Wednesday, April 30, 2025

How is it we feel a sense of agency over our movements?

UTokyo team demonstrates key mechanism for formation of sense of agency when learning new motor skills

University of Tokyo

From finger digits to binary digits — image:
Participants learned to control the cursor by finger movements via the data glove. Through trial-and-error learning, participants embodied the hand-to-screen mapping, which led them to perceive certain cursor movements as more reflective of their own actions. ©2025 Tanaka et al. CC-BY-ND
view more
Credit: ©2025 Tanaka et al. CC-BY-ND

The sensation of controlling one’s body and things in the environment is known as sense of agency (SoA). Not only is SoA pivotal for tasks and well-being in everyday life, but its mechanisms have become increasingly important for the development of human-computer interfaces in new technology. This need has fueled research in this area, in particular to understand how SoA is generated from scratch in unfamiliar environments. Researchers at the University of Tokyo performed experiments involving hand-to-screen mapping using a specialized glove and highlighted the role of motor exploration in generating experience of self-agency. Their findings could contribute to future health and technology applications.

Traditionally, SoA has been explained by a framework called the comparator model. “According to this model, the brain maps the predicted outcomes of actions, referred to as an internal model. SoA arises when those predictions match with actual sensory feedback,” said Takumi Tanaka, assistant professor at the University of Tokyo’s Graduate School of Humanities and Sociology and co-author of the current study.

An issue arises when considering acquisition of new motor skills, such as learning a sport or relearning everyday activities after a neurological injury. While the comparator model assumes that outcomes from actions are partly predictable, new learners instead often attempt actions first and explore what happens as a result. “This process is called motor exploration, which helps form the internal model and generalize the learned outcomes,” said Tanaka.

The team set out to understand how SoA develops in new motor learning scenarios. To address this, it was necessary to track changes in SoA from the pre-learning stage where predictions are yet to be formed.

The motor learning task chosen for the study involved a data glove to control a cursor on the screen through finger movements. The investigation was divided into two experiments. The first required participants to learn the hand-to-screen spatial mapping from scratch through trial and error. At each learning phase, the researchers measured how strongly participants felt that they were in control of the cursor, including when the cursor movement was subtly distorted in space or time. The results showed that during the pre-learning stage, participants primarily relied on temporal synchronicity of hand and cursor movements to judge whether they were in control. “After sufficient practice, however, participants would feel that the cursor movement aligned with their own when it adhered to the learned mapping — a trend that was more pronounced in those who achieved higher proficiency,” said Tanaka.

No similar enhancement in SoA was observed in the second experiment, which was designed to suppress motor exploration by having participants simply imitate presented gestures to transport the cursor to a target position. “This,” according to Tanaka, “indicates that merely memorizing gesture-to-cursor associations is not enough; to build a strong sense of agency, learners must actively discover the underlying rules, for instance, that bending the index finger moves the cursor to the right. We call this a structural representation, and it seems to develop through motor exploration.”

This study provides key insights into the evolution of SoA during the acquisition of new motor skills. It enriches existing SoA frameworks by elucidating the origin of the comparator process, and could help refine applications in rehabilitation, virtual reality and brain-machine interfaces in the future.

###

Journal article: Takumi Tanaka and Hiroshi Imamizu, “Sense of agency for a new motor skill emerges via the formation of a structural internal model”, Communications Psychology, https://www.nature.com/articles/s44271-025-00240-7

Funding: This work was funded by Japan Society for the Promotion of Science grants JP23K12928, JP19H05725, JP21H03780, and JP24H00172.

About The University of Tokyo:
The University of Tokyo is Japan's leading university and one of the world's top research universities. The vast research output of some 6,000 researchers is published in the world's top journals across the arts and sciences. Our vibrant student body of around 15,000 undergraduate and 15,000 graduate students includes over 4,000 international students. Find out more at www.u-tokyo.ac.jp/en/ or follow us on X (formerly Twitter) at @UTokyo_News_en.

Journal

Communications Psychology

DOI

10.1038/s44271-025-00240-7

Method of Research

Experimental study

Subject of Research

People

Article Title

Sense of agency for a new motor skill emerges via the formation of a structural internal model

Article Publication Date

29-Apr-2025

Prevention instead of reaction: Intelligent, networked systems for structural monitoring

A research team at Graz University of Technology has developed a system for structural monitoring that can be integrated directly into structural management and used sustainably for preventive maintenance planning.

News Release 30-Apr-2025

Graz University of Technology

image:
The system was tested at the Laxenburger bridge.
view more
Credit: IGMS - TU Graz

The safety and durability of transport and building infrastructure are in the interests of both operators and users. To maintain the best possible building condition at the lowest possible cost, effective monitoring is required to improve condition assessment. Although there are suitable monitoring systems, they are usually not properly networked, not directly integrated into the building management system and their data is often not usable in the long term due to incomplete documentation and non-standardised evaluation procedures. In the PreMainSHM project, a team at Graz University of Technology (TU Graz) led by Markus Krüger from the Institute of Technology and Testing of Construction Materials (IMBT) and Werner Lienhart from the Institute of Engineering Geodesy and Measurement Systems (IGMS) has tackled this issue and developed a preventive structural monitoring system with intelligent, networked systems that can be integrated directly into standard structural management software.

