150 Psychologie
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Institute
Lexical access speed and the development of phonological recoding during immediate serial recall
(2022)
A recent Registered Replication Report (RRR) of the development of verbal rehearsal during serial recall revealed that children verbalized at younger ages than previously thought, but did not identify sources of individual differences. Here, we use mediation analysis to reanalyze data from the 934 children ranging from 5 to 10 years old from the RRR for that purpose. From ages 5 to 7, the time taken for a child to label pictures (i.e. isolated naming speed) predicted the child’s spontaneous use of labels during a visually presented serial reconstruction task, despite no need for spoken responses. For 6- and 7-year-olds, isolated naming speed also predicted recall. The degree to which verbalization mediated the relation between isolated naming speed and recall changed across development. All relations dissipated by age 10. The same general pattern was observed in an exploratory analysis of delayed recall for which greater demands are placed on rehearsal for item maintenance. Overall, our findings suggest that spontaneous phonological recoding during a standard short-term memory task emerges around age 5, increases in efficiency during the early elementary school years, and is sufficiently automatic by age 10 to support immediate serial recall in most children. Moreover, the findings highlight the need to distinguish between phonological recoding and rehearsal in developmental studies of short-term memory.
Als Ausgangspunkt dieser Arbeit dienen Ansätze, die eine narrative Perspektive für das Verständnis von Psychopathologie und die psychotherapeutische Praxis vorschlagen. Im Hinblick auf die Fragen, welche Vorteile die Analyse von Patient*innenerzählungen bieten kann, und durch welche Merkmale psychopathologische Narrative sich auszeichnen, wird ein Überblick über ausgewählte Fallberichte, empirische Untersuchungen und theoretische Überlegungen gegeben. Diese werden unter den drei Kategorien Kohärenz, „Agency“ und Perspektiven beschrieben. Die Arbeit mag einen Impuls geben, ein tieferes Verständnis für narrative Dysfunktionen zu entwickeln und ihre Ursprünge sowie ihre Bedeutung für psychische Störungen und deren Behandlung vermitteln.
The neural processing of speech and music is still a matter of debate. A long tradition that assumes shared processing capacities for the two domains contrasts with views that assume domain-specific processing. We here contribute to this topic by investigating, in a functional magnetic imaging (fMRI) study, ecologically valid stimuli that are identical in wording and differ only in that one group is typically spoken (or silently read), whereas the other is sung: poems and their respective musical settings. We focus on the melodic properties of spoken poems and their sung musical counterparts by looking at proportions of significant autocorrelations (PSA) based on pitch values extracted from their recordings. Following earlier studies, we assumed a bias of poem-processing towards the left and a bias for song-processing on the right hemisphere. Furthermore, PSA values of poems and songs were expected to explain variance in left- vs. right-temporal brain areas, while continuous liking ratings obtained in the scanner should modulate activity in the reward network. Overall, poem processing compared to song processing relied on left temporal regions, including the superior temporal gyrus, whereas song processing compared to poem processing recruited more right temporal areas, including Heschl's gyrus and the superior temporal gyrus. PSA values co-varied with activation in bilateral temporal regions for poems, and in right-dominant fronto-temporal regions for songs. Continuous liking ratings were correlated with activity in the default mode network for both poems and songs. The pattern of results suggests that the neural processing of poems and their musical settings is based on their melodic properties, supported by bilateral temporal auditory areas and an additional right fronto-temporal network known to be implicated in the processing of melodies in songs. These findings take a middle ground in providing evidence for specific processing circuits for speech and music in the left and right hemisphere, but simultaneously for shared processing of melodic aspects of both poems and their musical settings in the right temporal cortex. Thus, we demonstrate the neurobiological plausibility of assuming the importance of melodic properties in spoken and sung aesthetic language alike, along with the involvement of the default mode network in the aesthetic appreciation of these properties.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ~ .20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the defaultmode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Significance Statement Spontaneous brain activity builds the foundation for intelligent processing - the ability of humans to adapt to various cognitive demands. Using resting-state EEG, we extracted multiple aspects of temporally highly resolved intrinsic brain dynamics to investigate their relationship with individual differences in intelligence. Single associations were of small effect sizes and varied critically across spatial and temporal scales. However, combining multiple measures in a multimodal cross-validated prediction model, allows to significantly predict individual intelligence scores in unseen participants. Our study adds to a growing body of research suggesting that observable associations between complex human traits and neural parameters might be rather small and proposes multimodal prediction approaches as promising tool to derive robust brain-behavior relations despite limited sample sizes.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. A huge number of neuroimaging studies identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, respective research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) in regard to their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ~ .20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Significance Statement Spontaneous brain activity builds the foundation for intelligent processing - the ability of humans to adapt to various cognitive demands. Using resting-state EEG, we extracted multiple aspects of temporally highly resolved intrinsic brain dynamics to investigate their relationship with individual differences in intelligence. Single associations were of small effect sizes and varied critically across spatial and temporal scales. However, combining multiple measures in a multimodal cross-validated prediction model, allows to significantly predict individual intelligence scores in unseen participants. Our study adds to a growing body of research suggesting that observable associations between complex human traits and neural parameters might be rather small and proposes multimodal prediction approaches as promising tool to derive robust brain-behavior relations despite limited sample sizes.
The implications of telework are discussed controversially and research on its positive and negative effects has produced contradictory results. We explore voluntariness of employee telework as a boundary condition which may underpin these contradictory findings. Under normal circumstances, individuals who do more telework should perceive fewer disadvantages. However, during the COVID-19 pandemic, employees could no longer voluntarily choose to telecommute, as many organizations were forced to introduce telework by governmental regulations. In two studies, we examine whether the voluntary nature of telework moderates the association between the amount of telework and perceptions of disadvantage. In Study 1, we collected data before and during the COVID-19 pandemic (N = 327). Results show that pre-pandemic participants (who were more likely to voluntarily choose this form of work) reported fewer disadvantages the more telework they did, but this was not the case for employees during the COVID-19 pandemic. To validate these findings, we measured employees’ voluntariness of telework in Study 2 (N = 220). Results support the importance of voluntariness: Individuals who experience a high degree of voluntariness in choosing telework perceive fewer disadvantages the more they telework. However, the amount of telework was not related to reduced perceptions of disadvantages for those who experienced low voluntariness regarding the telecommuting arrangement. Our findings help to understand when telework is related to the perception of disadvantages and they can provide organizations with starting points for practical interventions to reduce the negative effects of telework.
The implications of telework are discussed controversially and research on its positive and negative effects has produced contradictory results. We explore voluntariness of employee telework as a boundary condition which may underpin these contradictory findings. Under normal circumstances, individuals who do more telework should perceive fewer disadvantages. However, during the COVID-19 pandemic, employees could no longer voluntarily choose to telecommute, as many organizations were forced to introduce telework by governmental regulations. In two studies, we examine whether the voluntary nature of telework moderates the association between the amount of telework and perceptions of disadvantage. In Study 1, we collected data before and during the COVID-19 pandemic (N = 327). Results show that pre-pandemic participants (who were more likely to voluntarily choose this form of work) reported fewer disadvantages the more telework they did, but this was not the case for employees during the COVID-19 pandemic. To validate these findings, we measured employees’ voluntariness of telework in Study 2 (N = 220). Results support the importance of voluntariness: Individuals who experience a high degree of voluntariness in choosing telework perceive fewer disadvantages the more they telework. However, the amount of telework was not related to reduced perceptions of disadvantages for those who experienced low voluntariness regarding the telecommuting arrangement. Our findings help to understand when telework is related to the perception of disadvantages and they can provide organizations with starting points for practical interventions to reduce the negative effects of telework.
Free gaze and moving images are typically avoided in EEG experiments due to the expected generation of artifacts and noise. Yet for a growing number of research questions, loosening these rigorous restrictions would be beneficial. Among these is research on visual aesthetic experiences, which often involve open-ended exploration of highly variable stimuli. Here we systematically compare the effect of conservative vs. more liberal experimental settings on various measures of behavior, brain activity and physiology in an aesthetic rating task. Our primary aim was to assess EEG signal quality. 43 participants either maintained fixation or were allowed to gaze freely, and viewed either static images or dynamic (video) stimuli consisting of dance performances or nature scenes. A passive auditory background task (auditory steady-state response; ASSR) was added as a proxy measure for overall EEG recording quality. We recorded EEG, ECG and eye tracking data, and participants rated their aesthetic preference and state of boredom on each trial. Whereas both behavioral ratings and gaze behavior were affected by task and stimulus manipulations, EEG SNR was barely affected and generally robust across all conditions, despite only minimal preprocessing and no trial rejection. In particular, we show that using video stimuli does not necessarily result in lower EEG quality and can, on the contrary, significantly reduce eye movements while increasing both the participants’ aesthetic response and general task engagement. We see these as encouraging results indicating that — at least in the lab — more liberal experimental conditions can be adopted without significant loss of signal quality.
This cross-sectional study examined gender differences between male- and female-typed housework during the early COVID-19 lockdowns in 2020. Participants in Germany, India, Nigeria, and South Africa (N = 823) rated their housework share before and during the lockdown, then speculated about the division of housework performed by men and women in general, before and post-lockdown. Women spent more time on female-typed tasks and men (in Nigeria and South Africa) on male-typed tasks before and during the lockdown. Irrespective of participants’ gender, they speculated that men's and women's housework was more pronounced post-lockdown than before, but we only found gender differences in South Africa and India. Gender role ideology (GRI) moderated the gender‒housework relationship in Germany, but gender did not moderate the paid work hours and housework relationship in any country. Our findings suggest that gendered housework persisted in these countries and raises concerns that this pattern is likely to continue post-lockdown.
