Behavior Research Methods

Publisher: Psychonomic Society, Psychonomic Society

Description

Impact factor 2.12

  • 5-year impact
    3.90
  • Cited half-life
    6.80
  • Immediacy index
    0.23
  • Eigenfactor
    0.02
  • Article influence
    1.71
  • Other titles
    Behavior research methods (Online), Behavior research methods
  • ISSN
    1554-3528
  • OCLC
    57493288
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Psychonomic Society

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • On author's personal website or institutional repository
    • Published source must be acknowledged
    • Must link to publisher version
    • Set statement to accompany deposit
    • Funder's designated repository after 12 months or as a result of legal obligation
    • Publisher's version/PDF cannot be used
  • Classification
    ​ green

Publications in this journal

  • Behavior Research Methods 01/2016;
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    ABSTRACT: Van Hooft and Born (Journal of Applied Psychology 97:301-316, 2012) presented data challenging both the correctness of a congruence model of faking on personality test items and the relative merit (i.e., effect size) of response latencies for identifying fakers. We suggest that their analysis of response times was suboptimal, and that it followed neither from a congruence model of faking nor from published protocols on appropriately filtering the noise in personality test item answering times. Using new data and following recommended analytic procedures, we confirmed the relative utility of response times for identifying personality test fakers, and our obtained results, again, reinforce a congruence model of faking.
    Behavior Research Methods 11/2014;
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    ABSTRACT: Since the work of Taft and Forster (1976), a growing literature has examined how English compound words are recognized and organized in the mental lexicon. Much of this research has focused on whether compound words are decomposed during recognition by manipulating the word frequencies of their lexemes. However, many variables may impact morphological processing, including relational semantic variables such as semantic transparency, as well as additional form-related and semantic variables. In the present study, ratings were collected on 629 English compound words for six variables [familiarity, age of acquisition (AoA), semantic transparency, lexeme meaning dominance (LMD), imageability, and sensory experience ratings (SER)]. All of the compound words selected for this study are contained within the English Lexicon Project (Balota et al., 2007), which made it possible to use a regression approach to examine the predictive power of these variables for lexical decision and word naming performance. Analyses indicated that familiarity, AoA, imageability, and SER were all significant predictors of both lexical decision and word naming performance when they were added separately to a model containing the length and frequency of the compounds, as well as the lexeme frequencies. In addition, rated semantic transparency also predicted lexical decision performance. The database of English compound words should be beneficial to word recognition researchers who are interested in selecting items for experiments on compound words, and it will also allow researchers to conduct further analyses using the available data combined with word recognition times included in the English Lexicon Project.
    Behavior Research Methods 11/2014;
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    ABSTRACT: It is common to encounter latent variables with ordinal data in social or behavioral research. Although a mediated effect of latent variables (latent mediated effect, or LME) with ordinal data may appear to be a straightforward combination of LME with continuous data and latent variables with ordinal data, the methodological challenges to combine the two are not trivial. This research covers model structures as complex as LME and formulates both point and interval estimates of LME for ordinal data using the Bayesian full-information approach. We also combine weighted least squares (WLS) estimation with the bias-corrected bootstrapping (BCB; Efron Journal of the American Statistical Association, 82, 171-185, 1987) method or the traditional delta method as the limited-information approach. We evaluated the viability of these different approaches across various conditions through simulation studies, and provide an empirical example to illustrate the approaches. We found that the Bayesian approach with reasonably informative priors is preferred when both point and interval estimates are of interest and the sample size is 200 or above.
    Behavior Research Methods 11/2014;
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    ABSTRACT: This article introduces childLex, an online database of German read by children. childLex is based on a corpus of children's books and comprises 10 million words that were syntactically annotated and lemmatized. childLex reports linguistic norms for lexical, superlexical, and sublexical variables in three different age groups: 6-8 (grades 1-2), 9-10 (grades 3-4), and 11-12 years (grades 5-6). Here, we describe how childLex was collected and analyzed. In addition, we provide information about the distributions of word frequency, word length, and orthographic neighborhood size, as well as their intercorrelations. Finally, we explain how childLex can be accessed using a Web interface.
    Behavior Research Methods 10/2014;
