How do humans perform difficult forced-choice evaluations, e.g., of words that
have been previously rated as being neutral? Here we tested the hypothesis
that in this case, the valence of semantic associates is of significant
influence. From corpus based co-occurrence statistics as a measure of
association strength we computed individual neighborhoods for single neutral
words comprised of the 10 words with the largest association strength. We then
selected neutral words according to the valence of the associated words
included in the neighborhoods, which were either mostly positive, mostly
negative, mostly neutral or mixed positive and negative, and tested them using
a valence decision task (VDT). The data showed that the valence of semantic
neighbors can predict valence judgments to neutral words. However, all but the
positive neighborhood items revealed a high tendency to elicit negative
responses. For the positive and negative neighborhood categories responses
congruent with the neighborhood's valence were faster than incongruent
responses. We interpret this effect as a semantic network process that
supports the evaluation of neutral words by assessing the valence of the
associative semantic neighborhood. In this perspective, valence is considered
a semantic super-feature, at least partially represented in associative
activation patterns of semantic networks