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pyactr-book

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Repository for the "Computational Cognitive Modeling and Linguistic Theory" (Brasoveanu & Dotlacil 2020) book

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pyactr-book
---------------------------

Python package to create and run ACT-R cognitive models.

This includes the (frozen) version of pyactr for the ["Computational Cognitive Modeling and Linguistic Theory" book](https://link.springer.com/book/10.1007/978-3-030-31846-8), with all the book code included in the folder book-code.

The book is available open-access [here](https://link.springer.com/book/10.1007/978-3-030-31846-8) or by clicking on the link above.

Running the code
---------------------------

Go to the folder [notebooks](https://github.com/abrsvn/pyactr-book/tree/master/notebooks), then:

- click on the notebook you want to work through
- wait (or refresh) until github renders it for you; if this doesn't work (sometimes github has issues rendering notebooks), download the corresponding pdf and open it locally on your computer
- open the notebook in Google Colab by clicking on the `Open In Colab` badge at the top of the notebook (this works for both the rendered ipynb file or the pdf file opened locally on your computer)
- enjoy reading, running the code interactively, modifying the code etc.

The first two notebooks provide a basic introduction to Jupyter notebooks and Python3 based on various resources; feel free to skip them if you've worked with python and notebooks before. The remainder of the notebooks cover **Chapters 2 through 7** of the ["Computational Cognitive Modeling and Linguistic Theory" book](https://link.springer.com/book/10.1007/978-3-030-31846-8).

Some notebook images might not display correctly in Google Colab, and you'll have to set up access to your google drive if you want to load/save files (e.g., data, models, figures etc.), but you should be able to go through a good number of notebooks by just opening them in Google Colab and working through them interactively.

The current list of notebooks is provided below; click on the links to open the notebooks directly in Google Colab:

- [0a_intro_to_jupyter](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/0a_intro_to_jupyter.ipynb)
- [0b_intro_to_python_for_psycholinguists](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/0b_intro_to_python_for_psycholinguists.ipynb)
- [1_ACTR_cognitive_architecture_in_cog_sci_and_ling](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/1_ACTR_cognitive_architecture_in_cog_sci_and_ling.ipynb)
- [2_pyactr_implementation_of_ACTR](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/2_pyactr_implementation_of_ACTR.ipynb)
- [3_modules_and_buffers_and_writing_productions_in_pyactr](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/3_modules_and_buffers_and_writing_productions_in_pyactr.ipynb)
- [4_running_our_first_model](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/4_running_our_first_model.ipynb)
- [5_model_for_counting_in_ACTR](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/5_model_for_counting_in_ACTR.ipynb)
- [6_regular_grammars_in_ACTR](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/6_regular_grammars_in_ACTR.ipynb)
- [7_counter_automata_in_ACTR](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/7_counter_automata_in_ACTR.ipynb)
- [8_top_down_parsing_model_structure_and_dec_mem](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/8_top_down_parsing_model_structure_and_dec_mem.ipynb)
- [9_top_down_parsing_production_rules](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/9_top_down_parsing_production_rules.ipynb)
- [10_top_down_parsing_running_the_model](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/10_top_down_parsing_running_the_model.ipynb)
- [11_top_down_parsing_failures_to_parse_and_taking_snapshots_of_the_mind](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/11_top_down_parsing_failures_to_parse_and_taking_snapshots_of_the_mind.ipynb)
- [12_HOMEWORK1_extending_the_top_down_parser](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/12_HOMEWORK1_extending_the_top_down_parser.ipynb)
- [13_intro_to_visual_and_motor_modules](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/13_intro_to_visual_and_motor_modules.ipynb)
- [14_lexical_decision_model_production_rules](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/14_lexical_decision_model_production_rules.ipynb)
- [15_lexical_decision_model_running_model_understanding_ouput](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/15_lexical_decision_model_running_model_understanding_ouput.ipynb)
- [16_HOMEWORK2_optional_add_more_stimuli_to_lex_dec_model](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/16_HOMEWORK2_optional_add_more_stimuli_to_lex_dec_model.ipynb)
- [17_left_corner_parsing_intro_and_dec_mem](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/17_left_corner_parsing_intro_and_dec_mem.ipynb)
- [18_left_corner_parsing_production_rules](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/18_left_corner_parsing_production_rules.ipynb)
- [19_left_corner_parsing_running_model_understanding_ouput](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/19_left_corner_parsing_running_model_understanding_ouput.ipynb)
- [20_HOMEWORK3_extending_the_left_corner_parser](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/20_HOMEWORK3_extending_the_left_corner_parser.ipynb)
- [21_analyzing_quantitative_data_intro_and_dataset](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/21_analyzing_quantitative_data_intro_and_dataset.ipynb)
- [22_basics_of_pymc3_and_normal_priors](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/22_basics_of_pymc3_and_normal_priors.ipynb)
- [23_likelihood_function_and_dummy_coding](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/23_likelihood_function_and_dummy_coding.ipynb)
- [24_HOMEWORK4_synthetic_data_compared_to_observed_data](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/24_HOMEWORK4_synthetic_data_compared_to_observed_data.ipynb)
- [25_half_normal_prior_for_sigma](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/25_half_normal_prior_for_sigma.ipynb)
- [26_posterior_beliefs_and_answering_the_theoretical_question](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/26_posterior_beliefs_and_answering_the_theoretical_question.ipynb)
- [27_HOMEWORK5_changing_priors_and_likelihood](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/27_HOMEWORK5_changing_priors_and_likelihood.ipynb)
- [28_subsymbolic_dec_mem_ebbinghaus_data_log_transform](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/28_subsymbolic_dec_mem_ebbinghaus_data_log_transform.ipynb)
- [29_exponential_model_of_forgetting](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/29_exponential_model_of_forgetting.ipynb)
- [30_power_law_model_of_forgetting](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/30_power_law_model_of_forgetting.ipynb)
- [31_base_activation](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/31_base_activation.ipynb)
- [32_spreading_activation](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/32_spreading_activation.ipynb)
- [33_retrieval_probabillity_and_latency](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/33_retrieval_probabillity_and_latency.ipynb)
- [34_log_freq_model_of_lex_dec](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/34_log_freq_model_of_lex_dec.ipynb)
- [35_lex_dec_ACTR_model_1](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/35_lex_dec_ACTR_model_1.ipynb)
- [36_lex_dec_ACTR_model_2](https://colab.research.google.com/github/abrsvn/pyactr-book/blob/master/notebooks/36_lex_dec_ACTR_model_2.ipynb)

Documentation
--------------------------

More documentation about the pyactr package can be found in the pyactr GitHub repo. Go to:

https://github.com/jakdot/pyactr

Some of the documents are also available here in the folder docs.

Requirements
--------------------------

pyactr requires Python3 (>=3.3), numpy, simpy and pyparsing; it also requires tkinter if you want to see visual output of how ACT-R models interact with environment, but this is not necessary to run any models.

pyactr-book

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