Radboud University

While symptoms of stress are a major risk factor in the onset of depressive symptoms and major depression, a better understanding of intervening mechanisms in breaking down this positive association is urgently required. It is within this literature that we investigate (1) how symptoms of stress are associated with depressive symptoms and the onset of major depression, and (2) the buffering effect of hours spent on voluntary work on the stress-depression relationship. Using 3-wave longitudinal data, we estimated a direct and reverse auto-regressive path model. We found both cross-sectional and longitudinal support for the positive association between symptoms of stress and depressive symptoms. Next, we found that individuals who experienced more symptoms of stress at T1, T2, and T3 were 1.64 (95%CI [1.46;1.91]), 1.49 (95%CI [1.24;1.74]), and 1.40 (95%CI [1.21;1.60]) times more likely to be prescribed an anti-depression treatment at T3, respectively. Moreover, we found that the number of hours spent volunteering mitigated the (1) longitudinal—but not cross-sectional—stress-depression relationship, and (2) cross-sectional—but not the longitudinal—association between symptoms of stress at T3 and the likelihood of being prescribed an anti-depression treatment. These results point toward the pivotal role of voluntary work in reducing the development of depressive symptoms and major depression in relation to the experience of symptoms of stress.

We pose the problem of approximating optimally a given nonnegative signal with the scalar autoconvolution of a nonnegative signal. The I-divergence is chosen as the optimality criterion being well suited to incorporate nonnegativity constraints. After proving the existence of an optimal approximation, we derive an iterative descent algorithm of the alternating minimization type to find a minimizer. The algorithm is based on the lifting technique developed by Csiszár and Tusnádi and exploits the optimality properties of the related minimization problems in the larger space. We study the asymptotic behavior of the iterative algorithm and prove, among other results, that its limit points are Kuhn-Tucker points of the original minimization problem. Numerical experiments confirm the asymptotic results and exhibit the fast convergence of the proposed algorithm.

In the past two decades, a variety of cognitive training interventions have been developed to help people overcome their addictive behaviors. Conceptually, it is important to distinguish between programs in which reactions to addiction-relevant cues are trained (varieties of cognitive bias modification, CBM) and programs in which general abilities are trained such as working memory or mindfulness. CBM was first developed to study the hypothesized causal role in mental disorders: by directly manipulating the bias, it was investigated to what extent this influenced disorder-relevant behavior. In these proof-of-principle studies, the bias was temporarily modified in volunteers, either temporarily increased or decreased, with corresponding effects on behavior (e.g., beer consumption), in case the bias was successfully manipulated. In subsequent clinical randomized controlled trials (RCTs), training (away from the substance vs. sham training) was added to clinical treatment. These studies have demonstrated that CBM, as added to treatment, reduces relapse with a small effect of about 10% (similar effect size as for medication, with the strongest evidence for approach-bias modification). This has not been found for general ability training (e.g., working memory training), although effects on other psychological functions have been found (e.g., impulsivity). Mindfulness also has been found to help people overcome addictions, and different from CBM, also as stand-alone intervention. Research on (neuro-)cognitive mechanisms underlying approach-bias modification has pointed to a new perspective in which automatic inferences rather than associations are influenced by training, which has led to the development of a new variety of training: ABC training.KeywordsAddictionAlcohol use disorderApproach biasApproach bias retrainingCognitive-bias modificationCognitive trainingMindfulnessTreatmentWorking memory training

According to the Kunming-Montreal Global Biodiversity Framework, a “Whole-of-Society” approach is needed to initiate transitions to a nature-positive society. Many look at civil society to initiate and accelerate such transitions. In this article, we investigate strategies from Civil Society Actors (CSAs) to contribute to transformative change, with specific focus on Tiny Forests and Beach Clean-Ups in the Netherlands. Results show that CSAs have a clear Theory of Change to achieve their goals, and act upon that vision through assembling power and esources, developing policy-relevant environmental knowledge, mobilising public support and media coverage and initiating innovative sustainable practices. Adopting mosaic governance approaches, CSAs strategically position themselves in social and institutional networks, connecting professionals and citizens for political leverage. However, our findings show that the step from local impact towards transition remains a large one and the contribution of CSAs should be valued as emergent, co-produced and part of a broader transition movement.