Based on the stressor-detachment model, previous research has assumed that work-related ICT use in the evening impairs psychological detachment. However, since most of the studies to date have assessed cross-sectional relationships, little is known about the actual direction of effects. In this 5-day diary study, we implemented a day-level longitudinal model to shed light on the causal relationships between work-related ICT use, detachment, and task progress (N = 340 employees, N = 1289 day-level cases). We also investigated the role of unfinished work tasks because we assumed, based on boundary theory, that they are a driving force leading to impaired detachment and work-related ICT use in the evening. Contrary to current research consensus but in line with our expectations, we found that low psychological detachment increased work-related ICT use and task progress. We found no evidence for reversed lagged effects. These results applied both to planned and unplanned ICT use. Furthermore, our results support the notion that unfinished work tasks precede ICT use and detachment. Thus, our findings suggest that work-related ICT use should not be treated as a stressor in its own right in the stressor-detachment model. Instead, it needs to be investigated as a behavioral outcome that employees engage in when they cannot detach from work.
Rezension zu: Social preferences: an introduction to behavioural economics and experimental research, by Michalis Drouvelis, Newcastle upon Tyne: Agenda Publishing, 2021, 205 pages, £22.99, ISBN 978-1-78821-417-9 (paperback).
Innovation is considered essential for today's organizations to survive and thrive. Researchers have also stressed the importance of leadership as a driver of followers' innovative work behavior (FIB). Yet, despite a large amount of research, three areas remain understudied: (a) The relative importance of different forms of leadership for FIB; (b) the mechanisms through which leadership impacts FIB; and (c) the degree to which relationships between leadership and FIB are generalizable across cultures. To address these lacunae, we propose an integrated model connecting four types of positive leadership behaviors, two types of identification (as mediating variables), and FIB. We tested our model in a global data set comprising responses of N = 7,225 participants from 23 countries, grouped into nine cultural clusters. Our results indicate that perceived LMX quality was the strongest relative predictor of FIB. Furthermore, the relationships between both perceived LMX quality and identity leadership with FIB were mediated by social identification. The indirect effect of LMX on FIB via social identification was stable across clusters, whereas the indirect effects of the other forms of leadership on FIB via social identification were stronger in countries high versus low on collectivism. Power distance did not influence the relations.
Objectives: Interpersonal factors, such as impairments in social interaction or lack of social support, have an important share when it comes to the development, maintenance, and progression of various mental disorders.
Methods: Individuals suffering from prolonged grief disorder (PGD) and matched bereaved healthy controls (n = 54) underwent a thorough diagnostic procedure, further completed the Inventory of Interpersonal Problems (IIP-D-32), and participated in a finitely iterated prisoner's dilemma (FIPD).
Results: Individuals suffering from PGD reported significantly more interpersonal problems. Both groups behaved differently in the FIPD with healthy controls being more carefully, adapting their behavior more flexible, whereas PGD patients displayed a lower responsiveness, which may indicate an inability to adapt to changes in relationships.
Conclusion: We conclude that interpersonal problems appear to be a relevant feature of PGD. Future studies need to clarify the causal relation behind this link, and should also include measures of attachment, social support, and disconnectedness.
In a dynamic environment, the already limited information that human working memory can maintain needs to be constantly updated to optimally guide behaviour. Indeed, previous studies showed that working memory representations are continuously being transformed during delay periods leading up to a response. This goes hand-in-hand with the removal of task-irrelevant items. However, does such removal also include veridical, original stimuli, as they were prior to transformation? Here we aimed to assess the neural representation of task-relevant transformed representations, compared to the no-longer-relevant veridical representations they originated from. We applied multivariate pattern analysis to electroencephalographic data during maintenance of orientation gratings with and without mental rotation. During maintenance, we perturbed the representational network by means of a visual impulse stimulus, and were thus able to successfully decode veridical as well as imaginary, transformed orientation gratings from impulse-driven activity. On the one hand, the impulse response reflected only task-relevant (cued), but not task-irrelevant (uncued) items, suggesting that the latter were quickly discarded from working memory. By contrast, even though the original cued orientation gratings were also no longer task-relevant after mental rotation, these items continued to be represented next to the rotated ones, in different representational formats. This seemingly inefficient use of scarce working memory capacity was associated with reduced probe response times and may thus serve to increase precision and flexibility in guiding behaviour in dynamic environments.
Aims: This study aims to: (1) explore the links between past exposure to potentially traumatic events, fear of contracting COVID-19 and perceived stress; (2) investigate how the exposure to traumagenic experiences affects one's locus of control over their health; and (3) examine fear, stress reactions and differences in health locus of control across three different sociocultural contexts.
Methods: A total of 524 adult participants were recruited from Egypt, Germany, and Italy through online channels. Self-reporting instruments were used to assess previous exposure to potentially traumatic events, PTSD symptoms, fear of COVID-19, perceived stress, and health locus of control.
Results: Our findings highlight differences in reaction to COVID-19 in relation to past exposure to potentially traumatic events and country of residence, both of which may inform tailored community-based intervention practices.
Conclusion: The impact of COVID-19 might be particularly disruptive for people who survived potentially traumatic experiences. Nevertheless, the mass mental health impact of the COVID-19 pandemic varies across different sociocultural contexts.
Some pitfalls of measuring representational similarity using Representational Similarity Analysis
(2022)
A core challenge in cognitive and brain sciences is to assess whether different biological systems represent the world in a similar manner. Representational Similarity Analysis (RSA) is an innovative approach that addresses this problem by looking for a second-order isomorphisim in neural activation patterns. This innovation makes it easy to compare latent representations across individuals, species and computational models, and accounts for its popularity across disciplines ranging from artificial intelligence to computational neuroscience. Despite these successes, using RSA has led to difficult-to-reconcile and contradictory findings, particularly when comparing primate visual representations with deep neural networks (DNNs): even though DNNs have been shown to learn and behave in vastly different ways to humans, comparisons based on RSA have shown striking similarities in some studies. Here, we demonstrate some pitfalls of using RSA and explain how contradictory findings can arise due to false inferences about representational similarity based on RSA-scores. In a series of studies that capture increasingly plausible training and testing scenarios, we compare neural representations in computational models, primate cortex and human cortex. These studies reveal two problematic phenomena that are ubiquitous in current research: a “mimic effect”, where confounds in stimuli can lead to high RSA-scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on a number of influential findings, such as comparisons made between human visual representations and those of primates and DNNs, we provide recommendations to avoid these pitfalls and sketch a way forward to a more solid science of representation in cognitive systems.
The pitfalls of measuring representational similarity using representational similarity analysis
(2022)
A core challenge in cognitive and brain sciences is to assess whether different biological systems represent the world in a similar manner. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines ranging from artificial intelligence to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here, we demonstrate the pitfalls of using RSA and explain how contradictory findings arise due to false inferences about representational similarity based on RSA-scores. In a series of studies that capture increasingly plausible training and testing scenarios, we compare neural representations in computational models, primate cortex and human cortex. These studies reveal two problematic phenomena that are ubiquitous in current research: a “mimic” effect, where confounds in stimuli can lead to high RSA-scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on a number of influential findings and the inferences drawn by current practitioners in a wide range of disciplines, we provide recommendations to avoid these pitfalls and sketch a way forward to a more solid science of representation in cognitive systems.
The pitfalls of measuring representational similarity using representational similarity analysis
(2022)
A core challenge in neuroscience is to assess whether diverse systems represent the world similarly. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines from machine learning to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here we demonstrate the pitfalls of using RSA to infer representational similarity and explain how contradictory findings arise and support false inferences when left unchecked. By comparing neural representations in primate, human and computational models, we reveal two problematic phenomena that are ubiquitous in current research: a “mimic” effect, where confounds in stimuli can lead to high RSA scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on existing findings and inferences, we provide recommendations to avoid these pitfalls and sketch a way forward.
Objects that are congruent with a scene are recognised more efficiently than objects that are incongruent. Further, semantic integration of incongruent objects elicits a stronger N300/N400 EEG component. Yet, the time course and mechanisms of how contextual information supports access to semantic object information is unclear. We used computational modelling and EEG to test how context influences semantic object processing. Using representational similarity analysis, we established that EEG patterns dissociated between objects in congruent or incongruent scenes from around 300 ms. By modelling semantic processing of objects using independently normed properties, we confirm that the onset of semantic processing of both congruent and incongruent objects is similar (∼150 ms). Critically, after ∼275 ms, we discover a difference in the duration of semantic integration, lasting longer for incongruent compared to congruent objects. These results constrain our understanding of how contextual information supports access to semantic object information.
Personal values are considered as guiding principles for humans’ attitudes and behavior, what makes them an essential component of mental health. Although these notions are widely recognized, investigations in clinical samples examining the link between values and mental health are lacking. We assessed n = 209 patients with affective disorders, neurotic disorders, reaction to severe stress, and adjustment disorders and personality disorders and compared them to a stratified random sample (n = 209) drawn from the European Social Survey. Personal values were assessed using the Portraits Value Questionnaire. Severity of psychopathology was assessed using the Beck Depression Inventory and the Brief Symptom Inventory. Clinical participants showed a higher preference for the values power, achievement and tradition/conformity and a lower preference for hedonism compared to controls. Patients exhibited more incompatible value patterns than controls. Across diagnostic groups, patients with neurotic disorders reported incompatible values most frequently. Value priorities and value conflicts may have the potential to contribute to a better understanding of current and future actions and experiences in patients with mental disorders.
Depressive symptoms in youth with ADHD: the role of impairments in cognitive emotion regulation
(2022)
Youth with attention-deficit/hyperactivity disorder (ADHD) are at increased risk to develop co-morbid depression. Identifying factors that contribute to depression risk may allow early intervention and prevention. Poor emotion regulation, which is common in adolescents, is a candidate risk factor. Impaired cognitive emotion regulation is a fundamental characteristic of depression and depression risk in the general population. However, little is known about cognitive emotion regulation in youth with ADHD and its link to depression and depression risk. Using explicit and implicit measures, this study assessed cognitive emotion regulation in youth with ADHD (N = 40) compared to demographically matched healthy controls (N = 40) and determined the association with depressive symptomatology. As explicit measure, we assessed the use of cognitive emotion regulation strategies via self-report. As implicit measure, performance in an ambiguous cue-conditioning task was assessed as indicator of affective bias in the processing of information. Compared to controls, patients reported more frequent use of maladaptive (i.e., self-blame, catastrophizing, and rumination) and less frequent use of adaptive (i.e., positive reappraisal) emotion regulation strategies. This pattern was associated with the severity of current depressive symptoms in patients. In the implicit measure of cognitive bias, there was no significant difference in response of patients and controls and no association with depression. Our findings point to depression-related alterations in the use of cognitive emotion regulation strategies in youth with ADHD. The study suggests those alterations as a candidate risk factor for ADHD-depression comorbidity that may be used for risk assessment and prevention strategies.
Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might lead to selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modeled this selective entrainment with a Dynamic Causal Model for Cross-Spectral Densities and found that it can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.
The knowledge that brain functional connectomes are both unique and reliable has enabled behaviourally relevant inferences at a subject level. However, it is unknown whether such “fingerprints” persist under altered states of consciousness. Ayahuasca is a potent serotonergic psychedelic which elicits a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and FC inherency. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed a shared functional space, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FCs motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively, our findings offer an example as to how individualised connectivity markers can be used to trace a subject’s functional connectome across altered states of consciousness.
Background: Multiple traumata such as child sexual and/or physical abuse often result in complex psychopathologies and a range of associated dysfunctional behaviors. Although evidence-based interventions exist, some therapists are concerned that trauma-focused psychotherapy with exposure-based elements may lead to the deterioration of associated dysfunctional behaviors in adolescents and young adults. Therefore, we examined the course of suicidal ideation, self-injury, aggressive behavior and substance use in a group of abuse-related posttraumatic stress disorder (PTSD) patients during phase-based, trauma-focused PTSD treatment.
Methods: Daily assessments from a randomized controlled trial (RCT) of Developmentally adapted Cognitive Processing Therapy (D-CPT) were analyzed to test for differences in the stated dysfunctional behaviors between the four treatment phases. We conducted multilevel modeling and repeated measure ANOVAs.
Results: We did not find any significant differences between the treatment phases concerning the stated dysfunctional behaviors, either at the level of urge or at the level of actual actions. On the contrary, in some primary outcomes (self-injury, aggressive behavior), as well as secondary outcomes (distress caused by trauma, joy), we observed significant improvements.
Discussion: Overall, during D-CPT, adolescents and young adults showed no deterioration in dysfunctional behaviors, while even showing improvements in some, suggesting that trauma-focused treatment preceded by skills building was not deleterious to this population. Hence, the dissemination of effective interventions such as D-CPT should be fostered, whilst the concerns of the therapists regarding exposure-based components need to be addressed during appropriate training. Nevertheless, further studies with momentary assessment, extended measurement methods, a control group and larger sample sizes are needed to confirm our preliminary findings.
Trial registration: The trial was registered at the German Clinical Trial Registry (GCTR), DRKS00004787, 18 March 2013, https://www.drks.de/DRKS00004787.
Background: Intrusive mental imagery (MI) plays a crucial role in the maintenance of posttraumatic stress disorder (PTSD) in adults. Evidence on the characteristics of MI in adolescents suffering from PTSD is sparse. The aim of this study was to thoroughly assess MI in an adolescent sample suffering from PTSD after the experience of childhood sexual abuse and/or childhood physical abuse (CA).
Methods: Thirty-two adolescents with a primary diagnosis of PTSD after CA and 32 adolescents without any mental disorder and without a history of CA, matched for age and gender, completed questionnaires assessing the characteristics of negative and positive MI, as well as images of injury and death that lead to positive emotions (ID-images).
Results: The PTSD group reported significantly more frequent, more vivid, more distressing and more strongly autobiographically linked negative MI compared to the control group. Although positive MI was highly present in both groups (PTSD: 65.6%; controls: 71.9%), no significant differences emerged between the two groups regarding the distinct characteristics of positive MI. The frequency of the ID-images did not significantly differ between the two groups (PTSD: 21.9%; controls: 9.4%), although the ID-images were more vivid in the PTSD group.
Discussion: Negative MI appears to be crucial in adolescent PTSD, whilst positive MI are unexpectedly common in both the PTSD and the control group. The role of positive MI as well as that of ID-images remain unclear. Specific interventions for changing negative MI that are tailored to the developmental challenges in adolescents with PTSD should be developed.
Trial registration: Some of the PTSD patients in this study were also part of a randomized controlled trial on Developmentally adapted Cognitive Processing Therapy (D-CPT). This trial was registered at the German Clinical Trial Registry (GCTR), DRKS00004787, 18 March 2013.
Background: Standardized neuropsychological testing serves to quantify cognitive impairment in multiple sclerosis (MS) patients. However, the exact mechanism underlying the translation of cognitive dysfunction into difficulties in everyday tasks has remained unclear. To answer this question, we tested if MS patients with intact vs. impaired information processing speed measured by the Symbol Digit Modalities Test (SDMT) differ in their visual search behavior during ecologically valid tasks reflecting everyday activities.
Methods: Forty-three patients with relapsing-remitting MS enrolled in an eye-tracking experiment consisting of a visual search task with naturalistic images. Patients were grouped into “impaired” and “unimpaired” according to their SDMT performance. Reaction time, accuracy and eye-tracking parameters were measured.
Results: The groups did not differ regarding age, gender, and visual acuity. Patients with impaired SDMT (cut-off SDMT-z-score < −1.5) performance needed more time to find and fixate the target (q = 0.006). They spent less time fixating the target (q = 0.042). Impaired patients had slower reaction times and were less accurate (both q = 0.0495) even after controlling for patients' upper extremity function. Exploratory analysis revealed that unimpaired patients had higher accuracy than impaired patients particularly when the announced target was in unexpected location (p = 0.037). Correlational analysis suggested that SDMT performance is inversely linked to the time to first fixation of the target only if the announced target was in its expected location (r = −0.498, p = 0.003 vs. r = −0.212, p = 0.229).
Conclusion: Dysfunctional visual search behavior may be one of the mechanisms translating cognitive deficits into difficulties in everyday tasks in MS patients. Our results suggest that cognitively impaired patients search their visual environment less efficiently and this is particularly evident when top-down processes have to be employed.
Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the discrete Fourier transform (DFT) and the least square spectrum (LSS). DFT and LSS can be applied both on the average accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effects) or on the population (random-effects) of simulated participants. Multiple comparisons across frequencies were corrected using False Discovery Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated sensitivity, specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the average accuracy time course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effects approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the Discrete Fourier Transform (DFT) and the Least Square Spectrum (LSS). DFT and LSS can be applied both on the averaged accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effect) or on the population (random-effect) of simulated participants. Multiple comparisons across frequencies were corrected using False-Discovery-Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated Sensitivity, Specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the averaged-accuracy-time-course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved Sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest Specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effect approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models’ prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.
The human brain achieves visual object recognition through multiple stages of nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models’ prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output M/EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.
A person's intelligence level positively influences his or her professional success. Gifted and highly intelligent individuals should therefore be successful in their careers. However, previous findings on the occupational situation of gifted adults are mainly known from popular scientific sources in the fields of coaching and self-help groups and confirm prevailing stereotypes that gifted people have difficulties at work. Reliable studies are scarce. This systematic literature review examines 40 studies with a total of 22 job-related variables. Results are shown in general for (a) the employment situation and more specific for the occupational aspects (b) career, (c) personality and behavior, (d) satisfaction, (e) organization, and (f) influence of giftedness on the profession. Moreover, possible differences between female and male gifted individuals and gifted and non-gifted individuals are analyzed. Based on these findings, implications for practice as well as further research are discussed.
Stress influences health not only directly, but also indirectly through changes in health-related behaviours, such as diet. Research has shown that stress influences individuals’ eating behaviour in different ways: Some increase, some decrease food intake, while others show no change. Identifying individuals at risk for stress-induced eating is essential for the development of tailored strategies for the prevention and treatment of overweight and obesity. The individual-difference model of stress-induced eating suggests that individual differences in the dietary response to stress are determined by differences in learning history, attitudes, or biology. Even though many studies have tried to identify person-characteristics that explain individual differences in the dietary response to stress, evidence remains inconclusive. Considering that eating is a repeated-occurrence health behaviour which is performed multiple times a day, Ecological Momentary Assessment (EMA) seems particularly promising to study the complex relationship between stress and food intake when and where it naturally occurs. Despite its potential, the number of studies applying EMA to assess the stress and eating relationship is limited. Furthermore, previous EMA studies show two limitations: (1) Actual food intake is not assessed and (2) inappropriate data analysis approaches are applied to semicontinuous outcomes. Therefore, the first aim of the present dissertation was to address the lack of an EMA tool that allows the assessment of stress and actual food intake by developing and evaluating the APPetite-mobile-app. Feasibility and usability of the APPetite-mobile-app as well as validity of the incorporated food record were empirically examined (Paper 1). Given the lack of an appropriate data analysis procedure, the second aim of the present dissertation was the introduction of a sophisticated statistical approach for semicontinuous data (Paper 2): Multilevel two-part modelling allows studying the influence of stress on the occurrence (i.e., whether individuals eat) as well as the amount of food intake (i.e., how much individuals eat) while accounting for the potential dependency between the two. Lastly, the novel EMA tool and the advanced data analysis procedure were integrated in order to gain novel insights into individual differences in the dietary response to stress and thereby identify individuals at risk for stress-induced eating in daily life (Paper 3). Results of Paper 1 showed good feasibility and acceptable usability of the APPetite-mobile-app as well as validity of the incorporated food record. Findings of Paper 2 highlight that multilevel two-part models offer novel and distinct insights in terms of the occurrence and the amount of food intake and are therefore not only methodologically but also conceptually promising. Paper 3 provides first evidence that the dietary response to stress might not be as stable as yet assumed. Time-varying factors might moderate the relationship between stress and actual food intake. Therefore, an expansion of the individual-difference model is proposed which accounts for time-varying factors. Further EMA studies are needed to verify the expanded model and identify time-varying factors which influence the dietary response to stress. Beyond that, improvements in the dietary assessment are required in order to allow prolonged EMA periods as well as larger samples. The present dissertation contributes to the research on the stress and eating relationship as it overcomes limitations of previous EMA studies and yields novel insights into the relationship between stress and actual food intake in daily life. Not only identifying individuals at risk for stress-induced eating, but also the identification of situations with an increased risk for stress-induced eating appears to be important for the development of targeted strategies for the prevention and treatment of overweight and obesity.
