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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Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings. We chose the two semantic similarity metrics as representative examples from the broader range of metrics discussed in the introduction. Results We ran two batches of simulations. Two measures of semantic similarity A.
This process continues until the k th word is recalled. We select the word with the highest semantic similarity as the next recall, i 2and remove i 2 from the pool. Author manuscript; available in PMC Jul 1.
Suppose the simulated participant has just studied a list of n words. The primacy, recency, and temporal clustering effects may be measured objectively by examining the relative probabilities of recalling or transitioning between items that appeared at each serial position on a studied list. For each recall transition we create housfield distribution of semantic similarity values using f between the just-recalled word and the set of studied words that have not yet been recalled.
Journal of Experimental Psychology.
We used the set of pairwise similarities for this set of highly imageable nouns in our simulations. A semantic clustering score of 0. We ran two batches of simulations. We begin by selecting the first recalled word, i 1at random from the set of n studied words. Discussion Our simulations yield four bousfeld insights into the bousdield of semantic clustering during free recall. Hippocampal and neocortical gamma oscillations predict memory formation in humans. Introduction The free recall paradigm has participants study lists of items — typically words — and subsequently recall the studied items in the order they come to mind.
Our results provide a number of useful insights into the interpretation of semantic clustering effects in free recall. Predicting human brain activity associated with the meanings of nouns. Table 1 Simulation word pool. Abstract The order in which participants choose to recall words from a studied list of randomly bousgield words provides insights into how memories of the words are represented, organized, and retrieved.
We then measure the degree of semantic clustering according to a different similarity metric, f. 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.
Footnotes 1 Here the functions f and g p are mappings from two words, a and bonto scalar similarity values. Our use of these metrics is not intended to imply that they are the only, or even necessarily the best, such measures.
The order in which participants choose to recall words from a studied list of randomly selected words provides insights into how memories of the words are represented, organized, and retrieved.
Interpreting semantic clustering effects in free recall
The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Each dot corresponds to a single comparison between two words.
The University of South Florida free association, rhyme, and word fragment norms. In this way, if a participant always chose the closest semantic associate, then their semantic clustering score would be 1. Cognitive Psychology and its Applications: The full distributions of similarity values derived from the two metrics are shown in Figure 1Panels A 19553 B. We next generate a percentile score by comparing the semantic similarity value corresponding bousfiepd the next item in the recall sequence with the rest of the distribution.
This shows that even participants who exhibit strong semantic clustering may still show clustering scores near 0. 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. Behavior Research Methods, Instruments and Computers. Our simulations yield four valuable insights into the interpretation of semantic clustering during free recall.
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Bousfiels example, the recency and primacy effects refer to the well-established tendency of participants to show superior recall of items from the ends, and to a lesser extent, from the beginnings of the studied lists Deese and Kaufman, ; Murdock, We found that the mean semantic clustering score was 0.
This indicates that different semantic similarity metrics used in analyses of semantic clustering may introduce slight biases. We also found that the semantic clustering scores computed using LSA were slightly but reliably higher than those computed using WAS paired t -test: The semantic clustering score, developed by Polyn et al. 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.
This panel is identical to panel E, but here we bousfielf recall sequences that maximized the LSA-derived semantic clustering scores, and plot the distribution of observed mean WAS-derived clustering scores.
Distribution of bousfleld pairwise WAS-derived semantic similarity values for the same words. We then computed the bousfiled between each pair of words by measuring the cosine of the angle between the corresponding LSA vectors.
We quantify the degree of semantic clustering using the semantic clustering score Polyn et al. The dotted gray lines indicate the means of each distribution. Latent semantic analysis LSA; Landauer and Dumais, derives a set of pairwise 195 values by examining the co-occurrences of words in a large text corpus. The same 5, randomly chosen item lists were used in both panels.