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Journal articles on the topic 'Action prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Liu, Lydia T., Solon Barocas, Jon Kleinberg, and Karen Levy. "On the Actionability of Outcome Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22240–49. http://dx.doi.org/10.1609/aaai.v38i20.30229.

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Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting disease risk in healthcare. Practitioners recognize that the ultimate goal is not just to predict but to act effectively. Increasing evidence suggests that relying on outcome predictions for downstream interventions may not have desired results. In most domains there exists a multitude of possible interventions for each individual, making the challenge of taking effective action more acute. Even when causal mechanisms conn

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Cai, Yijun, Haoxin Li, Jian-Fang Hu, and Wei-Shi Zheng. "Action Knowledge Transfer for Action Prediction with Partial Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8118–25. http://dx.doi.org/10.1609/aaai.v33i01.33018118.

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Predicting action class from partially observed videos, which is known as action prediction, is an important task in computer vision field with many applications. The challenge for action prediction mainly lies in the lack of discriminative action information for the partially observed videos. To tackle this challenge, in this work, we propose to transfer action knowledge learned from fully observed videos for improving the prediction of partially observed videos. Specifically, we develop a two-stage learning framework for action knowledge transfer. At the first stage, we learn feature embeddi

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Hollinger, David, Ryan S. Pollard, Mark C. Schall, Howard Chen, and Michael Zabala. "A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition." Sensors 24, no. 23 (2024): 7857. https://doi.org/10.3390/s24237857.

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Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information is necessary for a more thorough approach to intent recognition. Therefore, a combination of action-level and joint-level information may offer a more comprehensive approach to predicting movement intent. In this study, we devised a novel h

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Liu, Dan, Mao Ye, and Jianwei Zhang. "Improving Action Recognition Using Sequence Prediction Learning." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2050029. http://dx.doi.org/10.1142/s0218001420500299.

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Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predic

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5

Springer, Anne, and Wolfgang Prinz. "Action Semantics Modulate Action Prediction." Quarterly Journal of Experimental Psychology 63, no. 11 (2010): 2141–58. http://dx.doi.org/10.1080/17470211003721659.

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Liu, Xiaoli, and Jianqin Yin. "Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction." Applied Sciences 12, no. 11 (2022): 5381. http://dx.doi.org/10.3390/app12115381.

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Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and future human motion prediction. Most of the existing works treat these subtasks separately, ignoring the correlations, leading to unsatisfying performance. By contrast, we jointly model these tasks and improve human motion predictions utilizing their action semantics. In terms of methodology, we propose a novel multi-task framework (Multi-head TrajectoryCN

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Yu, Tianyu, Cuiwei Liu, Zhuo Yan, and Xiangbin Shi. "A Multi-Task Framework for Action Prediction." Information 11, no. 3 (2020): 158. http://dx.doi.org/10.3390/info11030158.

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Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different characteristics at different temporal stages. To this end, we propose a novel multi-task deep forest framework, which treats temporal progress analysis as a relevant task to action prediction and takes advantage of observation ratio labels of incomplete videos during training. The proposed multi-task deep forest is a cascade structure of random forests an

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Kim, Jihwan, Miso Lee, Cheol-Ho Cho, Jihyun Lee, and Jae-Pil Heo. "Prediction-Feedback DETR for Temporal Action Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4266–74. https://doi.org/10.1609/aaai.v39i4.32448.

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Temporal Action Detection (TAD) is fundamental yet challenging for real-world video applications. Leveraging the unique benefits of transformers, various DETR-based approaches have been adopted in TAD. However, it has recently been identified that the attention collapse in self-attention causes the performance degradation of DETR for TAD. Building upon previous research, this paper newly addresses the attention collapse problem in cross-attention within DETR-based TAD methods. Moreover, our findings reveal that cross-attention exhibits patterns distinct from predictions, indicating a short-cut

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Pucci, Davide, Federico Becattini, and Alberto Del Bimbo. "Joint-Based Action Progress Prediction." Sensors 23, no. 1 (2023): 520. http://dx.doi.org/10.3390/s23010520.

