Original Research•

Event segmentation in ADHD: neglect of social information and deviant theta activity point to a mechanism underlying ADHD

Abstract

Background Attention-deficit/hyperactivity disorder (ADHD) is one of the most frequently diagnosed psychiatric conditions in children and adolescents. Although the symptoms appear to be well described, no coherent conceptual mechanistic framework integrates their occurrence and variance and the associated problems that people with ADHD face.

Aims The current study proposes that altered event segmentation processes provide a novel mechanistic framework for understanding deficits in ADHD.

Methods Adolescents with ADHD and neurotypically developing (NT) peers watched a short movie and were then asked to indicate the boundaries between meaningful segments of the movie. Concomitantly recorded electroencephalography (EEG) data were analysed for differences in frequency band activity and effective connectivity between brain areas.

Results Compared with their NT peers, the ADHD group showed less dependence of their segmentation behaviour on social information, indicating that they did not consider social information to the same extent as their unaffected peers. This divergence was accompanied by differences in EEG theta band activity and a different effective connectivity network architecture at the source level. Specifically, NT adolescents primarily showed error signalling in and between the left and right fusiform gyri related to social information processing, which was not the case in the ADHD group. For the ADHD group, the inferior frontal cortex associated with attentional sampling served as a hub instead, indicating problems in the deployment of attentional control.

Conclusions This study shows that adolescents with ADHD perceive events differently from their NT peers, in association with a different brain network architecture that reflects less adaptation to the situation and problems in attentional sampling of environmental information. The results call for a novel conceptual view of ADHD, based on event segmentation theory.

What is already known on this topic

  • Attention-deficit/hyperactivity disorder (ADHD) is one of the most frequently diagnosed psychiatric conditions in children and adolescents with manifold symptoms affecting cognitive and social domains, but no coherent mechanistic framework integrates the various manifestations.

What this study adds

  • We provide evidence that calls for a novel conceptual view of ADHD, based on event segmentation theory. Adolescents with ADHD exhibit altered patterns in partitioning continuously incoming information about their environment and make less use of social information to structure perceived events. This difference is also reflected by an altered brain network architecture that is not adapted to the processing of social information.

How this study might affect research, practice or policy

  • Using a testing approach that can be translated into standardised diagnostic instruments with high ecological validity, we provide a novel mechanistic perspective on ADHD in which young people with the condition perceive events in their environment differently from their typically developing peers.

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders in children and adolescents.1 2 People diagnosed with ADHD not only exhibit core symptoms (inattentiveness, hyperactivity and impulsivity) but also experience the apparent consequences of these symptoms, including problems in school and social interactions.1 2 As with other psychiatric disorders, diagnosis remains exclusively based on a checklist of symptoms. However, why symptoms manifest as they do and what mechanism, if any, they share remain elusive.3 Therefore, novel conceptual approaches are required to explain at least some of the symptoms and, ideally, to shift from a purely symptom-descriptive to an explanatory-mechanistic view.

Typical ADHD diagnostic symptoms such as interrupting or intruding on others violate general expectations regarding the structure of interpersonal exchange. So far, these behaviours have mostly been subsumed more generally under the core symptoms of impulsivity and inattentiveness,4–8 and possible underlying mechanisms have not been examined in detail. For a deeper understanding of the underlying processes that are altered in ADHD, we sought to move beyond observable core symptoms and test the hypothesis that these behaviours reflect differences in the ‘segmentation of events’. In this regard, we suggest that people with ADHD might perceive the pace of unfolding events differently from neurotypically developing (NT) peers, potentially viewing these events as unfolding more slowly. Alternatively, they might perceive cues marking the end and beginning of sequential events differently, so that, for example, the end of an unpleasant event is less predictable. In either case, a person diagnosed with ADHD might have the impression that situations last too long, and in turn, they may become bored more quickly or feel fatigued sooner than NT peers. This assumption also might be related to findings that timing and time perception are altered in ADHD so that the purely temporal perception of ‘events’ might differ as well.9–12

A theoretical framework for this partitioning of events can be derived from event segmentation theory (EST).13–15 EST suggests that each current situation and its expected trajectory are mentally represented in a so-called ‘working event model’ (WEM). The WEM is influenced by prior experiences with similar situations, stored in long-term memory (so-called ‘event schemata’) and by the sum of the perceptual input from the environment. Based on the WEM, expectations arise about how a situation will probably unfold, and these expectations are constantly compared with the actual perceptual input.13–15 Whenever the expectation is unmet, the current WEM is updated and adjusted based on available event schemata to better represent the current situation. Moreover, this updating of one WEM leads to its closure and to the opening of a new one,13–15 marking recognition that a new situation is beginning.

