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A C++ implementation of cognitive models of spatial language understanding as well as pertinent empirical data and analyses.
A C++ implementation of cognitive models of spatial language understanding as well as pertinent empirical data and analyses.
This data publication consists of two parts. First, it contains a C++ implementation of models of spatial language understanding. More specifically, the source code implements - the Attentional Vector Sum (AVS) model proposed by Regier and Carlson (2001), - the reversed AVS model proposed by Kluth, Burigo, and Knoeferle (2017), - the AVS-BB (AVS bounding box) and the rAVS-CoO (rAVS center-of-object) model proposed by Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018), - and a number of closely related modifications. The source code is fully documented and accompanied by instructions and empirical data to replicate the model simulations reported in Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018) and Kluth and Schultheis (2018). Second, this data publication contains the empirical data (rating data, eye movement data, and reaction time data) from the study described in Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018). Furthermore, R source code files with statistical analyses (mainly Bayesian regression models) are provided that can be used to replicate all statistical models and graphics reported in Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018). All source code is licensed under the GPLv3. All data are licensed under the ODbL v1.0.
- Bielefeld University Germany
visual attention, linguistic judgments, spatial language, cognitive modeling, 410
visual attention, linguistic judgments, spatial language, cognitive modeling, 410
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Popularity provided by BIP!- Bielefeld University Germany
This data publication consists of two parts. First, it contains a C++ implementation of models of spatial language understanding. More specifically, the source code implements - the Attentional Vector Sum (AVS) model proposed by Regier and Carlson (2001), - the reversed AVS model proposed by Kluth, Burigo, and Knoeferle (2017), - the AVS-BB (AVS bounding box) and the rAVS-CoO (rAVS center-of-object) model proposed by Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018), - and a number of closely related modifications. The source code is fully documented and accompanied by instructions and empirical data to replicate the model simulations reported in Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018) and Kluth and Schultheis (2018). Second, this data publication contains the empirical data (rating data, eye movement data, and reaction time data) from the study described in Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018). Furthermore, R source code files with statistical analyses (mainly Bayesian regression models) are provided that can be used to replicate all statistical models and graphics reported in Kluth, Burigo, Schultheis, and Knoeferle (accepted 2018). All source code is licensed under the GPLv3. All data are licensed under the ODbL v1.0.