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    ABSTRACT: The purpose of this study was to develop an auditory emotion recognition function that could determine the emotion caused by sounds coming from the environment in our daily life. For this purpose, sound stimuli from the International Affective Digitized Sounds (IADS-2), a standardized database of sounds intended to evoke emotion, were selected, and four psychoacoustic parameters (i.e., loudness, sharpness, roughness, and fluctuation strength) were extracted from the sounds. Also, by using an emotion adjective scale, 140 college students were tested to measure three basic emotions (happiness, sadness, and negativity). From this discriminant analysis to predict basic emotions from the psychoacoustic parameters of sound, a discriminant function with overall discriminant accuracy of 88.9 % was produced from training data. In order to validate the discriminant function, the same four psychoacoustic parameters were extracted from 46 sound stimuli collected from another database and substituted into the discriminant function. The results showed that an overall discriminant accuracy of 63.04 % was confirmed. Our findings provide the possibility that daily-life sounds, beyond voice and music, can be used in a human-machine interface.
    Behavior Research Methods 10/2014;
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    ABSTRACT: Studying the collective behavior of fishes often requires tracking a great number of individuals. When many fishes move together, it is common for individuals to move so close to each other that some fishes superimpose themselves on others during one or several units of time, which impacts on tracking accuracy (i.e., loss of fish trajectories, interchange of fish identities). Type 1 occlusions arise when two fishes swim so near each other that they look like one long fish, whereas type 2 occlusions occur when the fishes' trajectories cross to create a T- or X-shaped individual. We propose an image processing method for resolving these types of occlusions when multitracking shoals in two dimensions. We assessed processing effectiveness after videorecording shoals of 20 and 40 individuals of two species that exhibit different shoal styles: zebrafish (Danio rerio) and black neon tetras (Hyphessobrycon herbertaxelrodi). Results show that, although the number of occlusions depended on both the number of individuals and the species, the method is able to effectively resolve a great deal of occlusions, irrespective of the species and the number of individuals. It also produces images that can be used in a multitracking system to detect individual fish trajectories. Compared to other methods, our approach makes it possible to study shoals with water depths similar to those seen in the natural conditions of the two species studied.
    Behavior Research Methods 10/2014;
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    ABSTRACT: This article presents valence/pleasantness, activity/arousal, power/dominance, origin, subjective significance, and source-of-experience norms for 1,586 Polish words (primarily nouns), adapted from the Affective Norms for English Words list (1,040 words) and from my own previous research (546 words), regarding the duality-of-mind approach for emotion formation. This is a first attempt at creating affective norms for Polish words. The norms are based on ratings by a total of 1,670 college students (852 females and 818 males) from different Warsaw universities and academies, studying various disciplines in equal proportions (humanities, engineering, and social and natural sciences) using a 9-point Likert Self-Assessment Manikin scale. Each participant assessed 240 words on six different scales (40 words per scale) using a paper-and-pencil group survey procedure. These affective norms for Polish words are a valid and useful tool that will allow researchers to use standard, well-known verbal materials comparable to the materials used in other languages (English, German, Portuguese, Spanish, French, Dutch, etc.). The normative values of the Polish adaptation of affective norms are included in the online supplemental materials for this article.
    Behavior Research Methods 10/2014;
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    ABSTRACT: Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advancements in this area by affording the classification of these observables directly from images. While such methods have been successfully implemented in the classification of individual behavior, their potential in the study collective behavior is largely untested. In this paper, we compare three methods for the analysis of collective behavior: simple tracking (ST) without resolving occlusions, machine learning with real data (MLR), and machine learning with synthetic data (MLS). These methods are evaluated on videos recorded from an experiment studying the effect of ambient light on the shoaling tendency of Giant danios. In particular, we compute average nearest-neighbor distance (ANND) and polarization using the three methods and compare the values with manually-verified ground-truth data. To further assess possible dependence on sampling rate for computing ANND, the comparison is also performed at a low frame rate. Results show that while ST is the most accurate at higher frame rate for both ANND and polarization, at low frame rate for ANND there is no significant difference in accuracy between the three methods. In terms of computational speed, MLR and MLS take significantly less time to process an image, with MLS better addressing constraints related to generation of training data. Finally, all methods are able to successfully detect a significant difference in ANND as the ambient light intensity is varied irrespective of the direction of intensity change.
    Behavior Research Methods 10/2014;