Type-based bigram frequencies for five-letter words.

Researchers often require subjects to make judgments that call upon their knowledge of the orthographic structure of English words. Such knowledge is relevant in experiments on, for example, reading, lexical decision, and anagram solution. One common measure of orthographic structure is the sum of the frequencies of consecutive bigrams in the word. Traditionally, researchers have relied on token-based norms of bigram frequencies. These norms confound bigram frequency with word frequency because each instance (i.e., token) of a particular word in a corpus of running text increments the frequencies of the bigrams that it contains. In this article, the authors report a set of type-based bigram frequencies in which each word (i.e., type) contributes only once, thereby unconfounding bigram frequency from word frequency. The authors show that type-based bigram frequency is a better predictor of the difficulty of anagram solution than is token-based frequency. These norms can be downloaded from www.psychonomic.org/archive/.     

Type-based bigram frequencies for five-letter words

Researchers often require subjects to make judgments that call upon their knowledge of the orthographic structure of English words. Such knowledge is relevant in experiments on, for example, reading, lexical decision, and anagram solution. One common measure of orthographic structure is the sum of the frequencies of consecutive bigrams in the word. Traditionally, researchers have relied ontoken-based norms of bigram frequencies. These norms confound bigram frequency with word frequency because each instance (i.e., token) of a particular word in a corpus of running text increments the frequencies of the bigrams that it contains. In this article, the authors report a set oftype-based bigram frequencies in which each word (i.e., type) contributes only once, thereby unconfounding bigram frequency from word frequency. The authors show that type-based bigram frequency is a better predictor of the difficulty of anagram solution than is token-based frequency. These norms can be downloaded fromwww.psychonomic.org/archive/.     

The solution of anagrams: A reexamination of the effects of transition letter probabilities,letter moves,and word frequency on anagram difficulty

Word frequency (WF), number of letter moves, and solution word transition letter probabilities (TP) were related to anagram solution. The solution word TP measure was based on the relative frequencies of correct to incorrect bigrams within the pool of bigrams defined by the letters of the anagram rather than on the absolute frequencies of the correct bigrams. This bigram rank measure, which also took word length and letter position into account, was a powerful predictor of anagram difficulty (p < .001). Likewise, number of letter moves predicted anagram solution strongly (p < .001), but WF was only a marginal predictor (.05 < p < .10). In addition, there were no significant interactions among the three variables, nor wasanagram TP consistently related to anagram difficulty. The results were interpreted in terms of an approach which combined elements of an hypothesis and an S-R mediational theory.     

The Role of Syllables in Anagram Solution: A Rasch Analysis

Anagrams are frequently used by experimental psychologists interested in how the mental lexicon is organized. Until very recently, research has overlooked the importance of syllable structure in solving anagrams and assumed that solution difficulty was mainly due to frequency factors (e.g., bigram statistics). The present study uses Rasch analysis to demonstrate that the number of syllables is a very important factor influencing anagram solution difficulty for both good and poor problem solvers, with polysyllabic words being harder to solve. Furthermore, it suggests that syllable frequency may have an impact on solution times for polysyllabic words, with more frequent syllables being more difficult to solve. The study illustrates the advantages of Rasch analysis for reliable and unidimensional measurement of item difficulty.     

The role of syllables in anagram solution: a Rasch analysis

Anagrams are frequently used by experimental psychologists interested in how the mental lexicon is organized. Until very recently, research has overlooked the importance of syllable structure in solving anagrams and assumed that solution difficulty was mainly due to frequency factors (e.g., bigram statistics). The present study uses Rasch analysis to demonstrate that the number of syllables is a very important factor influencing anagram solution difficulty for both good and poor problem solvers, with polysyllabic words being harder to solve. Furthermore, it suggests that syllable frequency may have an impact on solution times for polysyllabic words, with more frequent syllables being more difficult to solve. The study illustrates the advantages of Rasch analysis for reliable and unidimensional measurement of item difficulty.     