Based on these considerations, we hypothesised that the mechanisms described in the EST might be altered in ADHD and that these alterations would in turn be reflected at the neurophysiological level. More specifically, we posited that altered event segmentation in ADHD might be related to alterations in specific electroencephalography (EEG) frequency bands that are linked to distinct, general cognitive functions16 and specifically to event segmentation.17 The ‘alpha frequency band’ reflects a selection mechanism guiding which information is further processed within a so-called ‘knowledge system’,18 19 presumably storing event schemata. The ‘beta frequency band’ is likely associated with maintenance of the ‘status quo’,20–22 which may reflect maintenance of the current WEM. The ‘theta frequency band’ is associated with prediction error signalling,23 24 which might underlie the operation of comparing the WEM and related expectations to current perceptual input from the environment. All of these processes, reflected by distinct frequency bands, could give rise to the alterations/deficits observed in ADHD.

Following the rationale of spatiotemporal neuroscience,25–27 it is not only the information about the power in a specific frequency band that will provide more detailed insights into the expected alterations in event segmentation in ADHD compared with neurotypical development. Instead, the functional neuroanatomical localisation of the activity in these frequency bands and particularly the communication among brain regions within these frequency bands are of equal importance. As suggested by a previous study,17 the interplay of brain regions associated with the activity of different frequency bands is important in the event segmentation process. For other aspects of cognitive functions, previous studies have already shown ADHD-related changes in structural and functional connectivity profiles between brain regions within these frequency bands.28 29

Here, we specifically examined communication among brain regions within the alpha, beta and theta frequency bands, evaluating which functional neuroanatomical structures are associated with altered dynamics in these frequency bands. Furthermore, to gain insights into information flow among brain regions as a neural basis for the segmentation of incoming information into discrete events, we examined the directed exchange of information among brain regions. To investigate the effective connectivity among the sources of the frequency band activity, we applied a machine-learning approach (non-linear Causal Relationship Estimation by Artificial Neural Network (nCREANN)).30 We hypothesised that, compared with NT participants, individuals with ADHD would have weaker directed communication among brain regions.

Methods

Detailed information about the sample characteristics and the described methods can be found in online supplemental material.

Participants

Our study consisted of 52 NT adolescents and 56 adolescents diagnosed with ADHD, aged 11–16 years. They were all recruited from an in-house database and via advertisements and invited to participate in the study after a brief telephone interview with them and/or their legal guardians. The flowchart of selection and exclusion of participants is shown in figure 1. Per parent report, four of the participants in the ADHD group were also diagnosed with a reading and spelling disability, and one individual in this group was also diagnosed with a social-emotional disorder. The study ultimately included 38 NT adolescents (14.2 (1.6) years, 14 male, male-to-female ratio 0.58) and 43 adolescents diagnosed with ADHD (13.7 (1.4) years, 35 male, male-to-female ratio 4.38).

Figure 1
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Figure 1

Flowchart of participant inclusion. ADHD, attention-deficit/hyperactivity disorder; EEG, electroencephalography; NT, neurotypically developing.

Before the study appointment, parents of participants were asked to fill in online questionnaires assessing ADHD symptoms: the Conners Short Form and the ADHD Questionnaire for Parents (‘Fremdbeurteilungsbogen ADHS’ (FBB-ADHS)). Scores on both questionnaires differed significantly between the two groups (Conners: t(77)≥2.05, p≤0.044; FBB-ADHS: t(77)≥6.96, p<0.001; details in online supplemental table 1). On two subtests of the Intelligence and Development Scales 2 (matrices completion, naming categories) that all participants completed, the two groups did not differ (matrices completion: t(79)=1.10, p=0.273; naming categories: t(79)=1.38, p=0.173). Details about additional questionnaires can be found in the online supplemental methods and table 2.

