AGSuite: Software to conduct feature analysis of artificial grammar learning performance

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite’s intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.     

Bayesian inference with Stan: A tutorial on adding custom distributions

When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation; Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325–337, 2000) and JAGS (Plummer, 2003) provide “turnkey”-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153–178. doi: 10.1016/j.cogpsych.2007.12.002, 2008).     

Norms of valence and arousal for 14,031 Spanish words

Most current models of research on emotion recognize valence (how pleasant a stimulus is) and arousal (the level of activation or intensity that a stimulus elicits) as important components in the classification of affective experiences (Barrett, 1998; Kuppens, Tuerlinckx, Russell, & Barrett, 2012). Here we present a set of norms for valence and arousal for a very large set of Spanish words, including items from a variety of frequencies, semantic categories, and parts of speech, including a subset of conjugated verbs. In this regard, we found that there were significant but very small differences between the ratings for conjugations of the same verb, validating the practice of applying the ratings for infinitives to all derived forms of the verb. Our norms show a high degree of reliability and are strongly correlated with those of Redondo, Fraga, Padrón, and Comesaña’s (2007) Spanish version of the influential Affective Norms for English Words (Bradley & Lang, 1999), as well as those from Warriner, Kuperman, and Brysbaert (2013), the largest available set of emotional norms for English words. Additionally, we included measures of word prevalence—that is, the percentage of participants that knew a particular word—for each variable (Keuleers, Stevens, Mandera, & Brysbaert, 2015). Our large set of norms in Spanish not only will facilitate the creation of stimuli and the analysis of texts in that language, but also will be useful for cross-language comparisons and research on emotional aspects of bilingualism. The norms can be downloaded and available as a supplementary materials to this article.     

Improved low-cost,MR-compatible olfactometer to deliver tobacco smoke odor

We describe a low-cost, MRI-compatible olfactometer that delivers fresh cigarette smoke odor, a challenging odorant to present, as well as other odorants. This new olfactometer retains all of the advantages of an earlier design that was capable of only delivering volatile odors (Lowen & Lukas, Behavior Research Methods, 38, 307–313, 2006). The new system incorporates a novel switching mechanism that allows it to deliver fresh smoke generated from a burning cigarette during a stimulus presentation paradigm that might be employed in a cue-reactivity experiment. An evaluation study established that the olfactometer reliably delivered smoke to the participants and that tobacco smoke was discriminated from other odorants; there were no adverse reactions to the device.     

The Calgary semantic decision project: concrete/abstract decision data for 10,000 English words

Psycholinguistic research has been advanced by the development of word recognition megastudies. For instance, the English Lexicon Project (Balota et al., 2007) provides researchers with access to naming and lexical-decision latencies for over 40,000 words. In the present work, we extended the megastudy approach to a task that emphasizes semantic processing. Using a concrete/abstract semantic decision (i.e., does the word refer to something concrete or abstract?), we collected decision latencies and accuracy rates for 10,000 English words. The stimuli were concrete and abstract words selected from Brysbaert, Warriner, and Kuperman’s (2013) comprehensive list of concreteness ratings. In total, 321 participants provided responses to 1,000 words each. Whereas semantic effects tend to be quite modest in naming and lexical decision studies, analyses of the concrete/abstract semantic decision responses show that a substantial proportion of variance can be explained by semantic variables. The item-level and trial-level data will be useful for other researchers interested in the semantic processing of concrete and abstract words.     

Cognitive extension,enhancement, and the phenomenology of thinking

This paper brings together several strands of thought from both the analytic and phenomenological traditions in order to critically examine accounts of cognitive enhancement that rely on the idea of cognitive extension. First, I explain the idea of cognitive extension, the metaphysics of mind on which it depends, and how it has figured in recent discussions of cognitive enhancement. Then, I develop ideas from Husserl that emphasize the agential character of thought and the distinctive way that conscious thoughts are related to one another. I argue that these considerations are necessary for understanding why forms of cognitive extension may diminish our cognitive lives in different ways. This does not lead to a categorical rejection of cognitive enhancement as unethical or bad for human flourishing, but does warrant a conservative approach to the design and implementation of cognitive artifacts.     

How Prevalent is Contract Cheating and to What Extent are Students Repeat Offenders?

