dataesr/scientific-paragraphs-categorization huggingface dataset:
Present aim Previous fMRI research examined adaptation and anticipation in separate studies and identified brain networks only qualitatively based on regional activation. The current study addresses the open question of how adaptation and anticipation interact, and how the related brain regions form functional networks that communicate within and between one another in order to produce precise yet flexible synchronization behavior. To this end, we combine advances in brain network identification  (Finn et al. , 2015; Rosenberg et al. , 2017; Rosenberg et al. , 2016)  with computational modelling of behavior using the adaptation and anticipation model (ADAM) of sensorimotor synchronization  (Harry and Keller, 2019; van der Steen et al. , 2015a; van der Steen and Keller, 2013). ADAM comprises an adaptation module that provides input to an internal model of the 'self', which plays a role in action planning  (Wolpert and Kawato, 1998) , an anticipation module that informs an internal model of the 'other', for prediction of external sequential events  (Wolpert et al.
, 2005; Tognoli and Kelso, 2014)  primarily through the inclusion of the anticipation module, which uses an extrapolation function for tempo-change prediction, and the joint module that combines these predictions with the output of the adaptation module (see section 2. 4). Applying ADAM to behavioral data allows the estimation of parameters that reflect individual participants' use of reactive (phase and period) error correction, temporal (stimulus) prediction, and anticipatory error correction, thus providing a window into self-other monitoring and integration capacities for real-time interpersonal coordination  (Mills et al. , 2019; van der Steen et al. , 2015a; van der Steen et al. , 2015b). To identify the brain networks associated with ADAM's adaptation, anticipation, and joint modules, we adopt a connectome-based predictive modelling approach  (Finn et al.
Study overview & design fMRI data were collected while participants completed two audiomotor sensorimotor synchronization tasks (Virtual Partner task and Tempo Change task) designed to assess temporal adaptation and anticipation under two cognitive load conditions (single task and dual task). The Virtual Partner task was used to examine parameters from the adaptation module of ADAM under steady tempo conditions that were not expected to show large effects of cognitive load  (Repp and Keller, 2004). The Tempo Change task interrogated the ADAM's adaptation, anticipation, and joint modules under more demanding conditions that were expected to be susceptible to effects of cognitive load  (Pecenka et al. , 2013). This selection of tasks and modelling approach was based on previous behavioral work investigating individual differences in sensorimotor synchronization (see  Harry and Keller, 2019).
1. Schematic diagrams of the Adaptation and Anticipation Model (ADAM) of sensorimotor synchronization and the behavioral tasks. A) Adaptation and Anticipation Model (ADAM) architecture. Phase correction (α) and period correction (β) mechanisms in the 'adaptation' module influence action planning in a 'self' internal model. The 'anticipation' module controls the weighting (δ) of two processes, one entailing linear extrapolation of previous inter-onset intervals in the external sequence and the other copying the previous interval, which inform temporal predictions in an 'other' internal model. An anticipatory error correction mechanism (γ) a 'joint' internal model reduces discrepancies between (self) plans and (other) predictions before motor commands are issued. B) Virtual Partner task in which participants synchronize finger taps with computercontrolled auditory sequences that adapt to the participants to varying degrees (controlled by the phase correction parameter 'alpha', α).
Participants Seventy-five right-handed participants (41 females and 34 males, aged 18-40 years) were recruited via social media and on-campus flyers at Western Sydney University. Individuals with varying amounts of musical experience were included to increase the likelihood that the participant sample represented a range of sensorimotor synchronization abilities. Thirty-five musicians had 5-22 years of experience at playing a musical instrument and had either engaged in musical instruction at a tertiary level or were professionally active. The remaining 40 individuals had no formal music training beyond mandatory classroom music education in primary/secondary school. All participants had participated in previous sensorimotor synchronization experiments, and were drawn from a dedicated project database maintained at the MARCS Institute for Brain, Behaviour and Development.