Task

The short movie ‘The Red Balloon’,31 which has a duration of 32′35″ min, was shown to participants. The movie was divided into three clips lasting about 10 min each, with breaks of self-chosen length between these clips (details provided in online supplemental material). While watching the video, the participants completed an event segmentation task. They were instructed to ‘press the space key whenever something in the movie ended and something else was about to start’, without specific instructions regarding the length, content or a total number of such segments. Participant responses were recorded and used as a behavioural measure of event segmentation.

In previous work, Zacks et al32 analysed this movie frame by frame (30 frames per second; 1955 s × 30 frames=58 650 frames in total) and defined nine types of situational changes between two consecutive frames: temporal changes (discontinuities in depicted time), large spatial changes (changes in a character’s location), small spatial changes (changes in the location of the camera perspective), character changes (modifications of the character currently in focus), character–character interaction changes (changes in the interaction between two characters), character–object changes (changes in a character’s use of objects), cause changes (when the reason for an action in the current frame did not result from something in the previous frame), goal changes (when a character’s goals changed) or scene changes (ie, cuts). Following the rationale of Zacks et al,32 we partitioned the movie into intervals of 2 s (=60 frames) for the behavioural analysis, resulting in a total of 978 intervals (978 intervals × 60 frames=58 650 frames in total). For each interval of 2 s, we counted the number of situational changes, resulting in 512 intervals with no situational change, 276 with one situational change, 104 with two situational changes, 46 with three situational changes, 30 with four situational changes and 10 with five or more situational changes.

Analysis of EEG data

EEG data were concomitantly recorded at a sampling rate of 500 Hz while the participants completed the event segmentation task. After data were down-sampled from 500 Hz to 300 Hz, preprocessing of the recorded EEG data was performed using the ‘Automagic’ pipeline33 and EEGLAB34 in MATLAB 2019a (The MathWorks). For segmentation of the EEG data, the FieldTrip toolbox35 was used to segment periods from 0.5 s before to 2 s after the exact time point at which situational changes (as defined by Zacks et al,32 see above) occurred. We limited this analysis only to those change categories that differed significantly between groups in the behavioural data. A different approach of segmentation of the EEG data considering the periods around the responses given by the participants is presented in the online supplemental analysis 1. For the periods segmented relative to situational changes, a time–frequency analysis was conducted to compare the activity in the theta (4–7 Hz), alpha (8–12 Hz) and beta (15–30 Hz) frequency bands between the NT and ADHD groups using cluster-based permutation tests. To localise the functional neuroanatomical sources of activity that differed between the two groups, beamforming and subsequent clustering of source activity were conducted. Only the top 3% most active voxels were considered for subsequent analysis steps, which included analysis of effective connectivity among the most active brain regions. To determine the effective connectivity among the identified brain regions, we used nCREANN.30 In contrast to more conventional methods, the nCREANN approach considers both linear and non-linear connectivity and thus represents linear and non-linear dynamics in the information flow among cortical regions.

Statistical analyses

To compare age between the two groups, we used the independent samples t-test, and we compared gender proportions with the χ2 test. Parent questionnaire data were compared using independent samples t-tests. The male-to-female ratio was calculated by dividing the number of male participants by the number of female participants in a group. For the analysis of behavioural effects, we used two mixed effects logistic regression models. In the first model, the number of situational changes in a 2 s interval (0–5) was used as the predictor and the occurrence of a response in this interval was used as the outcome variable (0: no response; 1: response occurred). In the second model, the occurrence (0: no occurrence; 1: occurrence) of a specific type of situational change in an interval was used as the predictor, and the occurrence of one or more responses in this interval was used as the outcome variable (0: no response; 1: response occurred). Moreover, group membership was used as the predictor in both statistical models (0: NT; 1: ADHD). Both models estimated the random intercept for participants to take the variability between participants into account. Moreover, for both models, ORs were computed from the coefficient results of the fixed effects. The variance inflation factor (VIF) was used to test for multicollinearity. In addition to the logistic regression models, an independent t-test was used to compare the mean length of the segments defined by the participants between groups. For the EEG data, as outlined above, cluster-based permutation tests were used for group comparisons. Further details about the EEG data analysis are given in online supplemental material. Two-tailed p value<0.05 was considered significant.