Based on stereotype threat and stereotype lift theory, this study explores implicit stereotype threat effects of gender stereotypes on the performance of primary school children in mathematics. Moreover, effects of implicit gender stereotypical cues (gender-specific task material) on motivational aspects were explored, which have revealed mixed results in stereotype threat research in the past. N = 151 German primary school children (47.7% female; mean age: M = 9.81, SD = 0.60) calculated either stereotypical or neutral mathematical text problems before motivational aspects were assessed. Contradicting our expectations, results neither revealed a stereotype threat effect on girls’ performance nor a lift effect on the boys. Instead, girls calculating stereotypical tasks outperformed girls in the control group, whereas boys’ performance did not significantly differ compared to the control group. Regarding motivational aspects, only traditional gender differences emerged as girls reported significantly more pressure and tension calculating the mathematical tasks. The discussion focuses on the way in which stereotypes can affect children’s cognitive performance and in turn, their mathematical performance.
Brookshire (2022) claims that previous analyses of periodicity in detection performance after a reset event suffer from extreme false-positive rates. Here we show that this conclusion is based on an incorrect implemention of a null-hypothesis of aperiodicity, and that a correct implementation confirms low false-positive rates. Furthermore, we clarify that the previously used method of shuffling-in-time, and thereby shuffling-in-phase, cleanly implements the null hypothesis of no temporal structure after the reset, and thereby of no phase locking to the reset. Moving from a corresponding phase-locking spectrum to an inference on the periodicity of the underlying process can be accomplished by parameterizing the spectrum. This can separate periodic from non-periodic components, and quantify the strength of periodicity.
The consequences of the current COVID-19 pandemic for mental health remain unclear, especially regarding the effects on suicidal behaviors. To assess changes in the pattern of suicide attempt (SA) admissions and completed suicides (CS) in association with the COVID-19 pandemic. As part of a longitudinal study, SA admissions and CS are systematically documented and analyzed in all psychiatric hospitals in Frankfurt/Main (765.000 inhabitants). Number, sociodemographic factors, diagnoses and methods of SA and CS were compared between the periods of March–December 2019 and March–December 2020. The number of CS did not change, while the number of SA significantly decreased. Age, sex, occupational status, and psychiatric diagnoses did not change in SA, whereas the percentage of patients living alone while attempting suicide increased. The rate and number of intoxications as a SA method increased and more people attempted suicide in their own home, which was not observed in CS. Such a shift from public places to home is supported by the weekday of SA, as the rate of SA on weekends was significantly lower during the pandemic, likely because of lockdown measures. Only admissions to psychiatric hospitals were recorded, but not to other institutions. As it seems unlikely that the number of SA decreased while the number of CS remained unchanged, it is conceivable that the number of unreported SA cases increased during the pandemic. Our data suggest that a higher number of SA remained unnoticed during the pandemic because of their location and the use of methods associated with lower lethality.
Electroencephalography (EEG) has been used for decades to identify neurocognitive processes related to intelligence. Evidence is accumulating for associations with neural markers of higher-order cognitive processes (e.g., working memory); however, whether associations are specific to complex processes or also relate to earlier processing stages remains unclear. Addressing these issues has implications for improving our understanding of intelligence and its neural correlates. The mismatch negativity (MMN) is an event-related brain potential (ERP) that is elicited when, within a series of frequent standard stimuli, rare deviant stimuli are presented. As stimuli are typically presented outside the focus of attention, the MMN is suggested to capture automatic pre-attentive discrimination processes. However, the MMN and its relation to intelligence has largely only been studied in the auditory domain, thus preventing conclusions about the involvement of automatic discrimination processes in humans’ dominant sensory modality vision. Electroencephalography was recorded from 50 healthy participants during a passive visual oddball task that presented simple sequence violations as well as deviations within a more complex hidden pattern. Signed area amplitudes and fractional area latencies of the visual mismatch negativity (vMMN) were calculated with and without Laplacian transformation. Correlations between vMMN and intelligence (Raven’s Advanced Progressive Matrices) were of negligible to small effect sizes, differed critically between measurement approaches, and Bayes Factors provided anecdotal to substantial evidence for the absence of an association. We discuss differences between the auditory and visual MMN, the implications of different measurement approaches, and offer recommendations for further research in this evolving field.
How much data do we need? Lower bounds of brain activation states to predict human cognitive ability
(2022)
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Despite their low frequency of occurrence, states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture (derived from resting-state fMRI) and to be highly subject-specific. However, it is currently unclear whether such network-defining states of high cofluctuation also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, an eigenvector-based prediction framework, we show that functional connectivity estimates from as few as 20 temporally separated time frames (< 3% of a 10 min resting-state fMRI scan) are significantly predictive of individual differences in intelligence (N = 281, p < .001). In contrast and against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not achieve significant prediction of intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest brain connectivity, temporally distributed information is necessary to extract information about cognitive abilities from functional connectivity time series. This information, however, is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Probing the association between resting-state brain network dynamics and psychological resilience
(2022)
Abstract
This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.
Author Summary
We tested the replicability and generalizability of a previously proposed negative association between dynamic brain network reconfigurations derived from multilayer modularity detection (node flexibility) and psychological resilience. Using multiband resting-state BOLD fMRI data and exploring several parcellation schemes, sliding window approaches, and temporal resolutions of the data, we could not replicate previously reported findings regarding the association between node flexibility and resilience. By extending this work to other measures of brain dynamics (node promiscuity, degree) we observe a rather inconsistent pattern of correlations with resilience that strongly varies across analysis choices. We conclude that further research is needed to understand the network neuroscience basis of mental health and discuss several reasons that may account for the variability in results.
Abstract
To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.
Author summary
Visual word recognition is a critical process for reading and relies on the human brain’s left ventral occipito-temporal (lvOT) regions. However, the lvOTs specific function in visual word recognition is not yet clear. We propose that these occipito-temporal brain systems are critical for lexical categorization, i.e., the process of determining whether an orthographic percept is a known word or not, so that further lexical and semantic processing can be restricted to those percepts that are part of our "mental lexicon". We demonstrate that a computational model implementing this process, the lexical categorization model, can explain seemingly contradictory benchmark results from the published literature. We further use functional magnetic resonance imaging to show that the lexical categorization model successfully predicts brain activation in the left ventral occipito-temporal cortex elicited during a word recognition task. It does so better than alternative models proposed so far. Finally, we provide causal evidence supporting this model by empirically demonstrating that training the process of lexical categorization improves reading performance.
The ability to extract regularities from the environment is arguably an adaptive characteristic of intelligent systems. In the context of speech, statistical learning is thought to be an important mechanism for language acquisition. By considering individual differences in speech auditory-motor synchronization, an independent component analysis of fMRI data revealed that the neural substrates of statistical word form learning are not fully shared across individuals. While a network of auditory and superior pre/motor regions is universally activated in the process of learning, a fronto-parietal network is instead additionally and selectively engaged by some individuals, boosting their performance. Furthermore, interfering with the use of this network via articulatory suppression (producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on language-related statistical learning and reconciles previous contrasting findings, while highlighting the need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, relative duration and relative distance in time of two sequentially-organized events —standard S, with constant duration, and comparison C, varying trial-by-trial— are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer ISI helps counteracting such serial distortion effect only the constant S is in first position, but not if the unpredictable C is in first position. These results suggest the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. We simulated our behavioral results with a Bayesian model and replicated the finding that participants disproportionately expand first-position dynamic (unpredictable) short events. Our results clarify the mechanics generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.
Research points to neurofunctional differences underlying fluent speech production in stutterers and non-stutterers. There has been considerably less work focusing on the processes that underlie stuttered speech, primarily due to the difficulty of reliably eliciting stuttering in the unnatural contexts associated with neuroimaging experiments. We used magnetoencephalography (MEG) to test the hypothesis that stuttering events result from global motor inhibition–a “freeze” response typically characterized by increased beta power in nodes of the action-stopping network. We leveraged a novel clinical interview to develop participant-specific stimuli in order to elicit a comparable amount of stuttered and fluent trials. Twenty-nine adult stutterers participated. The paradigm included a cue prior to a go signal, which allowed us to isolate processes associated with stuttered and fluent trials prior to speech initiation. During this pre-speech time window, stuttered trials were associated with greater beta power in the right pre-supplementary motor area, a key node in the action-stopping network, compared to fluent trials. Beta power in the right pre-supplementary area was related to a clinical measure of stuttering severity. We also found that anticipated words identified independently by participants were stuttered more often than those generated by the researchers, which were based on the participants’ reported anticipated sounds. This suggests that global motor inhibition results from stuttering anticipation. This study represents the largest comparison of stuttered and fluent speech to date. The findings provide a foundation for clinical trials that test the efficacy of neuromodulation on stuttering. Moreover, our study demonstrates the feasibility of using our approach for eliciting stuttering during MEG and functional magnetic resonance imaging experiments so that the neurobiological bases of stuttered speech can be further elucidated.
When speech is too fast, the tracking of the acoustic signal along the auditory pathway deteriorates, leading to suboptimal speech segmentation and decoding of speech information. Thus, speech comprehension is limited by the temporal constraints of the auditory system. Here we ask whether individual differences in auditory-motor coupling strength in part shape these temporal constraints. In two behavioral experiments, we characterize individual differences in the comprehension of naturalistic speech as function of the individual synchronization between the auditory and motor systems and the preferred frequencies of the systems. Obviously, speech comprehension declined at higher speech rates. Importantly, however, both higher auditory-motor synchronization and higher spontaneous speech motor production rates were predictive of better speech-comprehension performance. Furthermore, performance increased with higher working memory capacity (Digit Span) and higher linguistic, model-based sentence predictability – particularly so at higher speech rates and for individuals with high auditory-motor synchronization. These findings support the notion of an individual preferred auditory– motor regime that allows for optimal speech processing. The data provide evidence for a model that assigns a central role to motor-system-dependent individual flexibility in continuous speech comprehension.
