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The next question that was addressed for human data is whether
depression or the correlation between affective and semantic
performance better explains variation in the network's performance on
the lexical decision task. The answer to this question provides an
interesting comparison between strictly statistical and causal
modeling perspectives. Statistically, the correlation between valence
identification and lexical decision reaction times was, as shown in
Table 12, p. 116, very low. Depression did not explain a statistically
significant amount of variation in any of the lexical decision
variables, due to the very small observed effect. The relationship
between the lexical decision variables and valence identification
variables were sometimes greater than that between depression itself
and performance on either task. Yet, the underlying explanation for
this fact is not contained in the statistics. Performance on the
valence identification task is dependent on performance on the lexical
decision task and vice-versa are due to feedback within the affective
semantic loop. Overtraining on some negative stimulus, or the
induction of depression serves to change the feedback within the
affective-semantic loop somewhat. Still, the primary effect of the
initial training (potentially akin to premorbid functioning) is not
erased by differential training on negativity. Given the small effect
of negative overtraining on simulated lexical decision task
performance, none of the hierarchical regressions using negative
overtraining and valence identification variables as the independent
variables and lexical decision variables as dependent variables were
statistically significant, or accounted for more than 3
of the
variation in the simulated lexical decision reaction times.
Table:
Correlations Between Overtraining on Negative Exemplars (``Depression'')
and Performance on the Simulated Lexical Decision and Valence
Identification Tasks
| |
Depression |
LD positive |
LD negative |
LD neutral |
| Depression |
1 |
-.08 |
.15 |
.05 |
| VID-positive |
.57* |
.05 |
.08 |
.04 |
| VID-negative |
-.24* |
-.21* |
-.18 |
.01 |
| VID-neutral |
-.01 |
.03 |
-.01 |
.02 |
Next: Incorrect Responses
Up: Results of Simulations
Previous: Modeling Distribution Characteristics
Greg Siegle
1999-11-15