Behavior Research Methods

Publisher: Psychonomic Society, Springer Verlag

Current impact factor: 2.93

Impact Factor Rankings

Additional details

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

Springer Verlag

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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Despite its prevalence as one of the most highly influential models of spoken word recognition, the TRACE model has yet to be extended to consider tonal languages such as Mandarin Chinese. A key reason for this is that the model in its current state does not encode lexical tone. In this report, we present a modified version of the jTRACE model in which we borrowed on its existing architecture to code for Mandarin phonemes and tones. Units are coded in a way that is meant to capture the similarity in timing of access to vowel and tone information that has been observed in previous studies of Mandarin spoken word recognition. We validated the model by first simulating a recent experiment that had used the visual world paradigm to investigate how native Mandarin speakers process monosyllabic Mandarin words (Malins & Joanisse, 2010). We then subsequently simulated two psycholinguistic phenomena: (1) differences in the timing of resolution of tonal contrast pairs, and (2) the interaction between syllable frequency and tonal probability. In all cases, the model gave rise to results comparable to those of published data with human subjects, suggesting that it is a viable working model of spoken word recognition in Mandarin. It is our hope that this tool will be of use to practitioners studying the psycholinguistics of Mandarin Chinese and will help inspire similar models for other tonal languages, such as Cantonese and Thai.
    No preview · Article · Feb 2016 · Behavior Research Methods
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    ABSTRACT: With nonnormal data, the typical confidence interval of the correlation (Fisher z') may be inaccurate. The literature has been unclear as to which of several alternative methods should be used instead, and how extreme a violation of normality is needed to justify an alternative. Through Monte Carlo simulation, 11 confidence interval methods were compared, including Fisher z', two Spearman rank-order methods, the Box–Cox transformation, rank-based inverse normal (RIN) transformation, and various bootstrap methods. Nonnormality often distorted the Fisher z' confidence interval—for example, leading to a 95 % confidence interval that had actual coverage as low as 68 %. Increasing the sample size sometimes worsened this problem. Inaccurate Fisher z' intervals could be predicted by a sample kurtosis of at least 2, an absolute sample skewness of at least 1, or significant violations of normality hypothesis tests. Only the Spearman rank-order and RIN transformation methods were universally robust to nonnormality. Among the bootstrap methods, an observed imposed bootstrap came closest to accurate coverage, though it often resulted in an overly long interval. The results suggest that sample nonnormality can justify avoidance of the Fisher z' interval in favor of a more robust alternative. R code for the relevant methods is provided in supplementary materials.
    No preview · Article · Jan 2016 · Behavior Research Methods
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    ABSTRACT: Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http:// dora. erbe-matzke. com/ software. html.
    No preview · Article · Jan 2016 · Behavior Research Methods
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    ABSTRACT: In the vast literature exploring learning, many studies have used paired-associate stimuli, despite the fact that real-world learning involves many different types of information. One of the most popular materials used in studies of learning has been a set of Swahili–English word pairs for which Nelson and Dunlosky (Memory 2; 325–335, 1994) published recall norms two decades ago. These norms involved use of the Swahili words as cues to facilitate recall of the English translation. It is unclear whether cueing in the opposite direction (from English to Swahili) would lead to symmetric recall performance. Bilingual research has suggested that translation in these two different directions involves asymmetric links that may differentially impact recall performance, depending on which language is used as the cue (Kroll & Stewart, Journal of Memory and Language 33; 149–174,1994). Moreover, the norms for these and many other learning stimuli have typically been gathered from college students. In the present study, we report recall accuracy and response time norms for Swahili words when they are cued by their English translations. We also report norms for a companion set of fact stimuli that may be used along with the Swahili–English word pairs to assess learning on a broader scale across different stimulus materials. Data were collected using Amazon’s Mechanical Turk to establish a sample that was diverse in both age and ethnicity. These different, but related, stimulus sets will be applicable to studies of learning, metacognition, and memory in diverse samples.
    No preview · Article · Jan 2016 · Behavior Research Methods
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    ABSTRACT: The most important forms of idioms in Chinese, chengyus (CYs), have a fixed length of four Chinese characters. Most CYs are joined structures of two, two-character words—subject–verb units (SVs), verb–object units (VOs), structures of modification (SMs), or verb–verb units—or of four, one-character words. Both the first and second pairs of words in a four-word CY form an SV, a VO, or an SM. In the present study, normative measures were obtained for knowledge, familiarity, subjective frequency, age of acquisition, predictability, literality, and compositionality for 350 CYs, and the influences of the CYs’ syntactic structures on the descriptive norms were analyzed. Consistent with previous studies, all of the norms yielded a high reliability, and there were strong correlations between knowledge, familiarity, subjective frequency, and age of acquisition, and between familiarity and predictability. Unlike in previous studies (e.g., Libben & Titone in Memory & Cognition, 36, 1103–1121, 2008), however, we observed a strong correlation between literality and compositionality. In general, the results seem to support a hybrid view of idiom representation and comprehension. According to the evaluation scores, we further concluded that CYs consisting of just one SM are less likely to be decomposable than those with a VOVO composition, and also less likely to be recognized through their constituent words, or to be familiar to, known by, or encountered by users. CYs with an SMSM composition are less likely than VOVO CYs to be decomposable or to be known or encountered by users. Experimental studies should investigate how a CY’s syntactic structure influences its representation and comprehension.
    No preview · Article · Jan 2016 · Behavior Research Methods