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Action understanding is a fundamental computer vision branch for several applications, ranging from surveillance to robotics. Most works deal with localizing and recognizing the action in both time and space, without providing a characterization of its evolution. Recent works have addressed the prediction of action progress, which is an estimate of how far the action has advanced as it is performed. In this paper, we propose to predict action progress using a different modality compared to previous methods: body joints. Human body joints carry very precise information about human poses, which

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10

Rosenfeld, Ariel, and Sarit Kraus. "Predicting Human Decision-Making: From Prediction to Action." Synthesis Lectures on Artificial Intelligence and Machine Learning 12, no. 1 (2018): 1–150. http://dx.doi.org/10.2200/s00820ed1v01y201712aim036.

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11

Kong, Yu, Zhiqiang Tao, and Yun Fu. "Adversarial Action Prediction Networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 3 (2020): 539–53. http://dx.doi.org/10.1109/tpami.2018.2882805.

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Burgstaller, Jessica, Markus Paulus, and Michaela Pfundmair. "Oxytocin promotes action prediction." Hormones and Behavior 107 (January 2019): 46–48. http://dx.doi.org/10.1016/j.yhbeh.2018.09.004.

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CHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.

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Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendation

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Pesquita, Ana, Ulysses Bernardet, Bethany E. Richards, Ole Jensen, and Kimron Shapiro. "Isolating Action Prediction from Action Integration in the Perception of Social Interactions." Brain Sciences 12, no. 4 (2022): 432. http://dx.doi.org/10.3390/brainsci12040432.

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Previous research suggests that predictive mechanisms are essential in perceiving social interactions. However, these studies did not isolate action prediction (a priori expectations about how partners in an interaction react to one another) from action integration (a posteriori processing of both partner’s actions). This study investigated action prediction during social interactions while controlling for integration confounds. Twenty participants viewed 3D animations depicting an action–reaction interaction between two actors. At the start of each action–reaction interaction, one actor perfo

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Pickering, Martin J., and Simon Garrod. "An integrated theory of language production and comprehension." Behavioral and Brain Sciences 36, no. 4 (2013): 329–47. http://dx.doi.org/10.1017/s0140525x12001495.

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AbstractCurrently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models

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Ziaeetabar, Fatemeh, Jennifer Pomp, Stefan Pfeiffer, et al. "Using enriched semantic event chains to model human action prediction based on (minimal) spatial information." PLOS ONE 15, no. 12 (2020): e0243829. http://dx.doi.org/10.1371/journal.pone.0243829.

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Predicting other people’s upcoming action is key to successful social interactions. Previous studies have started to disentangle the various sources of information that action observers exploit, including objects, movements, contextual cues and features regarding the acting person’s identity. We here focus on the role of static and dynamic inter-object spatial relations that change during an action. We designed a virtual reality setup and tested recognition speed for ten different manipulation actions. Importantly, all objects had been abstracted by emulating them with cubes such that particip

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de Assis Chagas, Amanda Mara, Isabella Alves de Faria, Ghislain Saunier, Ruben E. Bittencourt-Navarrete, and Anaelli Aparecida Nogueira-Campos. "Implicit action prediction constrains observed biological action reconstruction." Heliyon 7, no. 2 (2021): e06189. http://dx.doi.org/10.1016/j.heliyon.2021.e06189.

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18

Wu, Xinxiao, Jianwei Zhao, and Ruiqi Wang. "Anticipating Future Relations via Graph Growing for Action Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 2952–60. http://dx.doi.org/10.1609/aaai.v35i4.16402.

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Predicting actions from partially observed videos is challenging as the partial videos containing incomplete action executions have insufficient discriminative information for classification. Recent progress has been made through enriching the features of the observed video part or generating the features for the unobserved video part, but without explicitly modeling the fine-grained evolution of visual object relations over both space and time. In this paper, we investigate how the interaction and correlation between visual objects evolve and propose a graph growing method to anticipate futur

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19

Hanappi, Hardy. "Predictions and Hopes: Global Political Economy Dynamics of the Next Ten Years." Advances in Social Sciences Research Journal 11, no. 8 (2024): 66–87. http://dx.doi.org/10.14738/assrj.118.17381.