The cowbell and woodblock stimuli were generated by a Roland TD-9 Percussion Sound Module that was situated in a control room adjacent to the scanning room. The sound module was connected to a Motu Microlite MIDI interface, which was in turn connected to an Acer laptop running Windows. This laptop presented the auditory stimuli and recorded the tapping data using custom-built software written in C++. Auditory stimuli were presented via over-ear scanner-compatible headphones, and tapping responses were collected via a custom built, fiberoptic laser light gate and converted to midi signal via an Arduino microcontroller (https://www. arduino. cc). In the Virtual Partner task, participants were required to tap their finger in time with an adaptive auditory pacing sequence consisting of the four cowbell lead-in sounds followed by the 48 woodblock sounds.
Timing of the woodblock sounds was controlled by an algorithm that implemented phase correction, as in ADAM's adaptation module (see section 2. 4). Sequence inter-stimulus intervals were thus adjusted in a manner that compensated for a proportion of each asynchrony between participant taps and virtual partner pacing sounds. Specifically, the onset time of the next pacing sound was determined by calculating the most recent tap-to-tone asynchrony, multiplying it by the relevant phase correction parameter value, and adding the result to the virtual partner timekeeper period (which dictated its base tempo) to obtain the current sequence inter-stimulus interval (e. g. ,  Fairhurst et al. , 2013; Mills et al. , 2015; Repp and Keller, 2008). The timekeeper period was set at 500 ms and three phrase correction values were employed to produce different of virtual partner adaptivity: 0 (a non-adaptive metronome), 0.
, 2005; Vorberg and Schulze, 2002; Vorberg and Wing, 1996)  by incorporating anticipatory processes, resulting in a model architecture with ). The weighting (δ) of extrapolation and perseveration processes in the anticipation module generate temporal predictions about the timing of the next sequence tone (S ′ i+1 ). Anticipatory error correction (γ) in the joint module reduces discrepancies between plans and predictions before a motor command is issued and the response is executed (R i+1 ). B) Equations for computing inter-stimulus (tone onset) intervals (1), inter-response (tap) intervals (  2 ), and asynchronies (3) from behavioral data, which serve as input to equations for ADAM's adaptation module (4 & 5), anticipation module (6-8), and joint module (9 & 10) used in parameter estimation.
S, stimulus (tone) onset; R, response (tap) onset; s, inter-stimulus interval, r, inter-response interval, A, asynchrony, T, timekeeper interval; R ′ , planned response onset; S ′ predicted stimulus onset; A ′ , anticipated error; e, extrapolated stimulus interval; p, perseverated stimulus interval; nT, timekeeper noise; nM, motor noise; α, phase correction, β, period correction; δ, prediction/tracking weight; γ, anticipatory error correction. Figure adapted from  Harry and Keller (2019). three computational modules: 'adaptation', 'anticipation', and 'joint. ' The conceptual architecture of ADAM was shown in Fig. 1A  and a process diagram showing the operation of the model is displayed in Fig. 2A. Equations for calculating timing measures are provided in Fig. 2B  (Equations (  1 )-(  3 )), along with equations for each of ADAM's modules (Equations (  4 )-(  10 ); for further details, see  Harry and Keller, 2019).
The anticipatory error correction parameter thus represents the balance of self-other integration and segregation. ADAM also includes two sources of noise, one that represents variability in a 'timekeeper' that is assumed to be instantiated in the central nervous system and the other representing 'motor' noise in the peripheral nervous system. Estimates of performance-based ADAM parameter values were derived for individual participants by fitting the adaptation module or the full model to behavioral data for each trial in each task condition. Previous work using computer simulations  (Harry and Keller, 2019)  indicates that stimulus sequences with a roughly steady tempo, such as those from the Virtual Partner task, are best fit with ADAM's adaptation module, which provides estimates of phase correction (α) and period correction (β), whereas sequences with gradual larger-scale variations in tempo, such as in the Tempo Change task, are best fit by including period correction (β), stimulus prediction (δ), and anticipatory error correction (γ) parameters from the full model.
