New Methods of Concurrent Checking: 42 (Frontiers in Electronic Testing)

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Further, if an observer executes concurrent self-focused movements, then prediction accuracy is degraded, because it would degrade congruency with observed movement and inhibit motor activation Spengler et al. Moreover, these effects come from concurrent imitative and self-focused movement i. As mentioned above, skilled athletes use motor simulation for predicting the action outcome of others. Therefore, no additional effect of imitative movement would be seen for skilled athletes in terms of prediction accuracy, because they already use a simulative process that would be induced by observing the action.

In contrast, prediction accuracy would be degraded through concurrent self-focused movement because it would inhibit motor simulation processes in progress during the prediction task. On the other hand, for less-skilled people, imitative movement may facilitate prediction accuracy. This implies that even novices engage in motor simulation during prediction tasks.

It is believed that motor simulation enhances prediction accuracy according to the internal forward model, which enables us to predict future sensory consequences and outcomes based on an efference copy of issued motor commands e. Therefore, the forward model would not produce appropriate predicted sensory consequences and outcomes. In other words, if individuals can produce the accurate motor commands during observation, then they can estimate the action outcomes correctly via the forward model.

Taking these considerations into account, it may be that, in less-skilled individuals, concurrent imitative movement during action observation enhances the production of appropriate motor commands; thereby prediction accuracy will be temporarily improved. In contrast, concurrent self-focused movement in less-skilled individuals will not affect prediction accuracy if the motor simulation process is inhibited, because there was no reliance on motor-based prediction processes Aglioti et al.

Thus, the purpose of the current study was to investigate how prediction accuracy is influenced by concurrent motor execution with different movement types during action observation. Accordingly, we recruited skilled basketball players, who were experts in motor-based outcome prediction Aglioti et al. The occlusion technique was used to assess outcome-prediction capabilities: The task consisted of four conditions: The observation condition was used to assess the baseline of prediction ability of each participant and to confirm the presence of skill-related differences in prediction ability.

The incongruent-action condition was used to verify that the skilled athletes used motor-based predictions in the present study. Previous studies have demonstrated that incongruent actions degrade prediction accuracy in observers who use motor-based predictions, but not in observers who do not have such a capability Mulligan et al. Therefore, if skilled participants in the present study had motor-based prediction abilities, then their prediction accuracy would degrade, whereas if less-skilled participants did not have well-developed motor-based prediction abilities, then their prediction accuracy would be unaffected by their execution of incongruent actions.

We hypothesized that prediction accuracy would be modulated by imitative-motion and by self-motion.

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Further, we hypothesized that these effects would vary, depending on the initial prediction ability i. All participants had normal or corrected-to-normal visual acuity in both eyes and always used their right hand to shoot a basketball. The less-skilled group had experience in playing basketball in physical education class, but no members of this group had experienced systematized training and competitive activities for basketball. This study was approved by the Ethics Committee of the National Institute of Fitness and Sports in Kanoya and was consistent with the institutional ethical requirements for human experimentation in accordance with the Declaration of Helsinki.

Prior to the measurement session, all participants were fully informed of the procedures and possible risks, as well as the purpose of the study, and their written informed consent was obtained. To create occlusion video clips for this experiment, basketball free throws performed by a right-handed male basketball player who had 10 years of experience were digitally recorded using a hybrid camera GC-PX1, JVC. The video camera was approximately 6 m from the player. A side-on perspective was recorded, such that the player and basketball hoop were visible.

The player was requested to perform 50 trials each of three types of basketball free throws. The stimulus movies were presented using a temporal-occlusion technique. All video clips were cut In addition, the ball was occluded to prevent participants from making judgments based on the ball trajectory. A movie consisted of a fixation cross 2 s , the edited free-throw video clip approximately 2 s , and a white-noise video clip 3 s; Figure 1.

