Investigating Shared Task Representations in the Social Simon Paradigm: Effects of Friendship and Empathy

2012 ◽  
Author(s):  
Ruth M. Ford ◽  
Brad Aberdein
Keyword(s):  
Shared Task ◽  
The Social ◽  
Shared Task Representations ◽  
Task Representations

You Are Right!

2017 ◽  
Vol 64 (6) ◽  
pp. 406-412 ◽  
Author(s):  
Kerstin Dittrich ◽  
Lydia Puffe ◽  
Karl Christoph Klauer
Keyword(s):  
Spatial Compatibility ◽  
Shared Task ◽  
Left Response ◽  
Spatial Response ◽  
Spatial Alignment ◽  
The Social ◽  
The Right ◽  
Response Coding ◽  
Shared Task Representations ◽  
Task Representations

Abstract. In the social Simon task, two participants perform a spatial compatibility task together, each of them responding to only one stimulus (e.g., one participant reacts to red, the other to green stimuli). Participants show joint spatial compatibility effects (SCEs), that is, they respond faster when their go-stimulus appears on their half of the screen. Effects are absent when the same go/no-go task is performed without a coactor. Joint SCEs were originally explained in terms of shared task representations, but recent research suggests that effects result from spatial response coding: in joint go/no-go tasks, participants perceive themselves as the right/left participant operating a right/left response key. While previous research showed that the spatial alignment of keys and seats influences the effect, the present research demonstrates that merely instructing participants to be the right/left participant operating a right/left response key instead of labeling participants and keys with arbitrary numbers substantially increases joint SCEs.

scholarly journals Majority Decision-Making Works Best Under Conditions of Leadership Ambiguity and Shared Task Representations

2021 ◽  
Vol 12 ◽  
Author(s):  
Michaéla C. Schippers ◽  
Diana C. Rus
Keyword(s):  
Decision Making ◽  
Decision Rules ◽  
Business Simulation ◽  
Shared Task ◽  
Team Members ◽  
Majority Decision ◽  
Shared Task Representations ◽  
Task Representations ◽  
Positive Effect

The effectiveness of decision-making teams depends largely on their ability to integrate and make sense of information. Consequently, teams which more often use majority decision-making may make better quality decisions, but particularly so when they also have task representations which emphasize the elaboration of information relevant to the decision, in the absence of clear leadership. In the present study we propose that (a) majority decision-making will be more effective when task representations are shared, and that (b) this positive effect will be more pronounced when leadership ambiguity (i.e., team members’ perceptions of the absence of a clear leader) is high. These hypotheses were put to the test using a sample comprising 81 teams competing in a complex business simulation for seven weeks. As predicted, majority decision-making was more effective when task representations were shared, and this positive effect was more pronounced when there was leadership ambiguity. The findings extend and nuance earlier research on decision rules, the role of shared task representations, and leadership clarity.

scholarly journals Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

2018 ◽  
Vol 25 (10) ◽  
pp. 1274-1283 ◽  
Author(s):  
Abeed Sarker ◽  
Maksim Belousov ◽  
Jasper Friedrichs ◽  
Kai Hakala ◽  
Svetlana Kiritchenko ◽  
...  
Keyword(s):  
Social Media ◽  
Text Classification ◽  
Training Data ◽  
Support Vector ◽  
Shared Task ◽  
Social Media Mining ◽  
The Social ◽  
Related Text ◽  
Health Related ◽  
Media Mining

AbstractObjectiveWe executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.Materials and MethodsWe organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.ResultsAmong 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.DiscussionAmong individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).ConclusionsData imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).

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