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    ABSTRACT: In animal behavioral biology, an automated observing/training system may be useful for several reasons: (a) continuous observation of animals for documentation of specific, irregular events, (b) long-term intensive training of animals in preparation for behavioral experiments, (c) elimination of potential cues and biases induced by humans during training and testing. Here, we describe an open-source-based system named CATOS (Computer Aided Training/Observing System) developed for such situations. There are several notable features in this system. CATOS is flexible and low cost because it is based on free open-source software libraries, common hardware parts, and open-system electronics based on Arduino. Automated video condensation is applied, leading to significantly reduced video data storage compared to the total active hours of the system. A data-viewing utility program helps a user browse recorded data quickly and more efficiently. With these features, CATOS has the potential to be applied to many different animal species in various environments such as laboratories, zoos, or even private homes. Also, an animal's free access to the device without constraint, and a gamified learning process, enhance the animal's welfare and enriches their environment. As a proof of concept, the system was built and tested with two different species. Initially, the system was tested for approximately 10 months with a domesticated cat. The cat was successfully and fully automatically trained to discriminate three different spoken words. Then, in order to test the system's adaptability to other species and hardware components, we used it to train a laboratory rat for 3 weeks.
    No preview · Article · Jan 2016 · Behavior Research Methods
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    ABSTRACT: Houses have often been used as comparison stimuli in face-processing studies because of the many attributes they share with faces (e.g., distinct members of a basic category, consistent internal features, mono-orientation, and relative familiarity). Despite this, no large, well-controlled databases of photographs of houses that have been developed for research use currently exist. To address this gap, we photographed 100 houses and carefully edited these images. We then asked 41 undergraduate students (18 to 31 years of age) to rate each house on three dimensions: typicality, likeability, and face-likeness. The ratings had a high degree of face validity, and analyses revealed a significant positive correlation between typicality and likeability. We anticipate that this stimulus set (i.e., the DalHouses) and the associated ratings will prove useful to face-processing researchers by minimizing the effort required to acquire stimuli and allowing for easier replication and extension of studies. The photographs of all 100 houses and their ratings data can be obtained at http://dx.doi.org/10.6084/m9.figshare.1279430 .
    No preview · Article · Jan 2016 · Behavior Research Methods
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    ABSTRACT: Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameters of these Gaussian mixtures cannot be estimated in closed form, so estimates are typically obtained via an iterative process. The most common estimation procedure is maximum likelihood via the expectation-maximization (EM) algorithm. Like many approaches for identifying subpopulations, finite mixture modeling can suffer from locally optimal solutions, and the final parameter estimates are dependent on the initial starting values of the EM algorithm. Initial values have been shown to significantly impact the quality of the solution, and researchers have proposed several approaches for selecting the set of starting values. Five techniques for obtaining starting values that are implemented in popular software packages are compared. Their performances are assessed in terms of the following four measures: (1) the ability to find the best observed solution, (2) settling on a solution that classifies observations correctly, (3) the number of local solutions found by each technique, and (4) the speed at which the start values are obtained. On the basis of these results, a set of recommendations is provided to the user.
    No preview · Article · Dec 2015 · Behavior Research Methods
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    ABSTRACT: Social network analysis has become a prominent tool to study animal social life, and there is an increasing need to develop new systems to collect social information automatically, systematically, and reliably. Here we explore the use of a freely accessible Automated Learning Device for Monkeys (ALDM) to collect such social information on a group of 22 captive baboons (Papio papio). We compared the social network obtained from the co-presence of the baboons in ten ALDM testing booths to the social network obtained through standard behavioral observation techniques. The results show that the co-presence network accurately reflects the social organization of the group, and also indicate under which conditions the co-presence network is most informative. In particular, the best correlation between the two networks was obtained with a minimum of 40 days of computer records and for individuals with at least 500 records per day. We also show through random permutation tests that the observed correlations go beyond what would be observed by simple synchronous activity, to reflect a preferential choice of closely located testing booths. The use of automatized cognitive testing therefore presents a new way of obtaining a large and regular amount of social information that is necessary to develop social network analysis. It also opens the possibility of studying dynamic changes in network structure with time and in relation to the cognitive performance of individuals.
    No preview · Article · Dec 2015 · Behavior Research Methods
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    ABSTRACT: The movements that we make with our body vary continuously along multiple dimensions. However, many of the tools and techniques presently used for coding and analyzing hand gestures and other body movements yield categorical outcome variables. Focusing on categorical variables as the primary quantitative outcomes may mislead researchers or distort conclusions. Moreover, categorical systems may fail to capture the richness present in movement. Variations in body movement may be informative in multiple dimensions. For example, a single hand gesture has a unique size, height of production, trajectory, speed, and handshape. Slight variations in any of these features may alter how both the speaker and the listener are affected by gesture. In this paper, we describe a new method for measuring and visualizing the physical trajectory of movement using video. This method is generally accessible, requiring only video data and freely available computer software. This method allows researchers to examine features of hand gestures, body movement, and other motion, including size, height, curvature, and speed. We offer a detailed account of how to implement this approach, and we also offer some guidelines for situations where this approach may be fruitful in revealing how the body expresses information. Finally, we provide data from a small study on how speakers alter their hand gestures in response to different characteristics of a stimulus to demonstrate the utility of analyzing continuous dimensions of motion. By creating shared methods, we hope to facilitate communication between researchers from varying methodological traditions.
    No preview · Article · Dec 2015 · Behavior Research Methods