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Time and space form an integral part of every human experience, and for the neuronal representation of these perceptual dimensions, previous studies point to the involvement of the right-hemispheric intraparietal sulcus and structures in the medial temporal lobe. Here we used multi-voxel pattern analysis (MVPA) to investigate long-term memory traces for temporal and spatial stimulus features in those areas. Participants were trained on four images associated with short versus long durations and with left versus right locations. Our results demonstrate stable representations of both temporal and spatial information in the right posterior intraparietal sulcus. Building upon previous findings of stable neuronal codes for directly perceived durations and locations, these results show that the reactivation of long-term memory traces for temporal and spatial features can be decoded from neuronal activation patterns in the right parietal cortex.

Paired-associate stimuli are an important tool in learning and memory research. In cognitive psychology, many studies use materials of which the learners are expected to have little to no prior knowledge. Despite their theoretical usefulness, conclusions from these studies are difficult to generalize to real-world learning contexts, where learners can be expected to have varying degrees of prior knowledge. Here, we present an ecologically valid stimulus set with 112 country outline-name pairs, and report response times and prior knowledge for these items in 285 largely Western European participants. Prior knowledge per item ranged from very high (94.4%) to zero (0.3%), thereby allowing researchers to select materials of which participants can be expected to have any given amount of prior knowledge. As such, this database provides a useful tool for research on real-world learning. The database can be accessed at: https://osf.io/q25rd/.

Memorising vocabulary is an important aspect of formal foreign-language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. One way these computer-based systems optimize learning is by measuring learning performance in real time to create optimal repetition schedules for individual learners. While such adaptive learning systems have been successfully applied to word learning using keyboard-based input, they have thus far seen little application in word learning where spoken instead of typed input is used. Here we present a framework for speech-based word learning using an adaptive model that was developed for and tested with typing-based word learning. We show that typing- and speech-based learning result in similar behavioral patterns that can be used to reliably estimate individual memory processes. We extend earlier findings demonstrating that a response-time based adaptive learning approach outperforms an accuracy-based, Leitner flashcard approach in learning efficiency (demonstrated by higher average accuracy and lower response times after a learning session). In short, we show that adaptive learning benefits transfer from typing-based learning, to speech based learning. Our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner’s pronunciations. We discuss the implications for our approach for the development of educationally relevant, adaptive speech-based learning applications.

The COVID-19 pandemic caused lockdowns and sudden school closures around the world in spring 2020, significantly impacting the education of students. Here, we investigate how the switch to distance learning affected study activity and performance in an online retrieval practice tool used for language learning in Dutch secondary education. We report insights from a rich data set consisting of over 115 million retrieval practice trials completed by more than 133 thousand students over the course of two consecutive school years. Our findings show that usage of the tool increased substantially at the start of lockdown, with the bulk of study activity occurring on weekday mornings. In general, students’ progress through the material was largely unaffected by lockdown, although students from the highest educational track were somewhat more likely to be on or ahead of schedule than students from lower tracks, compared to the previous year. Performance on individual study trials was generally stable, but accuracy and response time on open answer questions went up, perhaps as a result of students being more focused at home. These encouraging findings contribute to a growing literature on the educational ramifications of distance learning during lockdown.

In a world that is uncertain and noisy, perception makes use of optimization procedures that rely on the statistical properties of previous experiences. A well-known example of this phenomenon is the central tendency effect observed in many psychophysical modalities. For example, in interval timing tasks, previous experiences influence the current percept, pulling behavioural responses towards the mean. In Bayesian observer models, these previous experiences are typically modelled by unimodal statistical distributions, referred to as the prior. Here, we critically assess the validity of the assumptions underlying these models and propose a model that allows for more flexible, yet conceptually more plausible, modelling of empirical distributions. By representing previous experiences as a mixture of lognormal distributions, this model can be parametrized to mimic different unimodal distributions and thus extends previous instantiations of Bayesian observer models. We fit the mixture lognormal model to published interval timing data of healthy young adults and a clinical population of aged mild cognitive impairment patients and age-matched controls, and demonstrate that this model better explains behavioural data and provides new insights into the mechanisms that underlie the behaviour of a memory-affected clinical population.

People model other people's mental states in order to understand and predict their behavior. Sometimes they model what others think about them as well: ``He thinks that I intend to stop.'' Such second-order theory of mind is needed to navigate some social situations, for example, to make optimal decisions in turn-taking games. Adults sometimes find this very difficult. Sometimes they make decisions that do not fit their predictions about the other player. However, the main bottleneck for decision makers is to take a second-order perspective required to make a correct opponent model. We report a methodical investigation into supporting factors that help adults do better. We presented subjects with two-player, three-turn games in which optimal decisions required second-order theory of mind (Hedden and Zhang, 2002). We applied three ``scaffolds'' that, theoretically, should facilitate second-order perspective-taking: 1) stepwise training, from simple one-person games to games requiring second-order theory of mind; 2) prompting subjects to predict the opponent's next decision before making their own decision; and 3) a realistic visual task representation. The performance of subjects in the eight resulting combinations shows that stepwise training, but not the other two scaffolds, improves subjects' second-order opponent models and thereby their own decisions.