Data must be usable

“The technologies we have for infrastructure monitoring provide a lot of data that complement each other well,” says Werner Lienhart. “But simply collecting data aimlessly is not helpful for forecasting the development of the building’s condition and its service life, including proactive maintenance planning. Our aim was therefore to develop a holistic solution that would make it possible to gain directly usable findings from structural monitoring for service life forecasts and maintenance planning in a long-term and cost-efficient manner. This allows building operators to act proactively rather than reactively.”

The centrepiece of the project was the intelligent linking of various monitoring technologies. A crucial role is played by the high-precision fibre-optic monitoring developed at the Institute of Engineering Geodesy and Measurement Systems, which provides detailed insights into material behaviour, as well as cost-effective and flexible wireless sensor networks, which have been further developed at the IMBT. By combining these technologies, comprehensive data on the condition of and stress loads on structures can be recorded. The researchers paid particular attention to the application of the FAIR data principles (Findability, Accessibility, Interoperability, Reusability) in order to ensure the long-term usability and exchange of the data obtained.

Linking with BIM and GIS

To close the gap between data acquisition and building management, the project partners developed a simple but flexible, entity-based data model. This model enables a hierarchical structuring of the buildings and links the measurement data with the corresponding components and sensors. This facilitates access to relevant information and enables interoperability with other software systems such as BIM (Building Information Modelling) and GIS (Geographic Information Systems). A digital twin was also implemented to visualise and manage the building data.

It was also important to ensure that the sensor data collected was reliable. Although sensors are currently calibrated in advance, this is done under controlled conditions that are rarely found in a building. Instead, the temperatures and humidity change and there are other influences such as vibrations. To this end, the project team developed appropriate methods that take into account the influence of the environment on the sensor data.

Practical testing on Laxenburg Bridge

The practical testing and validation of the developed concepts took place at Laxenburg Bridge in Vienna. Various sensor technologies were used there, including wireless sensors for monitoring inclinations and crack widths as well as fibre-optic systems for high-resolution strain measurement and crack detection under traffic load.

“PreMainSHM has demonstrated the potential of intelligent and networked structural monitoring for the safe and sustainable maintenance of buildings and infrastructure,” says Markus Krüger. “This potential must now be utilised to make the management of bridges and other engineering structures fit for the future. That’s why we have also created a guidance document in the project, which should help to ensure that future monitoring projects not only provide data, but also usable information for well-founded decisions in building management.”

Sensory point cloud of the Laxenburg Bridge

Method of Research

Observational study

For the tests, a crane truck was used to test high loads.
The sensors were also tested for their temperature susceptibility.

Zoo life boosts object exploration in orangutans

Wild and zoo-housed orangutans explore the world differently, study finds

BECause they are bored!

Max Planck Institute of Animal Behavior

Orangutan with wood — image:
Wild juvenile orangutan called Eden manipulating wood
view more
Credit: S. Vilela

A new study comparing wild and zoo-housed Sumatran orangutans reveals that life in a zoo significantly alters how orangutans interact with their environment. Researchers analyzed over 12,000 instances of daily exploratory object manipulation (EOM)—the active manipulation and visual inspection of objects associated with learning and problem-solving—across 51 orangutans aged 0.5 to 76 years. The findings show that orangutans living in zoos engage in more frequent, more diverse, and more complex exploration than their wild counterparts.

“Our study shows that orangutans in zoos not only explore more, but they also explore differently,” said Isabelle Laumer, first author of the study. “What’s especially fascinating is that even when exploring the same kinds of objects, zoo-housed orangutans showed a richer repertoire of actions and were more likely to use tools or manipulate multiple objects at the same time.”

The study compared exploratory object manipulation (EOM) behaviors in wild and zoo-housed orangutans across a wide age range. Data were collected at the Suaq Balimbing research site in Indonesia from 33 wild individuals aged between six months and 76 years and at four zoos in Germany and Switzerland from 24 individuals aged between seven months and 49 years. In total, ~12,000 EOM events were analyzed.

The study showed that zoo-housed orangutans explore the objects in their surroundings more frequently than wild orangutans whilst there was no difference in exploration duration when individuals did explore. Wild orangutans primarily explored naturally occurring objects like plants, bark, and sticks, while zoo-housed orangutans engaged with a wider variety of enrichment items such as plastic toys, puzzles, and stackable objects designed to encourage manipulation and cognitive engagement. Importantly, the age at which orangutans first engaged in specific types of exploration was consistent across both settings, suggesting an innate developmental sequence. However, zoo orangutans continued to explore into adulthood, while wild individuals’ EOM declined sharply around weaning age at about 8 years of age — likely due to the demands of survival in the wild, where foraging and constant vigilance leave little time for exploration.

In human infants, object exploration enables learning about physical properties such as texture and weight while stimulating cognitive and motor development—a pattern observed in many non-human animals as well. The heightened exploration may enhance cognitive flexibility and problem-solving skills in zoo-housed orangutans, as they interact with varied enrichment items and have more time and energy to devote to learning through exploration.

“These findings underscore how profoundly the environment influences animal behavior and cognitive development,” said Caroline Schuppli, senior author of the study. “And it also offers unique opportunities—by comparing wild and zoo-housed animals, we can better understand the full extent of a species’ cognitive potential.”