Differentiation and achievement grouping are frequently implemented practices to adapt education to students’ varying educational needs based on achievement level. Potential didactical and... Show moreDifferentiation and achievement grouping are frequently implemented practices to adapt education to students’ varying educational needs based on achievement level. Potential didactical and socioemotional advantages and disadvantages of these practices have been discussed in the literature. However, little is known about the perspective of students themselves. This study examined how students (N = 428) perceived differentiation and within-class homogeneous achievement grouping in primary mathematics education, with attention for potential differences between students of diverse achievement levels. Students of Grades 1, 3 and 5 completed a questionnaire about various differentiated mathematics activities and (if applicable) within-class achievement grouping. In line with the didactical perspective on differentiation, extended instruction and less difficult tasks were appreciated most by low-achieving students whereas more difficult tasks were appreciated most by high-achieving students. Students of all achievement groups had largely positive attitudes about achievement grouping and about their own achievement group. However, some differences between achievement groups were found, with less favourable results for students placed in low achievement groups. Students’ responses to open-ended questions provided additional insights into the reasons behind students’ evaluations of differentiation and achievement grouping. Differences between grade levels were also explored. Show less
Background: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in... Show moreBackground: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously.New method: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs.Results: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. Comparison with other methods: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods.Conclusions: The successful performance of C-ICA indicates that it is a promising method to extract neuro-functional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity. Show less
De Gucht, V.; Woestenburg, D.H.A.; Wilderjans, T.F. 2022
In various scientific fields, researchers make use of partitioning methods (e.g., K-means) to disclose the structural mechanisms underlying object by variable data. In some instances, however, a... Show moreIn various scientific fields, researchers make use of partitioning methods (e.g., K-means) to disclose the structural mechanisms underlying object by variable data. In some instances, however, a grouping of objects into clusters that are allowed to overlap (i.e., assigning objects to multiple clusters) might lead to a better representation of the underlying clustering structure. To obtain an overlapping object clustering from object by variable data, Mirkin's ADditive PROfile CLUStering (ADPROCLUS) model may be used. A major challenge when performing ADPROCLUS is to determine the optimal number of overlapping clusters underlying the data, which pertains to a model selection problem. Up to now, however, this problem has not been systematically investigated and almost no guidelines can be found in the literature regarding appropriate model selection strategies for ADPROCLUS. Therefore, in this paper, several existing model selection strategies for K-means (a.o., CHull, the Calinski-Harabasz, Krzanowski-Lai, Average Silhouette Width and Dunn Index and information-theoretic measures like AIC and BIC) and two cross-validation based strategies are tailored towards an ADPROCLUS context and are compared to each other in an extensive simulation study. The results demonstrate that CHull outperforms all other model selection strategies and this especially when the negative log-likelihood, which is associated with a minimal stochastic extension of ADPROCLUS, is used as (mis)fit measure. The analysis of a post hoc AIC-based model selection strategy revealed that better performance may be obtained when a different-more appropriate-definition of model complexity for ADPROCLUS is used. Show less
Moort, M.L. van; Koornneef , A.W.; Wilderjans, T.F.; Broek, P.W. van den 2022
People read for many different reasons. These goals affect the cognitive processes and strategies they use during reading. Understanding how reading goals exert their effects requires investigation... Show morePeople read for many different reasons. These goals affect the cognitive processes and strategies they use during reading. Understanding how reading goals exert their effects requires investigation of whether and how they affect specific component processes, such as validation. We investigated the effects of reading goal on text-based and knowledge-based validation processes during reading and on the resulting offline mental representation. We employed a self-paced sentence-by-sentence contradiction paradigm with versions of texts containing target sentences that varied systematically in congruency with prior text and accuracy with background knowledge. Participants were instructed to read for general comprehension or for study. Memory for text information was assessed the next day. We also measured the degree to which each text topic was novel to a reader, as well as his or her working memory capacity. Results show that reading goals affect readers’ general processing as indicated by overall reading times, but provide no evidence that they influence validation processes. Reading goals did affect readers’ memory for target information but this effect depended on congruency between that information and the preceding text: Reading for study generally resulted in better memory for target information than reading for comprehension did, but not for target information that was incongruent with prior text. These results suggest that reading goals may not influence validation processes directly but affect subsequent representation-building processes after the detection of an (in)consistency—particularly in the case of incongruencies with prior text. Show less