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Predictions and hopes are different things. Predictions are based on past empirical observations. They single out what seem to be essential variables and the relationships between them and assume that their importance will prevail in the future. Hopes add a component to a prediction, namely an evaluation, which refers back to the entity that produces the prediction. More favourable predictions are hoped to become a reality while others, which would see the entity in a worse position, are not hoped for. A closer look reveals that with a consideration of what predictions are used for by an entit

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Feng, Yan, Alexander Carballo, Keisuke Fujii, Robin Karlsson, Ming Ding, and Kazuya Takeda. "MulCPred: Learning Multi-Modal Concepts for Explainable Pedestrian Action Prediction." Sensors 24, no. 20 (2024): 6742. http://dx.doi.org/10.3390/s24206742.

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Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations, including the following: (1) they cannot be directly applied to multi-modal cases; (2) they lack the locality needed to attend to details in the inputs; (3) they are susceptible to mode collapse. These limitations

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21

Sankatsing, Glenn. "Action Is the Best Prediction." CLR James Journal 25, no. 1 (2019): 71–80. http://dx.doi.org/10.5840/clrjames2019121764.

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In the Caribbean, we cannot stop the misconduct of irresponsible global actors who agitate the winds beyond their natural cycles and push the sea over our shores, but now, we should refuse to leave our destiny in the hands of those for whom nature’s only beauty is its monetary value. Humanity is reading on its earlier footprints before nature has had time to erase them. That undermines sustainability, the backbone of continuity, survival and development, which goes beyond the pleonasm of sustainable development invented by the dominant system in order to maintain its predatory economy rather t

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22

Kong, Yu, and Yun Fu. "Max-Margin Action Prediction Machine." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 9 (2016): 1844–58. http://dx.doi.org/10.1109/tpami.2015.2491928.

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23

Fiehler, Katja, Eli Brenner, and Miriam Spering. "Prediction in goal-directed action." Journal of Vision 19, no. 9 (2019): 10. http://dx.doi.org/10.1167/19.9.10.

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24

Springer, Anne, Simone Brandstädter, Roman Liepelt, et al. "Motor execution affects action prediction." Brain and Cognition 76, no. 1 (2011): 26–36. http://dx.doi.org/10.1016/j.bandc.2011.03.007.

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25

Walsh, Eamonn, and Patrick Haggard. "Action, prediction, and temporal awareness." Acta Psychologica 142, no. 2 (2013): 220–29. http://dx.doi.org/10.1016/j.actpsy.2012.11.014.

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26

Kimura, Motohiro, and Hiroshi Nittono. "Action, Prediction, and the Brain." International Journal of Psychophysiology 213 (July 2025): 113125. https://doi.org/10.1016/j.ijpsycho.2025.113125.

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27

Li, Meng, Liang Yan, and Qianying Wang. "Group Sparse Regression-Based Learning Model for Real-Time Depth-Based Human Action Prediction." Mathematical Problems in Engineering 2018 (December 24, 2018): 1–7. http://dx.doi.org/10.1155/2018/8201509.

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This paper addresses the problem of predicting human actions in depth videos. Due to the complex spatiotemporal structure of human actions, it is difficult to infer ongoing human actions before they are fully executed. To handle this challenging issue, we first propose two new depth-based features called pairwise relative joint orientations (PRJOs) and depth patch motion maps (DPMMs) to represent the relative movements between each pair of joints and human-object interactions, respectively. The two proposed depth-based features are suitable for recognizing and predicting human actions in real-

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Oved, Nadav, Amir Feder, and Roi Reichart. "Predicting In-Game Actions from Interviews of NBA Players." Computational Linguistics 46, no. 3 (2020): 667–712. http://dx.doi.org/10.1162/coli_a_00383.

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Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. Although there is an abundance of computational work on player metrics prediction based on past performance, very few attempts to incorporate out-of-game signals have been made. Specifically, it was previously unclear whether linguistic signals gathered from players’ interviews can add information that does not appear in performance metrics. To bridge that gap, we define text classification tasks of predicting deviations from mean in NBA players’ in-game a

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Monroy, Claire D., Sarah A. Gerson, and Sabine Hunnius. "Toddlers’ action prediction: Statistical learning of continuous action sequences." Journal of Experimental Child Psychology 157 (May 2017): 14–28. http://dx.doi.org/10.1016/j.jecp.2016.12.004.

