18th annual conference on manual control.pdf - Acgsc.org

variable(s) of interest. After parameters have been identified
in this manner and tested for significance, one can justifiably
impose constraints on the search procedure. A constrained search
then allows one to focus on the important effects and, as is
clear from a comparison of Tables la and ib, it greatly
facilitates presentation of the results of the analysis.
Two procedures were also explored for testing the
statistical significance of parameter differences: the standard
t-test, and a sensitivity analysis that we have termed the
"qualitative cross-comparison" procedure. Both methods yielded
the same conclusions in situations where practice effects were
consistent across subjects. Conclusions were different when
subject behavior was inconsistent; but, since we have no way of
determining which method is "right", it is prudent to retain both
techniques in our repertoire of analysis tools.
To some extent, the nature of the data base will determine
which testing procedure to use. Since the t-test requires a
number of replications per condition, the qualitative testing
procedure must be used to compare parameters identified from two
experimental trials (as might be desired if significant trial-totrial
learning occurs). The qualitative procedure can also be
used to minimize computational requirements when multiple trials
per condition are available, as this method allows one to
directly test the replicate-averaged data.
Either method may be used for testing average results for a
subject population. If parameters are identified for individual
subjects first (a recommended procedure if substantial subject
differences are expected), applying the t-test to subject-paired
parameter differences avoids the necessity for further model
analysis. If, on the other hand, parameters have been identified
only for subject-averaged data, the cross-comparison scheme must
be used, as there is only one set of model parameters per
experimental condition.
Practice
Effects
There are a number of reasons for attempting to characterize
the effects of practice in terms of a single independent model
parameter. First, it is this author's opinion that, in general,
the model having the greatest potential for predictive capability
is the most parsimonious model -- i.e., the model that explains
the greatest amount of experimental data with the fewest
independent parameters. Reduction of practice effects to changes
in a single independent parameter does not necessarily impose
limitations on modelling the complexity of the learning process.
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