Technological upgrades, progressive scientific and technical developments are a direct reflection of the challenges of the 21st century, the overcoming of which implies a review of the content of education and the results obtained from it, aimed at developing a creative, self-planning, results’ predicting personalities. The acquisition of the mentioned qualities is ensured by the introduction of a chess game in the educational process, the purpose of which is not only to learn chess, but also to develop cognitive, emotional qualities at young age (Karapetyan & Misakyan, 2020), action prediction, thinking quality, decision-making quality, performing analytical actions (Kasparov, 2007).
The ability to act together with others to achieve common goals is crucial in life, yet there is no full consensus on the underlying cognitive skills. While influential theoretical accounts suggest that interaction requires sophisticated insights into others’ minds, alternative views propose that high-level social skills might not be necessary because interactions are grounded on sensorimotor predictive mechanisms. At present, empirical evidence is insufficient to decide between the two. This study addressed this issue and explored the association between performance at joint action tasks and cognitive abilities in three domains - action prediction, perspective-taking, and theory of mind - in healthy adults (N=58). We found that, while perspective-taking played a role in reading the behaviour of others independently of the social context, action prediction abilities specifically influenced the agents’ performance in an interactive task but not in a control (social but non-interactive) task. In our study, performance at a theory of mind test did not play any role, as confirmed by Bayesian analyses. The results suggest that, in adults, sensorimotor predictive mechanisms might play a significant and specific role in supporting interpersonal coordination during motor interactions. We discuss the implications of our findings for the contrasting theoretical views described above and propose a way they might be partly reconciled.
Interpreting the behaviour of autonomous machines will be a daily activity for future generations. Yet, surprisingly little is currently known about how people ascribe intentions to human-like and non-human-like agents or objects. In a series of six experiments, we compared people’s ability to extract non-mentalistic (i.e., where an agent is looking) and mentalistic (i.e., what an agent is looking at; what an agent is going to do) information from identical gaze and head movements performed by humans, human-like robots, and a non-human-like object. Results showed that people are faster to infer the mental content of human agents compared to robotic agents. Furthermore, the form of the non-human entity may differently engage mentalizing processes depending on how human-like its appearance is. These results are not easily explained by non-mentalizing strategies (e.g., spatial accounts), as we observed no clear differences in control conditions across the three different agents. Overall, results suggest that human-like robotic actions may be processed differently from both humans’ and objects’ behaviour. We discuss the extent to which these findings inform our understanding of the relevance of an agents’ or objects’ physical features in triggering mentalizing abilities and its relevance for human–robot interaction.
Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have been released. In the benchmark, several state-of-the-art methods have been ranked. However, most of the ranked temporal models rely on recurrent architectures. In our case, we propose, as far as we are concerned, the first self-attention alternative, based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision. Our architecture is composed of various branches which fuse video and kinematic data. The video branch is based on two possible architectures: RubiksNet and TimeSformer. The kinematic branch is based on different configurations of transformer encoder. Several experiments have been performed mainly focusing on pre-processing input data, highlighting problems with two kinematic data sources: pose keypoints and ego-vehicle speed. Our proposed model results are comparable to PCPA, the best performing model in the benchmark reaching an F1 Score of nearly 0.78 against 0.77. Furthermore, by using only bounding box coordinates and image data, our model surpasses PCPA by a larger margin (F1=0.75 vs. F1=0.72). Our model has proven to be a valid alternative to recurrent architectures, providing advantages such as parallelization and whole sequence processing, learning relationships between samples not possible with recurrent architectures.
Evidence for goal‐ and mixed evidence for false belief‐based action prediction in two‐ to four‐year‐old children: A large‐scale longitudinal anticipatory looking replication study
