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[1] 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.[2],[3]). In either case, the work of Maki and Buchanan[4] 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).
| Feature | B | SE(B) | β |
| COALS | 0.105 | 0.002 | 0.196 * |
| ESA | 0.066 | 0.006 | 0.040 * |
| WLM | 0.047 | 0.002 | 0.117 * |
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[5]. 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[6]. These effects
have lead Gallo et al.[7] 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,
- ↑ Maki, W.; Buchanan, E. Latent structure in measures of associative, semantic, and thematic knowledge. Psychon. Bull. Rev. 2008, 15, 598–603.
- ↑ McRae, K.; Jones, M.N. Semantic Memory. In The Oxford Handbook of Cognitive Psychology; Reisberg, D., Ed.; Oxford University Press: Oxford, UK, 2012. In Press.
- ↑ McRae, K.; Khalkhali, S.; Hare, M. Semantic and Associative Relations in Adolescents and Young Adults: Examining a Tenuous Dichotomy. In The Adolescent Brain: Learning, Reasoning, and Decision Making; Reyna, V.F., Chapman, S.B., Dougherty, M.R., Confrey, J., Eds.; American Psychological Association: Washington, DC, USA, 2011; pp. 39–66.
- ↑ Maki, W.; Buchanan, E. Latent structure in measures of associative, semantic, and thematic knowledge. Psychon. Bull. Rev. 2008, 15, 598–603.
- ↑ Roediger, H.L.; Gallo, D.A. Associative Memory Illusions. In Cognitive Illusions: A Handbook on Fallacies and Biases in Thinking, Judgement and Memory; Pohl, R., Ed.; Psychology Press: East Sussex, UK, 2004.
- ↑ Gallo, D.A.; Roediger, H.L.; McDermott, K.B. Associative false recognition occurs without strategic criterion shifts. Psychon. Bull. Rev. 2001, 8, 579–586.
- ↑ Gallo, D.A.; Roediger, H.L.; McDermott, K.B. Associative false recognition occurs without strategic criterion shifts. Psychon. Bull. Rev. 2001, 8, 579–586.