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Bianco, Valentina, Alessandra Finisguerra, and Cosimo Urgesi. "Contextual Priors Shape Action Understanding before and beyond the Unfolding of Movement Kinematics." Brain Sciences 14, no. 2 (2024): 164. http://dx.doi.org/10.3390/brainsci14020164.

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Previous studies have shown that contextual information may aid in guessing the intention underlying others’ actions in conditions of perceptual ambiguity. Here, we aimed to evaluate the temporal deployment of contextual influence on action prediction with increasing availability of kinematic information during the observation of ongoing actions. We used action videos depicting an actor grasping an object placed on a container to perform individual or interpersonal actions featuring different kinematic profiles. Crucially, the container could be of different colors. First, in a familiarization

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Thomas, Emily R., Daniel Yon, Floris P. de Lange, and Clare Press. "Action Enhances Predicted Touch." Psychological Science 33, no. 1 (2021): 48–59. http://dx.doi.org/10.1177/09567976211017505.

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It is widely believed that predicted tactile action outcomes are perceptually attenuated. The present experiments determined whether predictive mechanisms necessarily generate attenuation or, instead, can enhance perception—as typically observed in sensory cognition domains outside of action. We manipulated probabilistic expectations in a paradigm often used to demonstrate tactile attenuation. Adult participants produced actions and subsequently rated the intensity of forces on a static finger. Experiment 1 confirmed previous findings that action outcomes are perceived less intensely than pass

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Ma, Jun, and Wenhui Rong. "Pedestrian Crossing Intention Prediction Method Based on Multi-Feature Fusion." World Electric Vehicle Journal 13, no. 8 (2022): 158. http://dx.doi.org/10.3390/wevj13080158.

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Pedestrians are important traffic participants and prediction of pedestrian crossing intention can help reduce pedestrian–vehicle collisions. For the problem of predicting an individual pedestrian’s action where there is crossing potential, a pedestrian crossing intention prediction method that considers multi-feature fusion is proposed in this study, which integrates information affecting pedestrians’ actions, such as pedestrian action and traffic environment. This study is based on the BPI dataset for training and validation, and the test results show that the model has good data fitting and

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Gite, Shilpa, and Himanshu Agrawal. "Early Prediction of Driver's Action Using Deep Neural Networks." International Journal of Information Retrieval Research 9, no. 2 (2019): 11–27. http://dx.doi.org/10.4018/ijirr.2019040102.

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Intelligent transportation systems (ITSs) are one of the most widely-discussed and researched topic across the world. The researchers have focused on the early prediction of a driver's movements before drivers actually perform actions, which might suggest a driver to take a corrective action while driving and thus, avoid the risk of an accident. This article presents an improved deep-learning technique to predict a driver's action before he performs that action, a few seconds in advance. This is considering both the inside context (of the driver) and the outside context (of the road), and fuse

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Sugimoto, Masashi, Naoya Iwamoto, Robert W. Johnston, Keizo Kanazawa, Yukinori Misaki, and Kentarou Kurashige. "A Study of Predicting Ability in State-Action Pair Prediction." International Journal of Artificial Life Research 7, no. 1 (2017): 52–66. http://dx.doi.org/10.4018/ijalr.2017010104.

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When a robot considers an action-decision based on a future prediction, it is necessary to know the property of disturbance signals from the outside environment. On the other hand, the properties of disturbance signals cannot be described simply, such as non-periodic function, nonlinear time-varying function nor almost-periodic function. In case of a robot control, sampling rate for control will be affected description of disturbance signals such as frequency or amplitude. If the sampling rate for acquiring a disturbance signal is not correct, the action will be taken far from its actual prope

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35

Synofzik, Matthis, Peter Thier, and Axel Lindner. "Internalizing Agency of Self-Action: Perception of One's Own Hand Movements Depends on an Adaptable Prediction About the Sensory Action Outcome." Journal of Neurophysiology 96, no. 3 (2006): 1592–601. http://dx.doi.org/10.1152/jn.00104.2006.

