Biology subjects

Nemeth, D.

Publications and source records attributed to Nemeth, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Flexible belief updating drives the childhood advantage in statistical learning

Children often outperform adults in probabilistic statistical learning tasks, yet the mechanisms underlying this developmental advantage remain poorly understood. Here, we used eye-tracking measures of belief updating to examine how children and adults acquire and update predictions in a probabilistic sequence-learning task. Using the standard (oculomotor) reaction time measure, children showed stronger statistical learning than adults, replicating previous behavioral findings while revealing a more detailed profile of developmental differences in statistical learning. Critically, children updated their predictions more frequently: they were less likely to repeat previous predictions and more likely to shift their expectations in response to new input. Adults, in contrast, showed greater persistence, tending to maintain prior predictions even when those predictions were inconsistent with the underlying statistical structure. Despite these pronounced differences in updating behavior, the processing and use of prediction errors were remarkably similar across age groups. These findings indicate that developmental differences in statistical learning do not primarily arise from how prediction errors are computed, but rather from how prior beliefs and incoming information are weighted during belief updating. Childrens enhanced learning may therefore reflect reduced reliance on stable priors and greater sensitivity to current sensory evidence, supporting a more exploratory learning strategy. Adults, by contrast, appear to favor an exploitative strategy that stabilizes existing predictions but reduces flexibility in probabilistic environments. More broadly, the results suggest that developmental changes in statistical learning may reflect age-related differences in how readily learners revise their predictions in response to incoming evidence. By integrating sensitive oculomotor measures with analyses that probe the mechanisms underlying belief updating, the present study provides a more fine-grained account of how predictive learning changes across development and offers a framework for reconciling previously inconsistent developmental findings in statistical learning.

neuroscience↗

bioRxiv2026

Relationship between cognitive flexibility and disordered eating attitudes across the non-clinical spectrum

Disordered eating attitudes exist on a continuum that extends well below clinical diagnostic thresholds, yet the cognitive correlates of this non-clinical variation remain incompletely understood. Previous research linking executive functioning to disordered eating in non-clinical samples has relied almost exclusively on self-report questionnaire measures of executive function, which show weak correspondence with performance-based assessments. This methodological reliance leaves open the question of how objective executive performance relates to eating behavior across the spectrum. The present study addressed this gap by examining the association between performance-based measures of executive function and disordered eating attitudes in a non-clinical sample of 243 university students via an online experiment, using a dimensional approach consistent with the Research Domain Criteria framework. Participants completed established neurocognitive tasks covering three executive function domains: working memory was assessed with Digit Span and an N-back task, inhibitory control with a Go/No-Go task, and cognitive flexibility with the Card Sorting Task. Disordered eating attitudes were indexed using the EAT-26 total score and its subscales. A notable correlation was identified between cognitive flexibility and disordered eating attitudes, while working memory and inhibitory control exhibited no such association. Overall, our findings provide evidence for associations between executive functioning and disordered eating attitudes in a non-clinical sample.

neuroscience↗

bioRxiv2026

Early reaction time variability predicts implicit statistical learning: a comparison of four variability indices

High intra-individual reaction time variability (RTV) is traditionally viewed through a deficit perspective and interpreted as a maladaptive signature of attentional lapses, cognitive inefficiency, and systemic noise. However, theories from motor learning and the competitive neurocognitive networks framework suggest that behavioral variability and reduced top-down control might actually facilitate certain forms of implicit skill acquisition. The present study addresses the apparent conflict between these perspectives by investigating whether elevated RTV serves as an adaptive, functional precursor to implicit statistical learning. Across two independent studies, participants completed the Alternating Serial Reaction Time (ASRT) task. We quantified early RTV during the initial task phase using multiple metrics -- coefficient of variation, inter-trial RTV, and ex-Gaussian parameters Sigma and Tau-- to predict subsequent statistical learning. Analyses controlled for baseline response speed and early learning artifacts, and test-retest reliability measures were also evaluated. Our results show that early RTV predicted later statistical learning measured via reaction times. This predictive relationship was most consistent for metrics capturing dynamic, moment-to-moment fluctuations (inter-trial RTV and Sigma) rather than extreme attentional lapses (Tau). While the effect size was relatively small, the association remained significant after controlling for potential statistical confounds. Furthermore, early RTV demonstrated strong test-retest stability. These findings challenge the exclusively deficit-oriented perspective on behavioral noise. Instead, we propose that elevated RTV may reflect an adaptive, exploratory processing tendency, analogous to kinematic exploration in motor learning, that could support the brains ability to implicitly extract and model probabilistic environmental regularities.