Speech imagery (the ability to generate internally quasi-perceptual experiences of speech) is a fundamental ability linked to cognitive functions such as inner speech, phonological working memory, and predictive processing. Speech imagery is also considered an ideal tool to test theories of overt speech. The study of speech imagery is challenging, primarily because of the absence of overt behavioral output as well as the difficulty in temporally aligning imagery events across trials and individuals. We used magnetoencephalography (MEG) paired with temporal-generalization-based neural decoding and a simple behavioral protocol to determine the processing stages underlying speech imagery. We monitored participants’ lip and jaw micromovements during mental imagery of syllable production using electromyography. Decoding participants’ imagined syllables revealed a sequence of task-elicited representations. Importantly, participants’ micromovements did not discriminate between syllables. The decoded sequence of neuronal patterns maps well onto the predictions of current computational models of overt speech motor control and provides evidence for hypothesized internal and external feedback loops for speech planning and production, respectively. Additionally, the results expose the compressed nature of representations during planning which contrasts with the natural rate at which internal productions unfold. We conjecture that the same sequence underlies the motor-based generation of sensory predictions that modulate speech perception as well as the hypothesized articulatory loop of phonological working memory. The results underscore the potential of speech imagery, based on new experimental approaches and analytical methods, and further pave the way for successful non-invasive brain-computer interfaces.
The papers in this Special Issue Part I “Revisioning, Rethinking, Restructuring Gender at Work: Quo Vadis Gender Stereotypes?” focus on the current state of gender inequality, particularly stereotypes. We present studies showing that differences in gender stereotypes still exist, confirm disadvantages for women in male-dominated roles and sectors and when the employment sector is not specified, but also disadvantages for men in female-dominated roles and sectors. In contrast to this general trend, one paper in Part II of this Special Issue found a preference for women over men as job candidates in their study. Incongruence emerged as a striking common theme to explain these gender differences, whereby some studies focused on the perceived incongruence from the actor's perspective and how external factors contribute to these perceptions, whereas others looked at the perceived incongruence from the observer's perspective. We summarize the papers and briefly discuss the key points of Part I at the end of this editorial.
Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t–distributed stochastic neighbour embedding (t–SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of ∼30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab.
Spatial attention increases both inter-areal synchronization and spike rates across the visual hierarchy. To investigate whether these attentional changes reflect distinct or common mechanisms, we performed simultaneous laminar recordings of identified cell classes in macaque V1 and V4. Enhanced V4 spike rates were expressed by both excitatory neurons and fast-spiking interneurons, and were most prominent and arose earliest in time in superficial layers, consistent with a feedback modulation. By contrast, V1-V4 gamma-synchronization reflected feedforward communication and surprisingly engaged only fast-spiking interneurons in the V4 input layer. In mouse visual cortex, we found a similar motif for optogenetically identified inhibitory-interneuron classes. Population decoding analyses further indicate that feedback-related increases in spikes rates encoded attention more reliably than feedforward-related increases in synchronization. These findings reveal distinct, cell-type-specific feedforward and feedback pathways for the attentional modulation of inter-areal synchronization and spike rates, respectively.
In the human brain, the incoming light to the retina is transformed into meaningful representations that allow us to interact with the world. In a similar vein, the RGB pixel values are transformed by a deep neural network (DNN) into meaningful representations relevant to solving a computer vision task it was trained for. Therefore, in my research, I aim to reveal insights into the visual representations in the human visual cortex and DNNs solving vision tasks.
In the previous decade, DNNs have emerged as the state-of-the-art models for predicting neural responses in the human and monkey visual cortex. Research has shown that training on a task related to a brain region’s function leads to better predictivity than a randomly initialized network. Based on this observation, we proposed that we can use DNNs trained on different computer vision tasks to identify functional mapping of the human visual cortex.
To validate our proposed idea, we first investigate a brain region occipital place area (OPA) using DNNs trained on scene parsing task and scene classification task. From the previous investigations about OPA’s functions, we knew that it encodes navigational affordances that require spatial information about the scene. Therefore, we hypothesized that OPA’s representation should be closer to a scene parsing model than a scene classification model as the scene parsing task explicitly requires spatial information about the scene. Our results showed that scene parsing models had representation closer to OPA than scene classification models thus validating our approach.
We then selected multiple DNNs performing a wide range of computer vision tasks ranging from low-level tasks such as edge detection, 3D tasks such as surface normals, and semantic tasks such as semantic segmentation. We compared the representations of these DNNs with all the regions in the visual cortex, thus revealing the functional representations of different regions of the visual cortex. Our results highly converged with previous investigations of these brain regions validating the feasibility of the proposed approach in finding functional representations of the human brain. Our results also provided new insights into underinvestigated brain regions that can serve as starting hypotheses and promote further investigation into those brain regions.
We applied the same approach to find representational insights about the DNNs. A DNN usually consists of multiple layers with each layer performing a computation leading to the final layer that performs prediction for a given task. Training on different tasks could lead to very different representations. Therefore, we first investigate at which stage does the representation in DNNs trained on different tasks starts to differ. We further investigate if the DNNs trained on similar tasks lead to similar representations and on dissimilar tasks lead to more dissimilar representations. We selected the same set of DNNs used in the previous work that were trained on the Taskonomy dataset on a diverse range of 2D, 3D and semantic tasks. Then, given a DNN trained on a particular task, we compared the representation of multiple layers to corresponding layers in other DNNs. From this analysis, we aimed to reveal where in the network architecture task-specific representation is prominent. We found that task specificity increases as we go deeper into the DNN architecture and similar tasks start to cluster in groups. We found that the grouping we found using representational similarity was highly correlated with grouping based on transfer learning thus creating an interesting application of the approach to model selection in transfer learning.
During previous works, several new measures were introduced to compare DNN representations. So, we identified the commonalities in different measures and unified different measures into a single framework referred to as duality diagram similarity. This work opens up new possibilities for similarity measures to understand DNN representations. While demonstrating a much higher correlation with transfer learning than previous state-of-the-art measures we extend it to understanding layer-wise representations of models trained on the Imagenet and Places dataset using different tasks and demonstrate its applicability to layer selection for transfer learning.
In all the previous works, we used the task-specific DNN representations to understand the representations in the human visual cortex and other DNNs. We were able to interpret our findings in terms of computer vision tasks such as edge detection, semantic segmentation, depth estimation, etc. however we were not able to map the representations to human interpretable concepts. Therefore in our most recent work, we developed a new method that associates individual artificial neurons with human interpretable concepts.
Overall, the works in this thesis revealed new insights into the representation of the visual cortex and DNNs...
Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.
Neuroscience studies in non-human primates (NHP) often follow the rule of thumb that results observed in one animal must be replicated in at least one other. However, we lack a statistical justification for this rule of thumb, or an analysis of whether including three or more animals is better than including two. Yet, a formal statistical framework for experiments with few subjects would be crucial for experimental design, ethical justification, and data analysis. Also, including three or four animals in a study creates the possibility that the results observed in one animal will differ from those observed in the others: we need a statistically justified rule to resolve such situations. Here, I present a statistical framework to address these issues. This framework assumes that conducting an experiment will produce a similar result for a large proportion of the population (termed ‘representative’), but will produce spurious results for a substantial proportion of animals (termed ‘outliers’); the fractions of ‘representative’ and ‘outliers’ animals being defined by a prior distribution. I propose a procedure in which experimenters collect results from M animals and accept results that are observed in at least N of them (‘N-out-of-M’ procedure). I show how to compute the risks α (of reaching an incorrect conclusion) and β (of failing to reach a conclusion) for any prior distribution, and as a function of N and M. Strikingly, I find that the N-out-of-M model leads to a similar conclusion across a wide range of prior distributions: recordings from two animals lowers the risk α and therefore ensures reliable result, but leaves a large risk β; and recordings from three animals and accepting results observed in two of them strikes an efficient balance between acceptable risks α and β. This framework gives a formal justification for the rule of thumb of using at least two animals in NHP studies, suggests that recording from three animals when possible markedly improves statistical power, provides a statistical solution for situations where results are not consistent between all animals, and may apply to other types of studies involving few animals.
The neural mechanisms that unfold when humans form a large group defined by an overarching context, such as audiences in theater or sports, are largely unknown and unexplored. This is mainly due to the lack of availability of a scalable system that can record the brain activity from a significantly large portion of such an audience simultaneously. Although the technology for such a system has been readily available for a long time, the high cost as well as the large overhead in human resources and logistic planning have prohibited the development of such a system. However, during the recent years reduction in technology costs and size have led to the emergence of low-cost, consumer-oriented EEG systems, developed primarily for recreational use. Here by combining such a low-cost EEG system with other off-the-shelve hardware and tailor-made software, we develop in the lab and test in a cinema such a scalable EEG hyper-scanning system. The system has a robust and stable performance and achieves accurate unambiguous alignment of the recorded data of the different EEG headsets. These characteristics combined with small preparation time and low-cost make it an ideal candidate for recording large portions of audiences.
Research on psychopathy has so far been largely limited to the investigation of high-level processes, such as emotion perception and regulation. In the present work, we investigate whether psychopathy has an effect on the estimation of fundamental physical parameters, which are computed in the brain during early stages of sensory processing. We employed a simple task in which participants had to estimate their interpersonal distance from a moving avatar and stop it at a given distance. The face expression of the avatars were positive, negative, or neutral. Participants carried out the task online on their home computers. We measured the psychopathy level via a self-report questionnaire. Regardless of the degree of psychopathy, the facial expression of the avatars showed no effect on distance estimation. Our results show that individuals with a high degree of psychopathy underestimate distance of approaching avatars significantly less (let the avatar approach them significantly closer) than did participants with a lesser degree of psychopathy. Moreover, participants who scored high in Self-Centered Impulsivity underestimate the distance to approaching avatars significantly less (let the avatar approach closer) than participants with a low score. Distance estimation is considered an automatic process performed at early stages of visual processing. Therefore, our results imply that psychopathy affects basic early sensory processes, such as feature extraction, in the visual cortex.
