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.
(10K) - A ZIP archive containing the following files:
- analysis.R - Run this one - R script that compiles
the simulation from C source code, performs 2000 runs of the
simulation, calculates the average ouput from these runs, and presents
outputs results as a graph in a PDF file ('bigsum.pdf')
- plotfunc.R - Custom graphing functions used
- main.c - Top-level source code file for Gluck-Myers
- include.h - Sets most model parameters for Gluck-Myers
- run.c - Source code for functions to initialize, train, and
test Gluck-Myers model.
- cort.c - Source code for the cortical network of the
- hipp.c - Source code for the hippocampal network of the
- utilities.c - Source code for a set of utility functions
used by other C files in this archive.
In order to run this simulation, you will need to install the
- R - a free
software environment for statistical computing and graphics.
- gcc - a free compiler. If you
did a full install of R, you probably have this already.
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.
- plym10pat.csv (6K) - Participant-level raw data for patient
group and matched control group. Legal/ethical issues mean this raw
data cannot be published. It may be possible to provide a confidential
copy for research purposes on request - contact me. Column headings
are as follows:
- subj: Participant number (unique within PLYM10)
- grp: Experiment condition (exp = patient group, ctrl = match
- datum: Specifies type of data presented in the current row:
- ph1error: Training phase error score.
- a12 ... a0: Number of category A elements in the test stimulus.
- age: Participant's age.
- value: Dependent on value of 'datum':
- where datum = 'ph1error': Number incorrect responses in training phase
- where datum = 'age': Participant's age at time of testing, in years.
- otherwise: Number of 'category A' responses during test phase.
(145K) - Trial-level raw data for unmatched control
group. Column headings are as follows:
- date: Date of testing.
- subj: Participant number (unique within PLYM10).
- phase: Experiment phase. 1 = training, 2 = test.
- trial: Trial number (resets between phases).
- stim: Phase specific:
- Phase 1: The category from which the training item was generated.
- Phase 2: Number of category B elements (0-12).
- resp: Participant's response.
- rt: Participant's reaction time (in milliseconds).
(6K) - R script for analyses reported in O'Connell et
al. (submitted). This script can be run by the command
source('plym10analysis.R'), or can be run interactively. It also
produces Figure 2C as a PDF. In addition to the two following files,
you will need to have installed the R packages ez
and perm. Both are available on CRAN.
- plym10plot.R (1K)
- R function to produce Figure 2C of O'Connell et al. (submitted).
(2K) - R function for calculation of difference-adjusted
between-subject confidence intervals. Implements same calculation
as bs.ci() from Baguley (2012). Written by Thom Baguley.
- cam1stim.tbz (11K) -
The individual icons making up the stimuli in this experiment. For
each participant, 24 icons are randomly selected from the set 1.png to
36.png (37.png to 40.png are not used).
- Baguley, T. (2012). Calculating and graphing within-subject
confidence intervals for ANOVA. Behavior Research Methods, 44,
- Gluck, M.A. and Myers, C.E. (1993). Hippocampal mediation of
stimulus representation: A computational theory. Hippocampus,
- O'Connell, G., Myers, C.E., Hopkins, R.O., McLaren, R.P., Gluck,
M.A. and Wills, A.J. (submitted). Amnesic patients show superior
generalization in category learning.
- Wills, A.J., Reimers, S., Stewart, N., Suret, M. and McLaren,
I.P.L. (2000). Tests of the ratio rule in categorization. Quarterly
Journal of Experimental Psychology, 53A,983-1011.