neuroscience↗

bioRxiv2026

Disentangling the Relationship Between Mind Wandering and Symptom Dimensions in a Non-Clinical Sample: ADHD as the Primary Driver

Introduction: Mind wandering has been linked to a wide range of psychiatric conditions, yet most studies have examined these associations in isolation. Given the substantial comorbidity across the psychopathological spectrum, it remains unclear whether specific symptom dimensions uniquely and independently predict mind wandering once their overlap is accounted for. Methods: To address this, we adopted a dimensional approach in a non-clinical sample (N = 384), simultaneously modeling seven symptom dimensions: ADHD, depression, obsessive-compulsive tendencies, schizotypy, autistic traits, hypomania, and eating disorder symptoms. Results: At the bivariate level, mind wandering correlated positively with all symptom dimensions. However, when the substantial shared variance across dimensions was accounted for in both frequentist and Bayesian multivariate regression models, only ADHD symptoms consistently emerged as a unique predictor ({beta} = 0.52; BF > 1000). Hypomania symptoms showed a weak, inconsistent positive association across analyses. All remaining predictors yielded negligible unique contributions, with Bayes factors supporting the null hypothesis. Conclusion: These findings suggest that once overlap between symptom dimensions is accounted for, ADHD symptoms remain the only consistent predictor of trait-level mind wandering. We speculate that previously reported associations between mind wandering and other psychopathological symptom dimensions may largely reflect variance shared across the symptom battery rather than dimension-specific mechanisms.

neuroscience↗

bioRxiv2026

Capturing learning on the fly: an eye-tracking method to quantify prediction errors and updating the prior

The ability to build predictive models of the environment fundamentally drives adaptive behavior. Yet, the real-time dynamics of how these internal models are formed and updated remain poorly understood. Conventional methods often rely on indirect, offline measures or noisy motor responses, limiting insight into the fine-grained computational processes underlying learning. Here, we introduce a generalizable, gaze-based analytical framework that directly tracks the trial-by-trial dynamics of expectation formation and updating. Applying this framework to an unsupervised probabilistic learning task, we categorized anticipatory saccades to dissociate prediction errors arising from environmental stochasticity from those reflecting an inaccurate internal model, and quantified how these predictions were iteratively revised. Learners differentiated between these error types: noise-driven errors were more likely to happen, and triggered less updates than errors reflecting insufficient knowledge of the regularity. At the same time, participants exhibited a strong preference to repeat their previous predictions. This repetition bias was amplified when predictions aligned with the underlying regularity, but was also present for non-aligned responses. Critically, updating depended more strongly on whether a prior belief was consistent with the tasks probabilistic structure than on whether the predicted stimulus matched the actual, presented stimulus. These findings suggest that statistical learning may not strongly be driven by errors; rather, it may rely on conservative updating with relatively low learning rate, or, on a Hebbian, repetition-based process. Our framework thus offers a dual contribution: a broadly applicable tool for quantifying real-time expectations, and evidence for a learning strategy that prioritizes model stability in noisy environments.

neuroscience↗

bioRxiv2025

No sex differences in predictive processing

Statistical learning, defined as the implicit extraction of environmental regularities, is considered a fundamental cognitive mechanism that supports predictive processing across individuals, domains, and species. However, whether it is modulated by gender remains unclear. This study investigated potential gender-related differences in statistical learning using an age-matched sample of 129 women and 129 men (N = 258) who completed a well-established visuomotor probabilistic learning task. Statistical learning was revealed in both reaction time and accuracy measures, with participants responding faster and more accurately to high-probability than to low-probability trials. Critically, neither the magnitude nor the trajectory of statistical learning differed between women and men. Furthermore, no baseline differences in visuomotor performance interacted with learning metrics. Overall, these findings suggest that implicit statistical learning is a robust cognitive mechanism that is highly resilient to gender-related variation. Highlights Implicit statistical learning was robust in both women and men, with no evidence for gender differences in the magnitude or trajectory of learning. Although a minor baseline difference in general response speed was observed, this effect did not interact with or influence statistical learning. Statistical learning was robust across menstrual cycle phases and hormonal contraceptive use groups. The findings suggest that the "predictive brain" relies on fundamental, evolutionarily conserved mechanisms that are largely resilient to gender-based variation.

