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Information 2012, 3 As in previous work, these results seem to give some level of support to both activation/monitoring theory and fuzzy-trace theory. Activation/monitoring theory explains false memory largely in terms of the spreading activation in an associative network between words, which is consistent with the large predictive role of backward associative strength. Fuzzy-trace theory explains false memory in terms of gist, and gist traces, which are fuzzy semantic representations hypothesized to be more durable than verbatim traces, consistent with the finding that recall of the non-presented target words is greater than recall of list words and that the recall for list words decays more rapidly than the recall of target words. The results in this section suggest that these two accounts might be different perspectives on the same underlying mental representation. Since a vector may be compared to another vector using all elements and therefore all contexts, a vector can be used to represent gist. Likewise, since the vector elements can be treated individually and the rest of the vector ignored, a vector element can be treated as an associative strength in a given dimension. However an important caveat is that in our models, an entire list of words is required to raise the activation level of the target dimension above the noise of the other dimensions. Thus this approach does not work for simple stimulus-response word association like the NMS task in Study 3.

7. Discussion

We believe that the results of Studies 1 through 4 substantiate the claim that humans and Wikipedia are part of the same cognitive-linguistic ecosystem. The literature described in Section 1 demonstrates how our cognitive-linguistic environment affects our language structure and categorization. If Wikipedia’s structure is an externalization of internal cognitive and linguistic processes, then there is strong reason to believe in the cognitive-linguistic influence of Wikipedia’s past authors on future readers. In other words, Wikipedia would appropriately be described by the process of niche construction.

There are some good reasons for taking this niche-construction concept seriously. It is perhaps trivially true that reading a book or similar work will have some effect on an individual, e.g., through learning. However, the argument being made here is stronger, that the influence of Wikipedia derives from both its language structure and its network of concepts/categories. Analogous to developmental studies, , one prediction would be that reading Wikipedia would affect a participant’s language structure and category structure. By creating a computational cognitive model based on Wikipedia and applying it to multiple semantic tasks, we indirectly tested this hypothesis and found support for it.

First, the unsupervised W3C3 model produced state of the art correlations with human data in Studies 1 to 3. We claim the model is unsupervised because in all cases the three constituent predictors were evenly weighted by taking their average, or equivalently, not weighted at all. Studies 1 and 2 are best characterized as semantic comparison tasks. Study 1’s comparison task included a mixture of semantic relations, e.g., synonym, antonym, part-of, whereas Study 2’s task involved overlap of semantic features. The high correlations in these studies, between 0.67 and 0.78, indicate that the information necessary make semantic comparisons is well represented in the structure of Wikipedia.

Second, the unsupervised W3C3 model had a higher correlation than any of its constituent models in Studies 1 to 4. Regressions conducted in these studies indicate that each constituent model explains a unique portion of the variance in the human data except COALS in Study 4, and that while their relative weights change slightly for each task, equal weighting is nearly identical to the