Semantic Priming of Anagram Solutions

White, Hedy

doi:10.2307/1423086

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…Semantic priming has been widely used across a number of tasks and settings. For example, people are faster to solve anagrams related to semantically primed than unprimed categories (Schuberth et al, 1979;White, 1988). In combination, these lines of research suggest that semantic priming can lead people toward both correct and incorrect solutions.…”

Section: Eliciting False Insights With Semantic Primingmentioning

confidence: 99%

Eliciting false insights with semantic priming

Hj¹,

Laukkonen²,

Tangen³

et al. 2021

Preprint

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The insight experience (or ‘Aha moment’) generally evokes strong feelings of certainty and confidence. An ‘Aha’ experience for a false idea could underlie many false beliefs and delusions. However, for as long as insight experiences have been studied, false insights have remained difficult to elicit experimentally. That difficulty, in turn, highlights the fact that we know little about what causes people to experience a false insight. Across two experiments (total N=300), we developed and tested a new paradigm to elicit false insights. In Experiment 1 we used a combination of semantic priming and visual similarity to elicit feelings of insight for incorrect solutions to anagrams. These false insights were relatively common but were experienced as weaker than correct ones. In Experiment 2 we replicated the findings of Experiment 1 and found that semantic priming had a greater impact than visual similarity on false insights, although the combination of the two manipulations produced the strongest effect. These studies highlight the importance of misleading semantic processing in the generation of false insights.

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Section: Eliciting False Insights With Semantic Primingmentioning

confidence: 99%

Eliciting false insights with semantic priming

Hj¹,

Laukkonen²,

Tangen³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The specific task used for anagram problem solving is an adaptation of one used by White (1988). In this task, participants are shown strings of letters, some of which are anagrams of real words and some of which are not.…”

Section: Experiments 1a and 1bmentioning

confidence: 99%

“…This portion of the task may occur entirely serially (as in Sternberg, 1969) after the anagram solution phase, or may overlap partially in a cascaded fashion (as in McClelland, 1979). Alternatively, if anagram solution is driven by tests of “candidate” words generated from some subset of the anagram's letters, as some have proposed (e.g., Dewing & Hetherington, 1974; Richardson & Johnson, 1980; White, 1988), and if people either generate words of known imageability or can test the imageability of generated words while simultaneously testing the anagram for a match to the generated words, this imageability rating of anagrams task should produce no partial information.…”

Section: Experiments 2a and 2bmentioning

confidence: 99%

Sudden insight: All-or-none processing revealed by speed–accuracy decomposition.

Smith¹,

Kounios²

1996

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Issues surrounding the discreteness or continuity of cognitive processes have played a major role in experimental psychology, although there has been relatively little work that directly addresses these topics. Nevertheless, there has been a shift away from discrete models and toward continuous ones. The research reported in this article demonstrates discrete processing of information in an anagram task selected because of its similarity to insight problems, which seem subjectively to produce discrete "illumination" during processing. The authors used speed-accuracy decomposition (SAD), a relatively new technique for investigating the time course of information processing. The results of 2 experiments indicate little or no partial information in the anagram tasks, in contrast to previous research with SAD, all of which has revealed partial information. General models of human information processing must therefore be able to account for both patterns.

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“…Anagrams play an important and pervasive role in psychological research. For example, anagrams provide a measure of problem-solving ability (i.e., where the task is to generate a word composed of the same letters as a presented string) that has been used to study a range of psychological issues, including insight (e.g., Smith & Kounios, 1996), aging (e.g., Witte & Freund, 1995), recognition memory (e.g., Weldon, 1991), semantic memory (e.g., White, 1988), and the topography of evoked brain activity (e.g., Skrandies, Reik, & Kunze, 1999). Anagrams have also been used extensively to study processes involved in word recognition, where a great deal of research involves comparing performances between stimuli from different linguistic categories, including frequency, imageability, concreteness, orthographic structure, and lexicality (words vs. nonwords).…”

mentioning

confidence: 99%

Generating anagrams from multiple core strings employing user-defined vocabularies and orthographic parameters

Jordan

Monteiro

2003

Behavior Research Methods, Instruments, & Computers

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Anagrams are used widely in psychological research. However, generating a range of strings with the same letter content is an inherently difficult and time-consuming task for humans, and current computer-based anagram generators do not provide the controls necessary for psychological research. In this article, we present a computational algorithm that overcomes these problems. Specifically, the algorithm processes automatically each word in a user-defined source vocabulary and outputs, for each word, all possible anagrams that exist as words (or as nonwords, if required) as defined by the same source vocabulary. Moreover, we show how the output of the algorithm can be filtered to produce anagrams within specific user-defined orthographic parameters. For example, the anagrams produced can be filtered to produce words that share, with each other or with other words in the source vocabulary, letters in only certain positions. Finally, we provide free access to the complete Windows-based program and source code containing these facilities for anagram generation.

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Semantic Priming of Anagram Solutions

Cited by 16 publications

References 22 publications

Eliciting false insights with semantic priming

Eliciting false insights with semantic priming

Sudden insight: All-or-none processing revealed by speed–accuracy decomposition.

Generating anagrams from multiple core strings employing user-defined vocabularies and orthographic parameters

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