Data and Analysis Unit: PLYM10

Last update: 2015-04-04 by Andy Wills


This section contains a description of, and source code for, the simulation reported in Figure 1 of O'Connell et al. (submitted).


The model architecture and parameters were as specified in Gluck and Myers (1993, Appendix), with the following exceptions: (a) in the current experiment, there was an outcome on every training trial (category A or category B), thus the US-present learning rate was used on all training trials, (b) input-to-hidden connections in the cortical model had a learning rate of 0.1, (c) weights were intialized to a [-3,+3] random uniform distribution.

The model had 24 units representing the stimuli (one for each icon), 12 hidden units, and 2 units representing the outcome (one for each category label). Activation from the outcome units was converted to a response probability using a standard exponential ratio decision mechanism (see e.g. Wills et al., 2000). This decision mechanism's single parameter (k) was arbitrarily set to 3; the conclusions of this simulation are robust to a wide range of values for k.

The model was trained and tested on the same number of trials as were the participants, with the stimuli generated using the same algorithms as the program which ran the experiment. As is standard in applications of the Gluck-Myers model, training was preceded by 500 blank trials (ie. no stimuli and no category label), in order to initialize the network.

Figure 1 of O'Connell et al. (submitted) is an average across 2000 simulated participants, each with a different set of randomly-selected initial weights.The simulation was conducted largely in C, for speed of execution, with non-time-critical collation and graphing performed in R. The source code for this simulation is provided below.

Source code

In order to run this simulation, you will need to install the following:

This simulation was conducted on a Linux machine. It should work with little or no modification on any UNIX-based system (including Apple's OS X). For MS Windows, you may have to modify the system() calls in analysis.R - happy to help you do this, on request.

Empirical data and analysis


This section publishes such participant data files as are permitted under the ethical and regulatory approvals governing the research reported in O'Connell et al. (submitted). Analysis scripts and stimulus image files are also provided.