dataesr/scientific-paragraphs-categorization huggingface dataset:
This area has a high density of subcutaneous sensitive fibres  [21]  that stimulate the tempoparietal junction  [22]. This area is similar to the parietal-insular-vestibular area found in monkeys by Gru ¨sser et al. [23, 24]  which has neurons that respond to somesthetic stimulation, especially from the neck (apart from visual and vestibular inputs). It has been postulated that GCS impacts these neurons, supplying data to the central nervous system regarding the position of the head and the trunk in space  [23, 24]. Therefore, GCS might improve the balance ability in the context of vestibular deficits  [25] , neglect patients  [26]  and in fixed simulators, where there is a lack of vestibular information (e. g. ,  [27] ). Previous research has found that reduction of head movements is related to less SAS symptoms  [27, 28] , finding that GCS reduces these head movements.
In this avenue, it has been proposed that GCS reduces SAS symptoms through an improvement of the orientation of the head and trunk, which results in a reduction of movements produced by the imposed motion in simulator  [27, 28]. This improvement of orientation perception has been supported by studies with galvanic vestibular stimulation (GVS), a similar technique to GCS where the electrodes are usually placed on the mastoid process. These studies found that GVS decrease MS  [29, 30]  and SAS  [31]  due to a reduction of sensory reliability of the vestibular system  [29, 30] , or a reconnection between visual cues and external vestibular cues  [31] , in line with the assumptions of the sensory conflict theory. Additionally, evidence suggests that the administration of GCS improves driving performance variables  [27] , such as faster speed in a high-speed turning maneuver.
Faster speed was negatively correlated with SAS symptoms. The authors concluded that these findings can be interpreted as a mere increase in speed owing to less SAS symptoms, taking into account the high requirement of the driving task. The mitigation of SAS could come from other sources apart from the body balance account (i. e. , an improvement of body balance to mitigate SAS), for example, distraction from sickness symptoms (e. g. , stomach discomfort, eyestrain). As Reason and Brand  [4]  remarked, individuals are less likely to experience MS when directing attention toward external events than when they focus on reporting their sensations. The observed attentional disengagement account is based on previous evidence. For example, some studies have approached the mitigation of SAS by employing different distractors.
Galvez-Garcı ´a et al. [32]  provided tactile stimulation to the quadriceps (TSQ), avoiding the stimulation of muscles that could provide somatosensory information to the central nervous system about the position of the head or the trunk in space. Thus, the reduction of SAS with this stimulation would be likely due to an attentional disengagement from the symptoms. Furthermore, they measured head postural stability (head sway) to reduce the possibility that postural adjustment fully accounted for the mitigated SAS symptoms, in line with previous studies where vibrotactile stimulation did not affect body balance  [33]. The authors found a significant reduction of SAS symptoms when TSQ was provided, without an increase in body balance and no adverse impact on driving performance variables. This finding reinforces the idea that the distraction of the participants from the symptoms without body balance improvement could reduce SAS, consistent with previous research where explicit mental distraction was effective in reducing SAS  [28].
However, they did not measure any index to corroborate whether mental distraction impacted on body balance. In addition, this attentional disengagement account might explain, for example, why different types of stimulation, like acupuncture  [34]  or pleasant odors  [20] , mitigate SAS. Thus, the existing body of literature demonstrates that SAS is a complex phenomenon, which can be alleviated by modifying body balance factors and other processes such as attention. Recent findings have proposed that a combination of different countermeasures could decrease MS and SAS more effectively than the administration of either vestibular or cognitive distractor countermeasures in isolation. In this avenue, Bos  [17]  found that the combination of head vibration and mental distraction was more effective at mitigating MS (39% reduction of MS symptoms) than vibration and mental distraction in isolation (25% and 19%, respectively).