neuroscience↗

bioRxiv2025

ADHD-like traits reshape the balance between inhibitory control and predictive processes

Adaptive behavior relies on a dynamic balance between flexible, goal-directed control and efficient, automatic processes. This equilibrium is often disrupted in Attention-Deficit/Hyperactivity Disorder (ADHD), a condition understood to exist along a continuum of traits in the general population. While ADHD is consistently linked to response inhibition deficits, its relationship with statistical learning (a mechanism for habit learning) and, crucially, the interaction between these functions, remains underexplored. Here, using a novel paradigm in a non-clinical sample of university students (n = 226), we investigated how ADHD-like traits modulate the interplay between response inhibition and statistical learning. We found that higher ADHD-like traits were associated with poorer response inhibition, confirming previous research. Importantly, we uncovered an antagonistic relationship between inhibition and statistical learning, where weaker inhibitory control typically led to enhanced learning of environmental regularities. However, this learning advantage progressively diminished across the ADHD trait continuum, becoming markedly reduced in individuals with high symptom prevalence. These findings provide novel evidence that ADHD-like traits influence not only isolated neurocognitive processes but also their dynamic interaction, highlighting a spectrum-based mechanism that may underlie the transition from adaptive variability to maladaptive behavioral patterns. This work advocates for a dimensional approach to ADHD, emphasizing early detection and targeted interventions for individuals with varying levels of symptomatic expression, broadening support beyond traditional diagnostic boundaries.

neuroscience↗

bioRxiv2025

Faster adult implicit probabilistic statistical learning following childhood adversity

According to deficit models, early life adversity disrupts normal development, leading to long-term emotional, behavioural, and cognitive difficulties. However, some evidence suggests that certain psychological skills may be preserved or even enhanced by early adversity. We hypothesised that implicit learning and memory would be equally effective in individuals exposed to childhood adversity and those from more favourable backgrounds, and compared the effects of childhood versus adult adversity. To this aim, retrospective childhood harshness and unpredictability measurements and current perceived socio-economic status were collected in a sample of 325 participants at a Hungarian university taking part in an online experiment. They also completed a task allowing the assessment of multiple components of implicit statistical learning, including initial acquisition of regularities, consolidation of established regularities, resistance of established regularities against interference, and acquisition of novel regularities. Results showed that although statistical learning reached the same eventual level, its pace was quicker in individuals with relatively greater early life adversity exposure. Conversely, lower current socio-economic status was linked to reduced learning performance. These findings partially support the hidden talents framework, suggesting that early adversity may promote certain adaptive cognitive skills.

neuroscience↗

bioRxiv2025

Frontal theta synchronization facilitates the acquisition of new statistical regularities, evidenced by predictive eye movements

Frontal midline theta oscillations are key neural markers for learning, set-shifting, and adaptive behavior, signaling cognitive control and the reorganization of neural representations. The present study explored how these oscillations mediate the extraction and updating of statistical regularities. We delivered 6 Hz in-phase transcranial alternating current stimulation (tACS) or sham tACS, synchronizing frontal midline theta during an eye-tracking probabilistic sequence learning task designed to test cognitive flexibility and assess pre-stimulus gaze direction changes. A novel probabilistic sequence with a partially overlapping structure was introduced that allowed us to distinguish between the retention of old sequences and the acquisition of new ones. Following comparable statistical learning in both groups during the stimulation session, our results showed that tACS reduced the erroneous anticipations of previously learned regularities, and allowed participants to show anticipations corresponding to the previously learnt regularities while being able to anticipate novel regularities flexibly. These results suggest a role of frontal midline theta in the flexible rewiring of the mental representations of prior probabilistic structures, and in making predictions more accurate.

neuroscience↗

bioRxiv2025

Learning Amidst Noise: The Complementary Roles of Neural Predictive Activity and Representational Changes

