Literature DB >> 16447386

What is preexisting strength? Predicting free association probabilities, similarity ratings, and cued recall probabilities.

Douglas L Nelson1, Gunvor M Dyrdal, Leilani B Goodmon.   

Abstract

Measuring lexical knowledge poses a challenge to the study of the influence of preexisting knowledge on the retrieval of new memories. Many tasks focus on word pairs, but words are embedded in associative networks, so how should preexisting pair strength be measured? It has been measured by free association, similarity ratings, and co-occurrence statistics. Researchers interpret free association response probabilities as unbiased estimates of forward cue-to-target strength. In Study 1, analyses of large free association and extralist cued recall databases indicate that this interpretation is incorrect. Competitor and backward strengths bias free association probabilities, and as with other recall tasks, preexisting strength is described by a ratio rule. In Study 2, associative similarity ratings are predicted by forward and backward, but not by competitor, strength. Preexisting strength is not a unitary construct, because its measurement varies with method. Furthermore, free association probabilities predict extralist cued recall better than do ratings and co-occurrence statistics. The measure that most closely matches the criterion task may provide the best estimate of the identity of preexisting strength.

Mesh:

Year:  2005        PMID: 16447386     DOI: 10.3758/bf03196762

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  7 in total

1.  The ties that bind what is known to the recall of what is new.

Authors:  D L Nelson; N Zhang
Journal:  Psychon Bull Rev       Date:  2000-12

2.  What is free association and what does it measure?

Authors:  D L Nelson; C L McEvoy; S Dennis
Journal:  Mem Cognit       Date:  2000-09

Review 3.  Processing implicit and explicit representations.

Authors:  D L Nelson; T A Schreiber; C L McEvoy
Journal:  Psychol Rev       Date:  1992-04       Impact factor: 8.934

4.  Semantic facilitation without association in a lexical decision task.

Authors:  I Fischler
Journal:  Mem Cognit       Date:  1977-05

5.  The large-scale structure of semantic networks: statistical analyses and a model of semantic growth.

Authors:  Mark Steyvers; Joshua B Tenenbaum
Journal:  Cogn Sci       Date:  2005-01-02

6.  Interpreting the influence of implicitly activated memories on recall and recognition.

Authors:  D L Nelson; V M McKinney; N R Gee; G A Janczura
Journal:  Psychol Rev       Date:  1998-04       Impact factor: 8.934

7.  Semantic distance norms computed from an electronic dictionary (WordNet).

Authors:  William S Maki; Lauren N McKinley; Amber G Thompson
Journal:  Behav Res Methods Instrum Comput       Date:  2004-08
  7 in total
  7 in total

1.  Scaling laws in emotion-associated words and corresponding network topology.

Authors:  Takuma Takehara; Fumio Ochiai; Naoto Suzuki
Journal:  Cogn Process       Date:  2014-11-16

2.  Resolving conflict: a response to Martin and Cheng (2006).

Authors:  Sharon L Thompson-Schill; Matthew M Botvinick
Journal:  Psychon Bull Rev       Date:  2006-06

3.  How implicitly activated and explicitly acquired knowledge contribute to the effectiveness of retrieval cues.

Authors:  Douglas L Nelson; Serena L Fisher; Umit Akirmak
Journal:  Mem Cognit       Date:  2007-12

4.  Latent structure in measures of associative, semantic, and thematic knowledge.

Authors:  William S Maki; Erin Buchanan
Journal:  Psychon Bull Rev       Date:  2008-06

5.  How activation, entanglement, and searching a semantic network contribute to event memory.

Authors:  Douglas L Nelson; Kirsty Kitto; David Galea; Cathy L McEvoy; Peter D Bruza
Journal:  Mem Cognit       Date:  2013-08

6.  Not just semantics: strong frequency and weak cognate effects on semantic association in bilinguals.

Authors:  Inés Antón-Méndez; Tamar H Gollan
Journal:  Mem Cognit       Date:  2010-09

7.  Reactivity from judgments of learning is not only due to memory forecasting: evidence from associative memory and frequency judgments.

Authors:  Nicholas P Maxwell; Mark J Huff
Journal:  Metacogn Learn       Date:  2022-04-29
  7 in total

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