e.g., bed may evoke sleep with high probability but not the reverse, Roediger et al.[1] conducted a multiple regression analysis to determine whether forward association strength (target word evoking a list member), backward association strength (list member evoking a target word), or other features were most predictive of false memory. That study found that backward association strength was strongly correlated with false recall, r(53) = 0.73. It is generally believed that properties of the word lists themselves are only part of the explanation: The major theories of false memory, activation/monitoring[2] and fuzzy trace theory[3], both allow for cognitive processes of monitoring or strategies that intervene in the process of rejecting false memories.
Several researchers have proposed computational models to implement gist-type semantic
representations that are consistent, but not tightly integrated with, fuzzy-trace theory[4],[5]. In general,
any distributional model, by pooling over many contexts, creates a gist-type representation. LSA has
been proposed to create a gist-type semantic representation[6]. The intuition is that LSA abstracts
meaning across many different contexts and averages them together. In LSA, although a single word
may have multiple senses, e.g., river bank and bank deposit, LSA has one vector representation for
each word which is pooled across all the documents in which that word occurs. More recently, latent
Dirichlet allocation (LDA, also known as a topic model) has been proposed as an alternative to LSA
for representing gist[7]. One notable advantage of LDA is that the conditional probabilities used to
compare two words are inherently asymmetric and therefore consistent with the asymmetries in human
similarity judgments[8]. Another advantage of LDA is that words have probabilistic representations
over latent variables corresponding roughly to word senses. Thus the two senses for bank above could
be preserved and represented in probability distributions over two distinct latent variables.
In this study we investigated the relationship between the W3C3 model and constituent models on
backward associative strength in the DRM paradigm. Following previous research, we chose to focus
on backward associative strength because it is highly correlated with false recall and may be considered
independently of the monitoring processes that mediate false recall. In order to investigate the W3C3
model in this context, several of the constituent models had to be amended to produce gist representations
for DRM lists. To create COALS gist vectors, the raw unnormalized vectors for each word were
summed, then normalized using correlation, and then projected into a 500 dimensional SVD solution
as described in Section 2.1. This operation ensured that each element of the gist vector was a correlation,
just as is the case in a normal COALS vector. ESA naturally creates gist vectors from multi-word
strings, so no additional algorithm needed to be developed. For WLM, we create a synthetic article
using inlinks/outlinks of the most likely sense for each word. The most likely sense for each word was
determined by considering only the previous word in the DRM list. The most likely sense is the sense
that has the highest similarity to the previous word. For example, if the previous word is baseball and
the current word is bat, then the club sense is more similar than the flying mammal sense according
to WLM. Then the inlinks/outlinks for these most likely senses were aggregated and used to create a
synthetic gist article with the union of inlinks and union of outlinks. This gist article was then compared
to the non-present target article in the standard way. As before, the W3C3 model was an unweighted
average of these three scores.
We used the above methods for calculating gist and applied it to the standard set of 55 DRM lists[9].
For each list, we computed the gist representation and then compared it to the representation for the
- ↑ Roediger, H.L.; Watson, J.M.; McDermott, K.B.; Gallo, D.A. Factors that determine false recall: A multiple regression analysis. Psychon. Bull. Rev. 2001, 8, 385–407.
- ↑ Roediger, H.L.; Watson, J.M.; McDermott, K.B.; Gallo, D.A. Factors that determine false recall: A multiple regression analysis. Psychon. Bull. Rev. 2001, 8, 385–407.
- ↑ Brainerd, C.J.; Yang, Y.; Reyna, V.F.; Howe, M.L.; Mills, B.A. Semantic processing in “associative” false memory. Psychon. Bull. Rev. 2008, 15, 1035–1053.
- ↑ Landauer, T.K.; Dumais, S.T. A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol. Rev. 1997, 104, 211–240.
- ↑ Griffiths, T.L.; Steyvers, M.; Tenenbaum, J.B. Topics in semantic representation. Psychol. Rev. 2007, 114, 211–244.
- ↑ Landauer, T.K.; Dumais, S.T. A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol. Rev. 1997, 104, 211–240.
- ↑ Griffiths, T.L.; Steyvers, M.; Tenenbaum, J.B. Topics in semantic representation. Psychol. Rev. 2007, 114, 211–244.
- ↑ Tversky, A. Features of similarity. Psychol. Rev. 1977, 84, 327–352.
- ↑ Roediger, H.L.; Watson, J.M.; McDermott, K.B.; Gallo, D.A. Factors that determine false recall: A multiple regression analysis. Psychon. Bull. Rev. 2001, 8, 385–407.
