neural network models of visual search behavior

# visual-search-nets

neural networks models of visual search behavior

Paper on how object recognition models account for visual search behavior, that uses this package: https://github.com/NickleDave/untangling-visual-search

Proceedings paper from 2019 Conference on Cognitive Computational Neuroscience that used previous versions of this library.

Tool that can be used to generate visual search stimuli to then carry out experiments with this library: https://github.com/NickleDave/searchstims

## Installation

The following commands were used to create the environment:

tu@computi:~$conda create -n searchnets python=3.6 numpy matplotlib imageio joblib tensorflow-gpu tu@computi:~$ source activate searchnets
tu@computi:~$git clone https://github.com/NickleDave/visual-search-nets.git tu@computi:~$ cd ./visual-search-nets
tu@computi:~$pip install .  ## usage Installing this package (by running pip install . in the source directory) makes it possible to run experiments from the command line with the searchnets command, like so: tu@computi:~$ searchnets train config.ini


The command-line interface accepts arguments with the syntax searchnets command config.ini,
where command is some command to run, and config.ini is the name of a configuration file with options that specify how the command will be executed.
For details on the commands, see this page in the docs. For details on the config.ini files, please see this other page.

## Acknowledgements

• Research funded by the Lifelong Learning Machines program, DARPA/Microsystems Technology Office, DARPA cooperative agreement HR0011-18-2-0019
• David Nicholson was partially supported by the 2017 William K. and Katherine W. Estes Fund to F. Pestilli, R. Goldstone and L. Smith, Indiana University Bloomington.

## Citation

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## Project details

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