We examine preference for randomization, and link it to conflicting preference-led indecisiveness in social settings. In an ultimatum game experiment where receivers may face conflicting preferences between material gains and equity, we allow receivers to assign non-zero probabilities to both acceptance and rejection (the randomized choice) in addition to the standard binary choice of acceptance or rejection. We further elicit receivers’ willingness to pay for using the randomized choice instead of the binary choice. We find that a theoretical model incorporating receivers’ conflicting preferences explains the experimental results well: most receivers randomized actively between acceptance and rejection, and many were willing to pay for randomization. Our results suggest that allowing people to randomize when making choices with conflicting preferences may improve individual welfare.

Hydrogen sulfide (H2S) and methane (CH4) are produced in anoxic environments through sulfate reduction and organic matter decomposition. Both gases diffuse upwards into oxic zones where aerobic methanotrophs mitigate CH4 emissions by oxidizing this potent greenhouse gas. Although methanotrophs in myriad environments encounter toxic H2S, it is virtually unknown how they are affected. Here, through extensive chemostat culturing we show that a single microorganism can oxidize CH4 and H2S simultaneously at equally high rates. By oxidizing H2S to elemental sulfur, the thermoacidophilic methanotroph Methylacidiphilum fumariolicum SolV alleviates the inhibitory effects of H2S on methanotrophy. Strain SolV adapts to increasing H2S by expressing a sulfide-insensitive ba3-type terminal oxidase and grows as chemolithoautotroph using H2S as sole energy source. Genomic surveys revealed putative sulfide-oxidizing enzymes in numerous methanotrophs, suggesting that H2S oxidation is much more widespread in methanotrophs than previously assumed, enabling them to connect carbon and sulfur cycles in novel ways.

Literacy acquisition is a complex process with genetic and environmental factors influencing cognitive and neural processes associated with reading. Previous research identified factors that predict word reading fluency (WRF), including phonological awareness (PA), rapid automatized naming (RAN), and speech-in-noise perception (SPIN). Recent theoretical accounts suggest dynamic interactions between these factors and reading, but direct investigations of such dynamics are lacking. Here, we investigated the dynamic effect of phonological processing and speech perception on WRF. More specifically, we evaluated the dynamic influence of PA, RAN, and SPIN measured in kindergarten (the year prior to formal reading instruction), first grade (the first year of formal reading instruction) and second grade on WRF in second and third grade. We also assessed the effect of an indirect proxy of family risk for reading difficulties using a parental questionnaire (Adult Reading History Questionnaire, ARHQ). We applied path modeling in a longitudinal sample of 162 Dutch-speaking children of whom the majority was selected to have an increased family and/or cognitive risk for dyslexia. We showed that parental ARHQ had a significant effect on WRF, RAN and SPIN, but unexpectedly not on PA. We also found effects of RAN and PA directly on WRF that were limited to first and second grade respectively, in contrast to previous research reporting pre-reading PA effects and prolonged RAN effects throughout reading acquisition. Our study provides important new insights into early prediction of later word reading abilities and into the optimal time window to target a specific reading-related subskill during intervention.