Cramwinckel, F.M.; Scheepers, D.T.; Wilderjans, T.F.; Rooij, R.J.B. de 2021
Prejudice against sexual and gender minorities (e.g., LGBT people) is quite prevalent and is harmful. We examined an existing-and often-used-contact intervention in pre-existing groups in an... Show morePrejudice against sexual and gender minorities (e.g., LGBT people) is quite prevalent and is harmful. We examined an existing-and often-used-contact intervention in pre-existing groups in an educational setting and assessed its effectiveness in reducing different forms of LGBT negativity. We focused particularly on modern LGBT negativity: a relatively subtle form of prejudice, involving ambivalence, denial, and/or the belief that there is too much attention for LGBT prejudice. We used a mixed design in which condition (experimental vs. control group) was the between-participants factor, which was randomized at the group level, and time (pretest vs. posttest vs. follow-up) was the within-participants factor (N = 117). Interventions were video recorded and the behavior of LGBT educators and participants was coded. Participants responded positively to the intervention, especially to the LGBT educator's "coming-out story." Exploratory analysis of the video data indicated that the perceived effectiveness of the intervention was higher in groups where participants were more engaged, although caution is necessary in interpreting this finding. The most important measure indicated that modern LGBT negativity decreased in the intervention groups directly after the intervention, but returned to baseline levels one week later. However, in the control condition, modern LGBT negativity had increased over time. Taken together, this suggests that an actual reduction in modern LGBT negativity was short-lived (i.e., the intervention effect disappeared within 7 days). Show less
Background: The current study aimed to investigate the possible interplay between self-compassion and affect during Mindfulness-Based Compassionate Living (MBCL) in recurrently depressed... Show moreBackground: The current study aimed to investigate the possible interplay between self-compassion and affect during Mindfulness-Based Compassionate Living (MBCL) in recurrently depressed individuals.Methods: Data was used from a subsample of a parallel-group randomized controlled trial investigating the efficacy of MBCL in recurrently depressed adults (n = 104). Self-reports of self-compassion and positive/negative affect were obtained at the start of each of the eight MBCL sessions.Results: Bivariate Autoregressive Latent Trajectory (ALT) modeling showed that, when looking at the interplay between self-compassion and positive/negative affect on a session-to-session basis, no significant reciprocal cross-lagged effects between self-compassion and positive affect were found. Although there were no cross-lagged effects from negative affect to self-compassion, higher levels of self-compassion at each session did predict lower levels of negative affect at the subsequent session (b(SC(t-1),NA(t)) = -0.182, s.e. = 0.076, p =.017).Conclusions: The current study shows that increases in self-compassion are followed by decreases in negative affect in MBCL for depression. Show less
In the DSM-5 Section III Alternative Model for Personality Disorders (AMPD), the severity of personality pathology (criterion A) and the presence of pathological personality trait domains and... Show moreIn the DSM-5 Section III Alternative Model for Personality Disorders (AMPD), the severity of personality pathology (criterion A) and the presence of pathological personality trait domains and facets (criterion B) are assessed independently. Recent studies have challenged this assumption of independence but the interplay between criterion A and criterion B remains subject of further research. Using model-based cluster analysis on the criterion B trait domains, we compared criterion B trait domain clusters with respect to criterion A severity measures. Results revealed a six-cluster solution. Four of the six clusters represented a gradual increase in criterion B trait domains, paralleling an increase in criterion A severity. Two clusters did not follow this pattern: an Anxious-Detached type exhibiting overall high criterion A severity scores, and an emotionally stable psychopathy type exhibiting a number of low criterion A severity scores. Our findings indicate that criterion B domain clusters are informative of criterion A severity, relevant for future conceptualizations of the AMPD. Show less
Larsen, M.; Goemans, A.; Baste, V.; Wilderjans, T.F.; Lehmann, S. 2020