Results

Behavioural results

The results of the mixed effects logistic regression are shown in figure 2. The prediction of segmentation behaviour from the number of situational changes in an interval and from group membership (intercept: −3.29, p<0.001; OR=0.04; 95% CI 0.03 to 0.05; VIF≤2.1) revealed significant coefficients of the number of situational changes in an interval (0.31, p<0.001; OR=1.36; 95% CI 1.32 to 1.41) and, crucially, of the interaction between the number of situational changes in an interval and group membership (−0.07, p=0.006; OR=0.94; 95% CI 0.89 to 0.98). The probability of segmentation was more dependent on the number of situational changes in an interval in the NT group (intercept: −3.28, p<0.001; 0.31, p<0.001; OR=1.36; 95% CI 1.32 to 1.41) than in the ADHD group (intercept: −3.17, p<0.001; 0.24, p<0.001; OR=1.27; 95% CI 1.23 to 1.32). However, group membership alone did not predict the segmentation probability (0.12, p=0.375; OR=1.13; 95% CI 0.87 to 1.47).

Figure 2
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Figure 2

Behavioural results. (A) Results of the logistic regression with segmentation probability as the criterion and the number of changes in a 2 s interval as the predictor for the two groups. (B) Results of the logistic regression with segmentation probability as the criterion and the types of changes in a 2 s interval as predictors. (C) The density of the mean event segment length. Values for NT adolescents given are in red and values for the ADHD group are given in blue. ADHD, attention-deficit/hyperactivity disorder; NT, neurotypically developing.

The second mixed effects logistic regression model (figure 2B), examining the prediction of segmentation behaviour from the type of situational changes and from group membership (intercept: −3.27, p<0.001; OR=0.04; 95% CI 0.03 to 0.04; VIF≤6.8), established an interaction of type of situational changes and group membership only for character changes (−0.17, p=0.045; OR=0.85; 95% CI 0.72 to 1.00) and for character–character interaction changes (−0.26, p=0.014; OR=0.77; 95% CI 0.63 to 0.95). Specifically, character changes were more predictive for segmentation behaviour in the NT group (0.70, p<0.001; OR=2.02; 95% CI 1.79 to 2.27) than in the ADHD group (0.53, p<0.001; OR=1.70; 95% CI 1.52 to 1.90). Furthermore, in the NT group, the occurrence of a character–character interaction change was a predictor of segmentation behaviour (0.36, p<0.001; OR=1.44; 95% CI 1.24 to 1.67), whereas in the ADHD group, it was not (0.10, p=0.161; OR=1.11; 95% CI 0.96 to 1.28). For all other types of situational changes, there was no interaction with the predictor group membership. Detailed values are given in online supplemental table 3. An additional analysis considering gender as a predictor in the logistic regression models can be found in the online supplemental analysis 2.

The comparison of the average segment length between the two groups showed no difference in the mean duration of the indicated segments (t(79)=0.20; p=0.844; figure 2C).

Frequency band activity and source localisation

The behavioural findings revealed that the NT group’s responses were more dependent on situational changes related to characters in the movie (character, character–character interaction) compared with the ADHD group. Consequently, we focused the neurophysiological data analysis on character/character–character interaction (CCC) periods. At the electrode level, we compared CCC periods between groups using cluster-based permutation testing to identify the electrode clusters showing significant activity differences in a specific frequency band. There was a negative cluster (p<0.010) indicating lower theta band activity (TBA) in the NT group compared with the ADHD group (figure 3A) and indicating higher TBA levels at the frontal, parietal, temporal and occipital electrodes during CCC periods in the ADHD group. The time courses of the power in the three frequency bands are illustrated in online supplemental figure 1. Because cluster-based permutation tests did not reveal any group differences for alpha and beta band activity, we limited source reconstruction to TBA group effects. The dynamical imaging of coherent sources (DICS) beamforming technique, applying the neural activity index, along with the Density-Based Spatial Clustering of Applications with Noise algorithm based on DICS beamforming, identified distinct clusters of activity for TBA. Regarding the CCC periods in the NT group, sources were localised in the medial frontal cortex (MF; BA6/BA24/BA8), the right occipitotemporal regions encompassing the right fusiform gyrus (r-FG; BA37/BA19), and the left occipitotemporal regions encompassing the left fusiform gyrus (l-FG; BA37/BA19; figure 3B). In contrast, the ADHD group exhibited activity clusters in the MF (BA6/BA24/BA8/BA32), right frontotemporal/insular regions (r-FT; BA22/BA41/BA13) and right inferior frontal regions (r-IF; BA9/BA44; figure 3B).