Experimental apparatus and setup. At the end of each movie presentation, three instruction frames appeared, which asked the participant to respond verbally as to where the basketball would land i. In the observation condition, participants predicted shot outcomes based on simple observation of the presented stimuli. In the incongruent-action condition, they executed right-wrist flexion with maximum speed.

In the self-motion condition, they executed their right-wrist flexion as if taking the shot themselves. The model player gave us the consent for the publication of this image. In the observation condition, participants predicted the shot outcomes based on simple observation of presented stimuli, consistent with previous studies Aglioti et al. In this task, they received instruction from the experimenter as follows: In this case, you do not need to perform any concurrent action.

In the other three conditions, participants were required to execute simple hand movements concurrently during stimulus observation. Thus, we employed hand flexion of the right wrist as the concurrent movement execution. In the incongruent-action condition, participants executed their right-wrist flexion with their maximum speed.

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Furthermore, in the three concurrent-movement conditions, they were also instructed to perform concurrent movement i. In these conditions, participants put their right elbow on a height-adjustable table. Their arm was maintained in position by themselves when they moved their wrist Figure 1. Each condition included 36 trials trials in total , which were randomly arranged. The instructions were provided before the 1st, 12th, and 24th trial in each condition by repetition. The order of conditions was randomly assigned in the skilled group and the order was matched in the less-skilled group.

No accuracy feedback was provided during the experimental task. First, to replicate previous findings i. The experimental condition was the within-subjects factor and group was the between-subjects factor. In the case of a significant interaction, unpaired t -tests with Bonferroni correction were used to examine the experimental conditions for which the difference between the skilled and less-skilled group was significant.

Additionally, to clarify individual differences in the effects of concurrent imitative and self-focused movement on prediction accuracy, correlations were obtained between the original prediction ability for each participant i. Figure 2 shows the prediction accuracies in the skilled and less-skilled groups in each condition. Consistent with previous findings Aglioti et al. Further, only the skilled group significantly decreased in prediction accuracy in the incongruent-action condition compared to the observation condition.

According to previous findings Mulligan et al. That is, participants in the present study are suitable for testing the effect of concurrent imitative-motion and self-motion on prediction accuracy. Percentage of correct responses in each condition observation, incongruent-action, imitative-motion, and self-motion for the skilled and less-skilled groups. The horizontal dashed line indicates the chance level.

Vertical error bars show standard errors. According to previous proposals regarding the characteristics of motor-system activation during action observation Buccino et al. Furthermore, we expect that, because skilled athletes strongly rely on motor-based prediction Aglioti et al. In contrast, less-skilled people who did not have well-developed motor-based prediction would not be affected by self-focused movement, but their prediction accuracy would be improved by concurrent imitative movement that induces appropriate efference copy.

Thus, the results indicate that the skilled group lost prediction accuracy when they executed flexion of the right wrist while imagining themselves taking the shot. In contrast, there was no significant correlation between the magnitude of degradation and prediction ability. That is, the amplitude of facilitation by imitative movement depends on the original prediction ability in less-skilled participants, while the amplitude of degradation does not depend on individual prediction ability, regardless of skill level. Relationship between the change in prediction accuracy from observation condition to imitative-motion and self-motion conditions, and prediction accuracy in the observation condition.

This study investigated the influence of different types of concurrent motor execution during action observation on prediction accuracy. The main results showed that concurrent imitative motor execution facilitated prediction accuracy, only in less-skilled participants, who did not have well-developed motor-based prediction. In contrast, motor execution, or taking a shot on your own, degraded prediction accuracy only in skilled participants, who strongly relied on motor-based prediction.

That is, the influence of imitative-motion and self-motion on prediction accuracy varied with skill level. Previous studies have indicated that motor activation during prediction tasks that relates to motor simulation is linked to the superior prediction ability of skilled athletes Wright and Jackson, ; Aglioti et al. New Methods of Concurrent Checking: Set up a giveaway.

