Learning the likelihood: using deepInference for the estimation of diffusion-model and Lévy flight parameters [dataset]
In the corresponding paper, we use the recently develop DeepInference architecture as a general likelihood-free method to estimate parameters of cognitive models. DeepInference is a machine-learning algorithm based on the training of convolutional neural networks. In a first step, the network has to...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Database Research Data |
| Language: | English |
| Published: |
Heidelberg
Universität
2018-06-22
|
| DOI: | 10.11588/data/HY4OBJ |
| Subjects: | |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.11588/data/HY4OBJ Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/HY4OBJ 
|
| Author Notes: | Andreas Voss, Ulf K. Mertens, Stefan T. Radev |
| Summary: | In the corresponding paper, we use the recently develop DeepInference architecture as a general likelihood-free method to estimate parameters of cognitive models. DeepInference is a machine-learning algorithm based on the training of convolutional neural networks. In a first step, the network has to be trained with simulated data to learn the relation of parameters and data. Then, the trained network can be used to re-estimate parameters for real data. The efficiency and robustness of this approach was tested for two decision models based on continuous evidence accumulation. Study 1 investigated the recovery of parameters of the diffusion model, and Study 2 addressed the same question for a Lévy-Flight model. Results demonstrate that the machine-learning approach is superior to traditional multidimensional search algorithms that maximize the likelihood, both in terms of correlations of estimated parameters with true parameters and with regard to absolute deviations. The new approach also excels the maximum likelihood based search pertaining the robustness in the presence of contaminated data. |
|---|---|
| Item Description: | Gesehen am 02.07.2018 Deposit date: 2018-06-21 Grant information: Deutsche Forschungsgemeinschaft (DFG): Vo-1288-2 |
| Physical Description: | Online Resource |
| DOI: | 10.11588/data/HY4OBJ |