CiteULike: briordan's word-association

Topics in semantic representation

Thomas Griffiths — 2008-06-26T18:39:29-00:00

Psychological Review, Vol. 114, No. 2. (2007), pp. 211-244.

Probabilistic topic models

Mark Steyvers — 2008-06-22T21:17:19-00:00

(2007), pp. 427-448.

Saying the right word at the right time: Syntagmatic and paradigmatic interference in sentence production

Gary Dell — 2008-05-03T00:32:52-00:00

Language and Cognitive Processes, Vol. 23, No. 4. (2008), pp. 583-608.

Retrieving a word in a sentence requires speakers to overcome syntagmatic, as well as paradigmatic interference. When accessing cat in The cat chased the string, not only are similar competitors such as dog and cap activated, but also other words in the planned sentence, such as chase and string. We hypothesise that both types of interference impact the same stage of lexical access, and review connectionist models of production that use an error-driven learning algorithm to overcome that interference. This learning algorithm creates a mechanism that limits syntagmatic interference, the syntactic traffic cop, a configuration of excitatory and inhibitory connections from syntactic-sequential states to lexical units. We relate the models to word and sentence production data, from both normal and aphasic speakers.

Eye Movements to Pictures Reveal Transient Semantic Activation During Spoken Word Recognition

Eiling Yee — 2008-04-08T13:54:18-00:00

Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 32, No. 1. (January 2006), pp. 1-14.

A database of associative strengths from the strength-sampling model: A theory-based supplement to the Nelson, McEvoy, and Schreiber word association norms

Maki — 2008-02-26T02:21:59-00:00

Behavior Research Methods, Vol. 40, No. 1. (February 2008), pp. 232-235.

Word associations: Network and semantic properties

De Deyne — 2008-02-26T02:21:59-00:00

Behavior Research Methods, Vol. 40, No. 1. (February 2008), pp. 213-231.

Word association spaces for predicting semantic similarity effects in episodic memory

Mark Steyvers — 2008-01-15T17:02:46-00:00

(2005)

Visualizing similarity data with a mixture of maps

James Cook — 2008-01-02T01:30:24-00:00

(2007)

Is there a "strength effect" in automatic semantic priming?

D Anaki — 2007-12-29T20:03:38-00:00

pp. 262-272.

According to spreading activation models of automatic priming, highly related associates should yield stronger priming effects than weakly related associates. The strength of relations is usually based on word association norms. However, this strength effect has been found in some studies but not in others. The present study suggests that one factor that might be responsible for this inconsistency is whether the weak associate is the primary or nonprimary response in the word association norms. This possibility was explored by comparing priming effects of weak nonprimary and primary associates with those of strong primary associates. Comparable priming effects were found for the strong and weak primary associates whereas the weak nonprimary associates did not yield any priming effects. These results were obtained both in paired (Experiment 1) and single (Experiment 2) presentation priming procedures. Thus, the rank of the associate is an important factor in predicting the magnitude of the priming effect.

Activating event knowledge

Hare — 2007-12-29T19:52:30-00:00

??? (submitted)

Lexical co-occurrence and association strength

Donald Spence — 2007-12-16T16:48:29-00:00

Journal of Psycholinguistic Research, Vol. 19, No. 5. (1990), pp. 317-330.

The 1-million-word Brown corpus was searched for co-occurrences of semantically related pairs of concrete nouns appearing within an arbitrary window of 250 characters. Related pairs of nouns (OCEAN-WATER) co-occur significantly more often than matched, unrelated pairs (OCEAN-HAND), and this difference remained significant within blocks of text up to 1000 characters in length. Frequency of co-occurrence, corrected for chance, is significantly correlated with association strength. Lexical distance between co-occurring members of a given pair is inversely correlated with association strength. Significantly more co-occurrences were found, per unit text, in the fictional sections of the corpus.

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

Douglas Nelson — 2007-12-16T16:46:19-00:00

pp. 711-719.

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.