Moving in synchrony to external rhythmic stimuli is an elementary function that humans regularly engage in. It is termed “sensorimotor synchronization” and it is governed by two main parameters, the period and the phase of the movement with respect to the external rhythm. There has been an extensive body of research on the characteristics of these parameters, primarily once the movement synchronization has reached a steady-state level. Particular interest has been shown about how these parameters are corrected when there are deviations for the steady-state level. However, little is known about the initial “tuning-in” interval, when one aligns the movement to the external rhythm from rest. The current work investigates this “tuning-in” period for each of the four limbs and makes various novel contributions in the understanding of sensorimotor synchronization. The results suggest that phase and period alignment appear to be separate processes. Phase alignment involves limb-specific somatosensory memory in the order of minutes while period alignment has very limited memory usage. Phase alignment is the primary task but then the brain switches to period alignment where it spends most its resources. In overall this work suggests a central, cognitive role of period alignment and a peripheral, sensorimotor role of phase alignment.
Temporal anticipation is a fundamental process underlying complex neural functions such as associative learning, decision-making, and motor-preparation. Here we study event anticipation in its simplest form in human participants using magnetoencephalography. We distributed events in time according to different probability density functions and presented the stimuli separately in two different sensory modalities. We found that the temporal dynamics in right parietal cortex correlate with reaction times to anticipated events. Specifically, after an event occurred, event probability was represented in right parietal activity, hinting at a functional role of event-related potential component P300 in temporal expectancy. The results are consistent across both visual and auditory modalities. The right parietal cortex seems to play a central role in the processing of event probability density. Overall, this work contributes to the understanding of the neural processes involved in the anticipation of events in time.
Nothing beyond the name : towards an eclipse of listening in the psychotherapeutic enterprise
(2022)
What are the different kinds of reduction that take place in a psychotherapeutic discipline? This article looks at the agonistic relations between the two types of reduction that fundamentally constitute a psychotherapeutic paradigm: naming and listening. At any given moment in the history of psychological theory, various schools and theories are in contention with each other over an institutional and state legitimation that will only be granted to one or some of them. It is argued that these disciplinary contentions for a dominant status subordinate the names and concepts that populate a particular psychotherapeutic paradigm to a property regime, thereby obscuring or compromising the attention paid to forms of listening that occur on the edge of naming and meaning.
Viewpoint effects on object recognition interact with object-scene consistency effects. While recognition of objects seen from “accidental” viewpoints (e.g., a cup from below) is typically impeded compared to processing of objects seen from canonical viewpoints (e.g., the string-side of a guitar), this effect is reduced by meaningful scene context information. In the present study we investigated if these findings established by using photographic images, generalise to 3D models of objects. Using 3D models further allowed us to probe a broad range of viewpoints and empirically establish accidental and canonical viewpoints. In Experiment 1, we presented 3D models of objects from six different viewpoints (0°, 60°, 120°, 180° 240°, 300°) in colour (1a) and grayscaled (1b) in a sequential matching task. Viewpoint had a significant effect on accuracy and response times. Based on the performance in Experiments 1a and 1b, we determined canonical (0°-rotation) and non-canonical (120°-rotation) viewpoints for the stimuli. In Experiment 2, participants again performed a sequential matching task, however now the objects were paired with scene backgrounds which could be either consistent (e.g., a cup in the kitchen) or inconsistent (e.g., a guitar in the bathroom) to the object. Viewpoint interacted significantly with scene consistency in that object recognition was less affected by viewpoint when consistent scene information was provided, compared to inconsistent information. Our results show that viewpoint-dependence and scene context effects generalize to depth rotated 3D objects. This supports the important role object-scene processing plays for object constancy.
The present diary study was conducted for the purpose of bridging and integrating empirical research on the antecedents and consequences of work-related ruminative processes in the evening. Based on the control theory, unfinished tasks and fatigue in the afternoon were considered as antecedents of affective rumination, while vitality was investigated as the outcome observed in the next morning to test for cyclical processes. During a 5-day diary study (including 3 weekdays and the weekend), 74 beginning teachers completed three diary entries per day. A total of 795 diary entries were obtained. Using multilevel structural equation modeling, the study supported that both fatigue and unfinished tasks explained unique shares of variance of affective rumination in the evening at the between- and within-person levels. Furthermore, affective rumination mediated the relationship between unfinished tasks and vitality as well as fatigue and vitality. However, this only held true at the between- and not the within-person level, as neither affective rumination nor fatigue and unfinished tasks predicted the following morning’s vitality at this level. The results offer insights into the antecedents of affective rumination and add to extant research on the negative consequences of affective rumination considering vitality as an outcome.
Hintergrund: Depressionen sind häufige, schwere und oft lebensbedrohliche Erkrankungen, bei denen es – trotz sehr guter Behandlungsmethoden – Versorgungslücken gibt. Hierzu tragen Vorbehalte gegen eine leitlinienkonforme pharmako- und/oder psychotherapeutische Behandlung bei. Ziel der Arbeit ist es zu ermitteln, in welchen soziodemographischen Bevölkerungssegmenten diese Vorbehalte besonders ausgeprägt sind.
Methodik: Die Untersuchung basiert auf Online-Befragungen der deutschen Bevölkerung aus den Jahren 2021, 2020 und 2019, darunter 1656 Personen (2021), 1775 Personen (2020) und 1729 Personen (2019) ohne Depressionserfahrungen. Mit einer CHAID-Analyse wurde geprüft, in welchen Bevölkerungssegmenten die Vorbehalte gegen eine leitliniengerechte Behandlung besonders groß sind.
Ergebnisse: Vorbehalte gegen Pharmakotherapie hatten insgesamt 69,8 % der Befragten ohne Depressionserfahrungen. Am größten waren die Vorbehalte unter jüngeren Personen (< 40 Jahre); hier lag der Anteil bei 74,2 %. Vorbehalte gegen Psychotherapie äußerten 31,4 % ohne Depressionserfahrungen; unter Frauen mit geringerer Schulbildung hatten 40,5 % Vorbehalte gegen eine Psychotherapie; unter Männern mit geringerer Schulbildung waren es 39,1 %. Vorbehalte gegen beide Behandlungsformen zeigten 27,7 %. Am größten waren die Vorbehalte unter Männern mit Schulbildung unterhalb der Hochschulreife (34,1 %). Die Ergebnisse sind signifikant (χ2-Test, p < 0,05).
Diskussion: Eine allgemeine Informationsstrategie wäre geeignet, Vorbehalten gegen Pharmakotherapie und Psychotherapie gleichermaßen zu verringern. Für eine spezifische Informationsstrategie müssen die Botschaften hinsichtlich Inhalt und Kommunikationskanälen so gestaltet werden, dass die jüngere Zielgruppe zuverlässig erreicht wird.
Im Rahmen der fortschreitenden Digitalisierung der Hochschullehre finden auch verstärkt elektronische Prüfungsformate Eingang in den Alltag von Hochschulen. Insbesondere elektronische Abschlussklausuren (E-Klausuren) bieten hier die Möglichkeit, die Prüfungsbelastung Hochschulehrender durch die Automatisierung weiter Teile der Klausurkonstruktion, -administration und -auswertung zu reduzieren. Die Integration digitaler Technologien in die Prüfungspraxis deutscher Hochschulen ermöglicht dabei nicht nur eine ökonomische Klausurkonstruktion, realitätsnähere Klausuren (z. B. durch die Nutzung fachspezifischer Standardsoftware), und den Einsatz innovativer Testbausteine (z. B. Integration von Multimediadateien in Items), sondern auch die Nutzung aktueller psychometrischer Methoden. Insbesondere die Konstruktion von Hochschulklausuren als kriteriumsorientierte, adaptive Tests (z. B. Spoden & Frey, 2021), hat das Potential Hochschulklausuren individualisierter, messpräzisier und fairer zu machen, sowie die Validität der aus der Klausurbearbeitung abgeleiteten Testwertinterpretationen zu steigern. Um kriteriumsorientierte, adaptive Hochschulklausuren in der Breite nutzbar zu machen, müssen allerdings zuvor einige Herausforderungen gemeistert werden, denen sich diese Arbeit widmet. Die in den vier Einzelarbeiten dieser Dissertation betrachteten Herausforderungen lassen sich auf einer psychometrischen, einer personalen und einer technischen Ebene verorten.
Auf der psychometrischen Ebene ist eine zentrale Herausforderung die ökonomische Kalibrierung des Itempools. Üblicherweise wird bei der Konstruktion adaptiver Tests eine dreistellige Anzahl an Items konstruiert und mittels einer separaten Kalibrierungsstudie im Vorlauf der operationalen Testanwendung mit mehreren hundert Testpersonen kalibriert. Die massierte Konstruktion vieler Items und die Durchführung einer zusätzlichen empirischen Studie lässt sich im Rahmen von Hochschulklausuren nur schwer realisieren. Im ersten Einzelbeitrag wird daher eine neuartige kontinuierliche Kalibrierungsstrategie (KKS) vorgestellt und im Rahmen einer Monte-Carlo-Simulation hinsichtlich ihrer psychometrischen Eigenschaften geprüft. Zusammenfassend ermöglicht die KKS, adaptive Tests während wiederkehrender Testanwendungen bei konstanter Berichtsmetrik, Kontrolle von Itemparameter-Drift und fortlaufender Ergänzung des Itempools zu kalibrieren. Es zeigt sich, dass die KKS selbst für sehr kleine Stichproben eine geeignete Methode darstellt, den Itempool über mehrere Testanwendungen hinweg fortlaufend zu kalibrieren.