Effects of solution word attributes on anagram difficulty: A regression analysis

A sample of 45 student subjects provided solution scores for 80 five-letter anagrams. These scores were analysed as a function of solution word imagery, con-creteness, familiarity, objective frequency, age-of-acquisition and associative meaningfulness using multiple regression techniques. Two bigram measures together with number of vowels, nature of starting letter (vowel or consonant), anagram pronounceability and anagram-solution similarity scores were also entered into the regression equations. The bigram measures, the starting letter and anagram-solution similarity emerged as having significant associations with the solution scores. Previous reports of imagery effects in anagram are discussed in the light of the present results.     

Personality Patterns in Adolescence as Portrayed by the Rorschach Ink-Blot Method: III. The “Erlebnistypus” (A Normative Study)

The effect of bigram cues on the solution of five-letter monosyllabic and bisyllabic solution-word anagrams was investigated. This was accomplished through the construction of anagrams with and without solution-word bigrams for both monosyllable and bisyllable words. The results revealed that the monosyllabic words were significantly easier to solve when bigram cues were provided, but that there was no difference between the two types of words when bigram cues were not present in the anagrams. Furthermore, no advantage was observed in the solution of bisyllable words even when the bigram in the anagram was also a syllable of the solution word. It was concluded that the facilitating effect of a bigram cue seems to be peculiar to monosyllabic words, and that the result appears to be a function of an initial solution process that favors a single-syllable response.     

Sublexical frequency measures for orthographic and phonological units in German

Many recent studies have demonstrated the influence of sublexical frequency measures on language processing, or called for controlling sublexical measures when selecting stimulus material for psycholinguistic studies (Aichert & Ziegler, 2005). The present study discusses which measures should be controlled for in what kind of study, and presents orthographic and phonological syllable, dual unit (bigram and biphoneme) and single unit (letter and phoneme) type and token frequency measures derived from the lemma and word form corpora of the CELEX lexical database (Baayen, Piepenbrock, & Gulikers, 1995). Additionally, we present the SUBLEX software as an adaptive tool for calculating sublexical frequency measures and discuss possible future applications. The measures and the software can be downloaded at www.psychonomic.org.     

The Syllable Effect in Anagram Solution: Unrecognised Evidence from Past Studies

Six previous studies of the variables affecting anagram solution are re-examined for the evidence that number of syllables contributes to solution difficulty. It was shown that the number of syllables in a solution word was confounded with imagery for one study and with diagram frequency for another. More importantly it was shown that the number of syllables has a large effect on anagram solution difficulty in the re-analysis of the results from the other four studies. In these studies, the number of syllables was either more important than the principal variable examined in the experiment or the second most important variable. Overall the effect size for the number of syllables was large, d = 1.14. The results are discussed in the light of other research and it is suggested that anagram solution may have more in common with other word identification and reading processes than has been previously thought.     

The use of phonemic information to solve anagrams

Two studies investigated the role of phonemic information in anagram solving. In the first study, subjects were given bigram clues to beginnings of solution words. In addition, some subjects pronounced the letters, either correctly or incorrectly with respect to their pronun-ciations in the solution words. Correct pronunciations facilitated and incorrect pronunciations inhibited anagram solving. The second study required subjects to repeat the pronunciation of the entire anagram prior to attempting solution. Again, correct pronunciations were solved more quickly than were anagrams containing incorrect phonemic units. Results of the two studies support an analysis of anagram solving in which both orthographic and phonemic information are used to search memory to retrieve possible solution words. The relationship of the present results to recent research concerning reading and other lexical access tasks is discussed.     