The ability to extract structured sensory patterns from a noisy environment is fundamental to cognition, yet how the brain learns complex regularities remains unclear. Using magnetoencephalography during a visuomotor task, we tracked the neural dynamics as humans learned non-adjacent temporal dependencies embedded in noise. We reveal that learning is supported by two temporally dissociable mechanisms. Neural predictive activity emerged rapidly, with stimulus-specific patterns appearing before stimulus onset and preceding measurable behavioral improvements. This is followed by a slower build-up of representational change, characterized by an increased neural pattern similarity between statistically dependent, non-adjacent elements. Both processes are supported by a distributed consortium of networks, with the sensorimotor and dorsal attentional networks playing a central role. These findings suggest that both neural predictive activity and representational changes contribute to learning regularities, revealing a temporal hierarchy in which neural predictive activity precedes behavioral improvement and is followed by neural representational changes, possibly facilitating the gradual consolidation of knowledge into stable neural representations.

neuroscience↗

bioRxiv2025

Obsessive-Compulsive Tendencies Shift the Balance Between Competitive Neurocognitive Processes

Theoretical models of Obsessive-Compulsive Disorder (OCD) emphasize that symptoms may arise from an imbalance between habitual and goal-directed processes, characterized by increased reliance on habitual behavior and reduced efficiency of goal-directed control. However, it remains unclear whether similar alterations appear at a more general functional level, beyond reward-driven mechanisms. The present study, therefore, investigated the relationship between statistical learning (SL), an implicit, reward-independent mechanism that supports the detection of environmental regularities and the formation of habitual behaviour, and cognitive flexibility, defined as the capacity to adapt behaviour and cognitive strategies to changing environmental demands. By adopting a dimensional approach to obsessive-compulsive (OC) tendencies in a university student sample, we aimed to clarify how continuous symptom variability relates to the interaction of these neurocognitive functions. A total of 404 participants completed an online experiment, including a probabilistic sequence learning task assessing SL and a card-sorting task measuring cognitive flexibility. Results revealed an antagonistic relationship between SL and cognitive flexibility. Importantly, this inverse association weakened as OC tendencies increased, suggesting that OC tendencies may alter the balance between automatic and goal-directed functions. HighlightsO_LICD theories propose an imbalance between automatic and goal-directed control systems. C_LIO_LItested whether this imbalance emerges at a functional level, beyond reward-based learning. C_LIO_LIlearning and cognitive flexibility showed an antagonistic relationship in a university student sample. C_LIO_LIinverse association weakened as obsessive-compulsive tendencies increased. C_LIO_LIC tendencies alter the interaction between automatic learning and executive control. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/669948v4_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@9f700corg.highwire.dtl.DTLVardef@1752b54org.highwire.dtl.DTLVardef@16f9a98org.highwire.dtl.DTLVardef@e99e6_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Inhibiting the right dorsolateral prefrontal cortex selectively enhances unsupervised statistical learning

The brain must balance the automatic extraction of environmental regularities with top-down cognitive control, yet the causal neural mechanisms governing this interplay are debated. In particular, the hemispheric contributions of the dorsolateral prefrontal cortex (DLPFC) remain unresolved. Here, we applied inhibitory repetitive transcranial magnetic stimulation (rTMS) to the left, right, or bilateral DLPFC in 95 healthy adults during a probabilistic sequence learning task. We found that inhibiting the right and bilateral DLPFC significantly enhanced statistical learning compared to sham stimulation, while the left DLPFC stimulation group remained similar to sham. Contrary to the hypothesis that this competition is mediated by the suppression of existing knowledge, episodic memory performance was unaffected across all stimulation groups. These findings challenge a direct episodic gating mechanism and suggest the DLPFCs influence on statistical learning is hemisphere-dependent. As an alternative, we propose that right DLPFC inhibition shifts cognitive processing toward a more exploratory information-sampling strategy, a view supported by our finding of significantly greater reaction time variability in the right and bilateral stimulation groups. Together, our results provide causal evidence for a right-lateralized suppressive influence of the DLPFC on unsupervised learning and suggest its modulatory role is linked to information processing style rather than direct competition with episodic memory systems.

neuroscience↗

bioRxiv2025

A functional trade-off between executive control and implicit statistical learning is dynamically gated by mind wandering