Um die Berichtsmetrik dabei über die verschiedenen Testanwendungen hinweg konstant zu halten, und somit Vergleichbarkeit der Ergebnisse verschiedener Testzeitpunkte (z. B. Semester) zu gewährleisten, nutzt die KKS Equating-Methoden (z. B. Kolen & Brennan, 2014) zum Herstellen einer statistischen Verbindung zwischen Klausurdurchläufen. Die Qualität dieser statistischen Verbindung hängt dabei von verschiedenen Parametern ab. Im zweiten Einzelbeitrag werden daher verschiedene Konfigurationen der in die KKS implementierten Equating-Prozedur hinsichtlich ihres Einflusses auf die Qualität der Parameterschätzungen im Rahmen einer Monte-Carlo-Simulation untersucht und auf Basis der Ergebnisse praktische Empfehlungen abgleitet. Hierfür werden unter anderem die Schwierigkeitsverteilung der genutzten Linkitems sowie die verwendete Skalentransformationsmethode variiert. Es zeigt sich, dass die KKS unter verschiedenen Konfigurationen in der Lage ist, die Skala über mehrere Testzyklen hinweg konstant zu halten. Normal- beziehungsweise gleichverteile Schwierigkeitsverteilungen der Linkitems sowie die Stocking-Lord-Skalentransformationsmethode (Stocking & Lord, 1983) erweisen sich hierbei am vorteilhaftesten.
Auf personaler Ebene stellt die Akzeptanz seitens der Hochschullehrenden einen kritischen Erfolgsfaktor für die Implementation neuer E-Learning Systeme in Lehrveranstaltungen dar. Angelehnt an Technologieakzeptanzmodellen (z. B. Technology Acceptance Model; Davis, 1989) wird im dritten Einzelbeitrag ein empirisch prüfbares Modell – das Technology-based Exams Acceptance Model (TEAM) – zur Vorhersage der Intention zur Nutzung von adaptiven und nicht-adaptiven E-Klausursystemen seitens Hochschullehrender vorgeschlagen und anhand der Daten von N = 993 deutschen Hochschullehrenden empirisch geprüft. Das postulierte Modell weist einen guten Modellfit auf. Die Ergebnisse weisen die wahrgenommene Nützlichkeit als Schlüsselprädiktor für die Nutzungsintention aus. Medienbezogene Variablen haben indirekte Effekte auf die wahrgenommene Nützlichkeit, mediiert über vorherige Nutzungserfahrungen mit Bildungstechnologien. Darüber hinaus spielt die subjektive Norm eine wichtige Rolle bei der Erklärung der Akzeptanz von E-Klausuren...
Über zwei Drittel aller Menschen erleben in ihrem Leben mindestens ein traumatisches Ereignis (Kessler et al., 2017). Gerade nach interpersonellen Traumatisierungen ist die Rate der Betroffenen, welche eine posttraumatische Belastungsstörung (PTBS) entwickeln, sehr hoch (z. B. ca. 50% nach sexuellem Missbrauch; Hauffa et al., 2011). In der Vergangenheit wurden Angst- und Ohnmachtsgefühle als zentrale der PTBS zu Grunde liegende Emotionen aufgefasst (Foa & Kozak, 1986). Neuere Forschungsbefunde legen jedoch nahe, dass traumabezogene Schuld- und Schamgefühle auch eine wichtige Rolle bei der Entstehung und Aufrechterhaltung der PTBS spielen (z. B. Badour et al., 2017). Dabei leiden besonders Betroffene von interpersonellen Gewalterfahrungen unter diesen Gefühlen (z. B. Badour et al., 2017).
Im Hinblick auf die psychotherapeutische Behandlung der PTBS haben sich traumafokussierte Verfahren als wirksam erwiesen (z. B. Lewis et al., 2020). Hohe Drop-out (z. B. Swift & Greenberg, 2014) und Nonresponse Raten (Fonzo et al., 2020) geben jedoch Hinweise darauf, dass nicht allen PTBS Patient*innen mit diesen Verfahren ausreichend geholfen werden kann, wobei insbesondere Patient*innen mit interpersonellen Traumatisierungen weniger gut davon zu profitieren scheinen (z. B. Karatzias et al., 2019). Zudem hat sich gezeigt, dass Schuldgefühle auch nach einer erfolgreichen PTBS Behandlung weiter persistieren (Larsen et al., 2019). Demnach besteht ein Bedarf an alternativen Therapieverfahren für Patient*innen mit interpersonellen Traumatisierungen und/oder Schuld- und Schamgefühlen.
Besonders vielversprechend sind hierbei achtsamkeitsbasierte Interventionen, die bereits in der PTBS Behandlung eine zunehmend bedeutsame Rolle spielen (Hopwood & Schutte, 2017). Eine wichtige Voraussetzung für die weitere Erforschung dieser Interventionen sind valide und reliable Verfahren zur Veränderungsmessung von Achtsamkeit (Isbel et al., 2020). So scheinen bisherige Studien jedoch hauptsächlich fragebogenbasierte Maße zur Erfassung von Veränderungen in Trait-Achtsamkeit eingesetzt zu haben, obwohl diese Interventionen eher auf die Steigerung von State-Achtsamkeit abzielen (Goodman et al., 2017). Darüber hinaus kristallisierten sich methodische Kritikpunkte in Bezug auf die Validität von Fragebögen zur Erfassung von Trait-Achtsamkeit heraus (van Dam et al., 2018). Demgegenüber erfassen Experience-Sampling Ansätze (z. B. Mindful-Breathing Exercise, MBE; Burg & Michalak, 2011) eher Aspekte der State-Achtsamkeit, sind jedoch in klinischen Untersuchungsstichproben bisher kaum untersucht worden. Darauf aufbauend fokussierte die erste Forschungsfrage der Dissertation die Untersuchung der MBE im klinischen Kontext. Ein Hauptbefund der Studie zeigte, dass die MBE bei PTBS Patient*innen hinsichtlich ihres Prädiktionswertes für die PTBS Symptome Übererregung und Intrusionen gegenüber fragebogenbasierter Trait-Achtsamkeit überlegen war. Mögliche Wirkmechanismen achtsamkeitsbasierter Interventionen könnten demnach durch den Einsatz der MBE besonders gut abgebildet werden.
Innerhalb der achtsamkeitsbasierten Interventionen kommt in der Behandlung der PTBS am häufigsten die Mindfulness Based Stress Reduction (MBSR; Kabat-Zinn, 2013) als standardisierte Gruppenintervention zum Einsatz (Boyd et al., 2018). Jedoch scheint die MBSR insbesondere für PTBS Patient*innen mit interpersonellen Traumatisierungen nicht eins-zu-eins anwendbar zu sein (Müller-Engelmann et al., 2017). Buddhistische Metta-Meditationen (dt.: Liebende Güte; Salzberg, 2002) sind vor diesem Hintergrund eine vielversprechende Ergänzung zu achtsamkeitsbasierten Interventionen. Metta-Meditationen zielen darauf ab, sich selbst sowie allen anderen Lebewesen bedingungsloses Wohlwollen und Freundlichkeit entgegen zu bringen (Bodhi, 2010). Metta-Meditationen sind noch weniger gut in der klinischen Forschung etabliert. Erste Befunde deuten jedoch darauf hin, dass sie bei PTBS Patient*innen zu einer Reduktion der PTBS Symptomatik führen können (z. B. Kearney et al., 2021). Folglich wurde im Rahmen der zweiten Forschungsfrage eine neue Intervention entwickelt und evaluiert, welche sich an den Bedürfnissen von PTBS Patient*innen mit interpersonellen Traumatisierungen orientiert. Sie kombiniert kürzere, PTBS spezifische Achtsamkeitsübungen mit angepassten Übungen aus MBSR sowie Metta-Meditationen (= Trauma-MILOKI). Trauma-MILOKI zeigte sich in einer multiplen Baseline Studie wirksam zur Reduktion der PTBS Symptome sowie zur Steigerung des Wohlbefindens.
Ein Wirkmechanismus von Metta-Meditationen ist die Förderung positiver Emotionen sowie des Gefühls sozialer Verbundenheit (Salzberg, 2002), weswegen sie auch besonders gut geeignet scheinen, traumabezogene Schuld- und Schamgefühle zu reduzieren. Darüber hinaus haben sich unter den etablierten Therapieverfahren v. a. kognitive Ansätze zur Reduktion von Schuldgefühlen als wirksam erwiesen (Resick et al., 2008)...
Background: Hebb repetition learning is a form of long-term serial order learning that can occur when sequences of items in an immediate serial recall task are repeated. Repetition improves performance because of the gradual integration of serial order information from short-term memory into a more stable long-term memory trace.
Aims: The current study assessed whether adolescents with non-specific intellectual disabilities showed Hebb repetition effects, and if their magnitude was equivalent to those of children with typical development, matched for mental age.
Methods: Two immediate serial recall Hebb repetition learning tasks using verbal and visuospatial materials were presented to 47 adolescents with intellectual disabilities (11–15 years) and 47 individually mental age-matched children with typical development (4–10 years).
Results: Both groups showed Hebb repetition learning effects of similar magnitude, albeit with some reservations. Evidence for Hebb repetition learning was found for both verbal and visuospatial materials; for our measure of Hebb learning the effects were larger for verbal than visuospatial materials.
Conclusions: The findings suggested that adolescents with intellectual disabilities may show implicit long-term serial-order learning broadly commensurate with mental age level. The benefits of using repetition in educational contexts for adolescents with intellectual disabilities are considered.
In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use “one shot” generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a “complementary mask” module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.
Since 2020, the COVID-19 pandemic had an impact on education worldwide. There is increased discussion of possible negative effects on students’ learning outcomes and the need for targeted support. We examined fourth graders’ reading achievement based on a school panel study, representative on the student level, with N = 111 elementary schools in Germany (total: N = 4,290 students, age: 9–10 years). The students were tested with the Progress in International Reading Literacy Study instruments in 2016 and 2021. The analysis focused on (1) total average differences in reading achievement between 2016 and 2021, (2) average differences controlling for student composition, and (3) changes in achievement gaps between student subgroups (i.e., immigration background, socio-cultural capital, and gender). The methodological approach met international standards for the analysis of large-scale assessments (i.e., multiple multi-level imputation, plausible values, and clustered mixed-effect regression). The results showed a substantial decline in mean reading achievement. The decline corresponds to one-third of a year of learning, even after controlling for changes in student composition. We found no statistically significant changes of achievement gaps between student subgroups, despite numerical tendencies toward a widening of achievement gaps between students with and without immigration background. It is likely that this sharp achievement decline was related to the COVID-19 pandemic. The findings are discussed in terms of further research needs, practical implications for educating current student cohorts, and educational policy decisions regarding actions in crises such as the COVID-19 pandemic.