Procura-PALavras (P-PAL): A Web-based interface for a new European Portuguese lexical database

In this article, we present Procura-PALavras (P-PAL), a Web-based interface for a new European Portuguese (EP) lexical database. Based on a contemporary printed corpus of over 227 million words, P-PAL provides a broad range of word attributes and statistics, including several measures of word frequency (e.g., raw counts, per-million word frequency, logarithmic Zipf scale), morpho-syntactic information (e.g., parts of speech [PoSs], grammatical gender and number, dominant PoS, and frequency and relative frequency of the dominant PoS), as well as several lexical and sublexical orthographic (e.g., number of letters; consonant–vowel orthographic structure; density and frequency of orthographic neighbors; orthographic Levenshtein distance; orthographic uniqueness point; orthographic syllabification; and trigram, bigram, and letter type and token frequencies), and phonological measures (e.g., pronunciation, number of phonemes, stress, density and frequency of phonological neighbors, transposed and phonographic neighbors, syllabification, and biphone and phone type and token frequencies) for ~53,000 lemmatized and ~208,000 nonlemmatized EP word forms. To obtain these metrics, researchers can choose between two word queries in the application: (i) analyze words previously selected for specific attributes and/or lexical and sublexical characteristics, or (ii) generate word lists that meet word requirements defined by the user in the menu of analyses. For the measures it provides and the flexibility it allows, P-PAL will be a key resource to support research in all cognitive areas that use EP verbal stimuli. P-PAL is freely available at http://p-pal.di.uminho.pt/tools.     

Determinants of information-seeking behavior

Two studies examined situational determinants of choice among anagram tests that varied both in difficulty and in diagnosticity (the information they provided about one's own ability). In both studies, subjects worked on a preliminary anagram test before making their choices. Study 1 manipulated level of performance on the preliminary test. Results showed that high performance led to preferring more difficult and more diagnostic tests. In Study 2, subjects were either paid or not paid for their performance on the preliminary test. Results showed that pay led to a preference for more diagnostic tests. Unexpectedly, results of both studies showed that although difficulty and diagnosticity were defined independently of one another, they were not perceived as such. Thus, high diagnostic tests were perceived as more difficult; more difficult tests were perceived as more diagnostic; and the difference between high and low diagnostic tests in perceived diagnosticity and choice of items (high diagnostic tests had higher scores on both measures) were more pronounced among more difficult tests. Motivational as well as cognitive interpretations of the results were discussed.     

The effects of superficial and structural information on online problem solving for good versus poor anagram solvers

The two experiments reported here tested two predictions concerning the sensitivity of good and poor problem solvers to superficial and structural information during online problem solving: (a) Superficial features have a greater effect on solution difficulty for poor problem solvers, whereas (b) structural features have a greater effect on solution difficulty for good problem solvers. The tests were conducted in the domain of anagram solution by manipulating or measuring several superficial and structural characteristics in this domain. The results supported both predictions. They also indicated that better problem solvers have access to structural information from the earliest stages of processing (within the first 2 s). The authors discuss the implications of their results for the types of solution strategies used by more and less competent anagram solvers.     

The effects of superficial and structural information on online problem solving for good versus poor anagram solvers

The two experiments reported here tested two predictions concerning the sensitivity of good and poor problem solvers to superficial and structural information during online problem solving: (a) Superficial features have a greater effect on solution difficulty for poor problem solvers, whereas (b) structural features have a greater effect on solution difficulty for good problem solvers. The tests were conducted in the domain of anagram solution by manipulating or measuring several superficial and structural characteristics in this domain. The results supported both predictions. They also indicated that better problem solvers have access to structural information from the earliest stages of processing (within the first 2 s). The authors discuss the implications of their results for the types of solution strategies used by more and less competent anagram solvers.     

Case-sensitive letter and bigram frequency counts from large-scale English corpora.

We tabulated upper- and lowercase letter frequency using several large-scale English corpora (approximately 183 million words in total). The results indicate that the relative frequencies for upper- and lowercase letters are not equivalent. We report a letter-naming experiment in which uppercase frequency predicted response time to uppercase letters better than did lowercase frequency. Tables of case-sensitive letter and bigram frequency are provided, including common nonalphabetic characters. Because subjects are sensitive to frequency relationships among letters, we recommend that experimenters use case-sensitive counts when constructing stimuli from letters.     

