of Clustering in the Recall of Randomly Arranged Associates · W. A. Bousfield et al. The Journal of Psychology. Volume 36, – Issue 1. Bousfield, W.A. BousfieldThe occurrence of clustering in the recall of randomly arranged associates. Journal of General Psychology, 49 (), pp. Psychol., 49 (), pp. Google Scholar. Bousfield et al., W.A. Bousfield, B.H. Cohen, G.A. WhitmarshAssociative clustering in the recall of words.

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Another measure of semantic similarity, termed the Google similarity distance Calibrasi and Vitanyi,uses the Google search engine to compute the number of web pages containing both word x and yrelative to the total number of pages containing each word individually; a similar metric boufsield on Wikipedia links to measure the similarities between topics Milne and Witten, The occurrence of clustering in the recall of randomly arranged associates. Latent semantic analysis LSA; Landauer and Dumais, derives a set of pairwise similarity values by examining the co-occurrences of words in a large text corpus.

Each simulated participant encounters many word lists, and we simulate a sequence of recalls after each studied list.

Each dot corresponds to a single comparison between two words. We then re-order the n – 2 remaining words in the pool by their semantic similarities to i 2 and select the word most similar to i 2 to be recalled next.

Interpreting semantic clustering effects in free recall.

Although the similarity values produced by each of these myriad similarity metrics are somewhat related, the pairwise correlations between the measures tend to be surprisingly low. Over the past decade, a number of techniques have been developed for systematically quantifying the relative meanings of words.

Clustering in free recall as a function of certain methodological variations. This panel is identical to panel E, but here we generated recall bousfiekd that maximized the LSA-derived semantic clustering scores, and plot the distribution of observed mean WAS-derived clustering scores. We then create a pool of the n – 1 remaining words from the studied list.



Discussion Our simulations yield four valuable insights into the interpretation of semantic clustering during free recall. Because this procedure ensures that each recall will be followed by the most similar word that is yet to be recalled, by definition it will maximize the semantic clustering score according to g p.

Interpreting semantic clustering effects in free recall

Serial effects in recall of unorganized and sequentially organized verbal material. Our simulations bousfirld four valuable insights into the interpretation of semantic clustering during free recall. For each participant we also constructed 50 lists of 15 unique items each, drawn from the word pool. Across bousfielsimulated recall sequences, and combining across the two semantic similarity measures, the observed semantic clustering scores ranged from 0.

This panel shows a binned variant of the scatterplot in panel C. We quantify the degree of semantic clustering using the semantic clustering score Polyn et al. Weobtain a single semantic clustering score for each simulated participant by averaging the semantic clustering scores across all lists that the participant encountered.

Distribution of the pairwise WAS-derived semantic bousffield values for the same words.

This shows that even participants who exhibit strong semantic clustering may still show clustering scores near 0. Given that the clustering scores obtained using any given model of semantic similarity are likely to be only noisy reflections of any true patterns in the data, one should use multiple models of semantic similarity whenever possible.

LSA represents one technique for deriving similarity values via automated text processing. By analyzing recall sequences during free recall, researchers have uncovered a number of trends that many participants exhibit. By contrast, WAS derives similarity values using experimental data from psychological experiments.


The binning reveals an approximately monotonic relation between the two similarity measures. We found that the mean semantic clustering score was obusfield. We ran two batches of simulations. Hippocampal and neocortical gamma oscillations predict memory formation in humans. Suppose the simulated participant has just studied a list of n words.

In such cases, one might use simulations analogous to those we present here to gain insights into the range of clustering scores one 19553 expect under various models e. We generated 5-item recall sequences that maximized the WAS-derived semantic clustering score forsimulated participants presented with 50 item bkusfield each see text for details. In particular, how should the magnitudes of semantic clustering effects be interpreted?

Theta and gamma oscillations during encoding predict subsequent recall.

Author information Copyright and License information Disclaimer. The serial position effect of free recall. The latent semantic analysis theory of acquisition, induction, and representation of knowledge. If one observes or fails to observe a similar pattern of clustering scores across experimental conditions when using multiple semantic similarity models e. Predicting human brain activity associated with the meanings of nouns.

For this reason the precise clustering score one observes is difficult to interpret, and one would be better served by instead comparing distributions of clustering scores obtained across conditions in an experiment or across participants. Measuring semantic clustering effects requires making assumptions about which words participants consider to be similar in meaning.