****readme file for ACT-R models which are described in the paper 'Using the ACT-R Architecture to Specify 39 Quantitative Process Models of Decision Making'***

This folder contains a lisp file for each model (below referred to as model files), as well as the folders input and input_Exp2. The input folders contain files that are loaded by the model-files when the models are run ('input' contains the files loaded by the models recognition and cue_retrieval and by the decision models for Experiment 1. 'input_Exp2' contains the files loaded by the decision models for Experiment 2). Below, we explain the files in some detail.

As explained in more detail in the paper, ACT-R is a hybrid cognitive architecture – it has both symbolic and subsymbolic components. The symbolic components are implemented by means of a production system, which requires the definition of the contents of declarative and procedural memory. The subsymbolic components require the setting of a number of parameters. Additionally to these model-specific components, ACT-R needs an environment with which it can interact. Below, we give a short explanation of each of these components.


***code for the environment***

At the beginning of the model files, preceding the actual model code, you find the code which defines the environment. The environment code is for example responsible for the presentation of stimuli on the screen on which these stimuli can be read by ACT-R. The environment also defines the parameters for the base-levels of the city chunks (and in the Model 4 and 1&4 classes, the base-level of the size chunk (big chunk)). Furthermore, it collects ACT-Rs responses and writes them out to an output file.

*stimuli*
For the decision models, stimuli are the RU pairs of cities as presented to the participants. They are loaded by the environment code from the file trials_test.txt which can be found in the input folders. (Note, that only those RU pairs, which were also used in the analysis of the human data are used as stimuli. That is, pairs where the recognition-memory task revealed that the R city was indeed recognized and the U city indeed not recognized by the participant.) 
For the recognition model, stimuli are the names of the cities as presented to participants in the recognition-memory task (see trials_recognition.txt). For the cue_retrieval models,  stimuli are the pairs of cities and cues as presented to participants in the cue-memory task (see trials_cues.txt). 

*individual knowledge*
Additionally to the stimuli, the environment code loads one file for each participant (complete_vpNR.txt). These individual files contain the participants' individual recognition- and cue- knowledge as identified in the recognition- and cue- memory task. Additionally, these files contain the definition of chunks for cities and their cues together with the parameter setting for the cues' base-levels and their setting of negative Sji values. 


***Subsymbolic model components - Parameter Settings for All Models***

Following the definition of the environment, you find the definition of the parameter settings as shared by all participants for the subsymbolic component of the models. The settings are identical for all models. Note, that the setting of baselevels and negative Sjis is not defined here but in the environment (see above).


***Symbolic model components***

*Declarative Memory*
Following the parameter definition, you find the definition of declarative memory. The content of each model’s declarative memory is defined in terms of chunks. Chunks are the elements of declarative knowledge in the ACT-R theory. Each chunk is defined by its chunk-type. 
In this section of the model file, we define general knowledge components as shared by each participant (e.g., the definition of chunk types). Additionally the models use participant-specific knowledge which is loaded for each participant individually (see above in the environment section). Apart from the big-chunk in the Model-4 and Model-1&4 classes, general declarative knowledge is identical in all models. This section of the model files also contains the definition of ACT-R's hand positions and manual noise.

*Production Rules for Each Model*
Finally, each model file contains the content of the model’s procedural memory. Procedural memory is defined in terms of production rules. All models have identical production rules for the basic processing steps (reading the city names and retrieving their meaning from delacrative memory). As described in the paper, the models differ in the processing steps that follow the initial assessment of recognition. In the model files, production rules are accompanied by short explanations for more clarity.