Connectome-based predictive modelling Connectome-based predictive modelling (CPM) was used to identify the brain networks associated with parameter estimates from the adaptation, anticipation, and joint modules of ADAM (Figs. 1A  & 2 ). This technique identifies networks associated with sensory-motor and cognitive mechanisms by measuring brain-behavior relationships between task-evoked functional connectivity strength and task performance  (Finn et al. , 2015; Shen et al. , 2017). The approach allows changes of functional connectivity among regions within the same brain network and between regions in different networks to be quantified. Within-network connectivity reflects the modulation of connections among nodes within a resting-state network in relation to individual differences in the process in question (here phase correction, period correction, stimulus prediction, or anticipatory correction), whereas between-network connectivity reflects the modulation of connections between nodes in different resting-state networks in relation to these processes.
5), which is an automatic process that is relatively resistant to dual-task interference  (Pecenka et al. , 2013). To assess the contribution of each ADAM parameter to behavioral performance, we examined how the variability in parameter estimates across participants was related to individual differences in synchronization accuracy and stability using mixed effects linear regression implemented by the lme4 package (version 1. 1-30) in R (version 4. 2. 1). ADAM parameter estimates and cognitive load condition were included as fixed effects and participant was included as a random effect (See Tables  S3 -S6 in the Supplementary Materials for details of the analyses, and Tables S7-S8 for tests of collinearity among fixed effects. ). While there was little evidence for reliable relationships with synchronization accuracy (mean asynchrony) (Fig.
Brain network identification CPM was used to identify brain networks associated with ADAM's three modules by measuring relationships between task-evoked functional connectivity strength and ADAM parameter estimates. ADAM's adaptation module was identified based on phase correction estimates from the Virtual Partner task and period correction estimates from the Tempo Change task. The anticipation module was examined based on stimulus prediction estimates, while the joint module was addressed based on anticipatory error correction estimates from the Tempo Change task. When applied to phase correction parameter estimates from the Virtual Partner task, the cross-validated CPM procedure identified networks in the single-task and dual-task conditions that could account for phase correction in the held-out dataset (single task to dual task cross validation; r 61 = 0.
Permutation tests did not reveal a particularly large concentration of hubs in any of the 10 networks of interest for the low period correction network. Nevertheless, the number of hubs in the salience/ ventral attention, limbic (frontal), control, and default mode restingstate networks (Fig. 7A , blue) was greater than expected by chance (Fig. 7B , blue), with the majority of connections observed within the default mode network and between control and ventral attention networks (Fig. 7C , blue). Networks associated with the parameter estimates for stimulus prediction revealed that each network accounted for stimulus prediction in the held-out dataset (single task to dual task cross validation; r 55 = 0. 39, P = 0. 003, dual task to single task cross validation; r 55 = 0. 35, P = 0. 008). Cross-task analysis identified a stimulus prediction network comprising 300 positive connections (Table  S13  & Fig.
Discussion The present study combined connectome-based predictive modelling (CPM) of fMRI data and computational modelling of sensorimotor synchronization behavior to identify brain networks that support temporal adaptation, anticipation, and the monitoring and integration of information about one's own actions and externally controlled auditory pacing sequences. Our analyses revealed variations in functional connectivity within and between distinct but overlapping brain networks that were associated with individual differences in parameter estimates reflecting processes instantiated in the adaptation and anticipation model (ADAM) of sensorimotor synchronization with regular and tempo-changing sequences  (Harry and Keller, 2019; van der Steen and Keller, 2013). Cross-validation and comparison across conditions of low and high cognitive load demonstrated the reliability of, and stability in global brain network structure accompanied by attention-related modulations of connectivity strength and hub configuration across networks linked to ADAM's adaptation, anticipation, and joint modules.
