โจทย์ ปรนัย
ข้อใดต่อไปนี้อธิบายแนวคิด การรับรู้จังหวะ (Beat Perception) ได้ดีที่สุดเนื่องจากเกี่ยวข้องกับความสามารถในการได้ยินของทารกแรกเกิด

Sleeping newborns process musical beat.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
จากการวิจัย ทารกแรกเกิดใช้วิธีทดลองตามข้อใดในการแยกแยะการรับรู้จังหวะจากการเรียนรู้ทางสถิติในทารกแรกเกิด

Sleeping newborns process musical beat.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
การตอบสนองที่ไม่ตรงกัน (MMR) ในการศึกษา EEG บ่งชี้อะไรเกี่ยวกับการประมวลผลการได้ยินของทารกแรกเกิด

Newborn infants have been shown to extract temporal regularities from sound sequences, both in the form of learning regular sequential properties, and extracting periodicity in the input

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
คำว่า "การเรียนรู้ทางสถิติ (Statistical Learning)" หมายถึงอะไรในบริบทของการประมวลผลการได้ยินในทารกแรกเกิด?

When combining the current results with our previous study, we now have converging evidence from two different paradigms suggesting that beat processing is functional in newborn infants (Winkler et al., 2009). This can be taken as support for a biological basis of beat perception, per se (Merchant & Honing, 2014; ten Cate & Honing, 2024). That is, while statistical learning is an essential function for extracting information from the world, the current results suggest that it is complemented by functions honed to extract the temporal structure of (at least) the acoustic environment. Their complementary nature is supported by findings showing that temporal predictability, which is enhanced by isochronous stimulus presentation, appears to improve statistical learning (Selchenkova, Jones, & Tillmann, 2014; Tsogli, Jentschke, & Koelsch, 2022). In fact, much of the research on statistical learning, especially in neonates, has been based on isochronous stimuli (Bosseler et al., 2016; Teinonen et al., 2009). Indeed, it is easy to speculate that extracting the temporal structure of a sound sequence helps to reduce its variability by allowing the brain to focus its processing efforts in time (entrainment, see e.g., Stefanics et al., 2010). A similar assumption has been suggested in the Dynamic Attention Theory (Jones, 1976).

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
สภาวะใดในการศึกษา EEG ไม่ได้ส่งผลให้เกิดความแตกต่างระหว่างการตอบสนองแบบจังหวะและการตอบสนองที่ผิดปกติในทารกแรกเกิด

In the current experiment we have established that newborn infants are capable of beat based processing, providing converging evidence for the conclusions of Winkler et al. (2009). Importantly, the paradigm of Bouwer et al. (2016) used here allowed for the separation of beat processing and statistical learning of transition probabilities in neonates. Although the results suggest the presence of beat perception in newborns, we could not show the presence of statistical learning of transition probabilities when sequence timing was not isochronous. Current results, and previous results that show better statistical learning for sequences of regular temporal structure, bring up the possibility that extracting the temporal structure and statistical learning work in a complementary fashion.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
กลไกทางประสาทใดที่คิดว่ารองรับการเคลื่อนไหวให้ตรงกันกับจังหวะ