Introduction: Sufficient CPR skills in the general population are essential to make them active bystanders and contribute to an effective chain of survival in cardiac arrest emergencies. However, having a large proportion of the population regularly retrained is practically infeasible.Objective: The aim of this study was to assess and retrain cardiopulmonary resuscitation (CPR) skills of individuals who received (limited) CPR training several months to years prior.Method: Ninety-nine German adults in a possession of a driver's license were asked to perform CPR on a Laerdal Resusci Anne® QCPR manikin (Laerdal, Stavanger, Norway). After initial assessment, participants watched an instructional video and completed short, isolated compression, and ventilation practice with live feedback. CPR competency was assessed again after retraining and after a retention interval of 45 min.Results: Our results indicate that only 2% of participants managed to reach the performance criteria set by the European Resuscitation Council Guidelines, with most failing to reach even the lowest levels of performance. This corroborates earlier observations that CPR skills have deteriorated almost completely after a long retention interval, calling into question “one-and-done” certification of this basic life-saving. However, we also demonstrated that performance strikingly increased after watching a 6-min instructional video and a short opportunity for isolated practice. This increase in performance was stable over 45 min with 96% of participants meeting performance levels specified in the Guidelines. Closer inspection of the isolated compression practice data suggests that performance was very high at the start of the practice already, indicating that short refresher videos might suffice to change bystanders that would not have initiated CPR due to lack of knowledge into active first responders.Conclusion: We suggest that short refresher trainings could be an effective and affordable means of improving basic lifesaving skills to increase the effective contribution of bystanders during emergencies.

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

In this data report, we describe a three-session experiment spanning six months. Several well-controlled laboratory tasks (Word Learning, Raven Matrices, and Tapping) and Cardiopulmonary Resuscitation (CPR), a complex but well-defined real-world task, were administered. Data are reported from 50 participants for the first session, 40 for the second, and 34 for the third. CPR is a useful domain for studying real-world performance inside the laboratory because clear performance standards can be applied to quantifying learners’ proficiency covering both the first steps that need to be taken prior to the initiation of CPR (declarative knowledge) as well as the compressions and ventilations themselves (procedural skill). This research resulted in a rich dataset with a range of different measures for all participants. For all tasks, the complete set of raw data are made available along with relevant aggregate performance scores (see https://osf.io/m8bxe/). The raw data in particular will enable other researchers to explore potential analyses and modeling beyond the scope of our own. The details of the data collection protocol and available data are documented here to facilitate this process.

Objective: Clock variance is an important statistic in many clinical and developmental studies. Existing methods require a large number of trials for accurate clock variability assessment, which is problematic in studies using clinical or either young or aged participants. Furthermore, these existing methods often implicitly convolute clock and memory processes, making it difficult to disentangle whether the clock or memory system are driving the observed deviations. Here we assessed whether 20 repeated productions of a well-engrained interval (1 s), a task that does not incorporate memory updating nor the processing of feedback, could provide an accurate assessment of clock variability.Method: Sixty-eight undergraduate students completed two tasks: a 1-s production task in which they were asked to produce a 1-s duration by ending a tone by a keypress, and a multi-duration reproduction task. Durations presented in the reproduction task were tones lasting 1.17, 1.4 and 1.68 s. No feedback was presented in either task, and the order of presentation was counterbalanced between participants.Results: The observed central tendency in the reproduction task was better explained by models including the measures of clock variability derived from the 1-s production task than by models without it. Three clock variability measures were calculated for each participant [standard deviation, root mean squared residuals (RMSRs) from an estimated linear slope, and RMSR scaled by mean production duration]. The model including the scaled RMSR was preferred over the alternative models, and no notable effects of the order of task presentation were observed. These results suggest that: (1) measures of variability should account for drift; (2) the presentation of another timing task before a 1-s production task did not influence the assessment of the clock variability; and (3) the observed variability adheres to the scalar property and predicts temporal performance, and is thus a usable index of clock variability.Conclusion: This study shows that just 20 repeated productions of 1 s provide a reliable index of clock variability. As administering this task is fast and easy, it could prove to be useful in a large variety of developmental and clinical populations.

