216 research outputs found
sj-docx-1-qjp-10.1177_17470218221111789 – Supplemental material for Cognitive control mechanisms in language processing: are there both within- and across-task conflict adaptation effects?
No full textSupplemental material, sj-docx-1-qjp-10.1177_17470218221111789 for Cognitive control mechanisms in language processing: are there both within- and across-task conflict adaptation effects? by Nicoletta Simi, Ian Grant Mackenzie, Hartmut Leuthold, Markus Janczyk and Carolin Dudschig in Quarterly Journal of Experimental Psychology</p
sj-docx-1-qjp-10.1177_17470218231184996 – Supplemental material for Generalisation of unpredictable action-effect features: Large individual differences with little on-average effect
No full textSupplemental material, sj-docx-1-qjp-10.1177_17470218231184996 for Generalisation of unpredictable action-effect features: Large individual differences with little on-average effect by Markus Janczyk and Jeff Miller in Quarterly Journal of Experimental Psychology</p
QJE-STD-17-354.R2-Supplementary_Material – Supplemental material for The central locus of self-prioritisation
No full textSupplemental material, QJE-STD-17-354.R2-Supplementary_Material for The central locus of
self-prioritisation by Markus Janczyk, Glyn W Humphreys and Jie Sui in Quarterly Journal
of Experimental Psychology</p
Dataset for: Two types of between-task conflict trigger respective processing adjustments within one dual-task
No full textDataset for Mahesan, D., Janczyk, M.. & Fischer, R. (accepted/in press). Two types of between-task conflict trigger respective processing adjustments within one dual-task. Acta Psychologica.Parts of the manuscript were funded and supported by the German Research Foundation (DFG FI 1624/5-1 to RF; JA 2307/6-1 to MJ)peerReviewe
sj-docx-1-qjp-10.1177_17470218221135603 – Supplemental material for Temporal aspects of two types of backward crosstalk in dual-tasks: An analysis of continuous mouse-tracking data
No full textSupplemental material, sj-docx-1-qjp-10.1177_17470218221135603 for Temporal aspects of two types of backward crosstalk in dual-tasks: An analysis of continuous mouse-tracking data by Carolin Schonard, Rolf Ulrich and Markus Janczyk in Quarterly Journal of Experimental Psychology</p
Learning and Transfer of Response-Effect Relations (Janczyk, Eichfelder, Liesefeld, Franz)
No full textData for Janczyk, M., Eichfelder, L., Liesefeld, H.R., & Franz, V. (accepted in 2024). Learning and transfer of response-effect relations. The Quarterly Journal of Experimental Psychology
Combining speed and accuracy to control for speed-accuracy trade-offs (?)
No full textWe tested various measures that combine reaction times and proportion of correct responses to determine whether these can control for speed-accuracy tradeoffs. This was done on data simulated using the diffusion model and these simulated data are provided here (see the paper for details). If you use these data, please cite: Liesefeld, H. R. & Janczyk, M. (in press). Combining speed and accuracy to control for speed-accuracy trade-offs(?). Behavior Research Methods. doi:10.3758/s13428-018-1076-
Pragmatic processing: An investigation of the (anti-)presuppositions of determiners using mouse-tracking
No full textSchneider C, Schonard C, Franke M, Jäger G, Janczyk M. Pragmatic processing: An investigation of the (anti-)presuppositions of determiners using mouse-tracking. Cognition. 2019;193: 104024
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
No full textSoftware repository for the reproduction of the test cases from
Thomas Richter, Rolf Ulrich, Markus Janczyk: Diffusion models with time-dependent parameters: "Analysis and computational effort and accuracy of different numerical methods"
This software is used in particular for the reproducibility of the results.
However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact [email protected].
General setup:
Python collects all Python script. Here, Python/PythonTools are several internal functions, e.g. the realizations of KFE and random walks. Python/results and Python/pics are the directories where the results (figures and text-files) are put.
C++ collects the C++ scripts.
Case I
Reproduces Case I of the paper (time-independent)
Python/TestCase1.py
runs the test-case with random walks and with KFE. It produces output in Python/pics and Python/results. These results will be used in C++/testcase1.cc (as reference solution) and by Python/TestCase1-Plot.py
C++/testcase1.cc
runs the stochastic Euler simulation. Script is started by C++/run-testcase1.sh. It reads in the reference solution generated by Python/TestCase1.py for computing errors.