The authors conclude that both countermeasures distract participants from their symptoms, although vibration applied to the head might affect vestibular balance. Nevertheless, body balance was not measured to corroborate this hypothesis. Furthermore, their study was not performed with a driving simulator, whereby this reduction might not be evident in multisensory driving tasks where subjects must be focused on driving maneuvers. In a similar vein, Galvez-Garcı ´a et al. [27]  studied the combination of GCS and auditory stimulation. The last one mitigated SAS by improving balance similarly to GCS  [28]. The additive effect of GCS and auditory stimulation was more effective at reducing SAS (73% reduction as compared to a baseline condition, where no stimulation was applied) than both countermeasures applied in isolation (50% and 48% reduction for GCS and auditory stimulation, respectively).
Both stimulations improved body balance (especially when they were presented in conjunction), with an improvement in the required driving task (i. e. , high speed when driving around a curve). Conversely, the combination of seat vibration and airflow was not effective at decreasing SAS because the null effect of seat vibration to mitigate SAS  [35]. All in all, previous evidence indicates that the use of simultaneous techniques to reduce SAS -specifically, those targeting the body balance account  [27]  and the attentional disengagement account of SAS  [32] -is more effective than their implementation in isolation. However, the combination of techniques from these two accounts has not been tested. This is important for two reasons. First, there is sparse literature regarding the exclusivity and additivity of techniques which mitigate SAS, and this study will provide further evidence for their use in combination.
Second, it is fundamental to test whether techniques that have high effectiveness in reducing SAS by improving body balance (e. g. , GCS), which also have evidenced positive effects on driving performance variables  [27] , can further reduce SAS along with other distracting techniques. In this way, techniques that have shown to impact body balance could be combined in the future with multiple distractors that have been tested in isolation, such as pleasant odors  [20]  and relaxing pleasant music  [19]. Following the aforementioned premises, in the present study, we aimed to determine whether two techniques that have proved to be effective at decreasing SAS in isolation-GCS  [27, 28, 36, 37]  and TSQ  [20] -have an additive effect when deployed simultaneously to decrease SAS. Furthermore, as previous studies have shown that an improvement in body balance (i.
e. , reduction of head movements produced by the imposed motion) is a crucial component when explaining the reduction of SAS by means of GCS  [27, 29] , we measured the head sway in all the conditions. Head sway was used during the simulator experience as an index of body balance, in line with previous research that highlighted the utility of this technique as a measure accounting for the variability of body balance when in the simulator  [27, 28] ; head balance is positively associated with SAS symptoms. As previously noted, studies with GCS (e. g. ,  [27] ) have shown evidence of improved driving performance (i. e. , faster speed in curves), whereas no adverse effects have been revealed when using TSQ (e. g. ,  [32] ). However, in this latter, the participants were able to adjust their speed on the curves to perform this maneuver efficiently and thus, reducing the differences in driving performance between the TSQ and the baseline conditions.
To address this concern in the current study, we measured driving performance variables in a demanding driving task (i. e. , high-speed turning maneuver) to prevent the participants from reducing the speed during the driving task  [27]. We anticipate that the combined administration of GCS and TSQ will be more effective at reducing SAS than their administration in isolation with an improvement of the driving performance. Importantly, we do not expect body balance to be modulated when TSQ is deployed, in line with previous research  [32].
Apparatus and stimuli During the experiment, the participants were sitting in an adjustable car seat at a distance of 3. 5 m from a screen (dimensions: 2. 0 m wide × 1. 4 m high; field-of-view: 31˚horizontally, 24v ertically), on a fixed-base simulator. The experiment room was temperature-controlled (mean temperature = 23 ± 1. 4˚C). Open-source driving simulator software OpenDS Pro Complete version 5. 0 was used to design a specific scenario and to render a driver's front view. Participants controlled the car using a steering wheel, pedals and a gearbox. The simulated environment consisted of a 24. 6 km flat route through an urban environment, where participants had to negotiate 27 curves to the left and 27 to the right (50% of gradual turn and 50% of sharp 90t urns). Gradual turns consisted of a 70 m entry, a 140 m curve and a 70 m exit, summing up a total of 250 m.
