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Liu CL, Cheng X, Choo BL, Hong M, Teo JL, Koo WL, Tan JYJ, Ubrani MB, Suckling J, Gulyás B, Leong V, Kourtzi Z, Sahakian B, Robbins T, Chen ASH. Potential cognitive and neural benefits of a computerised cognitive training programme based on Structure Learning in healthy adults: study protocol for a randomised controlled trial. Trials 2023; 24:517. [PMID: 37568212 PMCID: PMC10422731 DOI: 10.1186/s13063-023-07551-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Cognitive flexibility refers to the capacity to shift between conceptual representations particularly in response to changes in instruction and feedback. It enables individuals to swiftly adapt to changes in their environment and has significant implications for learning. The present study focuses on investigating changes in cognitive flexibility following an intervention programme-Structure Learning training. METHODS Participants are pseudo-randomised to either the Training or Control group, while matched on age, sex, intelligence and cognitive flexibility performance. In the Training group, participants undergo around 2 weeks of training (at least 13 sessions) on Structure Learning. In the Control group, participants do not have to undergo any training and are never exposed to the Structure Learning task. The effects of Structure Learning training are investigated at both the behavioural and neural level. We measured covariates that can influence an individual's training performance before the training phase and outcome measures that can potentially show training benefits after the training phase. At the behavioural level, we investigated outcomes in both cognitive and social aspects with a primary focus on executive functions. At the neural level, we employed a multimodality approach and investigated potential changes to functional connectivity patterns, neurometabolite concentration in the frontal brain regions, and brain microstructure and myelination. DISCUSSION We reported the development of a novel training programme based on Structure Learning that aims to hone a general learning ability to potentially achieve extensive transfer benefits across various cognitive constructs. Potential transfer benefits can be exhibited through better performance in outcome measures between Training and Control participants, and positive associations between training performance and outcomes after the training in Training participants. Moreover, we attempt to substantiate behavioural findings with evidence of neural changes across different imaging modalities by the Structure Learning training. TRIAL REGISTRATION National Institutes of Health U.S. National Library of Medicine ClinicalTrials.gov NCT05611788. Registered on 7 November 2022. PROTOCOL VERSION 11 May 2023.
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Affiliation(s)
- Chia-Lun Liu
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore.
| | - Xiaoqin Cheng
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
- Department of Psychology, University of Innsbruck, Innsbruck, Austria
| | - Boon Linn Choo
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
| | - Min Hong
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
| | - Jia Li Teo
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
- School of Social Sciences, Nanyang Technological University, Singapore, Singapore
| | - Wei Ler Koo
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
| | - Jia Yuan Janet Tan
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
| | - Marisha Barth Ubrani
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Balázs Gulyás
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Victoria Leong
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
- School of Social Sciences, Nanyang Technological University, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Barbara Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Trevor Robbins
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Annabel Shen-Hsing Chen
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
- School of Social Sciences, Nanyang Technological University, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- National Institute of Education, Nanyang Technological University, Singapore, Singapore
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Tong K, Chan YN, Cheng X, Cheon B, Ellefson M, Fauziana R, Feng S, Fischer N, Gulyás B, Hoo N, Hung D, Kalaivanan K, Langley C, Lee KM, Lee LL, Lee T, Melani I, Melia N, Pei JY, Raghani L, Sam YL, Seow P, Suckling J, Tan YF, Teo CL, Uchiyama R, Yap HS, Christopoulos G, Hendriks H, Chen A, Robbins T, Sahakian B, Kourtzi Z, Leong V. Study protocol: How does cognitive flexibility relate to other executive functions and learning in healthy young adults? PLoS One 2023; 18:e0286208. [PMID: 37471399 PMCID: PMC10358919 DOI: 10.1371/journal.pone.0286208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 05/10/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Cognitive flexibility (CF) enables individuals to readily shift from one concept or mode of practice/thoughts to another in response to changes in the environment and feedback, making CF vital to optimise success in obtaining goals. However, how CF relates to other executive functions (e.g., working memory, response inhibition), mental abilities (e.g., creativity, literacy, numeracy, intelligence, structure learning), and social factors (e.g., multilingualism, tolerance of uncertainty, perceived social support, social decision-making) is less well understood. The current study aims to (1) establish the construct validity of CF in relation to other executive function skills and intelligence, and (2) elucidate specific relationships between CF, structure learning, creativity, career decision making and planning, and other life skills. METHODS This study will recruit up to 400 healthy Singaporean young adults (age 18-30) to complete a wide range of cognitive tasks and social questionnaires/tasks. The richness of the task/questionnaire battery and within-participant administration enables us to use computational modelling and structural equation modelling to examine connections between the latent constructs of interest. SIGNIFICANCE AND IMPACT The current study is the first systematic investigation into the construct validity of CF and its interrelationship with other important cognitive skills such as learning and creativity, within an Asian context. The study will further explore the concept of CF as a non-unitary construct, a novel theoretical proposition in the field. The inclusion of a structure learning paradigm is intended to inform future development of a novel intervention paradigm to enhance CF. Finally, the results of the study will be useful for informing classroom pedagogy and the design of lifelong learning policies and curricula, as part of the wider remit of the Cambridge-NTU Centre for Lifelong Learning and Individualised Cognition (CLIC).