Learners differ in their learning aptitude. Modern computerized fact-learning systems take these individual differences into account by adapting repetition schedules to the learner's characteristics. Adaptation is based on monitoring responses during learning and using these responses to inform the model's decisions about when to introduce and repeat material by updating the model's internal parameters. Typically, adaptive systems start a learning session with a set of default parameters, with these parameters being updated and adapted to the learner's characteristics when responses are collected. Here we explore whether domain-general individual differences such as working-memory capacity or measures of general intelligence, which can be assessed prior to learning sessions, can inform the choice of initial model parameters. Such an approach is viable if the domain-general individual differences are related to the model parameters estimated during learning. In the current study, we asked participants to learn factual information, and assessed whether their learning performance, operationalized as (1) a model-parameter that captures the rate of forgetting, and (2) the results on an immediate and delayed post-test, was related to two common measures of individual differences: working memory capacity (WMC) and general cognitive ability (GCA). We failed to find evidence in favor for such relations, suggesting that, at least in this relatively small and homogeneous sample, executive functioning and attentional control did not play important roles in predicting delayed recall. The model parameters estimated during learning, on the other hand, are highly correlated with delayed recall of the studied material.

The serial reaction time task is widely used to study learning and memory. The task is traditionally administered by showing target positions on a computer screen and collecting responses using a button box or keyboard. By comparing response times to random or sequenced items or by using different transition probabilities, various forms of learning can be studied. However, this traditional laboratory setting limits the number of possible experimental manipulations. Here, we present a virtual reality version of the serial reaction time task and show that learning effects emerge as expected despite the novel way in which responses are collected. We also show that response times are distributed as expected. The current experiment was conducted in a blank virtual reality room to verify these basic principles. For future applications, the technology can be used to modify the virtual reality environment in any conceivable way, permitting a wide range of previously impossible experimental manipulations.

Timing is key to accurate performance, for example when learning a new complex sequence by mimicry. However, most timing research utilizes artificial tasks and simple stimuli with clearly marked onset and offset cues. Here we address the question whether existing interval timing findings generalize to 'real-world' timing tasks. In this study, animated video clips of a person performing different everyday actions were presented and participants had to reproduce the main action’s duration. Although reproduced durations are more variable then observed in laboratory studies, the data adheres to two interval timing laws: Relative timing sensitivity is constant across durations ('scalar property'), and the subjective duration of a previous action influenced the current action’s perceived duration ('temporal context effect'). Taken together, this demonstrates that laboratory findings generalize, and paves the way for studying interval timing as a component of complex, everyday cognitive performance. A short video summary of the research here can be found at https://vimeo.com/252673651

Magnitudes or quantities of the different dimensions that define a stimulus (e.g., space, speed or numerosity) influence the perceived duration of that stimulus, a phenomenon known as (temporal) interference effects. This complicates studying the neurobiological foundation of the perception of time, as any signatures of temporal processing are tainted by interfering dimensions. In earlier work, in which judgements on either time or numerosity were made while EEG was recorded, we used Maximum Likelihood Estimation (MLE) to estimate, for each participant separately, the influence of temporal and numerical information on making duration or numerosity judgements. We found large individual differences in the estimated magnitudes, but ML-estimates allowed us to partial out interference effects. However, for such analyses, it is essential that estimates are meaningful and stable. Therefore, in the current study, we examined the reliability of the MLE procedure by comparing the interference magnitudes estimated in two sessions, spread a week apart. In addition to the standard paradigm, we also presented task variants in which the interfering dimension was manipulated, to assess which aspects of the numerosity dimension exert the largest influence on temporal processing. The results indicate that individual interference magnitudes are stable, both between sessions and over tasks. Further, the ML-estimates of the time-numerosity judgement tasks were predictive of performance in a standard temporal judgement task. Thus, how much temporal information participants use in time estimations tasks seems to be a stable trait that can be captured with the MLE procedure. ML-estimates are, however, not predictive of performance in other interference-tasks, here operationalized by a numerical Stroop task. Taken together, the MLE procedure is a reliable tool to quantify individual differences in magnitude interference effects and can therefore reliably inform the analysis of neuroimaging data when contrasts are needed between the accumulation of a temporal and an interfering dimension.

Are difficult decisions best made after a momentary diversion of thought? Previous research addressing this important question has yielded dozens of experiments in which participants were asked to choose the best of several options (e.g., cars or apartments) either after conscious deliberation, or after a momentary diversion of thought induced by an unrelated task. The results of these studies were mixed. Some found that participants who had first performed the unrelated task were more likely to choose the best option, whereas others found no evidence for this so-called unconscious thought advantage (UTA). The current study examined two accounts of this inconsistency in previous findings. According to the reliability account, the UTA does not exist and previous reports of this effect concern nothing but spurious effects obtained with an unreliable paradigm. In contrast, the moderator account proposes that the UTA is a real effect that occurs only when certain conditions are met in the choice task. To test these accounts, we conducted a meta-analysis and a large-scale replication study (N = 399) that met the conditions deemed optimal for replicating the UTA. Consistent with the reliability account, the large-scale replication study yielded no evidence for the UTA, and the meta-analysis showed that previous reports of the UTA were confined to underpowered studies that used relatively small sample sizes. Furthermore, the results of the large-scale study also dispelled the recent suggestion that the UTA might be gender-specific. Accordingly, we conclude that there exists no reliable support for the claim that a momentary diversion of thought leads to better decision making than a period of deliberation.