The sharp turns were designed on a T-junction, with a 40 m lead-in and a 40 m curve, summing up 80 m in total. Between the curves, there were straight sections with a distance between 200 and 300 m. A high-speed digital camera (S-MOTION), placed above the screen in the middle, recorded head movements. Data processing of head movements was performed using an algorithm in MatlabR2016a software  [42] , where movements of the nose tip were measured in pixels along the X-and Y-axes. TSQ was delivered by a small metallic rod of 1. 5 mm (TSD190 tactile stimulator connected to STM100C module of Biopac MP160) with pulses of 0. 5 s during all the curves followed a previously tested protocol  [32, 43, 44]. Equally, a similar protocol to that used in previous studies (e. g. ,  [27, 45] ) was used to provide the GCS.
Data analyses We used RStudio (version 1. 1. 383; RStudio Team  [49] ), for all our analyses. In line with previous research (e. g. ,  [27, 28, 32] ), the SSQ total score, the head sway (i. e. , the standard deviation of head movements in pixels along the X-and Y-axes during the curves of the circuit) and two driving performance variables were considered as dependent variables. The driving performance variables corresponded to the average speed (km/h) during the curves, and the steering wheel variability (SD of steering wheel position) during the curves. Both slower speed and less steering wheel movements have been associated with more conservative driving  [50]. Whereas the speed has been negatively correlated to SAS  [27] , the relationship between steering movements and SAS has not been directly tested.
Thus, we included steering wheel movements as an additional driving performance variable. We computed linear mixed-effects models (LME) for each dependent variable using the nlme  [51]  and the lme4  [52]  packages. Mixed-effects modelling offers a series of advantages when it comes to repeated-measures designs as compared to, for instance, ANOVA tests. Most relevant, it allows researchers to take into account all the potential factors that might contribute to the explanation of a dataset simultaneously (i. e. , fixed and random effects) and provides enhanced statistical power. For more details on the advantages of mixed-effects modelling as compared to more traditional methods, we refer the interested reader to Baayen et al. [53]. It should be noted that none of the dependent variables was normally distributed, as indicated by the Shapiro-Wilk normality test (SSQ total score: W = 0.
TSQ vs. GCS + TSQ), and "participants" as a random effect to account for a more general estimate of the fixed effect by taking into account the stochastic variability in the dataset  [55]. Following a model comparison approach  [56] , we contrasted the computed LME models, considering the model with the lowest Akaike information criterion (AIC) as the best fitting model  [57] ; we computed the exponential of the difference between the models' AIC to account for the relative likelihood of a given model [AIC RL = exp(ΔAIC/2)] (e. g. ,  [57] ). Thus, for each dependent variable, we compared the model including the fixed factor (condition) against a null model including no fixed effect. We performed likelihood ratio tests to extract the p-values. As the fixed factor condition included four levels, we carried out multiple comparisons by using the lsmeans package  [58].
SSQ scores A significant main effect of condition was found on the SSQ total scores (see Fig  1A ), χ 2 (3) = 192. 57, p < 0. 001, AIC RL > 100, R 2 m = 0. 668, R 2 c = 0. 767. Multiple comparisons revealed that lower SSQ scores were reported in the GCS + TSQ condition (12. 4 ± 6. 6) than in the baseline [58. 4 ± 13. 5, t(39) = 20. 787, p < 0. 001], the GCS [29. 9 ± 13. 2, t(39) = 7. 901, p < 0. 001] and the TSQ [30. 3 ± 12. 6, t(39) = 8. 070, p < 0. 001] conditions. The SSQ scores were also significantly lower for both the GCS [t(39) = 12. 886, p < 0. 001] and the TSQ [t(39) = 12. 717, p < 0. 001] conditions as compared to the baseline condition. Instead, the scores did not differ significantly between the GCS and the TSQ conditions [t(39) = -0. 169, p = 0. 998]. In short, the delivery of GCS + TSQ was more effective at decreasing the SAS symptoms than their isolated presentation.