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Affiliation(s)
- Ke Tong
- Nanyang Technological University, Singapore, Singapore
| | - Yuan Ni Chan
- Nanyang Technological University, Singapore, Singapore
| | - Xiaoqin Cheng
- Nanyang Technological University, Singapore, Singapore
| | - Bobby Cheon
- National Institutes of Health, Bethesda, Maryland, United States of America
| | | | | | | | | | - Balázs Gulyás
- Nanyang Technological University, Singapore, Singapore
| | - Natalie Hoo
- Nanyang Technological University, Singapore, Singapore
| | - David Hung
- National Institute of Education, Singapore, Singapore
| | | | | | - Kean Mun Lee
- Nanyang Technological University, Singapore, Singapore
| | - Li Ling Lee
- Nanyang Technological University, Singapore, Singapore
| | - Timothy Lee
- National Institute of Education, Singapore, Singapore
| | - Irene Melani
- Nanyang Technological University, Singapore, Singapore
| | | | - Jia Ying Pei
- Nanyang Technological University, Singapore, Singapore
| | - Lisha Raghani
- Nanyang Technological University, Singapore, Singapore
| | - Yoke Loo Sam
- Nanyang Technological University, Singapore, Singapore
| | - Peter Seow
- National Institute of Education, Singapore, Singapore
| | | | - Yan Fen Tan
- Nanyang Technological University, Singapore, Singapore
| | - Chew Lee Teo
- National Institute of Education, Singapore, Singapore
| | | | - Hui Shan Yap
- Nanyang Technological University, Singapore, Singapore
| | | | | | - Annabel Chen
- Nanyang Technological University, Singapore, Singapore
| | | | | | - Zoe Kourtzi
- University of Cambridge, Cambridge, United Kingdom
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A common probabilistic framework for perceptual and statistical learning. Curr Opin Neurobiol 2019; 58:218-228. [PMID: 31669722 DOI: 10.1016/j.conb.2019.09.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 08/24/2019] [Accepted: 09/09/2019] [Indexed: 11/20/2022]
Abstract
System-level learning of sensory information is traditionally divided into two domains: perceptual learning that focuses on acquiring knowledge suitable for fine discrimination between similar sensory inputs, and statistical learning that explores the mechanisms that develop complex representations of unfamiliar sensory experiences. The two domains have been typically treated in complete separation both in terms of the underlying computational mechanisms and the brain areas and processes implementing those computations. However, a number of recent findings in both domains call in question this strict separation. We interpret classical and more recent results in the general framework of probabilistic computation, provide a unifying view of how various aspects of the two domains are interlinked, and suggest how the probabilistic approach can also alleviate the problem of dealing with widely different types of neural correlates of learning. Finally, we outline several directions along which our proposed approach fosters new types of experiments that can promote investigations of natural learning in humans and other species.
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Kourtzi Z, Welchman AE. Learning predictive structure without a teacher: decision strategies and brain routes. Curr Opin Neurobiol 2019; 58:130-134. [PMID: 31569060 DOI: 10.1016/j.conb.2019.09.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 09/03/2019] [Accepted: 09/12/2019] [Indexed: 11/17/2022]
Abstract
Extracting the structure of complex environments is at the core of our ability to interpret the present and predict the future. This skill is important for a range of behaviours from navigating a new city to learning music and language. Classical approaches that investigate our ability to extract the principles of organisation that govern complex environments focus on reward-based learning. Yet, the human brain is shown to be expert at learning generative structure based on mere exposure and without explicit reward. Individuals are shown to adapt to-unbeknownst to them-changes in the environment's temporal statistics and predict future events. Further, we present evidence for a common brain architecture for unsupervised structure learning and reward-based learning, suggesting that the brain is built on the premise that 'learning is its own reward' to support adaptive behaviour.
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Affiliation(s)
- Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, UK.
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