22 papers · ranked by Valyu relevance
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…
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…
Sarah Esser, Hilde Haider, Clarissa Lustig, Takumi Tanaka + 1 more
'Kanji Tanaka'] The ability to anticipate the sensory consequences of our actions (i.e., action-effects) is known to be important for intentional action initiation and control. Learned action-effects can select the responses that previously have been associated with them. What has been largely unexplored is how learned…
Lea Eichfelder, Volker H. Franz, Markus Janczyk
Ideomotor theory is an influential approach to understand goal-directed behavior. In this framework, response-effect (R-E) learning is assumed as a prerequisite for voluntary action: Once associations between motor actions and their effects in the environment have been formed, the anticipation of these effects will…
Marcel R. Schreiner, Wilfried Kunde
The experiment consisted of a learning and a test phase, similar to the induction paradigm (Elsner & Hommel, ). The procedure is shown in Fig. [Fig1]. In the learning phase, participants performed a forced-choice effect-production task. In each trial, participants were first presented with a white box on a black…
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…
Eric Peh, Paritosh Parmar, Basura Fernando
We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding. CATE can have applications in areas like task planning and learning from demonstration. We identify and explore two different aspects of the concept of CATE: Action Selection (AS) and Effect-Affinity…
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…
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…
Ruth Pauli, Inti Brazil, Gregor Kohls, Miriam C. Klein-Flügge + 13 more
Theoretical and empirical accounts suggest that adolescence is associated with heightened reward learning and impulsivity. Experimental tasks and computational models that can dissociate reward learning from the tendency to initiate actions impulsively (action initiation bias) are thus critical to characterise the…
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…
Jonathan M Wood, Hyosub E Kim, Susanne M Morton
When learning a new motor skill, people often must use trial and error to discover which movement is best. In the reinforcement learning framework, this concept is known as exploration and has been observed as increased movement variability in motor tasks. For locomotor tasks, however, increased variability decreases…
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…
Federico Nardi, A. Aldo Faisal, Shlomi Haar
This study examines the distinctiveness of error-based and reward-based mechanisms in motor learning, which are traditionally isolated in laboratory tasks but co-occur in real-world scenarios. Using Embodied Virtual Reality (EVR) of pool billiards - allowing for full proprioception via interaction with the physical…
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…