Discussion The main aim of this study was to test the effectiveness of GCS and TSQ at mitigating SAS during a driving task, both when delivered in isolation and in conjunction. Concerning the application techniques in isolation, our data confirmed that GCS alleviates SAS with a reduction of 48. 8% of symptoms as compared to the baseline condition (where no stimulation was delivered), consistent with previous research  [2, 27, 28, 36]. This reduction of symptoms is related to an improvement of postural adjustment (less head sway for the X-and Y-axes) according to body balance account  [27]. Furthermore, our data aligned with previous evidence showing that TSQ mitigates SAS  [32] , with a reduction of 48. 1% of as compared to the baseline condition. Importantly, this reduction does not depend on an improvement in balance ability (i.
e. , no differences between the baseline and TSQ condition regarding head sway). We argue that TSQ produces a distraction from being aware of their symptoms, in line with attentional disengagement account  [34]. Besides, other attentional factors could coexist, such as an attenuation of the sensation of motion by distraction reducing the conflict between the visual and the other senses  [46]. The importance of corroborating a measure that decreases SAS without impacting balance ability should be noted. This finding aligns with previous studies suggesting that impairment of balance ability is not the only explanatory factor of MS (e. g. ,  [60] ). In fact, although GCS showed significantly less head sway for the X-and Y-axes and TSQ did not, both sources of stimulation decreased SAS in a similar proportion.
More generally, this provides additional evidence that SAS is not a simple phenomenon that only affects body balance factors, but also other processes, such as attention, in line with previous research on SAS  [20]  and MS  [17]. The idea that SAS relates to multiples factors is reinforced by the drastic decrease of SAS symptoms when GCS and TSQ are applied in conjunction (78. 8%), as compared to the baseline condition. Importantly, the SSQ total scores for the GCS and the TSQ conditions were 29. 9 and 30. 3, respectively, which according to Webb et al. [61] , still represents discomfort (i. e. , scores > 20). However, for the combined administration of GCS and TSQ, the SSQ total score was 12. 4, which suggests that the participants barely experienced any discomfort. It should be remarked that the reduction of SAS for the GCS + TSQ condition is not related to a greater improvement of body balance compared with the GCS condition (i.
e. , no significant difference regarding head sway along the X-and Y-axes). Thus, our results support the idea that the combination of techniques that impact on body balance and attentional factors is an effective method to alleviate SAS. The results of driving performance also support the application of GCS and TSQ separately and, especially, in conjunction. Both techniques enhanced the success of performance on the proposed demanding driving task (i. e. , speed constraints), such that faster speeds led to more steering movements  [50] , in line with previous studies where GCS was tested with a similar demanding driving task  [27]. This improvement in driving performance was higher when GCS and TSQ are applied in conjunction. Moreover, we found a positive correlation between speed and steering movements with SAS in all conditions.
This confirms that SAS produces a more conservative driving style owing to SAS  [50]. It should be pointed out that previous research did not find an improvement in driving performance when using TSQ  [32]. This could be because participants could adjust their speed on the curves to perform this maneuver efficiently, reducing the differences in driving performance between conditions. In line with our expectations, the inclusion of a more demanding task is the key to capture differences regarding driving variables (i. e. , speed and steering movements) between the TSQ and the baseline conditions. Furthermore, it should be noted that GCS and TSQ could have generated some interference with driving because of multiple sources of stimulation. However, this seems not to have been the case, as an improvement in driving performance was observed.
Overall, our results reveal that GCS and TSQ-especially when presented in conjunction-improve driving performance, which is a fundamental point for their recommendation for future interventions aiming to reduce SAS. Some consideration should be noted about the processes by which GCS and TSQ reduce SAS. Our results suggest that the reduction of symptoms by TSQ is due to a distraction from symptoms  [32] , as supported by the null effect in body balance. Furthermore, our data show that the reduction of SAS by GCS is mainly related to the improvement of body balance-as indexed by reduced head movements-being the head sway positively correlated with SAS symptoms. More concretely, GCS could reduce SAS symptoms improving the orientation of the head and trunk, resulting in a reduction of movements produced by the imposed motion in simulators  [27, 28].
