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Although eye movements during reading are modulated by cognitive processing demands, they also reflect visual sampling of the input, and possibly preparation of output for speech or the inner voice. By simultaneously recording eye movements and the voice during reading aloud, we obtained an output measure that constrains the length of time spent on cognitive processing. Here we investigate the dynamics of the eye-voice span (EVS), the distance between eye and voice. We show that the EVS is regulated immediately during fixation of a word by either increasing fixation duration or programming a regressive eye movement against the reading direction. EVS size at the beginning of a fixation was positively correlated with the likelihood of regressions and refixations. Regression probability was further increased if the EVS was still large at the end of a fixation: if adjustment of fixation duration did not sufficiently reduce the EVS during a fixation, then a regression rather than a refixation followed with high probability. We further show that the EVS can help understand cognitive influences on fixation duration during reading: in mixed model analyses, the EVS was a stronger predictor of fixation durations than either word frequency or word length. The EVS modulated the influence of several other predictors on single fixation durations (SFDs). For example, word-N frequency effects were larger with a large EVS, especially when word N-1 frequency was low. Finally, a comparison of SFDs during oral and silent reading showed that reading is governed by similar principles in both reading modes, although EVS maintenance and articulatory processing also cause some differences. In summary, the EVS is regulated by adjusting fixation duration and/or by programming a regressive eye movement when the EVS gets too large. Overall, the EVS appears to be directly related to updating of the working memory buffer during reading.
Several authors highlighted that the time course of an experiment itself could have a substantial influence on the interpretability of experimental effects. Since mixed effects modeling had enabled researchers to investigate more complex problems with more precision than before, two naming experiments were conducted with college students, with and without non-words intermixed, and analyzed with regard to frequency, quality, interactive and trial-history effects. The present analyses build on and extend the Bates, Kliegl, Vasishth, and Baayen (2015) approach in order to converge on a parsimonious model that accounts for autocorrelated errors caused by trial history. For three of four cases, a history-sensitive model improved the model fit over a history-naïve model and explained more deviance. In one of these cases, the herein presented approach helped reveal an interaction between stimulus frequency and quality that was not significant without a trial history account. Main and joint effects, limitations, as well as directions for further research, are briefly discussed.
There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.
There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.
Although eye movements during reading are modulated by cognitive processing demands, they also reflect visual sampling of the input, and possibly preparation of output for speech or the inner voice. By simultaneously recording eye movements and the voice during reading aloud, we obtained an output measure that constrains the length of time spent on cognitive processing. Here we investigate the dynamics of the eye-voice span (EVS), the distance between eye and voice. We show that the EVS is regulated immediately during fixation of a word by either increasing fixation duration or programming a regressive eye movement against the reading direction. EVS size at the beginning of a fixation was positively correlated with the likelihood of regressions and refixations. Regression probability was further increased if the EVS was still large at the end of a fixation: if adjustment of fixation duration did not sufficiently reduce the EVS during a fixation, then a regression rather than a refixation followed with high probability. We further show that the EVS can help understand cognitive influences on fixation duration during reading: in mixed model analyses, the EVS was a stronger predictor of fixation durations than either word frequency or word length. The EVS modulated the influence of several other predictors on single fixation durations (SFDs). For example, word-N frequency effects were larger with a large EVS, especially when word N-1 frequency was low. Finally, a comparison of SFDs during oral and silent reading showed that reading is governed by similar principles in both reading modes, although EVS maintenance and articulatory processing also cause some differences. In summary, the EVS is regulated by adjusting fixation duration and/or by programming a regressive eye movement when the EVS gets too large. Overall, the EVS appears to be directly related to updating of the working memory buffer during reading.
Individual differences in justice sensitivity and rejection sensitivity have been linked to differences in aggressive behavior in adults. However, there is little research studying this association in children and adolescents and considering the two constructs in combination. We assessed justice sensitivity from the victim, observer, and perpetrator perspective as well as anxious and angry rejection sensitivity and linked both constructs to different forms (physical, relational), and functions (proactive, reactive) of self-reported aggression and to teacher- and parent-rated aggression in N=1,489 9- to 19-year olds in Germany. Victim sensitivity and both angry and anxious rejection sensitivity showed positive correlations with all forms and functions of aggression. Angry rejection sensitivity also correlated positively with teacher-rated aggression. Perpetrator sensitivity was negatively correlated with all aggression measures, and observer sensitivity also correlated negatively with all aggression measures except for a positive correlation with reactive aggression. Path models considering the sensitivity facets in combination and controlling for age and gender showed that higher victim justice sensitivity predicted higher aggression on all measures. Higher perpetrator sensitivity predicted lower physical, relational, proactive, and reactive aggression. Higher observer sensitivity predicted lower teacher-rated aggression. Angry rejection sensitivity predicted higher proactive and reactive aggression, whereas anxious rejection sensitivity did not make an additional contribution to the prediction of aggression. The findings are discussed in terms of social information processing models of aggression in childhood and adolescence. Aggr. Behav. 41:353-368, 2015. (c) 2014 Wiley Periodicals, Inc.
School attacks are attracting increasing attention in aggression research. Recent systematic analyses provided new insights into offense and offender characteristics. Less is known about attacks in institutes of higher education (e.g., universities). It is therefore questionable whether the term “school attack” should be limited to institutions of general education or could be extended to institutions of higher education. Scientific literature is divided in distinguishing or unifying these two groups and reports similarities as well as differences. We researched 232 school attacks and 45 attacks in institutes of higher education throughout the world and conducted systematic comparisons between the two groups. The analyses yielded differences in offender (e.g., age, migration background) and offense characteristics (e.g., weapons, suicide rates), and some similarities (e.g., gender). Most differences can apparently be accounted for by offenders’ age and situational influences. We discuss the implications of our findings for future research and the development of preventative measures.