Figure 3
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Figure 3

Neurophysiological results. (A) Results of the TF analyses (TF plot) and the cluster-based permutation testing (topographic plot) for TBA. The TF plot displays the mean difference in TBA between the NT and ADHD groups (NT minus ADHD) across the significant electrodes identified through cluster-based permutation testing. The topographic plot illustrates the average difference in TBA between the NT and ADHD groups (NT minus ADHD) within the significant time windows identified through cluster-based permutation testing. (B) The non-linear Causal Relationship Estimation by Artificial Neural Network linear (left) and non-linear (right) connectivity between brain regions for TBA. Blue arrows represent linear connectivity, and red arrows represent non-linear connectivity. ADHD, attention-deficit/hyperactivity disorder; IFG, left fusiform gyrus; MF, medial frontal cortex; NT, neurotypically developing; rFG, right fusiform gyrus; r-FT, right frontotemporal/insular regions; rIT, right inferior temporal; TBA, theta band activity; TF, time–frequency.

Effective connectivity results

Figure 3B shows the results of the effective connectivity analysis with nCREANN. In assessing model fitting, the mean square error values during training and testing were, respectively, 0.029 and 0.038 for the NT group and 0.027 and 0.037 for the ADHD group. The average training and testing R2 values were both 0.992 in the NT group; in the ADHD group, the average training value was 0.992 and the average testing value was 0.991.

In the NT group, the linear connectivity values were as follows: 1.00 from MF to l-FG and 0.74 from l-FG to MF; 1.11 from MF to r-FG and 0.84 from r-FG to MF; 1.40 from l-FG to r-FG and 1.43 from r-FG to 1-FG; 3.59 from MF to l-FG and 6.18 from l-FG to MF; 3.80 from MF to r-FG and 5.99 from r-FG to MF; and 6.22 from l-FG to r-FG and 7.12 from r-FG to 1-FG.

In the ADHD group, the linear values were as follows: 1.79 from MF to r-FT and 1.65 from r-FT to MF; 1.27 from MF to r-IF and 1.23 from r-IF to MF; 1.93 from r-FT to r-IF and 2.07 from r-IF to r-FT; 3.51 from MF to r-FT and 7.03 from r-FT to MF; 2.33 from MF to r-IF and 5.04 from r-IF to MF; and 7.44 from r-FT to r-IF and 6.14 from r-IF to r-FT.

Discussion

We assessed a potential and novel conceptual approach to explaining specific aspects of ADHD core symptoms as a way to facilitate a shift from a symptom-descriptive to an explanatory-mechanistic view. Using EST,13–15 we examined whether and to what extent adolescents with ADHD perceived the unfolding of events in their environment differently from NT adolescents and thus had a different conception of ‘what is happening now’ compared with same-age peers. We examined whether young people with ADHD exhibited differences in segmentation of events based on the number of changes in a situation and on the types of these changes. To identify the neural underpinnings of any behavioural group differences, we compared oscillatory activity, including its neuroanatomical sources and directed communication between cortical regions, between the two groups.

Main findings

The behavioural data imply that participants with ADHD were less aware than their NT peers of changes in their environment, being less reactive to situational changes and showing comparatively altered segmentation of incoming information. In other words, the ADHD group appeared to be less stringent in organising incoming information to guide their behaviour. Of interest, our findings show that, compared with the NT group, the ADHD group appeared to consider changes in social interactions to a lesser extent for segmenting events. This comparative lack of consideration of social information in event segmentation adds to previous research indicating that people diagnosed with ADHD rely less than NT adolescents on social and affective cues for decision-making and orienting attention.5 36 Intriguingly, the participants diagnosed with ADHD did not demarcate longer or shorter periods of events than their NT peers but did seem to be less dependent on and thus less attentive to changes in the current situation. Thus, the alterations in event segmentation in the ADHD group do not appear to arise solely from altered time perception and the resulting timing of events.9 Of note, the two groups did not differ in intelligence scores, so this altered event segmentation behaviour and reduced reliance on social information in the ADHD group are unlikely to be attributable to differences in cognitive functioning.