Reaction time (RT) data are often pre-processed before analysis by rejecting outliers and errors and aggregating the data. In stimulus–response compatibility paradigms such as the approach–avoidance task (AAT), researchers often decide how to pre-process the data without an empirical basis, leading to the use of methods that may harm data quality. To provide this empirical basis, we investigated how different pre-processing methods affect the reliability and validity of the AAT. Our literature review revealed 108 unique pre-processing pipelines among 163 examined studies. Using empirical datasets, we found that validity and reliability were negatively affected by retaining error trials, by replacing error RTs with the mean RT plus a penalty, and by retaining outliers. In the relevant-feature AAT, bias scores were more reliable and valid if computed with D-scores; medians were less reliable and more unpredictable, while means were also less valid. Simulations revealed bias scores were likely to be less accurate if computed by contrasting a single aggregate of all compatible conditions with that of all incompatible conditions, rather than by contrasting separate averages per condition. We also found that multilevel model random effects were less reliable, valid, and stable, arguing against their use as bias scores. We call upon the field to drop these suboptimal practices to improve the psychometric properties of the AAT. We also call for similar investigations in related RT-based bias measures such as the implicit association task, as their commonly accepted pre-processing practices involve many of the aforementioned discouraged methods. Highlights • Rejecting RTs deviating more than 2 or 3 SD from the mean gives more reliable and valid results than other outlier rejection methods in empirical data • Removing error trials gives more reliable and valid results than retaining them or replacing them with the block mean and an added penalty • Double-difference scores are more reliable than compatibility scores under most circumstances • More reliable and valid results are obtained both in simulated and real data by using double-difference D-scores, which are obtained by dividing a participant’s double mean difference score by the SD of their RTs

To understand language, we need to recognize words and combine them into phrases and sentences. During this process, responses to the words themselves are changed. In a step towards understanding how the brain builds sentence structure, the present study concerns the neural readout of this adaptation. We ask whether low-frequency neural readouts associated with words change as a function of being in a sentence. To this end, we analyzed an MEG dataset by Schoffelen et al. (2019) of 102 human participants (51 women) listening to sentences and word lists, the latter lacking any syntactic structure and combinatorial meaning. Using temporal response functions and a cumulative model-fitting approach, we disentangled delta- and theta-band responses to lexical information (word frequency), from responses to sensory- and distributional variables. The results suggest that delta-band responses to words are affected by sentence context in time and space, over and above entropy and surprisal. In both conditions, the word frequency response spanned left temporal and posterior frontal areas; however, the response appeared later in word lists than in sentences. In addition, sentence context determined whether inferior frontal areas were responsive to lexical information. In the theta band, the amplitude was larger in the word list condition around 100 milliseconds in right frontal areas. We conclude that low-frequency responses to words are changed by sentential context. The results of this study speak to how the neural representation of words is affected by structural context, and as such provide insight into how the brain instantiates compositionality in language. Significance statement: Human language is unprecedented in its combinatorial capacity: we are capable of producing and understanding sentences we have never heard before. Although the mechanisms underlying this capacity have been described in formal linguistics and cognitive science, how they are implemented in the brain remains to a large extent unknown. A large body of recent work from the cognitive neuroscientific literature implies a role for delta-band neural activity in the representation of linguistic structure and meaning. In this work, we combine these insights and techniques with findings from psycholinguistics to show that meaning is more than the sum of its parts: the delta-band MEG signal differentially reflects lexical information in- and outside of sentence structures.

Research suggests that people’s capacity to successfully pursue hedonic goals is at least as important for well-being as trait self-control. Extending this research, we tested whether trait hedonic capacity is related to more time spent with hedonic goal pursuit (i.e., hedonic quantity) and whether this explains its positive relationship with well-being. Second, we explored whether this may come at a cost for people’s performance. Results show that people with higher trait hedonic capacity do spend more time with hedonic goal pursuit (Study 1 and 2). However, hedonic quality not hedonic quantity accounts for its positive relationship with well-being. Further, people higher vs. lower in trait hedonic capacity perform equally well in their studies (Study 2) and their jobs (Study 3 and 4). Thus, trait hedonic capacity seems to allow people to invest more time into their hedonic goals in a way that does not jeopardize their academic and job performance.

Based on prior findings of content-specific beta synchronization in working memory and decision making, we hypothesized that beta oscillations support the (re-)activation of cortical representations by mediating neural ensemble formation. We found that beta activity in monkey dorsolateral prefrontal cortex (dlPFC) and pre-supplementary motor area (preSMA) reflects the content of a stimulus in relation to the task context, regardless of its objective properties. In duration- and distance-categorization tasks, we changed the boundary between categories from one block of trials to the next. We found that two distinct beta-band frequencies were consistently associated with the two relative categories, with activity in these bands predicting the animals’ responses. We characterized beta at these frequencies as transient bursts, and showed that dlPFC and preSMA are connected via these distinct frequency channels. These results support the role of beta in forming neural ensembles, and further show that such ensembles synchronize at different beta frequencies.