Human cognition must balance goal-directed behavior with the need to learn from environmental regularities. Mind wandering (MW), a state of attentional decoupling from the task at hand, is paradoxically associated with both executive failures and enhanced implicit statistical learning, yet the direct relationship between these phenomena remains unclear. Here, we provide direct behavioral evidence for a functional trade-off between these competing demands. Using a task that concurrently measured response inhibition, statistical learning, and self-reported task focus, we show that MW is associated with impaired inhibitory control but enhanced learning of probabilistic sequences. Critically, we reveal that these effects are related: the magnitude of the learning enhancement during MW depends on the efficacy of response inhibition. These findings demonstrate that transient lapses in top-down executive control are associated with the enhanced implicit extraction of environmental statistics, supporting neurocompetition models, and framing MW as a cognitive state that may be evolutionarily preserved to promote the unsupervised acquisition of predictive models.

neuroscience↗

bioRxiv2024

The interplay between executive functions and updating predictive representations

Modifying habits, particularly unwanted behaviors, is often challenging. Cognitive research has focused on understanding the mechanisms underlying habit formation and how habits can be rewired. A key mechanism is statistical learning, the continuous, implicit extraction of probabilistic patterns from the environment, which forms the basis of predictive processing. However, the interplay between executive functions (EF) and the rewiring - or updating - of these probabilistic representations remains largely unexplored. To address this gap, we conducted an experiment consisting of four sessions: 1) Learning Phase - acquisition of probabilistic representations, 2) Rewiring Phase - updating these probabilistic representations, 3) Retrieval Phase - accessing learned representations, and 4) EF assessment, targeting five key aspects: attentional control, inhibition, working memory, flexibility, and verbal fluency. We focused on the relationship between these EF measures and the updating of previously acquired knowledge using an interindividual differences approach. Our results revealed a positive relationship between rewiring and inhibition, suggesting that better inhibitory control may facilitate the adaptive restructuring of probabilistic predictive representations. Conversely, a negative relationship was identified between rewiring and semantic fluency, implying that certain underlying aspects of verbal fluency tasks, such as access to long-term memory representations, may hinder the updating process. We interpret this relationship through the lens of competitive memory network models. Our findings indicate that the rewiring of implicit probabilistic representations is a multifaceted cognitive process requiring both the suppression of proactive interference from prior knowledge through cognitive inhibition and a strong reliance on model-free functioning.

neuroscience↗

bioRxiv2024

Individual differences in probabilistic learning and updating predictive representations in individuals with obsessive-compulsive tendencies

Obsessive-compulsive (OC) tendencies involve intrusive thoughts and rigid, repetitive behaviours that also manifest at the subclinical level in the general population. The neurocognitive factors driving the development and persistence of the excessive presence of these tendencies remain highly elusive, though emerging theories emphasize the role of implicit information processing. Despite various empirical studies on distinct neurocognitive processes, the incidental retrieval of environmental structures in dynamic and noisy environments, such as probabilistic learning, has received relatively little attention. In this study, we aimed to unravel potential individual differences in implicit probabilistic learning and the updating of predictive representations related to OC tendencies in the general population. We conducted two independent online experiments (NStudy1 = 164, NStudy2 = 257) with young adults. Probabilistic learning was assessed using a reliable implicit visuomotor probabilistic learning task, which involved sequences with second-order non-adjacent dependencies. Our findings revealed that even among individuals displaying a broad spectrum of OC tendencies within a non-clinical population, implicit probabilistic learning remained remarkably robust. Furthermore, the results highlighted effective updating capabilities of predictive representations, which were not influenced by OC tendencies. These results offer new insights into individual differences in probabilistic learning and updating in relation to OC tendencies, contributing to theoretical, methodological, and practical approaches for understanding the maladaptive behavioural manifestations of OC disorder and subclinical tendencies.

neuroscience↗

bioRxiv2024

MIND WANDERING DURING IMPLICIT LEARNING IS ASSOCIATED WITH INCREASED PERIODIC EEG ACTIVITY AND IMPROVED EXTRACTION OF HIDDEN PROBABILISTIC PATTERNS