In any case, it should be noted that our pattern of improvement of body balance could be related to different theories about the ateology of SAS. On the one hand, the improvement of orientation perception could be one of the key factors to reduce SAS, and thus our pattern of data could support the vertical subjective theory  [8] , previous studies with GVS  [29] [30] [31]  and more generally, previous work where it has been highlighted that GCS improves orientation perception  [23, 24]. On the other hand, the theory of postural instability  [9]  states that SAS is caused by an inability to maintain a stable pattern of movement, whether this stability is slowly degraded or outright lost. Our data do not conflict with the latter theory. Our findings support that GCS improves body balance (and consequently reduces SAS).
Thus, GCS could prevent the inability to maintain a stable pattern of movement by improving the orientation of the head. However, although not directly addressed, it has been suggested that GCS could additionally distract the participants from the symptoms (i. e. , from being conscious of their sickness; e. g. ,  [28] ). Thus, although our data mostly support that GCS decreases SAS by an improvement in body balance, a distraction from the symptoms may occur with GCS. However, to the best of our knowledge, there is no empirical data that support this idea. Further, our results support the idea that SAS is not a simple phenomenon that only affected body balance factors, but also other processes, such as attention. These attentional processes are not restricted to disengagement from the symptoms.
For example, Seno et al. [62, 63]  found that attention could moderate the severity of the induced self-motion sensation caused by visual stimulation, and limits the conflict between visual and the other senses. In this vein, Wei et al. [64]  found that allocating less attention to central vision produces less MS symptoms. Thus, the attentional disengagement account could coexist with other attentional factors. All in all, our study confirms that GCS and TSQ are effective to reduce SAS in a fixed-base simulator when applied in isolation. More importantly, the combination of GCS and TSQ has proved to be an especially effective countermeasure at mitigating SAS because of their impact on body balance and attentional components, respectively. Furthermore, the joint and individual use of GCS and TSQ improves driving performance.
Finally, identifying attention-related factors which mitigate SAS is relevant, as a series of stimulating devices can be implemented into the driving simulation set-up. However, care should be taken regarding the intensity of the source of distraction, as high levels of distraction might lead to a significant driving performance impairment. Future research is needed to generalize our results, for instance, by testing different driving scenarios and populations such as elderly drivers who might be more prone to SAS  [65]. In addition, the electrodes and tappers for GCS and TSQ, respectively, were placed in the baseline condition to rule out the mere presence of the electrodes and tappers as an explicatory factor of the results. Future research could compare the baseline condition used in the current study with a condition where no electrodes and tappers to study this manipulation as a passive technique to distract from the SAS symptoms.
Finally, more studies focused on SAS using a broader and multicausal perspective are needed. In this line, Lackner  [71]  showed how MS is a complex syndrome that transgresses nausea and vomiting and could cause sopite syndrome  [72, 73]. This syndrome is featured by the profound drowsiness and persistent fatigue following motion stimulation. In this vein, different distractive techniques could countermeasure this SAS-related syndrome.
BayesMallows is an R package for analyzing data in the form of rankings or preferences with the Mallows rank model, and its finite mixture extension, in a Bayesian probabilistic framework. The Mallows model is a well-known model, grounded on the idea that the probability density of an observed ranking decreases exponentially fast as its distance to the location parameter increases. Despite the model being quite popular, this is the first Bayesian implementation that allows a wide choice of distances, and that works well with a large amount of items to be ranked. BayesMallows supports footrule, Spearman, Kendall, Cayley, Hamming and Ulam distances, allowing full use of the rich expressiveness of the Mallows model. This is possible thanks to the implementation of fast algorithms for approximating the partition function of the model under various distances.
Although developed for being used in computing the posterior distribution of the model, these algorithms may be of interest in their own right. BayesMallows handles non-standard data: partial rankings and pairwise comparisons, even in cases including non-transitive preference patterns. The advantage of the Bayesian paradigm in this context comes from its ability to coherently quantify posterior uncertainties of estimates of any quantity of interest. These posteriors are fully available to the user, and the package comes with convienient tools for summarizing and visualizing the posterior distributions.