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Pavlovian-to-instrumental transfer (PIT) tasks examine the influence of Pavlovian stimuli on ongoing instrumental behaviour. Previous studies reported associations between a strong PIT effect, high-risk drinking and alcohol use disorder. This study investigated whether susceptibility to interference between Pavlovian and instrumental control is linked to risky alcohol use in a community sample of 18-year-old male adults. Participants (N = 191) were instructed to 'collect good shells' and 'leave bad shells' during the presentation of appetitive (monetary reward), aversive (monetary loss) or neutral Pavlovian stimuli. We compared instrumental error rates (ER) and functional magnetic resonance imaging (fMRI) brain responses between the congruent and incongruent conditions, as well as among high-risk and low-risk drinking groups. On average, individuals showed a substantial PIT effect, that is, increased ER when Pavlovian cues and instrumental stimuli were in conflict compared with congruent trials. Neural PIT correlates were found in the ventral striatum and the dorsomedial and lateral prefrontal cortices (lPFC). Importantly, high-risk drinking was associated with a stronger behavioural PIT effect, a decreased lPFC response and an increased neural response in the ventral striatum on the trend level. Moreover, high-risk drinkers showed weaker connectivity from the ventral striatum to the lPFC during incongruent trials. Our study links interference during PIT to drinking behaviour in healthy, young adults. High-risk drinkers showed higher susceptibility to Pavlovian cues, especially when they conflicted with instrumental behaviour, indicating lower interference control abilities. Increased activity in the ventral striatum (bottom-up), decreased lPFC response (top-down), and their altered interplay may contribute to poor interference control in the high-risk drinkers.
Much work has shown that differences in the timecourse of language processing are central to comparing native (L1) and non-native (L2) speakers. However, estimating the onset of experimental effects in timecourse data presents several statistical problems including multiple comparisons and autocorrelation. We compare several approaches to tackling these problems and illustrate them using an L1-L2 visual world eye-tracking dataset. We then present a bootstrapping procedure that allows not only estimation of an effect onset, but also of a temporal confidence interval around this divergence point. We describe how divergence points can be used to demonstrate timecourse differences between speaker groups or between experimental manipulations, two important issues in evaluating L2 processing accounts. We discuss possible extensions of the bootstrapping procedure, including determining divergence points for individual speakers and correlating them with individual factors like L2 exposure and proficiency. Data and an analysis tutorial are available at https://osf.io/exbmk/.
Language production ultimately aims to convey meaning. Yet words differ widely in the richness and density of their semantic representations, and these differences impact conceptual and lexical processes during speech planning. Here, we replicated the recent finding that semantic richness, measured as the number of associated semantic features according to semantic feature production norms, facilitates object naming. In contrast, intercorrelational semantic feature density, measured as the degree of intercorrelation of a concept's features, presumably resulting in the coactivation of closely related concepts, has an inhibitory influence. We replicated the behavioral effects and investigated their relative time course and electrophysiological correlates. Both the facilitatory effect of high semantic richness and the inhibitory influence of high feature density were reflected in an increased posterior positivity starting at about 250 ms, in line with previous reports of posterior positivities in paradigms employing contextual manipulations to induce semantic interference during language production. Furthermore, amplitudes at the same posterior electrode sites were positively correlated with object naming times between about 230 and 380 ms. The observed effects follow naturally from the assumption of conceptual facilitation and simultaneous lexical competition and are difficult to explain by language production theories dismissing lexical competition.
Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise. Even with powerful software, the analyst is responsible for verifying the utility of their model. To demonstrate this point, we introduce a principled Bayesian workflow (Betancourt, 2018) to cognitive science. Using a concrete working example, we describe basic questions one should ask about the model: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks. The running example for demonstrating the workflow is data on reading times with a linguistic manipulation of object versus subject relative clause sentences. This principled Bayesian workflow also demonstrates how to use domain knowledge to inform prior distributions. It provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model. Given the increasing use of Bayesian methods, we aim to discuss how these methods can be properly employed to obtain robust answers to scientific questions.
When researchers carry out a null hypothesis significance test, it is tempting to assume that a statistically significant result lowers Prob(H0), the probability of the null hypothesis being true. Technically, such a statement is meaningless for various reasons: e.g., the null hypothesis does not have a probability associated with it. However, it is possible to relax certain assumptions to compute the posterior probability Prob(H0) under repeated sampling. We show in a step-by-step guide that the intuitively appealing belief, that Prob(H0) is low when significant results have been obtained under repeated sampling, is in general incorrect and depends greatly on: (a) the prior probability of the null being true; (b) type-I error rate, (c) type-II error rate, and (d) replication of a result. Through step-by-step simulations using open-source code in the R System of Statistical Computing, we show that uncertainty about the null hypothesis being true often remains high despite a significant result. To help the reader develop intuitions about this common misconception, we provide a Shiny app (https://danielschad.shinyapps.io/probnull/). We expect that this tutorial will help researchers better understand and judge results from null hypothesis significance tests.
