Children do not distinguish efficient from inefficient actions during observation

AbstractObservation is a powerful way to learn efficient actions from others. However, the role of observers’ motor skill in assessing efficiency of others is unknown. Preschoolers are notoriously poor at performing multi-step actions like grasping the handle of a tool. Preschoolers (N = 22) and adults (N = 22) watched video-recorded actors perform efficient and inefficient tool use. Eye tracking showed that preschoolers and adults looked equally long at the videos, but adults looked longer than children at how actors grasped the tool. Deep learning analyses of participants’ eye gaze distinguished efficient from inefficient grasps for adults, but not for children. Moreover, only adults showed differential action-related pupil dilation and neural activity (suppressed oscillation power in the mu frequency) while observing efficient vs. inefficient grasps. Thus, children observe multi-step actions without “seeing” whether the initial step is efficient. Findings suggest that observer’s own motor efficiency determines whether they can perceive action efficiency in others.

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Looking without seeing: Children do not distinguish efficient from inefficient means to achieve a goal

10.31234/osf.io/et8yb ◽

2020 ◽

Author(s):

Ori Ossmy ◽

Danyang Han ◽

Brianna Kaplan ◽

Melody Xu ◽

Catherine Bianco ◽

...

Keyword(s):

Deep Learning ◽

Eye Tracking ◽

Neural Activity ◽

Eye Gaze ◽

Physiological Responses ◽

Pupil Dilation ◽

Action Perception ◽

Learning Analysis

Observing actions provides important information about other people’s goals and the means they use to achieve their goals. Preschoolers (N=22) and adults (N=22) watched videorecorded actors use efficient and inefficient means of grasping a hammer to pound a peg. Eye tracking showed that participants at both ages looked equally long at the goal target (the peg) but adults looked longer than children at the means—how actors grasped the hammer. Deep learning analysis of participants’ eye gaze distinguished observation of efficient from inefficient grasps for adults, but not for children. Moreover, only adults showed differential physiological responses while observing efficient versus inefficient grasps in action-related neural activity (EEG) and pupil dilation. Thus, children can actively direct their gaze to look at goal-directed actions without seeing whether the means are efficient or not. Moreover, findings suggest that the development of action perception is built from children’s own motor experiences.

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Faculty Opinions recommendation of An important role of neural activity-dependent CaMKIV signaling in the consolidation of long-term memory.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1000758.12183 ◽

2001 ◽

Author(s):

Alcino Silva

Keyword(s):

Neural Activity ◽

Long Term Memory ◽

Term Memory ◽

Activity Dependent

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The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach

Energy Economics ◽

10.1016/j.eneco.2021.105140 ◽

2021 ◽

Vol 95 ◽

pp. 105140

Author(s):

Yuze Li ◽

Shangrong Jiang ◽

Xuerong Li ◽

Shouyang Wang

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Volatility Forecasting ◽

Data Decomposition ◽

Oil Futures ◽

News Sentiment

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Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽

10.3390/s21113936 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3936

Author(s):

Yannis Spyridis ◽

Thomas Lagkas ◽

Panagiotis Sarigiannidis ◽

Vasileios Argyriou ◽

Antonios Sarigiannidis ◽

...

Keyword(s):

Deep Learning ◽

Heuristic Method ◽

Sensor Nodes ◽

Convolutional Network ◽

Received Signal Strength Indicator ◽

Anchor Nodes ◽

Time Required ◽

Rf Signal ◽

Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

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Role of Deep Learning in Early Detection of COVID-19: Scoping Review

Computer Methods and Programs in Biomedicine Update ◽

10.1016/j.cmpbup.2021.100025 ◽

2021 ◽

pp. 100025

Author(s):

Mahmood Alzubaidi ◽

Haider Dhia Zubaydi ◽

Ali Bin-Salem ◽

Alaa A Abd-Alrazaq ◽

Arfan Ahmed ◽

...

Keyword(s):

Deep Learning ◽

Early Detection ◽

Scoping Review

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Tool-Use Model Considering Tool Selection by a Robot Using Deep Learning

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) ◽

10.1109/humanoids.2018.8625048 ◽

2018 ◽

Author(s):

Namiko Saito ◽

Kitae Kim ◽

Shingo Murata ◽

Tetsuya Ogata ◽

Shigeki Sugano

Keyword(s):

Deep Learning ◽

Tool Use ◽

Tool Selection

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Educational environment and psychological distress of medical students: The role of a deep learning approach

Journal of Taibah University Medical Sciences ◽

10.1016/j.jtumed.2015.08.005 ◽

2015 ◽

Vol 10 (4) ◽

pp. 411-418 ◽

Cited By ~ 2

Author(s):

Muhamad S.B. Yusoff ◽

Wan N. Arifin

Keyword(s):

Deep Learning ◽

Psychological Distress ◽

Medical Students ◽

Educational Environment ◽

Learning Approach

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Talking Fingers: An Interview Study of Pianists' Views on Fingering

Musicae Scientiae ◽

10.1177/102986499700100106 ◽

1997 ◽

Vol 1 (1) ◽

pp. 87-107 ◽

Cited By ~ 12

Author(s):

Eric Clarke ◽

Richard Parncutt ◽

Matti Raekallio ◽

John Sloboda

Keyword(s):

Motor Skill ◽

Common Ground ◽

Procedural Knowledge ◽

Contemporary Music ◽

Declarative Knowledge ◽

Standard Approach ◽

Primary Determinant ◽

Role Of Teachers ◽

Technical Considerations

Seven professional pianists were interviewed to gather their views on various aspects of piano fingering. The issues covered included technical considerations, the influence of interpretation and composers' markings on fingering, the effects of different Performance circumstances, and the role of teachers in determining fingering strategies. An analysis of the participants' responses revealed both a considerable amount of common ground in their attitudes to these matters, and some strikingly different attitudes. Five primary themes emerged: i) while Standard fingerings form the basis for these performers' strategies, a greater use of Standard fingerings in sight-read as opposed to rehearsed or memorised performance was predicted by the participants; ii) those performers with a deeper involvement in, and earlier exposure to, contemporary music considered themselves to have a less Standard approach to fingering; iii) physical considerations not only represent constraints on fingering, but also offer opportunities to employ positively pleasurable fingerings which may be highly idiosyncratic; iv) while interpretation is universally regarded as the primary determinant of finger choice, attitudes range from a refusal to contemplate a fingering until musical matters have been resolved to the belief that a Single best fingering can be found onto which interpretative choices are mapped; v) the participants appear to have good declarative knowledge of the more abstract and Strategic aspects of their approach to fingering, and essentially procedural knowledge of what they do in any particular circumstance, as found in previous research on motor skill.

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