Python/TestCase1-Plot.py
produces Fig. 6 of the paper. It requires the outputs of Python/TestCase1.py and C++/testcase1.cc
Case II
Reproduces Case II of the paper (time-dependent thresholds and drift)
Python/TestCase2.py
runs the test-case with random walks and with KFE. It produces output in Python/pics and Python/results. These results will be used in C++/testcase2.cc (as reference solution) and by Python/TestCase2-Plot.py
C++/testcase2.cc
runs the stochastic Euler simulation. Script is started by C++/run-testcase2.sh. It reads in the reference solution generated by Python/TestCase2.py for computing errors.
Python/TestCase2-Plot.py
produces Fig. 7 of the paper. It requires the outputs of Python/TestCase2.py and C++/testcase2.cc
Python/TestCase2-AdjustRandomWalks.py
runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.
Case III
Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)
Python/TestCase3.py
runs the test-case with random walks and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.
C++/testcase3.cc
runs the stochastic Euler simulation. Script is started by C++/run-testcase3.sh. It reads in the reference solution generated by Python/TestCase3.py for computing errors.
Python/TestCase3-Plot.py
produces second part of Fig. 8. Depends on the output of Python/TestCase3.py
Data Fitting
Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in
R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78 , 148–174
Python/DataFitting-Simon.py
runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.
Python/Eriksen-Fletcher.py
runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.
Installation & running the examples
Python
The python skripts can just be started. Just note that they depend on each other, i.e.: Python/TestCase1.py produces a reference solution that is required by C++/testcase1.cc and the results of both are needed in Python/TestCase1-Plot.py
The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.
C++
The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials N_tr and a given time step dt and this simulation is repeated 64 times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.
Configuration
The C++ test cases must be compiled. The test cases are set up to use cmake. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):
Create a directory for compilation, e.g. C++/bin now called the bin-dir
In the bin-dir calls cmake by cmake .. (adjust the path, if the bin-dir is not a subdirectory of the C++-dir.
Several options can be adjusted. In C++/bin call ccmake . to make all necessary changes.
If you change the location of the bin-dir you will have to modify the run-scripts run-testcase[123].sh.
Compilation
Initially and whenever you change the code, the programs must be re-compiled
In C++/bin just call make
Running the examples
The programs are started in C++. For each of the test-case there is a skript to start the program.
In C++ call sh ./run-testcase1.sh (or sh ./run-testcase2.sh, etc.)
Each script will start the programs several times. For Case I and Case II the simulation is started on a sequence of finer and finer discretizations, for Case III the value of tau will be changed.
The scripts store the output in C++/results. Old outputs will be overwritten! Further, the scripts read information about the reference solution from Python/resuts.
The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling
export OMP_NUM_THREADS=8
before calling the run-scripts.
License Information
Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022 ([email protected])
You are free to use the scripts under the Creative Commons Attribution 4.0 License.Software to reproduce the results from the paper
Thomas Richter, Markus Janczyk, Rolf Ulrich: Diffusion models with time-dependent parameters: "Analysis and computational effort and accuracy of different numerical methods
Visual and tactile action effects determine bimanual coordination performance
No full textJanczyk M, Skirde S, Weigelt M, Kunde W. Visual and tactile action effects determine bimanual coordination performance. Human Movement Science. 2009;28(4):437-449.Effect-based models of motor control assign a crucial role to anticipated perceptual feedback in action planning. Two experiments were conducted to test the validity of this proposal for discrete bimanual key press responses. The results revealed that the normally observed performance advantage for the preparation of two responses with homologous rather than non-homologous fingers becomes inverted when homologous fingers produce nonidentical visual effects, and non-homologous fingers produce identical visual effects. in the second experiment the finger homology effect was strongly reduced when homologous fingers produced non-identical tactile feedback. The results show that representations of to-be-produced visual and tactile action effects both contribute to action planning, though possibly to a varying degree. Implications of these results for effect-based models of motor control are considered. (C) 2009 Elsevier B.V. All rights reserved
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