Pavlovian-to-instrumental transfer (PIT) tasks examine the influence of Pavlovian stimuli on ongoing instrumental behaviour. Previous studies reported associations between a strong PIT effect, high-risk drinking and alcohol use disorder. This study investigated whether susceptibility to interference between Pavlovian and instrumental control is linked to risky alcohol use in a community sample of 18-year-old male adults. Participants (N = 191) were instructed to 'collect good shells' and 'leave bad shells' during the presentation of appetitive (monetary reward), aversive (monetary loss) or neutral Pavlovian stimuli. We compared instrumental error rates (ER) and functional magnetic resonance imaging (fMRI) brain responses between the congruent and incongruent conditions, as well as among high-risk and low-risk drinking groups. On average, individuals showed a substantial PIT effect, that is, increased ER when Pavlovian cues and instrumental stimuli were in conflict compared with congruent trials. Neural PIT correlates were found in the ventral striatum and the dorsomedial and lateral prefrontal cortices (lPFC). Importantly, high-risk drinking was associated with a stronger behavioural PIT effect, a decreased lPFC response and an increased neural response in the ventral striatum on the trend level. Moreover, high-risk drinkers showed weaker connectivity from the ventral striatum to the lPFC during incongruent trials. Our study links interference during PIT to drinking behaviour in healthy, young adults. High-risk drinkers showed higher susceptibility to Pavlovian cues, especially when they conflicted with instrumental behaviour, indicating lower interference control abilities. Increased activity in the ventral striatum (bottom-up), decreased lPFC response (top-down), and their altered interplay may contribute to poor interference control in the high-risk drinkers.
Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each other, a priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary 'omnibus' F test" (Hays, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, polynomial, custom, nested, interaction contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more useful confirmatory tests of experimental hypotheses than standard omnibus F-tests. Reproducible code is available from https://osf.io/7ukf6/.
Drunk decisions
(2018)
Background: Studies in humans and animals suggest a shift from goal-directed to habitual decision-making in addiction. We therefore tested whether acute alcohol administration reduces goal-directed and promotes habitual decision-making, and whether these effects are moderated by self-reported drinking problems. Methods: Fifty-three socially drinking males completed the two-step task in a randomised crossover design while receiving an intravenous infusion of ethanol (blood alcohol level=80 mg%), or placebo. To minimise potential bias by long-standing heavy drinking and subsequent neuropsychological impairment, we tested 18- to 19-year-old adolescents. Results: Alcohol administration consistently reduced habitual, model-free decisions, while its effects on goal-directed, model-based behaviour varied as a function of drinking problems measured with the Alcohol Use Disorders Identification Test. While adolescents with low risk for drinking problems (scoring <8) exhibited an alcohol-induced numerical reduction in goal-directed choices, intermediate-risk drinkers showed a shift away from habitual towards goal-directed decision-making, such that alcohol possibly even improved their performance. Conclusions: We assume that alcohol disrupted basic cognitive functions underlying habitual and goal-directed decisions in low-risk drinkers, thereby enhancing hasty choices. Further, we speculate that intermediate-risk drinkers benefited from alcohol as a negative reinforcer that reduced unpleasant emotional states, possibly displaying a novel risk factor for drinking in adolescence.
Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early-abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcohol-related stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their long-term goals. Such behaviors may include, but are not limited to, approaching alcohol.
How is reading development reflected in eye-movement measures? How does the perceptual span change during the initial years of reading instruction? Does parafoveal processing require competence in basic word-decoding processes? We report data from the first cross-sectional measurement of the perceptual span of German beginning readers (n = 139), collected in the context of the large longitudinal PIER (Potsdamer Intrapersonale Entwicklungsrisiken/Potsdam study of intra-personal developmental risk factors) study of intrapersonal developmental risk factors. Using the moving-window paradigm, eye movements of three groups of students (Grades 1-3) were measured with gaze-contingent presentation of a variable amount of text around fixation. Reading rate increased from Grades 1-3, with smaller increases for higher grades. Perceptual-span results showed the expected main effects of grade and window size: fixation durations and refixation probability decreased with grade and window size, whereas reading rate and saccade length increased. Critically, for reading rate, first-fixation duration, saccade length and refixation probability, there were significant interactions of grade and window size that were mainly based on the contrast between Grades 3 and 2 rather than Grades 2 and 1. Taken together, development of the perceptual span only really takes off between Grades 2 and 3, suggesting that efficient parafoveal processing presupposes that basic processes of reading have been mastered.