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Causal coherence improves episodic memory of dynamic events
No full text“Episodes” in memory are formed by the experience of dynamic events that unfold over time. However, just because a series of events unfold sequentially does not mean that they are related. Sequences can have a high degree of causal coherence, each event connecting to the next through a cause-and-effect relationship, or be a fragmented series of unrelated occurrences. Are causally coherent events remembered better? And if coherence leads to better recall, which attributes of episodic memories are particularly affected by it? Past work has investigated similar questions by manipulating the causal structure of language-based, narrative stimuli. In this study, across three experiments, we used dynamic visual stimuli showing unfamiliar events to test the effect of causal structure on episodic recall in a cued memory task. Experiment 1 found that the order of three-part causally coherent sequences of events is better remembered than that of fragmented events. Experiment 2 extended this finding to longer sequences and further demonstrated that causal structure is not confounded with low-level characteristics of the stimuli: Reversing the order of coherent stimuli led to task performances indistinguishable from those on fragmented stimuli. Experiment 3 replicated the results of improved order recall from the previous experiments and additionally showed that recall of causally relevant details of coherent stimuli is superior to recall for details of focal events in fragmented sequences. In sum, these findings show that the episodic memory system is sensitive to the causal structure of events and suggest coherence usually leads to better recall
The two faces of worker specialization
No full textWe study how worker specialization — the distance between a worker’s skill set and those prevalent in the labor market — shapes employment outcomes. Using US and French data, we first document that specialized jobs are characterized by asymmetric skill profiles and a scarcity of nearby employment opportunities. We incorporate these features into a random search model with multidimensional skills, mismatch penalties and skill complementarity. We show that specialization lowers job-finding rates due to a lack of suitable jobs, but raises re-employment wages via improved productivity. Empirical evidence from displaced workers in both countries confirms these predictions. Our findings reconcile competing views in the literature by showing that specialization entails trade-offs and is neither uniformly beneficial nor harmful
Public procurement cartels:A large-sample testing of screens using machine learning
No full textDue to the high budgetary costs of public procurement cartels, it is crucial to measure and understand them. The literature developed screens that work well for selected cartel types and with high quality data, but it didn’t produce generalisable knowledge supporting policy and law enforcement on typically available datasets. We simultaneously measure multiple cartel behaviours on publicly available data of 73 cartels from 7 European countries covering 2004–2021. We apply machine learning methods, using diverse cartel screens characterising pricing and bidding behaviours in a predictive model. Combining many indicators in a random forest algorithm achieves 70–84 % prediction accuracy, distinguishing behavioural traces of confirmed cartels from non-cartels across different cartel types and countries (accuracy is 97 % when trained and tested on a single cartel case, typical of the literature). Most screens contribute to prediction in line with theory. These results could improve cartel detection and investigations and support pro-competition policies
Spatio-temporal visual statistical learning in context
No full textVisual Statistical Learning (VSL) is classically investigated in a restricted format, either as temporal or spatial VSL, and void of any effect or bias due to context. However, in real-world environments, spatial patterns unfold over time, leading to a fundamental intertwining between spatial and temporal regularities. In addition, their interpretation is heavily influenced by contextual information through internal biases encoded at different scales. Using a novel spatio-temporal VSL setup, we explored this interdependence between time, space, and biases by moving spatially defined patterns in and out of participants' views over time in the presence or absence of occluders. First, we replicated the classical VSL results in such a mixed setup. Next, we obtained evidence that purely temporal statistics can be used for learning spatial patterns through internal inference. Finally, we found that motion-defined and occlusion-related context jointly and strongly modulated which temporal and spatial regularities were automatically learned from the same visual input. Overall, our findings expand the conceptualization of VSL from a mechanistic recorder of low-level spatial and temporal co-occurrence statistics of single visual elements to a complex interpretive process that integrates low-level spatio-temporal information with higher-level internal biases to infer the general underlying structure of the environment
Student Mobilities to an ‘Offbeat’ or ‘Onbeat’ Destination? The Stipendium Hungaricum Scholarship Programme in Illiberal Hungary
No full textThis paper examines international student mobility under Hungary's illiberal regime through the experiences of Stipendium Hungaricum (SH) participants. Launched in 2013, SH seeks to internationalise Hungarian higher education and strengthen ties with non-EU countries, particularly those in the East. The research reveals contradictions between Hungary's anti-immigration stance and its selective policies that position international students as desirable immigrants. But do international students actually want Hungary as their destination? This study shows that Hungary functions as both an ‘onbeat’ and ‘offbeat’ study destination. Students view it as ‘onbeat’ when they see it as ‘Europe's gateway’ and a place of ‘peace and public safety.’ Yet they also describe it as ‘offbeat’ due to local anti-immigration and Islamophobic sentiments they encounter. Interviews conducted between 2020 and 2022 demonstrate how Hungary's illiberal policies shape student perceptions, revealing the complex processes behind its dual characterisation as both attractive and challenging
Pre-Decisional Information Search in 2- to 4-Year-Olds:Young Children Select the Relevant Cues When Looking for a Hidden Reward
No full textThis paper examines the capacity of 2- to 4-year-olds (n = 111, 56 female, age 25 to 59 months) to identify and select the most informative cue among several options when searching for a target—a task commonly used in decision-making research and one in which young children have traditionally struggled. Across two experiments—including a preregistered replication—we tested children using a simplified version of Lindow's (2021) finding-presents game. Children were presented with three cue cards (e.g., color, shape, icon) they could look up to determine which of the three boxes contained a hidden reward. Only one feature actually distinguished the target box—for example, the boxes all shared the same color, and all had a sticker on top, but differed in shape—so only one cue card offered genuinely informative guidance. In the first test phase, children were presented with the same set of boxes from the training phase. In the second test phase, children were presented with a new set of boxes and cue cards featuring a novel combination of cues. In Experiment 1, overall, children were more likely to select the relevant cue card, although 2-year-olds were successful only in the Same-set test phase. Experiment 2 focused specifically on 2-year-olds and showed that they succeeded even in a version that ruled out the use of a cue-saliency heuristic. Together, these findings reveal the early emergence of pre-decisional information search abilities in preschoolers and introduce a straightforward, effective paradigm for assessing early information-seeking skills
