22 papers · ranked by Valyu relevance
Yevhen Damanskyy, Torsten Martiny-Huenger, Elizabeth J. Parks-Stamm
According to the ideomotor principle, repeated experience with an action and its perceivable consequences (effects) establish action-effect associations. Research on verbal instructions indicates that such associations are also acquired from verbal information. In the present experiment (N = 651), first, we aimed to…
Yevhen Damanskyy, Torsten Martiny-Huenger, Elizabeth J. Parks-Stamm
Action-effect learning is based on a theoretical concept that actions are associated with their perceivable consequences through bidirectional associations. Past research has mostly investigated how these bidirectional associations are formed through actual behavior and perception of the consequences. The present…
Frédérique Bunlon, Peter J. Marshall, Lorna C. Quandt, Cedric A. Bouquet + 1 more
'Cedric A. Bouquet' 'Marco Iacoboni'] According to the ideomotor theory, actions are represented in terms of their perceptual effects, offering a solution for the correspondence problem of imitation (how to translate the observed action into a corresponding motor output). This effect-based coding of action is assumed…
Uta Wolfensteller, Hannes Ruge
It is well-established that we can pick up action effect associations when acting in a free-choice intentional mode. However, it is less clear whether and when action effect associations are learnt and actually affect behavior if we are acting in a forced-choice mode, applying a specific stimulus-response (S-R) rule.…
Oliver Herbort, Martin V. Butz
In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies’ use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often studied in settings…
Marko Zaric, Jakob Hollenstein, Justus Piater, Erwan Renaudo
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics. Current state-of-theart action representations in robotics lack proper effect-driven learning of the robot's actions. Although successful in solving manipulation tasks, deep learning methods…
Mehmet Eren, Erhan Öztop
In self-supervised robot learning, robots actively explore their environments and generate data by acting on entities in the environment. Therefore, an exploration policy is desired that ensures sample efficiency to minimize robot execution costs while still providing accurate learning. For this purpose, the robotic…
Hongsang Yoo, Haopeng Li, Qiuhong Ke, Liangchen Liu + 1 more
Human action recognition has drawn a lot of attention in the recent years due to the research and application significance. Most existing works on action recognition focus on learning effective spatial-temporal features from videos, but neglect the strong causal relationship among the precondition, action and effect.…
Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba
Version Authors: ['Argaman Mordoch' 'Enrico Scala' 'Roni Stern' 'Brendan Juba'] Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designing…
Taisei Sugiyama, Nicolas Schweighofer, Jun Izawa
Reinforcement learning enables the brain to learn optimal action selection, such as go or not go, by forming state-action and action-outcome associations. Does this mechanism also optimize the brain’s willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., reinforcement meta-learning, is a…
Sascha Frölich, Marlon Esmeyer, Tanja Endrass, Michael N. Smolka + 1 more
Human behaviour consists in large parts of action sequences that are often repeated in mostly the same way. Through extensive repetition, sequential responses become automatic or habitual, but our environment often confronts us with events to which we have to react flexibly and in a goal-directed manner. To assess how…
Eric Legler, Darío Cuevas Rivera, Sarah Schwöbel, Ben J. Wagner + 1 more
Humans tend to repeat past actions due to rewarding outcomes. Recent computational models propose that the probability of selecting a specific action is also, in part, based on how often this action was selected before, independent of previous outcomes or reward. However, these new models so far lack empirical support.…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
Haley Hegefeld, Juliet Y. Davidow
Adolescence is a period marked by profound changes in both capacities for learning and the motivational drives that guide behavior. Motivated learning, including the ability to associate cues with actions that lead to positive or negative outcomes, is a fundamental component of adaptive behavior and is essential for…
Naser Al-Fawakhiri, Vikram Chib, Samuel D. McDougle
Multiple learning signals can shape motor output, including reward and punishment (via value-based reinforcement learning) and sensorimotor error (via motor adaptation). However, it is unclear if action values, learned via reinforcement learning, interact with error-based motor learning. Here, we asked if the learned…
Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev + 6 more
Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Scott E. Allen, A. David Redish, René F. Kizilcec
Learning underlies nearly all human behavior and is central to education and education reform. Although recent advances in neuroscience have revealed the fundamental structure of learning processes, these insights have yet to be integrated into research and practice. Specifically, neuroscience has found that…
Cameron Reid
Transfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem. This is useful when one wishes…
Lucie Denisart, Diana Zapata-Dominguez, Xavier David, Aubin Leclere + 5 more
The manufacturing process of batteries can be complex and time-consuming. We introduce a new version of the digital twin of our lithium ion battery pilot line, Simubat 4.0 Gen-2, based on a new combination of Virtual Reality and Mixed Reality. This digital twin is designed to deliver training on the lithium-ion battery…
Adam Steel, Chris I. Baker, Charlotte J. Stagg
In real-world settings, learning is often characterised as intentional: learners are aware of the goal during the learning process, and the goal of learning is readily dissociable from the awareness of what is learned. Recent evidence has shown that reward and punishment (collectively referred to as valenced feedback)…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…