@article{VivLsCol-26-PS-IJ,
abstract = {This study explored the temporal dynamics of motor imagery (MI) following the observation of robotic versus human actions using a temporal judgment task. Participants observed goal-directed action videos by a human or a NAO robot, performed MI until an auditory stop signal, then completed a two-alternative forced choice task to identify the frame corresponding to their imagery stop point, choosing between the correct frame and either a preceding (`Before') or succeeding (`After') frame. Results revealed a systematic temporal bias in MI towards `After'incorrect frames. Crucially, this bias was significantly smaller for imagining robotic actions compared to human actions. This difference was primarily driven by an increased number of `Before'errors for longer robotic actions, suggesting a perceived slowing down of MI (leading to an attenuated forward bias) when simulating less biologically plausible robotic movements, especially over extended durations. For human actions, video duration did not significantly modulate temporal errors. This finding indicates that the temporal distortion in MI is modulated by the observed agent's movement characteristics and action duration, challenging simplistic embodied simulation accounts.},
author = {Viviani, Lorenzo and Liso, Alba and Colotti, Lisa and Romano, Sara and Fogo, Valentina and Riguzzi, Fabrizio and Buccino, Giovanni and Craighero, Laila},
date = {2026/02/14},
date-added = {2026-02-14 15:07:49 +0100},
date-modified = {2026-02-14 15:07:49 +0100},
doi = {10.1007/s00426-026-02259-9},
isbn = {1430-2772},
journal = {Psychological Research},
number = {1},
pages = {34},
title = {Differential temporal dynamics in motor imagery shaped by agent type and action duration},
volume = {90},
year = {2026}
}
@inproceedings{ManGiaAli-24-IJCLR-IC,
author = {Manhaeve, Robin
and Giannini, Francesco
and Ali, Mehdi
and Azzolini, Damiano
and Bizzarri, Alice
and Borghesi, Andrea
and Bortolotti, Samuele
and De Raedt, Luc
and Dhami, Devendra
and Diligenti, Michelangelo
and Duman{\v{c}}i{\'{c}}, Sebastijan
and Faltings, Boi
and Gentili, Elisabetta
and Gerevini, Alfonso
and Gori, Marco
and Guns, Tias
and Homola, Martin
and Kersting, Kristian
and Lehmann, Jens
and Lombardi, Michele
and Lorello, Luca
and Marconato, Emanuele
and Melacci, Stefano
and Passerini, Andrea
and Paul, Debjit
and Riguzzi, Fabrizio
and Teso, Stefano
and Yorke-Smith, Neil
and Lippi, Marco},
editor = {Dai, Wang-Zhou},
title = {Benchmarking in Neuro-Symbolic AI},
booktitle = {Learning and Reasoning: 4th International Joint Conference on Learning and Reasoning, IJCLR 2024, and 33rd International Conference on Inductive Logic Programming, ILP 2024, Nanjing, China, September 20--22, 2024, Proceedings},
year = {2026},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {238--249},
abstract = {Neural-symbolic (NeSy) AI has gained a lot of popularity by enhancing learning models with explicit reasoning capabilities. Both new systems and new benchmarks are constantly introduced and used to evaluate learning and reasoning skills. The large variety of systems and benchmarks, however, makes it difficult to establish a fair comparison among the various frameworks, let alone a unifying set of benchmarking criteria. This paper analyzes the state-of-the-art in benchmarking NeSy systems, studies its limitations, and proposes ways to overcome them. We categorize popular neural-symbolic frameworks into three groups: model-theoretic, proof-theoretic fuzzy, and proof-theoretic probabilistic systems. We show how these three categories have distinct strengths and weaknesses, and how this is reflected in the type of tasks and benchmarks to which they are applied.},
doi = {10.1007/978-3-032-09087-4_17},
isbn = {978-3-032-09087-4}
}
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