Overall results indicate that a core network of hub regions-a potential ADAM 'rich club' (Table  1  & Fig. 10 )-modulates functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner that supports sensorimotor synchronization under conditions that differ in terms of tempo profile and concurrent cognitive demands (Fig. 12 ). This study thus contributes to efforts to understand how the functional organization of resting-state networks supports flexible cognition and behavior  (Cohen & D'Esposito, 2016; Deco et al. , 2015; Dixon et al. , 2017; Sporns, 2013; Wang et al. , 2021)  by extension to the domain of real-time rhythmic coordination. In particular, our findings are informative about how brain network reconfiguration fulfills functions associated with different ADAM modules by (1) shifting focus on internal and external information, and (2) varying the degree of simultaneous integration and segregation of these different information sources.
ADAM common regions Resting    Tognoli and Kelso, 2014) , where the brain is in a state of readiness to respond to changes in the internal and external environment by shifting between endogenous and endogenous focus  (Corbetta et al. , 2008; Fox et al. , 2006; Sridharan et al. , 2008). With regard to temporal prediction, ADAM's anticipation module is primarily externally oriented insofar as it weights the extrapolation versus perseveration of earlier inter-stimulus intervals in environmental event sequences  (Harry and Keller, 2019; van der Steen and Keller, 2013). Endogenous processes nevertheless play a role in temporal prediction to the extent that auditory beat prediction involves internally driven action simulation  (Cannon and Patel, 2021; Keller, 2012b; Keller et al. , 2014; Patel and Iversen, 2014) , especially when the simulation process supports beat maintenance based on the perseverative mimicking of the most recent inter-stimulus interval  (Repp et al.
Simultaneous integration and segregation in the joint network While brain network reconfiguration related to ADAM's adaptation and anticipation modules is assumed to influence the relative focus on internal and external information, network characteristics associated with the joint module influence the degree of simultaneous integration and segregation of these different information sources. Studies of largescale brain connectivity have shown that balancing local segregation and global integration in the functional organization of resting-state networks regulates information flow in a manner conducive to flexible task-related behavior  (Cohen & D'Esposito, 2016; Deco et al. , 2015; Di Plinio, Perrucci, Aleman and Ebisch, 2020; Sporns, 2013; Wang et al. , 2021). During sensorimotor synchronization, integration of information about the relationship between one's own action timing and the external sequence is necessary to monitor and evaluate overall performance, whereas segregation is required to maintain autonomous movement control and a distinction between action planning and predictions about external event timing  (Heggli et al.
We assume that regions within the ADAM 'rich club' (Table  1  & Fig. 10 ) enable the joint network to tune the relative integration versus segregation of internal self-related information and external otherrelated information through interactions with ADAM's adaptation and anticipation modules. This assumption is based on the fact that each of the main resting-state networks is represented by at least one common node in this 'rich club', which provides an exhaustive set of potential links for controlling information flow and network reconfiguration. Among these node regions, the posterior cingulate cortex/precuneus, the inferior frontal gyrus, the insula, and the temporoparietal junction (spanning parts of the supramarginal gyrus, the lateral occipital cortex, and the angular gyrus) have been previously linked to regulating the integration and segregation of internal and external information in rhythmic contexts  (Fairhurst et al.
, 2019; Rauschecker, 2011). In the context of sensorimotor synchronization, the role of the putamen in beat maintenance  (Cannon and Patel, 2021; Grahn and Rowe, 2013 ) may be functionally relevant to timing interactions and information flow across ADAM brain networks. Finally, the thalamus regulates functional connectivity within and between cortical resting-state networks in a manner that allows information processed throughout the cortex to be integrated while the modular structure of functional networks is maintained  (Guimerà and Nunes Amaral, 2005; Guimerà et al. , 2007; Hwang, Bertolero, Liu, & D'Esposito, 2017; Nakajima and Halassa, 2017). It is well-suited to support collaboration between ADAM modules via a gating process that allows coordinated interactions between elementary sensorimotor processes and higher-level cognitive operations (e.