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Extensive work on learning in reaching and pointing tasks has demonstrated high degrees of plasticity in our ability to optimize goal-directed motor behavior. However, studies focusing on the perceptual awareness of our own actions during motor adaptation are still rare. Here we present the first simultaneous investigation of sensorimotor adaptation on both levels, i.e., action and action perception. We hypothesized that self-action perception relies on internal predictions about the sensory action outcome that are updated in a way similar to that of motor control. Twenty human subjects perfor

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Podoprosvetov, Aleksei Valerievich, Margarita Evgenievna Luzina, Anton Pavlovich Aliseychik, Evgeny Vladimirovich Pavlovsky, and Igor Aleksandrovich Orlov. "Neural networks for prediction of human actions." Keldysh Institute Preprints, no. 109 (2021): 1–16. http://dx.doi.org/10.20948/prepr-2021-109.

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This work is devoted to the prediction of human actions. The article proposes a machine learning algorithm for predicting human physical actions, created using a software package for collecting data, processing data and training a regression algorithm on the processed data. The results obtained are associated with the automatic determination of the beginning and type of physical action performed by a person. The work is aimed at improving control systems for industrial use of exoskeletons designed to increase human strength through an external frame. In the future, it is possible to use the re

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Wang, Yina, Wenjie Hao, Yanjun Yu, Junyou Yang, and Guang Yang. "A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot." Sensors 23, no. 24 (2023): 9674. http://dx.doi.org/10.3390/s23249674.

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The excretion care robot’s (ECR) accurate recognition of transfer-assisted actions is crucial during its usage. However, transfer action recognition is a challenging task, especially since the differentiation of actions seriously affects its recognition speed, robustness, and generalization ability. We propose a novel approach for transfer action recognition assisted by a bidirectional long- and short-term memory (Bi-LSTM) network combined with a multi-head attention mechanism. Firstly, we utilize posture sensors to detect human movements and establish a lightweight three-dimensional (3D) mode

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Wang, Hongyang, Qingfei Meng, Ju Fan, et al. "Social Influence Does Matter: User Action Prediction for In-Feed Advertising." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 246–53. http://dx.doi.org/10.1609/aaai.v34i01.5357.

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   Social in-feed advertising delivers ads that seamlessly fit inside a user’s feed, and allows users to engage in social actions (likes or comments) with the ads. Many businesses pay higher attention to “engagement marketing” that maximizes social actions, as social actions can effectively promote brand awareness. This paper studies social action prediction for in-feed advertising. Most existing works overlook the social influence as a user’s action may be affected by her friends’ actions. This paper introduces an end-to-end approach that leverages social influence for actio

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Zheng, Kaikui, Shuai Liu, Jinxing Yang, Metwalli Al-Selwi, and Jun Li. "sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning." Sensors 22, no. 24 (2022): 9949. http://dx.doi.org/10.3390/s22249949.

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Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a slidi

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Dignath, David, Andrea Kiesel, Christian Frings, and Bernhard Pastötter. "Electrophysiological evidence for action-effect prediction." Journal of Experimental Psychology: General 149, no. 6 (2020): 1148–55. http://dx.doi.org/10.1037/xge0000707.

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Lai, Shaofan, Wei-Shi Zheng, Jian-Fang Hu, and Jianguo Zhang. "Global-Local Temporal Saliency Action Prediction." IEEE Transactions on Image Processing 27, no. 5 (2018): 2272–85. http://dx.doi.org/10.1109/tip.2017.2751145.

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Su, J., J. J. A. van Boxtel, and H. Lu. ""What" and "when" in action prediction." Journal of Vision 12, no. 9 (2012): 462. http://dx.doi.org/10.1167/12.9.462.

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Hu, Jian-Fang, Wei-Shi Zheng, Lianyang Ma, Gang Wang, Jianhuang Lai, and Jianguo Zhang. "Early Action Prediction by Soft Regression." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 11 (2019): 2568–83. http://dx.doi.org/10.1109/tpami.2018.2863279.

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Moore, James, and Patrick Haggard. "Awareness of action: Inference and prediction." Consciousness and Cognition 17, no. 1 (2008): 136–44. http://dx.doi.org/10.1016/j.concog.2006.12.004.