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Get fast, free shipping with Amazon Prime. Your recently viewed items and featured recommendations. View or edit your browsing history. Get to Know Us. English Choose a language for shopping. Not Enabled Word Wise: Not Enabled Enhanced Typesetting: Would you like to report this content as inappropriate? Do you believe that this item violates a copyright? Amazon Music Stream millions of songs. Amazon Drive Cloud storage from Amazon. This maintains an environment close to the in situ conditions Hansen et al. The following manipulations were all done under strict anoxic conditions in a glove box Plas-Lab, Inc.

Sediments from Haps cores were sliced into six 2 cm segments, five in the top 10 cm, and one around 15 cm. The sliced segments were passed through a 2 mm sieve to remove macrofauna and hard objects such as carbonate shells which could puncture the bag, and then loaded into the gas-tight bag, carefully avoiding headspace and removing gas bubbles. The sediment was homogenized by manual kneading of the bag for 20 min Hansen et al.

At each sampling time, triplicate samples were taken by squeezing the bag to push the sediment through the glass outlet, where a PVC tube connected the outlet to a cut-off 2.

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The model here presumes that the concentration of CH 4 changes during the experiment through time due to constant methane consumption i. In case the rate of methane production, p, is different from rate of methane consumption, r, the relationship between R t and time can be described with the following expression:. Therefore p and r can each be calculated from the combination of these slopes.

Two microliters of carrier-free 35 S-sulfate solution containing approximately kBq ca. The formation of radiolabeled products was analyzed after incubation for 12—19 h at the in situ temperature. The ex situ SRRs were then calculated accordingly with a correction factor of 1. Sediment subsamples of 2. The rates of organic carbon mineralization rates from SRR were calculated with a 1: Aliquots of frozen sediment samples 0. Sediment samples were thawed at room temperature in a lysis buffer mixture containing 0.

The supernatant was extracted with equal volumes of phenol: The PCR mixture contained 0. Negative controls without template were included to test for reagent contamination in each set of PCR reactions. The final purified PCR products were quantified by a Qubit 2. An average of 39, ranging from 16, to 66, reads were generated for all samples, and more than Merged reads with length shorter than bp or longer than bp, or homopolymeric stretches longer than 7 bp were all removed from the dataset in the initial quality filtering.

Sequences not aligning within the expected region flanked by the PCR primers were removed from further analysis. Methanogen taxonomic lineages were classified into different physiological categories depending on their presumed substrate preference Canfield et al. Profiles of geochemical parameters showed similar pattern in cores from the two cruises in December and May Figure 1. Methane pore-water concentrations increased with depth, from 0.

There was a slight gradient of CH 4 from the sediment to the overlying seawater Figure 1 , enlarged section. The estimated methane flux was 0. This is a minimal estimate, as the effect of bioturbation was not taken into account. The sulfate—methane transition zone was shallow, at around 50 cm, but there was a long tailing of methane up through the sulfate zone. Depth-integrated 0—16 cm rates of different processes in surface sediments of this study. The initial concentration of CH 4 in each bag did not increase with depth as it did in CH 4 profiles, which was due to loss of CH 4 during slicing and sieving.

During incubations, the concentrations of CH 4 decreased with time for all depths except for the 0—2 cm Figure 2. In this top section, CH 4 increased from 1. This is consistent with the idea that that some active substrates were available in the top 0—2 cm for methanogenesis, which became depleted rapidly in a few days. Therefore, at 0—2 cm the modeling and calculation of methane turnover rates was separated in two phases, 0—4 days and 6—50 days.

Concentration and 13 C isotopic composition of CH 4 in the bags during incubation of December samples.

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As assumed by the isotope dilution model, the correlation coefficients were all above 0. Similar trends and good fitting were also observed in experiments repeated in May Supplementary Figure S4 , and in the second phase of 0—2 cm Supplementary Figure S5. Based on the modeling data, production and oxidation rates of CH 4 were calculated Figure 4.

Both production and oxidation rates peaked in the top layer with initial rates of over pmol cm -3 d -1 , and then decreased steeply to below pmol cm -3 d -1 in all other layers, with no clear depth trend. The depth-integrated rates of CH 4 production and oxidation were slightly higher in December compared to May Table 1 , which were all around 1 nmol cm -2 d -1 in 0—16 cm.

