Self‐Priming in Production: Evidence for a Hybrid Model of Syntactic Priming

Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error‐driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation‐based accounts (Pickering & Branigan, 1999; Reitter, Keller, & Moore, 2011). Both theories predict that speakers should be primed by the syntactic choices of others, but only activation‐based accounts predict that speakers should be able to prime themselves. Here we test whether speakers can be primed by their own productions in three behavioral experiments and find evidence of structural persistence following both comprehension and speakers’ own productions. We also find that comprehension‐based priming effects are larger for rarer syntactic structures than for more common ones, which is most consistent with error‐driven accounts. Because neither error‐driven accounts nor activation‐based accounts fully explain the data, we propose a hybrid model.     

Risky behavior in young adult pedestrians: Personality determinants,correlates with risk perception,and gender differences

Based on the risk homeostasis theory, the aim of this study was to assess acceptable risk, measured by personality factors, and risk perception, measured by physiological arousal, as predictors of risky behavior in young adult pedestrians, as well as to assess for gender differences. Eighty-two young adults, aged 18–30, completed self-report measures about risky pedestrian behaviors and these personality traits: impulsiveness, conscientiousness and openness to experience. Sixty-four of these participants then took part in an experimental task designed to assess their perception of the level of risk in situations involving different interactions between cars and pedestrians. Risk perception was assessed by these physiological responses: heart rate, skin conductance level, and skin conductance response. Results showed that risky behavior had significant correlations with impulsiveness, conscientiousness, and skin conductance level. Gender differences also emerged in risky behavior, impulsiveness, conscientiousness, and skin conductance level. Finally, a structural equation model showed that impulsiveness, conscientiousness and the difference in skin conductance level between risky and neutral situations predicted risky pedestrian behaviors, and explained 23.6% of the its variance, after controlling the effect of gender. Implications for evaluation, prevention, and intervention for risky pedestrian behaviors are discussed.     

Asymmetric cost and benefit perceptions in willingness-to-donate decisions

Charitable giving entails the act of foregoing personal resources in order to improve the conditions of other people. In the present paper, we systematically examine two dimensions integral to donation decisions that have thus far received relatively little attention but can explain charitable behavior rather well: the perceptions of cost for the donor and benefit for the recipients. In line with current theories in judgment and decision making, we hypothesize that people weigh these dimensions subjectively and perceive them asymmetrically, consistent with prospect theory. Costs for the donor are typically perceived as losses, whereas benefits for recipients are perceived as gains. In four studies, we presented several scenarios to participants in which both donation amounts (costs) and number of lives helped (benefits) were manipulated while keeping the ratio of costs and benefits constant. Results from Studies 1 and 2 showed that willingness to help decreased as donation amounts and number of lives helped increased. Additionally, Studies 3 and 4 provide evidence for a solution to reduce the asymmetry and increase donation amounts as the number of lives at risk increases.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

The theory of learning styles applied to distance learning

Distance Education (DE) associated with the use of Virtual Learning Environments (VLE) as interaction tools between the student and the educator has become a large research niche spread around the world. Techniques to improve learning effectiveness in VLEs seek to find relation-ships connections between pedagogical advances and educational technological resources available in VLEs. In this context, this work sought to associate the theory of Learning Styles with the behavior of the student of Distance Education, observing their interaction with the Virtual Learning Environment and trying to associate them with their learning style identified by the CHAEA questionnaire. For this purpose, the CHAEA questionnaire was applied to a group of distance learning students and the correlation between their interactions with VLE and their learning styles was verified. The results show that there is no correlation between these elements, which may show, in fact, the lack of coherence between these theories. Seeking a model for data, a linear regression was applied and the results were, then, confirmed.     

Social learning under the labeling effect: Exploring travellers’ behavior in social dilemmas

This paper presents results of a laboratory experiment, in which subjects made choices between public and private transportation when managing Personal Carbon Allowances (PCAs). The objectives of this study were twofold. Firstly, it was aimed at investigating the labeling effect under the social dilemma context. Secondly, it showed insights into the decision-making process when feedback about others’ choices was provided. A combination of the labeling effect and feedback helped individuals change their commuting pattern and increase the number of trips by public transportation under the PCAs regime. Feedback on others’ choices resulted in social learning and incentivized choice of public transportation more frequently in social dilemma situations.     

Competing stimulus assessments: A systematic review

The current review summarizes the literature on competing stimulus assessments (CSAs). CSAs are pretreatment assessments designed to systematically identify stimuli that reduce problem behavior (PB), ostensibly through reinforcer competition or substitution. We report on the participant characteristics, outcomes, and predictive validity of published CSAs that included (a) no-stimulus control trial(s), (b) test trials during which each stimulus was available singly and noncontingently, and (c) measurement of PB and stimulus engagement or contact. Results showed that CSAs have broad utility across a variety of topographies and functions of PB. In the majority of CSA applications for which extended analyses, or validations, were performed, stimuli shown to reduce PB during the CSA produced similar reductions during extended analysis. This was the case regardless of topography or function of PB, or whether the stimuli were assumed to be “matched” to the stimulation thought to be produced by PB. Implications for future research are discussed.     

Cognitive Task Analysis for Implicit Knowledge About Visual Representations With Similarity Learning Methods

Visual representations are prevalent in STEM instruction. To benefit from visuals, students need representational competencies that enable them to see meaningful information. Most research has focused on explicit conceptual representational competencies, but implicit perceptual competencies might also allow students to efficiently see meaningful information in visuals. Most common methods to assess students’ representational competencies rely on verbal explanations or assume explicit attention. However, because perceptual competencies are implicit and not necessarily verbally accessible, these methods are ill‐equipped to assess them. We address these shortcomings with a method that draws on similarity learning, a machine learning technique that detects visual features that account for participants’ responses to triplet comparisons of visuals. In Experiment 1, 614 chemistry students judged the similarity of Lewis structures and in Experiment 2, 489 students judged the similarity of ball‐and‐stick models. Our results showed that our method can detect visual features that drive students’ perception and suggested that students’ conceptual knowledge about molecules informed perceptual competencies through top‐down processes. Furthermore, Experiment 2 tested whether we can improve the efficiency of the method with active sampling. Results showed that random sampling yielded higher accuracy than active sampling for small sample sizes. Together, the experiments provide the first method to assess students’ perceptual competencies implicitly, without requiring verbalization or assuming explicit visual attention. These findings have implications for the design of instructional interventions that help students acquire perceptual representational competencies.     

Representing decision-makers using styles of behavior: An approach designed for group decision support systems

Supporting decision-making processes when the elements of a group are geographically dispersed and on a tight schedule is a complex task. Aiming to support decision-makers anytime and anywhere, Web-based group decision support systems have been studied. However, the limitations in the decision-makers’ interactions associated to this scenario bring new challenges. In this work, we propose a set of behavioral styles from which decision-makers’ intentions can be modelled into agents. The goal is that, besides having agents represent typical preferences of the decision-makers (towards alternatives and criteria), they can also represent their intentions. To do so, we conducted a survey with 64 participants in order to find homogeneous operating values so as to numerically define the proposed behavioral styles in four dimensions. In addition, we also propose a communication model that simulates the dialogues made by decision-makers in face-to-face meetings. We developed a prototype to simulate decision scenarios and found that agents are capable of acting according to the decision-makers’ intentions and fundamentally benefit from different possible behavioral styles, just as a face-to-face meeting benefits from the heterogeneity of its participants.