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Desai, Padmashree, C. Sujatha, Saumyajit Chakraborty, Saurav Ansuman, Sanika Bhandari, and Sharan Kardiguddi. "Next frame prediction using ConvLSTM." Journal of Physics: Conference Series 2161, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1742-6596/2161/1/012024.

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Abstract Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames

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Knoblich, Günther, Eva Seigerschmidt, Rüdiger Flach, and Wolfgang Prinz. "Authorship effects in the prediction of handwriting strokes: Evidence for action simulation during action perception." Quarterly Journal of Experimental Psychology Section A 55, no. 3 (2002): 1027–46. http://dx.doi.org/10.1080/02724980143000631.

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Does the action system contribute to action perception? Recent evidence suggests that actions are simulated while being observed. Given that the planning and simulating system are the same only when one observes one's own actions, it might be easier to predict the future outcomes of actions when one has carried them out oneself earlier on. In order to test this hypothesis, three experiments were conducted in which participants observed parts of earlier self- and other-produced trajectories and judged whether another stroke would follow or not. When the trajectories were produced without constr

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Stenner, Max-Philipp, Markus Bauer, Hans-Jochen Heinze, Patrick Haggard, and Raymond J. Dolan. "Parallel processing streams for motor output and sensory prediction during action preparation." Journal of Neurophysiology 113, no. 6 (2015): 1752–62. http://dx.doi.org/10.1152/jn.00616.2014.

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Sensory consequences of one's own actions are perceived as less intense than identical, externally generated stimuli. This is generally taken as evidence for sensory prediction of action consequences. Accordingly, recent theoretical models explain this attenuation by an anticipatory modulation of sensory processing prior to stimulus onset (Roussel et al. 2013) or even action execution (Brown et al. 2013). Experimentally, prestimulus changes that occur in anticipation of self-generated sensations are difficult to disentangle from more general effects of stimulus expectation, attention and task

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Cao, Liyu, Wilfried Kunde, and Barbara Haendel. "Rapid and Accumulated Modulation of Action-Effects on Action." Journal of Cognitive Neuroscience 32, no. 12 (2020): 2333–41. http://dx.doi.org/10.1162/jocn_a_01633.

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Auditory feedback to a keypress is used in many devices to facilitate the motor output. The timing of auditory feedback is known to have an impact on the motor output, yet it is not known if a keypress action can be modulated on-line by an auditory feedback or how quick an auditory feedback can influence an ongoing keypress. Furthermore, it is not clear if the prediction of auditory feedback already changes the early phase of a keypress action independent of sensory feedback, which would suggest that such prediction changes the motor plan. In the current study, participants pressed a touch-sen

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Ambrosini, Ettore, Giovanni Pezzulo, and Marcello Costantini. "The eye in hand: predicting others' behavior by integrating multiple sources of information." Journal of Neurophysiology 113, no. 7 (2015): 2271–79. http://dx.doi.org/10.1152/jn.00464.2014.

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The ability to predict the outcome of other beings' actions confers significant adaptive advantages. Experiments have assessed that human action observation can use multiple information sources, but it is currently unknown how they are integrated and how conflicts between them are resolved. To address this issue, we designed an action observation paradigm requiring the integration of multiple, potentially conflicting sources of evidence about the action target: the actor's gaze direction, hand preshape, and arm trajectory, and their availability and relative uncertainty in time. In two experim

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Dong, Wenkai, Zhaoxiang Zhang, and Tieniu Tan. "Attention-Aware Sampling via Deep Reinforcement Learning for Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8247–54. http://dx.doi.org/10.1609/aaai.v33i01.33018247.

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Deep learning based methods have achieved remarkable progress in action recognition. Existing works mainly focus on designing novel deep architectures to achieve video representations learning for action recognition. Most methods treat sampled frames equally and average all the frame-level predictions at the testing stage. However, within a video, discriminative actions may occur sparsely in a few frames and most other frames are irrelevant to the ground truth and may even lead to a wrong prediction. As a result, we think that the strategy of selecting relevant frames would be a further import

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