Internal models of self, other, and self-other integration Using ADAM to link behavioral patterns to functional connectivity profiles of resting-state networks has shed light on the brain bases of individual differences in sensorimotor synchronization skills. These skills are crucial for human activities involving rhythmic interpersonal coordination, including musical ensemble performance and group dance. Going beyond present findings, we propose a neurophysiological model that provides a tool for understanding task-based configurations of resting-state networks associated with ADAM's adaptation, anticipation, and joint modules, as well as for scaling up from basic sensorymotor mechanisms to processes that support multi-person interaction in such social contexts  (Keller et al. , 2014; Müller et al.
, 2020). ADAM's adaptation module informs a 'self' internal model (Fig. 1A ) that plans the timing of one's own next action by implementing inverse models, which transform action timing goals into motor programs, and forward models, which represent causal relationships between motor commands and their sensory effects, in the cerebellum and a cortical circuit that includes the premotor cortex, inferior frontal gyrus, and supplementary motor area  (Ito, 2008; Todd and Lee, 2015a). In this system, timing goals are updated based on the comparison of feedback from superior temporal auditory regions and somatosensory cortex with desired states in the inferior frontal gyrus, whereas motor programs are updated based on the comparison of predictions generated by 'self' forward models with desired states.
These comparisons take place subcortically in the basal ganglia  (Milardi et al. , 2019)  and cortically in superior temporal and inferior parietal regions  (Rauschecker and Scott, 2009). Our results suggest that phase correction might thus rely to a greater degree on cerebellar internal models to enable rapid, automatic processing, while period correction recruits cortical circuits that are flexible but relatively effortful to implement. In our account, ADAM's anticipation module informs internal models of others' actions (Table  1A ) within cerebro-cerebellar loops that allow an individual to predict the actions of interaction partners via feedforward processes  (Ito, 2008; Van Overwalle et al. , 2020). Within the cortical circuits, predictions about the timing of others' upcoming actions can evolve via two routes.
In addition to these cortical contributions, the immunity of ADAM's anticipation module to effects of cognitive load might reflect reliance on rapid temporal predictions enabled by subcortical processes including cerebellar internal models  (Ito, 2008; Schubotz, 2007)  and a thalamostriatal beat processing loop  (Cannon and Patel, 2021; Patel and Iversen, 2014). Joint internal models implemented by ADAM's joint module (Table  1A ) allow potential asynchronies to be simulated and corrected before they occur  (Keller et al. , 2016; van der Steen and Keller, 2013). This process entails first integrating the outputs of 'self' and 'other' internal models, and then modifying 'own' inverse models to compensate for any discrepancies between these outputs. In terms of neurophysiological implementation, the posterior cingulate cortex, precuneus, and superior frontal gyrus might work together to evaluate own and other predicted states against a representation of a joint desired state (e.
Conclusions The current fMRI study used connectome-based predictive modelling to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates derived from the adaptation and anticipation model (ADAM) of sensorimotor synchronization. This approach allowed us to identify distinct but overlapping brain networks that support temporal error correction, prediction, and the monitoring and integration of information about one's own action and external events during rhythmic coordination with auditory sequences. Our results suggest that ADAM's adaptation, anticipation, and joint modules house common hub regions that modulate functional connectivity within and between resting-state networks and additional sensory-motor regions and subcortical structures in a manner that reflects coordination skill.
Observatory Control System (OCS) WEAVE introduces a new top-level Observatory Control System (OCS) to the WHT that permits queue-based observations  (Picó et al. 2018). The XML files generated by are converted to Observation Blocks (OBs), which are stored in a database and can be scheduled in a queue for the operator, based on the prevailing conditions and target priorities. At the heart of the OCS is a sequencer task that coordinates the actions of the telescope, spectrograph, positioner and calibration system using a CORBA-based 16  architecture and a centralised noticeboard. In this context, the positioner has two functions: one for the observation of the current field and performing any actions required of the focal-plane imager, and one for the positioning robots concerning the parallel setting-up of the next observation.