The surface area of anisotropic polymeric assemblies is a critical parameter concerning their properties. However, it is still a grand challenge for traditional techniques to determine the surface area. Here, a molecular probe loading (MPL) method is developed to measure the surface area of anisotropic polymersomes in the shape of tube, disc, and stomatocyte. This method uses an amphiphilic molecular probe, comprising hydrophobic pyrene as the anchor and hydrophilic tetraethylene glycol (EG4) as the float. The surface area of spherical polymersomes determined by dynamic light scattering is quantitatively correlated with the loading amount of probes, allowing the calculation of the average separation distance between the loaded probes. With the separation distance, we successfully determine the surface area of anisotropic polymersomes by measuring the loading amount. We envision that the MPL method will assist in the real-time surface area characterization, enabling the customization of functions.

Complex coacervates are phase-separated liquid droplets composed of oppositely charged multivalent molecules. The unique material properties of the complex coacervate interior favours the sequestration of biomolecules and facilitates reactions. Recently, it is shown that coacervates can be used for direct cytosolic delivery of sequestered biomolecules in living cells. Here, it is studied that the physical properties required for complex coacervates composed of oligo-arginine and RNA to cross phospholipid bilayers and enter liposomes penetration depends on two main parameters: the difference in ζ-potential between the complex coacervates and the liposomes, and the partitioning coefficient (Kp ) of lipids into the complex coacervates. Following these guidelines, a range of complex coacervates is found that is able to penetrate the membrane of living cells, thus paving the way for further development of coacervates as delivery vehicles of therapeutic agents.

In the near future, humans will increasingly be required to offload tasks to artificial systems to facilitate daily as well as professional activities. Yet, research has shown that humans are often averse to offloading tasks to algorithms (so-called "algorithmic aversion"). In the present study, we asked whether this aversion is also present when humans act under high cognitive load. Participants performed an attentionally demanding task (a multiple object tracking (MOT) task), which required them to track a subset of moving targets among distractors on a computer screen. Participants first performed the MOT task alone (Solo condition) and were then given the option to offload an unlimited number of targets to a computer partner (Joint condition). We found that participants significantly offloaded some (but not all) targets to the computer partner, thereby improving their individual tracking accuracy (Experiment 1). A similar tendency for offloading was observed when participants were informed beforehand that the computer partner's tracking accuracy was flawless (Experiment 2). The present findings show that humans are willing to (partially) offload task demands to an algorithm to reduce their own cognitive load. We suggest that the cognitive load of a task is an important factor to consider when evaluating human tendencies for offloading cognition onto artificial systems.

The relation between politics, ontology, and space remains one of the most contested concerns in human geography, often leading to a dismissal of ontology in favor of the politicization of space. In contrast, this article mobilizes post-foundationalism to propose a political ontology of space. After reviewing geographers’ engagements with politics, post-politics and the political, the article demonstrates how a post-foundational geography radically uproots geographic understandings of political and socio-spatial realities. Grounded upon parameters of negativity, contingency, and antagonism, the article equips geographers to grapple with the crumbling foundations of an uncertain present, and unknown futures.

For both political and ideological reasons, return is the most favoured future imagined for refugees by policy makers and protection actors. This article analyses how humanitarian migrants in a context of limited durable solutions can be supported to reclaim ownership of their futures, as well as how this can result in deeper insights for social scientists and policy makers. For the case of Syrians, this study deploys futures literacy labs as a participatory and capability-based research methodology that allows participants to become researchers of their own lives. Based on two futures literacy labs with two Syrian families in Lebanon in 2020 and 2021, the article demonstrates that a futures capability-based approach provides humanitarian migrants with the cognitive space and agency needed to go beyond foreclosed decision-making processes. The research methodology allows researchers to become witnesses to intimate reappropriation and learning processes by humanitarian migrants themselves. As a result, we are able to argue that ‘returns’ as a durable solution are essentially about a return to a state of well-being and possibilities, which may or not entail a spatial return.