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Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Hem, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. Reproducible code is available from haps://osf.io/y354c/. <br /> Translational Abstract <br /> In psychology and related areas, scientific hypotheses are commonly tested by asking questions like "is [some] effect present or absent." Such hypothesis testing is most often carried out using frequentist null hypothesis significance testing (NIIST). The NHST procedure is very simple: It usually returns a p-value, which is then used to make binary decisions like "the effect is present/abscnt." For example, it is common to see studies in the media that draw simplistic conclusions like "coffee causes cancer," or "coffee reduces the chances of geuing cancer." However, a powerful and more nuanced alternative approach exists: Bayes factors. Bayes factors have many advantages over NHST. However, for the complex statistical models that arc commonly used for data analysis today, computing Bayes factors is not at all a simple matter. In this article, we discuss the main complexities associated with computing Bayes factors. This is the first article to provide a detailed workflow for understanding and computing Bayes factors in complex statistical models. The article provides a statistically more nuanced way to think about hypothesis testing than the overly simplistic tendency to declare effects as being "present" or "absent".
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/.
No association of goal-directed and habitual control with alcohol consumption in young adults
(2017)
Alcohol dependence is a mental disorder that has been associated with an imbalance in behavioral control favoring model-free habitual over model-based goal-directed strategies. It is as yet unknown, however, whether such an imbalance reflects a predisposing vulnerability or results as a consequence of repeated and/or excessive alcohol exposure. We, therefore, examined the association of alcohol consumption with model-based goal-directed and model-free habitual control in 188 18-year-old social drinkers in a two-step sequential decision-making task while undergoing functional magnetic resonance imaging before prolonged alcohol misuse could have led to severe neurobiological adaptations. Behaviorally, participants showed a mixture of model-free and model-based decision-making as observed previously. Measures of impulsivity were positively related to alcohol consumption. In contrast, neither model-free nor model-based decision weights nor the trade-off between them were associated with alcohol consumption. There were also no significant associations between alcohol consumption and neural correlates of model-free or model-based decision quantities in either ventral striatum or ventromedial prefrontal cortex. Exploratory whole-brain functional magnetic resonance imaging analyses with a lenient threshold revealed early onset of drinking to be associated with an enhanced representation of model-free reward prediction errors in the posterior putamen. These results suggest that an imbalance between model-based goal-directed and model-free habitual control might rather not be a trait marker of alcohol intake per se.
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.
The influence of Pavlovian conditioned stimuli on ongoing behavior may contribute to explaining how alcohol cues stimulate drug seeking and intake. Using a Pavlovian-instrumental transfer task, we investigated the effects of alcohol-related cues on approach behavior (i.e., instrumental response behavior) and its neural correlates, and related both to the relapse after detoxification in alcohol-dependent patients. Thirty-one recently detoxified alcohol-dependent patients and 24 healthy controls underwent instrumental training, where approach or non-approach towards initially neutral stimuli was reinforced by monetary incentives. Approach behavior was tested during extinction with either alcohol-related or neutral stimuli (as Pavlovian cues) presented in the background during functional magnetic resonance imaging (fMRI). Patients were subsequently followed up for 6 months. We observed that alcohol-related background stimuli inhibited the approach behavior in detoxified alcohol-dependent patients (t = -3.86, p < .001), but not in healthy controls (t = -0.92, p = .36). This behavioral inhibition was associated with neural activation in the nucleus accumbens (NAcc) (t((30)) = 2.06, p < .05). Interestingly, both the effects were only present in subsequent abstainers, but not relapsers and in those with mild but not severe dependence. Our data show that alcohol-related cues can acquire inhibitory behavioral features typical of aversive stimuli despite being accompanied by a stronger NAcc activation, suggesting salience attribution. The fact that these findings are restricted to abstinence and milder illness suggests that they may be potential resilience factors.
In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men (n = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers (b = 0.09, SE = 0.03, z = 2.7, p < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala (t = 3.25, p(SVC) = 0.04, x = 26, y = -6, z = -12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption (r(s) = 0.17, p = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.
Background Aversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown. Methods To elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations. Results Whereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms. Conclusion Our data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.