Contract cheating, or plagiarism via paid ghostwriting, is a significant academic ethical issue, especially as reliable methods for its prevention and detection in students’ assignments remain elusive. Contract cheating in academic assessment has been the subject of much recent debate and concern. Although some scandals have attracted substantial media attention, little is known about the likely prevalence of contract cheating by students for their university assignments. Although rates of contract cheating tend to be low, criminological theories suggest that people who employ ghostwriters for their assignments are likely to re-offend, and little is known about re-offence rates in this form of academic misconduct. We combined previously-collected datasets (N = 1378) and conducted additional, and previously-unreported, analyses on self-report measures of contract cheating prevalence. We found that few students (3.5%), on aggregate, ever engaged in contract cheating but this varied substantially among samples (from 0.3% to 7.9%). Of those who ever engaged in contract cheating, 62.5% did so more than once. The data also suggested that engagement in contract cheating is influenced by opportunity. These figures may help policy makers, and researchers who are creating contract cheating detection methods, to estimate base rates of contract cheating and the likelihood of re-offence.     

The Antecedents of Ethical Climates in the Spanish Business Higher Education Institutions

Ethical climate in organisations has been studied widely and its influence on ethical behaviour has been documented. However, little is known about the ethical climate at university context and about its antecedents. Universities are social change institutions and their ethical climate could influence the ethical behaviour of future economic, social and political leaders. The current study analyses the perceived ethical climate in Business Studies Higher Education Institutions in Spain and whether university’s ownership, size and signing up for international initiatives influence it. The study is based on survey responses of 385 lecturers of Spanish Business Studies Faculties to an ethical climate questionnaire and on survey responses of 33 Business Studies Deans to a questionnaire related to the commitment to ethics and social responsibility in each Faculty. An exploratory factor analysis has been used as well as ANOVA tests and lineal regressions. The results reveal the predominance of an instrumental ethical climate in Spanish Business Faculties, an ethical climate that does not promote ethical behaviour or organisational commitment among its members. The results also confirm the significant influence of university’s ownership and signing up for Principles for Responsible Management Education on the ethical climate in Business Higher Education institutions. This study advances the understanding of the ethical climate in Business Studies institutions, and suggests measures to be taken by university managers in order to improve ethical judgment and behaviour of the university community.     

Metronome LKM: An open source virtual keyboard driver to measure experiment software latencies

Experiment software is often used to measure reaction times gathered with keyboards or other input devices. In previous studies, the accuracy and precision of time stamps has been assessed through several means: (a) generating accurate square wave signals from an external device connected to the parallel port of the computer running the experiment software, (b) triggering the typematic repeat feature of some keyboards to get an evenly separated series of keypress events, or (c) using a solenoid handled by a microcontroller to press the input device (keyboard, mouse button, touch screen) that will be used in the experimental setup. Despite the advantages of these approaches in some contexts, none of them can isolate the measurement error caused by the experiment software itself. Metronome LKM provides a virtual keyboard to assess an experiment’s software. Using this open source driver, researchers can generate keypress events using high-resolution timers and compare the time stamps collected by the experiment software with those gathered by Metronome LKM (with nanosecond resolution). Our software is highly configurable (in terms of keys pressed, intervals, SysRq activation) and runs on 2.6–4.8 Linux kernels.     

Development and psychometric validation of a child Racial Attitudes Index (RAI)

The Racial Attitudes Index (RAI) measures a child’s racial attitudes. Designed for children aged 5–9 years, the RAI is delivered over the Internet using Audio Computer Assisted Self-Interviewing (ACASI). Unlike traditional binary forced-choice instruments, the RAI uses an expanded response format permitting a more nuanced understanding of patterns of children’s racial attitudes. In addition to establishing psychometric evidence of the RAI technical adequacy, hypotheses about RAI item response patterns were tested. The racial attitudes of 336 Black and White children in grades K–3 were assessed using a forced-choice instrument (Preschool Racial Attitudes Measure II) and the RAI. Findings from this study indicate measures obtained with the RAI are technically adequate, and the measure functions invariantly across racial groups. Also, patterns of children’s racial attitudes measured with the RAI are more nuanced than those obtained using the forced-choice response format.