Enhancing expectancy formation in healthy aging through statistical and sensorimotor learning

Informazioni relative al progetto

Expectancy learning

ID dell’accordo di sovvenzione: 707865

DOI

10.3030/707865

Progetto chiuso

Data della firma CE 10 Marzo 2016

Data di avvio 1 Maggio 2016

Data di completamento 4 Giugno 2018

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 165 598,80

Contributo UE

€ 165 598,80

Coordinato da

UNIVERSITEIT MAASTRICHT
Netherlands

Periodic Reporting for period 1 - Expectancy learning (Enhancing expectancy formation in healthy aging through statistical and sensorimotor learning)

Periodo di rendicontazione: 2016-05-01 al 2018-04-30

Sintesi del contesto e degli obiettivi generali del progetto

To function in a dynamic environment, humans must learn which events to expect, and when to expect them. Anticipating upcoming events is particularly important for making sense of complex auditory information such as speech or music. How does learning give rise to the ability to anticipate upcoming events, and how does this ability change over the lifespan? In addition, can learning be enhanced by regularities in the environment and by linking movements with those events?
These questions have remained unanswered until now, yet they are crucial to understanding how the human brain develops the ability to predict future events and how this system changes in healthy aging. This understanding provides a basis for selecting and informing interventions, therapies, technologies, and even policies that can maximize the independence and well-being of elderly individuals.
The main goal of the project is to understand how individuals learn to anticipate what events will occur and when they will occur within novel series of information. The aims of the project are to reveal 1) how people learn to anticipate both particular events and higher-level structures of events, 2) how learning changes in healthy aging, 3) whether regularities in the environment and linking movements to external events can enhance learning, and 4) how subcortical brain regions are involved in these learning processes. To address these objectives we applied behavioural, electrophysiological, and neuroimaging approaches, as well as theoretical development. These approaches revealed how learning of events and event structures unfold, and results confirmed that regularities and body movement enhance learning. These approaches also revealed unexpected insights, including different ways in which movement enhances learning, individual differences in temporal structure learning, and a theoretical framework for prediction and learning in healthy aging.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

1) Developed novel stimuli and experimental paradigms.
2) Collected, processed, and analysed behavioural and EEG pilot data and experimental data.
3) Acquired and transferred knowledge in advanced EEG measurement, processing, and analysis.
4) Acquired certification in high-field 3T and 7T MRI acquisition, acquired and transferred knowledge in MRI acquisition and multi-variate analysis, and acquired data.
5) Developed novel theoretical frameworks.
6) Prepared and submitted papers for publication.
Main results:
The aim of the project was to understand how individuals learn to anticipate what events will occur and when they will occur within novel series of auditory information, and how learning changes in healthy aging. We aimed to reveal how expectations for individual events (event-based expectancies) and for event structures (structure-based expectancies) are formed, and whether learning is enhanced by movement-feedback coupling (sensorimotor integration) and regularities in the environment.
The results of the project can be summarized as follows: 1) Event-based expectancies unfold more efficiently than structure-based expectancies. 2) Event-based and structure-based temporal expectancies interact to facilitate sensorimotor learning. 3) High regularity enhances learning of movement-sound associations. 4) Movement without auditory feedback facilitates the formation of expectancies for novel auditory patterns, while movement coupled with auditory feedback (sensorimotor integration) facilitates learning of novel motor-sound associations. 5) Individuals learn temporal structures differently, some by chunking events and some by detecting equally-spaced temporal intervals. 6) Expectancy learning in healthy aging can be understood by interacting changes in predictive capacity and learning capacity.
Presentations:
Huisman, G. & Brown, R.M. Does movement facilitate learning? MaRBLe Presentations Spring Semester, University College Maastricht, July 2018.
Brown, R.M. Project overview at the first meeting of the NL-BE Music Research Network, June 2018, Leiden, Netherlands. Oral presentation.
Brown, R.M. & Penhune, V.B. Auditory and motor learning for skilled and novice performers. International Conference on Music Perception and Cognition, July 2016, San Francisco, CA. Poster presentation.
Brown, R.M. & Penhune, V.B. Time-course of auditory and motor learning for skilled and novice performers. Human Brain Mapping, June 2016, Geneva, Switzerland. Poster presentation.
Brown, R.M. Auditory-motor learning: past and future research. Lunch Talk, Neuropsychology and Psychopharmacology, May 2016. Oral presentation.
Brown, R.M. Learning statistical regularities: investigating the role of the cerebellum and basal ganglia. 7th Annual Neuropsychology and Psychopharmacology Research Day, March 2016. Oral presentation.
Dissemination also included two presentations outside of the host institution, collaborations, meetings with professors within and outside the faculty including potential collaborators and industry partners, and our research group's website.
Further dissemination will include presentations at international conferences, research network meetings, presentations within and outside the host institution, submission to high-impact journals, and general public engagements.
Publications and Manuscripts:
Brown, R. M., & Penhune, V. B. (accepted). Efficiency of auditory and motor learning for skilled and novice performers. Journal of Cognitive Neuroscience.
Stephan, M.A. Brown, R. M., Lega, C., & Penhune, V. B. (2016) Melodic priming of motor sequence performance: The role of the dorsal premotor cortex. Frontiers in Human Neuroscience, doi: 10.3389/fnins.2016.00210.
Kotz, S. A., Brown, R. M., Schwartze, M. (2016). Cortico-striatal circuits and the timing of action and perception. Current Opinion in Behavioral Sciences, 8, 42-45. doi: 10.1016/j.cob eha.2016.01.010
Brown, R. M., & Kotz, S. A. (submitted). The aging brain may favor prediction over learning to conserve energy resources: An integrative framework for cognitive, computational, and neurological aging.
Schwartze, M., Brown, R.M. Biau, E., Kotz, S.A. (submitted). Timing the “magical number seven”: effects of temporal structure on verbal working memory.
Brown, R. M.*, Schultz, B.*, & Kotz, S. A. (in preparation). Stimulating the motor system through salient acoustic features. *co-first authors

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The prevailing view of the human brain is that it is a predictive system, actively generating expectations about upcoming events. What is not yet understood is how predictive capacity arises through learning and how this capacity changes over the lifespan. With behavioural, electrophysiological, and neuroimaging techniques, as well as theoretical development, we reveal how expectations at different timescales emerge over learning and how sensorimotor integration and regularity can enhance learning, both independently and in combination. These results provide the basis for targeted and individual-tailored intervention strategies to enhance learning. We also reveal new insights into why and how predictive and learning capacity should both change in healthy aging at the cognitive, computational, and neural network levels. Our theoretical framework provides a unified understanding of the empirical advances in healthy aging to date, and it motivates a multi-faceted approach to optimizing lifelong learning.

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