop test 9
2.3.6 Attention network test 9
2.3.7 Go/Nogo test 10
2.4 Alternating serial reaction time (ASRT) task 11
2.5 Data analysis
2.5.1 Investigation of participants’ performance in SL 15
2.5.2 Modeling of the SL score 16
2.5.3 Response bias 18
2.5.4 Correlation analysis 19
III. Results
3.1 Success in SL: Higher accuracy and faster RT in Random-High than Random-Low 20
3.2 Decrease of accuracy in Random-Low triggered by response bias 20
3.3 Modeling SL score for the investigation of individuals’ potential and efficiency of SL 24
3.4 A significant correlation between the efficiency of SL and inhibition across all the participants 26
IV. Discussion
4.1 A negative correlation between the efficiency of SL with an inhibitory control 28
4.2 A significance of response bias in SL 30
4.3 Limitations 31
4.4 Conclusions 32
V. References 33
dc.format.extent 39 -
dc.language eng -
dc.publisher DGIST -
dc.source /home/dspace/dspace53/upload/200000286145.pdf -
dc.title Individual differences in statistical learning -
dc.type Thesis -
dc.identifier.doi 10.22677/Theses.200000286145 -
dc.description.alternativeAbstract 통계학습은 사람들이 확률적 규칙성을 파악을 하는데 필요한 학습 메커니즘이다. 기존에 통계학습의 질적 연구가 많이 되어 왔지만, 통계 학습에서의 질과 효율성을 모두 확인한 연구는 없었다. 그리고 학습에 의해 도달할 수 있는 질적 최대치인 학습의 잠재력이 학습의 효율성과 분리되어 있다는 것이 알려져왔다. 따라서 본 연구에서는 학습의 잠재력과 효율성을 구분하여 세밀히 살피고, 나아가 집행기능 중 어떤 기능이 주가 되어 학습의 잠재력과 효율성을 설명할 수 있는지 알아보았다. 특히, 수학적 모델링을 통해 alternating serial reaction time (ASRT) 과제에서의 수행 능력으로부터 통계학습의 잠재력과 효율성을 정량화 하였으며, 정량화된 값을 통해 개개인의 집행기능중 어떤 기능 (주의 전환, set shifting -
dc.description.alternativeAbstract 반응 억제, inhibition)과 상관관계가 있는지 보았다. 그 결과, 억제기능 (inhibition)과 통계학습의 효율성 사이에서 유의한 부적 관련성을 확인할 수 있었다. 하지만, 통계학습의 잠재력은 어떠한 집행기능과도 관련성을 찾을 수 없었다. 결론적으로, 본 연구에서는 수학적 모델링 방법을 통해 새로운 접근을 시도하였고, 억제기능이 통계학습에서의 효율성에 중요한 역할을 한다는 사실을 확인할 수 있었다. -
dc.description.alternativeAbstract 정보 갱신, updating - Master -
dc.contributor.department Brain and Cognitive Sciences -
dc.contributor.coadvisor Wookyung Yu - 2020-02 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.BM 박74 202002 - 2020-01-20 -
dc.contributor.alternativeDepartment 뇌인지과학전공 -
dc.contributor.affiliatedAuthor Park, Jungtak -
dc.contributor.affiliatedAuthor Yu, Wookyung -
dc.contributor.affiliatedAuthor Jeon, Hyeon-Ae -
dc.contributor.alternativeName 박정탁 -
dc.contributor.alternativeName 유우경 -
dc.contributor.alternativeName Hyeon-Ae Jeon -
Files in This Item:


기타 데이터 / 975.51 kB / Adobe PDF download
Appears in Collections:
Department of Brain Sciences Theses Master


  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.