Case-sensitive letter and bigram frequency counts from large-scale English corpora

We tabulated upper- and lowercase letter frequency using several large-scale English corpora (∼183 million words in total). The results indicate that the relative frequencies for upper- and lowercase letters are not equivalent. We report a letter-naming experiment in which uppercase frequency predicted response time to uppercase letters better than did lowercase frequency. Tables of case-sensitive letter and bigram frequency are provided, including common nonalphabetic characters. Because subjects are sensitive to frequency relationships among letters, we recommend that experimenters use case-sensitive counts when constructing stimuli from letters.     

An hypothesis approach to the solution of anagrams

The attempts of subjects to reorganize the letters of an anagram were construed as a series of hypotheses about the correct letter order. It was predicted, consequently, that variables which reduce the number of tenable hypotheses or influence the order in which hypotheses are generated will affect problem difficulty. Five such variables, plus solution word frequency, were used to predict solution probabilities in two studies. The multiple Rs obtained were .92 and .82 and the two regression equations were effectively interchangeable. The process of anagram solution was described as entailing the retrieval of words from memory storage on the basis of letter order cues generated by the subject or, less usually, present in the anagram itself.     

The processing and representation of Dutch and English compounds: peripheral morphological and central orthographic effects

In this study, we use the association between various measures of the morphological family and decision latencies to reveal the way in which the components of Dutch and English compounds are processed. The results show that for constituents of concatenated compounds in both languages, a position-related token count of the morphological family plays a role, whereas English open compounds show an effect of a type count, similar to the effect of family size for simplex words. When Dutch compounds are written with an artificial space, they reveal no effect of type count, which shows that the differential effect for the English open compounds is not superficial. The final experiment provides converging evidence for the lexical consequences of the space in English compounds. Decision latencies for English simplex words are better predicted from counts of the morphological family that include concatenated and hyphenated but not open family members.     

WordGen: A tool for word selection and nonword generation in Dutch,English, German,and French

WordGen is an easy-to-use program that uses the CELEX and Lexique lexical databases for word selection and nonword generation in Dutch, English, German, and French. Items can be generated in these four languages, specifying any combination of seven linguistic constraints: number of letters, neighborhood size, frequency, summated position-nonspecific bigram frequency, minimum position-nonspecific bigram frequency, position-specific frequency of the initial and final bigram, and orthographic relatedness. The program also has a module to calculate the respective values of these variables for items that have already been constructed, either with the program or taken from earlier studies. Stimulus queries can be entered through WordGen’s graphical user interface or by means of batch files. WordGen is especially useful for (1) Dutch and German item generation, because no such stimulus-selection tool exists for these languages, (2) the generation of nonwords for all four languages, because our program has some important advantages over previous nonword generation approaches, and (3) psycholinguistic experiments on bilingualism, because the possibility of using the same tool for different languages increases the cross-linguistic comparability of the generated item lists. WordGen is free and available athttp://expsy.ugent.be/wordgen.htm.     

WordGen: a tool for word selection and nonword generation in Dutch, English, German, and French.

WordGen is an easy-to-use program that uses the CELEX and Lexique lexical databases for word selection and nonword generation in Dutch, English, German, and French. Items can be generated in these four languages, specifying any combination of seven linguistic constraints: number of letters, neighborhood size, frequency, summated position-nonspecific bigram frequency, minimum position-nonspecific bigram f requency, position-specific frequency of the initial and final bigram, and orthographic relatedness. The program also has a module to calculate the respective values of these variables for items that have already been constructed, either with the program or taken from earlier studies. Stimulus queries can be entered through WordGen's graphical user interface or by means of batch files. WordGen is especially useful for (1) Dutch and German item generation, because no such stimulus-selection tool exists for these languages, (2) the generation of nonwords for all four languages, because our program has some important advantages over previous nonword generation approaches, and (3) psycholinguistic experiments on bilingualism, because the possibility of using the same tool for different languages increases the cross-linguistic comparability of the generated item lists. WordGen is free and available at http://expsy.ugent.be/wordgen.htm.