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Information 2012, 3

These findings are consistent with previous work that suggests measures of word association (e.g., NMS), semantic feature overlap (e.g., MCSM), and text-based distributional similarity are in fact measuring something different in each case. Maki and Buchanan conducted a factor analysis in which these three types of measure loaded onto separate factors that were coherently either associative, semantic, or distributional in nature. One interpretations of these findings is that the observed separation is due to three separate cognitive representations aligned with these three measures. Alternatively, it could be the case that task-adaptive processing is acting on the same representation yet manifesting three different measures (cf., ). In either case, the work of Maki and Buchanan suggests that modeling both semantic relatedness and word association data with a single representation and procedure is unlikely to be successful. The low correlations in Table 11 lend additional evidence to this claim.

As in the previous study, a linear regression was conducted to assess the relative contributions of each constituent model to NMS performance. The regression used COALS, ESA, and WLM raw scores to predict the NMS forward associative strength. The results of the linear regression are presented in Table 12. Tolerance analyses were conducted to test for multicollinearity of COALS, ESA, and WLM by regressing each on the other two. The obtained tolerances, all between 0.78 and 0.82, suggest that the three models are not collinear. As was previously found in Study 2, the constituent models are not equally weighted. The magnitudes of β in Table 12 show that COALS, WLM, and ESA may be rank ordered in terms of their contribution to the overall model. Thus compared to Table 9, the relative order of WLM and ESA is reversed. Interestingly, the correlation produced by the W3C3 model and the correlation from the regression equation in Table 12 are identical, again supporting the robustness of equally weighting the three constituent models.

Table 12. Regression of COALS, ESA, and WLM raw scores on NMS similarity scores (N = 72,176).

Notes: R = 0.28, ∗p < 0.0001.

6. Study 4: False Memory

Perhaps the most striking evidence of semantic relatedness’ influence on cognitive processing can be found in the Deese–Roediger–McDermott (DRM) paradigm. In this paradigm, participants are presented with a list of words highly associated with a target word in previous word association norms experiments. For example, a list containing bed, rest and dream will likely lead to false recall of sleep. Participants in the DRM paradigm are highly likely to recall the non-presented target word—in some cases even when they are warned about such false memory illusions. These effects have lead Gallo et al. to conclude that the influence of semantic relatedness on retrieval is intrinsic and beyond the participant’s conscious control. Because word association norms are asymmetric,