Responses to the COVID-19 pandemic prompted people and institutions to turn to online virtual environments for a wide variety of social gatherings. In this perspectives article, we draw upon our previous work and interviews with Ghanaian Christian leaders to consider implications of this shift. Specifically, we propose that the shift from physical to virtual interactions mimics and amplifies the neoliberal individualist experience of abstraction from place associated with Eurocentric modernity. On the positive side, the shift from physical to virtual environments liberates people to selectively pursue the most fulfilling interactions, free from constraints of physical distance. On the negative side, the move from physical to virtual space necessitates a shift from material care and tangible engagement with the local community to the psychologization of care and pursuit of emotional intimacy in relations of one’s choosing—a dynamic that further marginalizes people who are already on the margins. The disruptions of the pandemic provide an opportunity to re-set social relations, to design ways of being that better promote sustainable collective well-being rather than fleeting personal fulfillment.
Individualization can be defined as the adaptation of instructional parameters to relevant characteristics of a specific learner. This definition raises several questions, however: Which characteristics are actually relevant? Which parameters of instruction need to be adjusted, and in which way, to positively interact with those characteristics? In a classroom context, additional questions arise: how can information about the relevant learner characteristics be delivered to the teacher? How can individualized instruction be delivered to each learner in a context that has originally been designed for whole-class instruction? By focusing on the measurement and modelling of learner characteristics and instructional adaptations, this dissertation aims to provide an insight into each of these issues.
This dissertation is divided into two parts. The first part is concerned with the theoretical (Paper 1) and statistical (Paper 2) modeling of learner characteristics in the context of individualized instruction. The second part is concerned with the measurement (Paper 3) and implementation (Paper 4) of individualized instruction in the classroom context.
Paper 1 summarizes existing research on individualization from different research traditions. From this summary I derive the need for a dynamic conceptualization of learner characteristics (acknowledging that learners change during and in interaction with the learning process) and synthesize a dynamic framework that details the opportunities for individualization on three different timescales. Paper 2 reports results from an exploratory study that investigated the potential benefits of utilizing person-centered analysis for the assessment of multivariate learner prerequisites and their interaction with instruction. We found that latent profiles over several reading related abilities could explain differential effectiveness of self-reported teaching foci in German third grade reading lessons. These findings indicate not just a need for stronger individualization of teaching but also an advantage of multivariate conceptualizations of learner characteristics. Additionally, they show the utility of person-centered approaches for the investigation of such multivariate learner characteristics and their interaction with instruction.
In the second part, I investigate possible approaches to the implementation and measurement of individualization in a classroom context. Paper 3 investigates whether teacher-, student- and observer perspectives converge when rating the amount of individualization present in regular classroom instruction. We found considerable agreement between the perspectives, indicating a common understanding of the construct at the classroom level as well as providing some evidence for the validity of the used measurement instruments. Paper 4 replicates findings concerning the effectiveness of formative assessment procedures for fostering reading education, supplemented by a moderator analysis showing that only children with low performance at the beginning of the school-year profited from its implementation. This indicates that the information provided by formative assessment procedures helps teachers to identify struggling readers but does not seem to be utilized for adapting instruction to specific deficits of average or high performing children.
In sum, this dissertation contributes to research on individualized instruction by demonstrating necessary conditions for its effectiveness. It posits the need for a dynamic conceptualization of learner characteristics, demonstrates the advantage of multivariate learner profiles, and points out ways towards the successful implementation of individualized instruction in the classroom.
Der Ukrainekrieg und seine psychologischen Folgen : Hilfe für Geflüchtete in Frankfurt und vor Ort
(2022)
Die Psychosoziale Beratungsstelle für Flüchtlinge der Goethe-Universität (PBF) unter Leitung von Prof. Dr. Ulrich Stangier beschäftigt sich angesichts der aktuellen Zuwanderung Geflüchteter aus der Ukraine mit den psychischen Folgen von Migration und Flucht im Rahmen der Ukrainekrise. Durch die Unterstützung der Freunde und Förderer der Goethe-Universität sowie die Polytechnische Gesellschaft konnten in Kooperation mit der Stadt Frankfurt die Angebote der Beratungsstelle an den gestiegenen Bedarf und die Bedürfnisse ukrainischer Geflüchteter angepasst werden. Weiterhin werden im Rahmen einer Kooperation mit ukrainischen Kolleg*innen in der Ukraine Workshops zum Umgang mit psychischen Kriegsfolgen angeboten.
Academic self-efficacy (ASE) refers to a student’s global belief in his/her ability to master the various academic challenges at university and is an essential antecedent of wellbeing and performance. The five-item General Academic Self-Efficacy Scale (GASE) showed promise as a short and concise measure for overall ASE. However, of its validity and reliability outside of Scandinavia is limited. Therefore, this paper aimed to investigate the psychometric properties, longitudinal invariance, and criterion validity of the GASE within a sample of university students (Time 1: n = 1056 & Time 2: n = 592) in the USA and Western Europe. The results showed that a unidimensional factorial model of overall ASE fitted the data well was reliable and invariant across time. Further, criterion validity was established by finding a positive relationship with task performance at different time stamps. Therefore, the GASE can be used as a valid and reliable measure for general ASE.
Previous research has demonstrated the efficacy of psychological interventions to foster resilience. However, little is known about whether the cultural context in which resilience interventions are implemented affects their efficacy on mental health. Studies performed in Western (k = 175) and Eastern countries (k = 46) regarding different aspects of interventions (setting, mode of delivery, target population, underlying theoretical approach, duration, control group design) and their efficacy on resilience, anxiety, depressive symptoms, quality of life, perceived stress, and social support were compared. Interventions in Eastern countries were longer in duration and tended to be more often conducted in group settings with a focus on family caregivers. We found evidence for larger effect sizes of resilience interventions in Eastern countries for improving resilience (standardized mean difference [SMD] = 0.48, 95% confidence interval [CI] 0.28 to 0.67; p < 0.0001; 43 studies; 6248 participants; I2 = 97.4%). Intercultural differences should receive more attention in resilience intervention research. Future studies could directly compare interventions in different cultural contexts to explain possible underlying causes for differences in their efficacy on mental health outcomes.
Children often perform worse than adults on tasks that require focused attention. While this is commonly regarded as a sign of incomplete cognitive development, a broader attentional focus could also endow children with the ability to find novel solutions to a given task. To test this idea, we investigated children’s ability to discover and use novel aspects of the environment that allowed them to improve their decision-making strategy. Participants were given a simple choice task in which the possibility of strategy improvement was neither mentioned by instructions nor encouraged by explicit error feedback. Among 47 children (8—10 years of age) who were instructed to perform the choice task across two experiments, 27.5% showed a full strategy change. This closely matched the proportion of adults who had the same insight (28.2% of n = 39). The amount of erroneous choices, working memory capacity and inhibitory control, in contrast, indicated substantial disadvantages of children in task execution and cognitive control. A task difficulty manipulation did not affect the results. The stark contrast between age-differences in different aspects of cognitive performance might offer a unique opportunity for educators in fostering learning in children.
Cross-sectional findings suggest that volumes of specific hippocampal subfields increase in middle childhood and early adolescence. In contrast, a small number of available longitudinal studies reported decreased volumes in most subfields over this age range. Further, it remains unknown whether structural changes in development are associated with corresponding gains in children’s memory. Here we report cross-sectional age differences in children’s hippocampal subfield volumes together with longitudinal developmental trajectories and their relationships with memory performance. In two waves, 109 participants aged 6–10 years (wave 1: MAge=7.25, wave 2: MAge=9.27) underwent high-resolution magnetic resonance imaging to assess hippocampal subfield volumes (imaging data available at both waves for 65 participants) and completed tasks assessing hippocampus dependent memory processes. We found that cross-sectional age-associations and longitudinal developmental trends in hippocampal subfield volumes were discrepant, both by subfields and in direction. Further, volumetric changes were largely unrelated to changes in memory, with the exception that increase in subiculum volume was associated with gains in spatial memory. Longitudinal and cross-sectional patterns of brain-cognition couplings were also discrepant. We discuss potential sources of these discrepancies. This study underscores that children’s structural brain development and its relationship to cognition cannot be inferred from cross-sectional age comparisons.
Humans accumulate knowledge throughout their entire lives. In what ways does this accumulation of knowledge influence learning of new information? Are there age-related differences in the way prior knowledge is leveraged for remembering new information? We review studies that have investigated these questions, focusing on those that have used the memory congruency effect, which provides a quantitative measure of memory advantage because of prior knowledge. Regarding the first question, evidence suggests that the accumulation of knowledge is a key factor promoting the development of memory across childhood and counteracting some of the decline in older age. Regarding the second question, evidence suggests that, if available knowledge is controlled for, age-related differences in the memory congruency effect largely disappear. These results point to an age-invariance in the way prior knowledge is leveraged for learning new information. Research on neural mechanisms and implications for application are discussed.
Das Projekt »Re:Start nach der Krise« der Psychotherapeutischen Beratungsstelle an der Goethe-Universität hat das Ziel, Studierenden bei der Rückkehr an die Universität Hilfe und Orientierung anzubieten. Gerhard Hellmeister, Psychologe und Therapeut, hat mit seinem Team das Angebot entwickelt und spricht im Interview mit dem UniReport über das Prinzip des Design Thinking, über die Bedeutung von Krisen und Neuanfängen und über die Unterschiede des Angebots zu Selbstoptimierungsseminaren und Karrierecoachings.