Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Karlaftis, Vasilis M; Giorgio, Joseph; Vértes, Petra E.; Wang, Rui; Shen, Yuan; Tiňo, Peter; Welchman, Andrew E.; Kourtzi, Zoe

doi:10.1038/s41562-018-0503-4

Cited by 28 publications

(32 citation statements)

References 61 publications

(94 reference statements)

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“…In total, participants completed 10 online experimental tasks assessing aspects of executive functioning (cognitive flexibility, working memory, inhibitory control), learning and verbal intelligence. These tasks were delivered using three different experimental web platforms: i-ABC [53], Inquisit (Millisecond) and CANTAB (Cambridge Cognition); or delivered verbally by the experimenter, as summarised in Table 3. Both groups also completed a short online demographics questionnaire.…”

Section: Tasksmentioning

confidence: 99%

mentioning

confidence: 99%

“…Summary of experimental tasks administered and respective delivery platforms2.4.1 i-ABC PlatformThree experimental tasks were administered on the i-ABC platform[53]. The Wisconsin Card Sort Test (WCST;[54][55]) and the Probabilistic Learning & Reversal Task (PR;[56]) were measures of cognitive flexibility, whilst the structure learning (SL) task[53,57] assessed statistical learning. The i-ABC website enabled the administration of the three tasks on a platform that simulated playing a "space themed" video game and participants earned points for completing the tasks.Wisconsin Card Sort Test (WCST).The WCST is a measure of cognitive flexibility that examines rule acquisition and set shifting[54][55].…”

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confidence: 99%

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A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

Leong¹,

Raheel²,

Sim³

et al. 2021

Preprint

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Background. The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods. A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. Results. The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. Conclusions. These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

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Section: Tasksmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

Leong¹,

Raheel²,

Sim³

et al. 2021

Preprint

Self Cite

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“…Statistical learning also influences human actions and decision-making (Friston et al, 2015;Monroy et al, 2017a;Monroy et al, 2018;Monroy et al, 2017c;Pezzulo et al, 2015;Schwartenbeck et al, 2013;Zubicaray et al, 2013). Using sequential paradigms based on various-order Markov models, Karlaftis et al (2019) demonstrated that the brain alters its decision strategies and engages dissociable neural circuits in response to changes in statistics pertaining to the environment: the ability to extract exact sequence statistics relates to neural plasticity in the motor corticostriatal circuits, while the ability to select the most probable outcomes relates to plasticity in the motivational and executive corticostriatal circuits. The same study also provided evidence that learning-dependent changes in these circuits predict individual decision strategy.…”

Section: Action and Productionmentioning

confidence: 99%

“…Using sequential paradigms based on various-order Markov models, Karlaftis et al (2019) have demonstrated that individuals alter their decision strategies in response to changes in the statistics of their environments and engage dissociable circuits; extraction of exact sequence statistics is related to neural plasticity in the motor corticostriatal circuits, while selection of the most probable outcomes is related to plasticity in the motivational and executive corticostriatal circuits. Karlaftis et al (2019) also provided evidence that learning-dependent changes in these circuits predict individual decision strategies.…”

Section: Probability Encodingmentioning

confidence: 99%

Discovering the Neuroanatomical Correlates of Music with Machine Learning In Eduardo Reck Miranda (Eds.). Handbook of Artificial Intelligence for Music. Part 1. Springer

Daikoku¹

2020

Preprint

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Music is ubiquitous in our lives yet unique to humans. The interaction between music and the brain is complex, engaging a variety of neural circuits underlying sensory perception, learning and memory, action, social communication, and creative activities. Over the past decades, a growing body of literature has revealed the neural and computational underpinnings of music processing including not only sensory perception (e.g., pitch, rhythm, and timbre), but also1local/non-local structural processing (e.g., melody and harmony). These findings have also influenced Artificial Intelligence and Machine Learning systems, enabling computers to possess human-like learning and composing abilities. Despite the plenty of evidence, more study is required for complete account of music knowledge and creative mechanisms in human brain. This chapter reviews the neural correlates of unsupervised learning with regard to the computational and neuroanatomical architectures of music processing. Further, we offer a novel theoretical perspective on the brain’s unsupervised learning machinery that considers computational and neurobiological constraints, highlighting the connections between neuroscience and machine learning.

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Intrinsic brain activity patterns across large‐scale networks predict reciprocity propensity

Pei

Zhu

et al. 2022

Human Brain Mapping

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Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

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Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Cited by 28 publications

References 61 publications

A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

Discovering the Neuroanatomical Correlates of Music with Machine Learning In Eduardo Reck Miranda (Eds.). Handbook of Artificial Intelligence for Music. Part 1. Springer

Intrinsic brain activity patterns across large‐scale networks predict reciprocity propensity

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