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.
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age-and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
For the first time a molecularly imprinted polymer (MIP) with direct electron transfer (DET) and bioelectrocatalytic activity of the target protein is presented. Thin films of MIPs for the recognition of a hexameric tyrosine-coordinated heme protein (HTHP) have been prepared by electropolymerization of scopoletin after oriented assembly of HTHP on a self-assembled monolayer (SAM) of mercaptoundecanoic acid (MUA) on gold electrodes. Cavities which should resemble the shape and size of HTHP were formed by template removal. Rebinding of the target protein sums up the recognition by non-covalent interactions between the protein and the MIP with the electrostatic attraction of the protein by the SAM. HTHP bound to the MIP exhibits quasi-reversible DET which is reflected by a pair of well pronounced redox peaks in the cyclic voltammograms (CVs) with a formal potential of -184.4 +/- 13.7 mV vs. Ag/AgCl (1 M KCl) at pH 8.0 and it was able to catalyze the cathodic reduction of peroxide. At saturation the MIP films show a 12-fold higher electroactive surface concentration of HTHP than the non-imprinted polymer (NIP).
In detoxified alcohol-dependent patients, alcohol-related stimuli can promote relapse. However, to date, the mechanisms by which contextual stimuli promote relapse have not been elucidated in detail. One hypothesis is that such contextual stimuli directly stimulate the motivation to drink via associated brain regions like the ventral striatum and thus promote alcohol seeking, intake and relapse. Pavlovian-to-Instrumental-Transfer (PIT) may be one of those behavioral phenomena contributing to relapse, capturing how Pavlovian conditioned (contextual) cues determine instrumental behavior (e.g. alcohol seeking and intake). We used a PIT paradigm during functional magnetic resonance imaging to examine the effects of classically conditioned Pavlovian stimuli on instrumental choices in n=31 detoxified patients diagnosed with alcohol dependence and n=24 healthy controls matched for age and gender. Patients were followed up over a period of 3 months. We observed that (1) there was a significant behavioral PIT effect for all participants, which was significantly more pronounced in alcohol-dependent patients; (2) PIT was significantly associated with blood oxygen level-dependent (BOLD) signals in the nucleus accumbens (NAcc) in subsequent relapsers only; and (3) PIT-related NAcc activation was associated with, and predictive of, critical outcomes (amount of alcohol intake and relapse during a 3 months follow-up period) in alcohol-dependent patients. These observations show for the first time that PIT-related BOLD signals, as a measure of the influence of Pavlovian cues on instrumental behavior, predict alcohol intake and relapse in alcohol dependence.
Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus–action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian–instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction.
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.
A nanohybrid consisting of poly(3-aminobenzenesulfonic acid-co-aniline) and multiwalled carbon nanotubes [MWCNT-P(ABS-A)]) on a gold electrode was used to immobilize the hexameric tyrosine-coordinated heme protein (HTHP). The enzyme showed direct electron transfer between the heme group of the protein and the nanostructured surface. Desorption of the noncovalently bound heme from the protein could be excluded by control measurements with adsorbed hemin on aminohexanthiol-modified electrodes. The nanostructuring and the optimised charge characteristics resulted in a higher protein coverage as compared with MUA/MU modified electrodes. The adsorbed enzyme shows catalytic activity for the cathodic H2O2 reduction and oxidation of NADH.
The interruption of learning processes by breaks filled with diverse activities is common in everyday life. This study investigated the effects of active computer gaming and passive relaxation (rest and music) breaks on auditory versus visual memory performance. Young adults were exposed to breaks involving (a) open eyes resting, (b) listening to music, and (c) playing a video game, immediately after memorizing auditory versus visual stimuli. To assess learning performance, words were recalled directly after the break (an 8:30 minute delay) and were recalled and recognized again after 7 days. Based on linear mixed-effects modeling, it was found that playing the Angry Birds video game during a short learning break impaired long-term retrieval in auditory learning but enhanced long-term retrieval in visual learning compared with the music and rest conditions. These differential effects of video games on visual versus auditory learning suggest specific interference of common break activities on learning.