The set-up of comprehensive studies in life sciences involving a longitudinal dimension-as appears in time-scale metabolomics-calls for the use of dimension reduction techniques for three-way data... Show moreThe set-up of comprehensive studies in life sciences involving a longitudinal dimension-as appears in time-scale metabolomics-calls for the use of dimension reduction techniques for three-way data structures (e.g., samples by variables by time points). For this purpose, a clustering around latent variables for three-way data approach, CLV3W, has been proposed. CLV3W aims at both partitioning the variables into nonoverlapping clusters and estimating within each cluster a rank-one Parafac model consisting of a latent component (resp. a weighting system) associated with the first mode (resp. third mode) and a vector of loadings reflecting the degree of closeness of each variable of the second mode to its cluster. In this paper, two constrained CLV3W models are discussed. First, a nonnegativity constraint is defined implying that clusters are composed of positively correlated variables. Second, it is proposed to constrain the weighting system to be the same for all clusters. These two constraints aim at providing more parsimonious models with configurations that are easier to interpret. The appropriateness of both constraints is evaluated in a simulation study and illustrated on two case studies pertaining to sensory evaluation and metabolomics data. Regarding the first case study, CLV3W yields the identification of two consumer segments together with one common emotional pleasantness dimension associated with coffee aromas. CLV3W analysis of human preterm breast milk metabolomics data provided three clusters of lipid species that are responsible for specific functions (i.e., milk fat globules membrane-constituents, fatty acid oxidation-products, lipid mediators as eicosanoids and endocannabinoids). Show less
Skvortsova, A.; Veldhuijzen, D.S.; Pacheco-Lopez, G.; Bakermans-Kranenburg, M.; IJzendoorn, M. van; Smeets, M.A.M.; ... ; Evers, A.W.M. 2020
Objective There is evidence that placebo effects may influence hormone secretion. However, few studies have examined placebo effects in the endocrine system, including oxytocin placebo effects. We... Show moreObjective There is evidence that placebo effects may influence hormone secretion. However, few studies have examined placebo effects in the endocrine system, including oxytocin placebo effects. We studied whether it is possible to trigger oxytocin placebo effects using a classical conditioning paradigm. Methods Ninety-nine women were assigned to a conditioned, control, or drug control group. In the two-phase conditioning paradigm, participants in the conditioned and drug control groups received an oxytocin nasal spray combined with a distinctive smell (conditioned stimulus [CS]) for three acquisition days, whereas the control group received placebo spray. Subsequently, the conditioned and control groups received placebo spray with the CS and the drug control group received oxytocin spray for three evocation days. Salivary oxytocin was measured several times during each day. Pain sensitivity and facial evaluation tests previously used in oxytocin research were also administered. Results On evocation day 1, in the conditioned group, oxytocin significantly increased from baseline to 5 minutes after CS (B[slope] = 19.55, SE = 5.88, p < .001) and remained increased from 5 to 20 (B = -10.42, SE = 5.81, p = .071) and 50 minutes (B = -0.70, SE = 3.37, p = .84). On evocation day 2, a trend for increase in oxytocin was found at 5 minutes (B = 15.22, SE = 8.14, p = .062). No placebo effect was found on evocation day 3 (B = 3.57, SE = 3.26, p = .28). Neither exogenous nor conditioned oxytocin affected pain or facial tasks. Conclusions Results indicate that oxytocin release can be conditioned and that this response extinguishes over time. Triggering hormonal release by placebo manipulation offers various clinical possibilities, such as enhancing effects of pharmacological treatments or reducing dosages of medications. Show less
In neuroscience, clustering subjects based on brain dysfunctions is a promising avenue to subtype mental disorders as it may enhance the development of a brain-based categorization system for... Show moreIn neuroscience, clustering subjects based on brain dysfunctions is a promising avenue to subtype mental disorders as it may enhance the development of a brain-based categorization system for mental disorders that transcends and is biologically more valid than current symptom-based categorization systems. As changes in functional connectivity (FC) patterns have been demonstrated to be associated with various mental disorders, one appealing approach in this regard is to cluster patients based on similarities and differences in FC patterns. To this end, researchers collect three-way fMRI data measuring neural activation over time for different patients at several brain locations and apply Independent Component Analysis (ICA) to extract FC patterns from the data. However, due to the three-way nature and huge size of fMRI data, classical (two-way) clustering methods are inadequate to cluster patients based on these FC patterns. Therefore, a two-step procedure is proposed where, first, ICA is applied to each patient’s fMRI data and, next, a clustering algorithm is used to cluster the patients into homogeneous groups in terms of FC patterns. As some clustering methods used operate on similarity data, the modified RV-coefficient is adopted to compute the similarity between patient specific FC patterns. An extensive simulation study demonstrated that performing ICA before clustering enhances the cluster recovery and that hierarchical clustering using Ward’s method outperforms complete linkage hierarchical clustering, Affinity Propagation and Partitioning Around Medoids. Moreover, the proposed two-step procedure appears to recover the underlying clustering better than (1) a two-step procedure that combines PCA with clustering and (2) Clusterwise SCA-ECP, which performs PCA and clustering in a simultaneous fashion. Additionally, the good performance of the proposed two-step procedure using ICA and Ward’s hierarchical clustering is illustrated in an empirical fMRI data set regarding dementia patients. Show less