Mind wandering, occupying 30-50% of our waking time, remains an enigmatic phenomenon in cognitive neuroscience. Predominantly viewed negatively, mind wandering is often associated with detrimental impacts on attention-demanding (model-based) tasks in both natural settings and laboratory conditions. Mind wandering however, might not be detrimental for all cognitive domains. We proposed that mind wandering may facilitate model-free processes, such as probabilistic learning, which relies on the automatic acquisition of statistical regularities with minimal attentional demands. We administered a well-established implicit probabilistic learning task combined with mind wandering thought probes in healthy adults (N = 37, 30 females). To explore the neural correlates of mind wandering and probabilistic learning, participants were fitted with high-density electroencephalography. Our findings indicate that probabilistic learning was not only immune to periods of mind wandering, but was positively associated with it. Spontaneous, as opposed to deliberate mind wandering, was particularly beneficial for extracting the probabilistic patterns hidden in the visual stream. Additionally, cortical oscillatory activity in the low-frequency (slow and delta) range, indicative of covert sleep-like states, was associated with both mind wandering and improved probabilistic learning, particularly in the early stages of the task. Given the importance of probabilistic implicit learning in predictive processing, our findings provide novel insights into the potential cognitive benefits of task-unrelated thoughts in addition to shedding light on its neural mechanisms. This surprising benefit challenges the predominant view of mind wandering as solely detrimental and highlights its complex role in human cognition, especially in memory consolidation. Statement of significanceMind wandering poses an unresolved puzzle for cognitive neuroscience: it is associated with poor performance in various cognitive domains, yet humans spend 30-50% of their waking time mind wandering. We proposed that mind wandering may be beneficial for less attention-demanding cognitive processes requiring automatic, habitual learning. We assessed an implicit probabilistic learning task measuring the ability to extract (without awareness) hidden regularities from the information stream. Participants showed superior performance in probabilistic learning during periods of mind wandering, especially when such task-unrelated thoughts occurred spontaneously without intention. Moreover, mind wandering and probabilistic learning were both associated with slow frequency neural activity, suggesting that mind wandering may reflect a transient, offline state facilitating rapid learning and memory consolidation.

neuroscience↗

bioRxiv2024

Enhancing Retrieval Capacity of the Predictive Brain through Dorsolateral Prefrontal Cortex Intervention

The ability to extract spatial or temporal regularities across experiences is crucial for skill development and predictive processes. The prefrontal cortex (PFC) plays a key role in modulating competitive memory systems, supporting declarative/episodic memory as opposed to statistical learning. This regulatory role may explain findings of improved acquisition and consolidation of statistical regularities following the suppression of dorsolateral PFC (DLPFC) by repetitive transcranial magnetic stimulation (rTMS). This raises a key question: Is access to models and prior statistical knowledge also modulated by the DLPFC? This preregistered study provides new insights by examining the role of the DLPFC in retrieving pre-existing knowledge of temporally distributed statistical regularities. Using a probabilistic learning task, healthy participants engaged in implicit statistical learning for 25 minutes. After a 24-hour consolidation period, participants received either 1 Hz rTMS or sham stimulation over the left, right, or bilateral DLPFC for 10 minutes before retesting. We found more effective access to statistical regularities in the bilateral DLPFC group compared to the sham group. Our results suggest that DLPFC suppression enhances the retrieval of statistical knowledge, particularly when interhemispheric compensatory mechanisms are prevented. These findings contribute to understanding competitive memory systems and offer implications for cognitive enhancement strategies.

neuroscience↗

bioRxiv2024

Gamma-TuRC proteins contribute to the dynamic organization of MTOCs during Drosophila spermatogenesis

The initiation of microtubule formation is facilitated by {gamma}-tubulin and {gamma}-Tubulin Ring Complex ({gamma}-TuRC) in various microtubule-organizing centers (MTOCs). While the heterogeneity of tissue-specific MTOCs and {gamma}-TuRC in Drosophila testis has been described, their molecular composition and physiological significance are poorly understood. We investigated the testis-specific distribution and biochemical interaction of the canonical {gamma}-TuRC proteins Grip163 and Grip84. We found that while Grip163 is present on the centrosome and basal body, Grip84 localizes to the centrosome and Golgi in spermatocytes and colocalizes with the testis-specific {gamma}-TuRC at the basal body, apical nuclear tip, and near the elongated mitochondria after meiosis. We also show the apical nuclear tip localization of some {gamma}-TuRC interacting partners and prove their binding to testis-specific {gamma}-TuRC proteins. These results highlight and prove the importance of the different {gamma}-TuRCs in organizing the diverse MTOCs present during the extensive rearrangement of cell organelles during the spermatogenesis of Drosophila.

cell biology↗