To elucidate the neural underpinnings of this difference, we examined the electrophysiological responses to the occurrence of social situational changes. We found that, after such changes, TBA was higher in the ADHD group than in the NT group, corroborating suggestions that TBA might be a marker of psychiatric vulnerability.37 TBA relative to social situational changes was associated with medial prefrontal regions, indicating a signalling of mismatches between the predicted and actual trajectories of the situation. However, the two groups differed in other regions that were most active, with the NT group showing high TBA in the bilateral occipitotemporal regions encompassing the fusiform gyri. Considering directed connectivity between the regions in the NT group, the nCREANN analysis revealed strong bidirectional linear and non-linear connectivity between the left and right occipitotemporal regions, suggesting a marked information exchange between the visual association cortices, but less impact of the MF on activity in these areas. Thus, in the NT group, the error signalling about mismatching incoming information appears to have occurred already in the visual association cortices that are important for processing social information.38 39 Only recently has it been shown that sensory cortices can process mismatching sensory information.40 41 In contrast to the NT group, the ADHD group showed high TBA in r-IF and r-FT regions. Thus, in the NT group, regions associated with processing social information38 39 were highly active after the social information occurred, whereas the ADHD group did not appear to activate these regions to the same extent. The ADHD group did, however, seem to activate insular cortex regions associated with the more general integration of perception and action, and the r-IF. The latter has been associated with attentional sampling of information42 43 for which studies have also revealed a role of TBA.44 There was a strong bidirectional connectivity between the r-IF and MF, as well as between the r-IF and the insular cortex, implying that the r-IF might be a hub region in the ADHD brain during event segmentation. Deficits in attentional processes are the key diagnostic feature of ADHD, and the higher TBA observed here in the ADHD group might reflect problems deploying attentional control. This interpretation is corroborated by previous findings that TBA is upregulated during effortful cognitive processes.45–47 Thus, the interplay of alterations in specific frequency bands and differences in the involved brain areas may underlie altered cognitive processing in ADHD, highlighting the relevance of spatiotemporal neuroscientific considerations in psychopathology.27 48

Limitations

While our study provides important, new insights into the underpinning of ADHD, it has some limitations. To gain more clarity about the role of event segmentation as a fundamental process, future studies need to confirm that the differences in event segmentation are attributable to ADHD symptoms. In the current study, gender and medication might have contributed to the obtained effects in event segmentation. This should be taken into account in future studies, for instance by matching the participants in the patient and control groups by gender and by setting stricter exclusion criteria regarding the use of medication. In interpreting neurophysiological results, challenges arise related to the different brain structures involved in the networks. Although the current results point to the involvement of different brain regions, a direct connection with ADHD symptoms could not be established. The findings offer no immediate diagnostic or therapeutic benefit, but they open the way for potential advances in the treatment of ADHD with the basic findings serving as a starting point for further research.

Implications

The current findings suggest that adolescents diagnosed with ADHD appear to perceive the unfolding of events in their environment differently from NT adolescents. In particular, the ADHD group seemed to differ from the NT group in the kind of information they used to segment incoming information into discrete periods. The observed differences in event segmentation between individuals with ADHD and NT peers imply a specific cognitive processing style in ADHD that may influence the perception of and interaction with the environment. The related changes in neural processing, particularly in theta oscillations, indicate that specific neurophysiological markers may be associated with these differences. In this context, a less situationally adapted architecture of directional communication between neuroanatomical structures suggests a possible neural basis for the difficulties that people diagnosed with ADHD can face in adapting to changing, particularly social contexts. These findings may be taken to offer a paradigm shift in the conceptualisation of ADHD and underscore the need for a new understanding of how people with ADHD perceive and navigate in their environment. In this regard, the findings call for a conceptually novel view on ADHD and on how individuals with ADHD perceive and represent their environment.

Astrid Prochnow graduated from the Technische Universität Dresden, Germany, in 2019 after completing a bachelor's degree in psychology and a consecutive master's degree with a focus on cognitive-affective neuroscience. She obtained a PhD degree at the Chair of Cognitive Neurophysiology at Technische Universität Dresden in November 2023. In addition to her research work, she also conducted neurofeedback training with ADHD patients during her PhD student time. Since 2022, she holds a post-doc position at the Chair of Cognitive Neurophysiology at Technische Universität Dresden. Her main research interests include response inhibition, the integration of perception and action, event segmentation and ADHD.

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