Depth distributions of CH 4 production and oxidation rates in December left panel and May right panel , as calculated from bag incubation data of total CH 4 concentrations and 13 C-CH 4 relative abundance. The highest SRRs of up to nmol cm -3 d -1 were detected at 0—2 cm in December , and then decreased gradually to below 20 nmol cm -3 d -1 at The rates of organic carbon mineralization rates calculated from SRR gave values close to production rates of DIC Figure 5 , right panel, and Supplementary Figure S6 , except at 0—2 cm, where the DIC production rate nmol cm -3 d -1 was higher than the mineralization rate calculated only from SRR.

The depth-integrated rates of SRR and carbon mineralization were two to three orders of magnitude higher than both flux and turnover rates of CH 4 in 0—16 cm Table 1. The in situ archaeal communities and their depth distribution were similar at the phylum level during the two sampling occasions Supplementary Figure S7 , and dominated by Woesearchaeota, Euryarchaeota, Thaumarchaeota, and Bathyarchaeota formerly known as the Miscellaneous Crenarchaeotal Group Meng et al.

Thus, the absolute abundances of methanogens, estimated by multiplying their relative abundances with total archaeal 16S rRNA gene copies, were two orders of magnitude lower than the total archaeal communities.

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Both the relative abundances and absolute abundances of methanogens decreased with depth, except at around 13 cm Figure 6. Most methanogen-related sequences in the surface sediments belonged to unclassified Methanomicrobiales, Methanococcoides , and Methermicoccus Supplementary Figure S8. The distribution of methanogens classified according to their inferred metabolic type is summarized in Figure 7 , and hydrogenotrophic and methylotrophic methanogens dominated in all samples, while putative acetoclastic methanogens appeared only in a few samples with low abundances.

A minor proportion of methanogens could not be assigned to a specific metabolic type. Depth distributions of different groups of methanogens classified according to presumed substrate usage: Bag incubation of sediment combines the merits of slurry incubations with jars and whole-core incubations, and provides low heterogeneity close to natural conditions during anoxic incubation of sediment Hansen et al. This bag incubation method has proven to be a useful tool in the study of anaerobic biogeochemical processes, such as degradation of organic matter, iron reduction, manganese reduction, and sulfate reduction Canfield, ; Canfield et al.

Here we extend this method to quantify CH 4 turnover by combing it with an isotope dilution technique. For a gas like CH 4 , it is critical to avoid air pockets in the bag, as even a small headspace might cause a significant loss of CH 4 from pore-water into the gas phase Yamamoto et al.

Our method calculates methane turnover rates during an incubation period, and thus averages out some finer temporal variations that may occur.


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Therefore, information about non-constant rates is lost, but few artifacts are created. The sieving of sediment before incubation and kneading of bag before each sampling aims to make sediment homogeneous in the bag, which allows repeated sampling during a time-course experiment Hansen et al. However, these operations inevitably alter the structure of the original sediment, and may influence the rates of CH 4 turnover and sulfate reduction, for example, influx of oxidants through bioturbation and bio-irrigation is blocked in bag incubations, which could stimulate sulfate reduction. Thamdrup and Canfield found that carbon mineralization rates in bag incubations were close to rates from flux measurements in sediment core in continental margin sediments off central Chile, and Hansen et al.

Our latest measurements in new samples from the same site confirmed that SRR and methanogenesis rates based on 14 CO 2 labeling determined in incubation bags were quite close to rates determined in intact sediment cores Xiao et al. In spite of potential for methanogenesis in surface sediment which has been demonstrated by several earlier studies Oremland et al. The closely coupled CH 4 production and oxidation in surface sediment in our study Figure 4 may partially explain this.

Methane production almost balanced methane oxidation so that a cryptic cycling of CH 4 existed, leaving little imprint in the pore water chemistry.