Processing sound sequences is crucial for both music and speech perception (Kotz, Ravignani, & Fitch, 2018, Patel, 2008). Studying the development of sound processing in infants can provide insights into the bootstrapping of speech and music acquisition during a period in which learning and brain maturation go hand in hand (Finlay & Uchiyama, 2020). In sound sequences, several types of structure can be discerned that contribute to efficient processing, including regularities in the temporal aspects of a sequence (i.e., the ‘beat’), regularities in order of events (i.e., statistical regularities), and hierarchical temporal stress patterns (i.e., the meter; Langus, Mehler, & Nespor, 2017). While both the processing of temporal (Winkler, Háden, Ladinig, Sziller, & Honing, 2009) and statistical (Bosseler, Teinonen, Tervaniemi, & Huotilainen, 2016) regularities have been suggested to be present in neonates, the relationship between these remains unclear, since sequences used to probe statistical regularities often also contain temporal regularities and vice versa (Bouwer, Van Zuijen, & Honing, 2014). Here we aim to disentangle the ability of neonates to detect regularities in time (the beat or pulse), and regularities in order (statistical learning). Temporal structure in rhythm often comes in the form of a regular beat: a series of perceived regularly recurring salient events (Cooper & Meyer, 1963, Honing, 2012, Honing and Bouwer, 2019). The ability to synchronize and coordinate movement with a regular beat is called rhythmic entrainment. This coordination is thought to result from the coupling of internal low frequency oscillations in auditory and motor areas with the external rhythmic signal (neural entrainment; see Large, Herrera, & Velasco, 2015). Beat-based timing has been suggested to be somewhat separate from other timing processes, like the perception of (a sequence of) single absolute temporal intervals (Bouwer, Fahrenfort, Millard, Kloosterman and Slagter, 2023, Bouwer, Honing and Slagter, 2020, Breska & Deouell, 2017, Honing & Merchant, 2014, Teki, Grube, Kumar, & Griffiths, 2011). Note that we will refer to the detection of beat as shown by brain responses as “beat perception” to keep the terminology compatible with previous literature, although sleeping infants cannot describe their experience and no overt behavior is involved here, making this term incorrect in the literal. In humans, we have previously suggested that beat perception is already functional at birth (Winkler et al., 2009). However, the results of this study could have been biased by comparing between responses to acoustically different sounds and contexts (detailed in Bouwer et al., 2014). Hence, the presence of beat perception at birth remains to be confirmed. Specifically, beat perception needs to be dissociated from the detection of statistical regularities in the order of events, such as transitional/conditional probabilities of item succession, as was described for language-like (Saffran, Newport, & Aslin, 1996) and in tonal stimuli (Saffran, Johnson, Aslin, & Newport, 1999). Statistical learning in its narrowest sense is the extraction of these regularities from the order of the elements of the input without explicit feedback or even awareness after prolonged exposure (Conway, 2020). Statistical learning is thought to be a domain general mechanism manifest in different domains, including audition (Saffran et al., 1996), music (Pearce, 2018; Saffran et al., 1999), and vision (Duncan & Theeuwes, 2020; Fiser & Aslin, 2001), and most prominently in language. There is evidence that statistical learning is already functional at birth (Bosseler et al., 2016; Bulf, Johnson, & Valenza, 2011; Teinonen, Fellman, Näätänen, Alku, & Huotilainen, 2009). However, there is an ongoing debate whether statistical learning in children is an unchanging domain general mechanism or it shows developmental changes that differ between the visual and the auditory modality (e.g., Saffran, Newport, Aslin, Tunick, & Barrueco, 1997; Raviv & Arnon, 2018; reviewed in Conway, 2020). Understanding the processing of sound sequences in infants requires one to dissociate the processing of the beat, absolute temporal intervals, and statistical regularities in the order of sounds. Firstly, to show beat perception, the rhythmic stimulus needs to be acoustically and/or temporally varied, so that these can be distinguished from interval-based perception (Bouwer et al., 2020; Bouwer, Werner, Knetemann, & Honing, 2016; Honing, Bouwer, Prado, & Merchant, 2018). Secondly, while the presence of acoustic variation in a sequence (e.g., differences in pitch or intensity between events) can aid the detection of a beat, especially in musical novices (Bouwer, Burgoyne, Odijk, Honing, & Grahn, 2018), it also creates possible confounds when probing beat perception, as differences in responses related to the temporal structure (e.g., different responses to events on and off the beat) may in fact be caused by differences in acoustic and sequential order properties when using acoustically rich and varied rhythmic sequences (Bouwer et al., 2014). Thus, to show beat perception, it must be carefully dissociated from learning transitional probabilities. To disentangle beat perception from statistical learning and the perception of interval-based temporal structure (e.g., learning an absolute interval), an auditory oddball paradigm was proposed by Bouwer et al. (2016), also used in Honing et al. (2018). The paradigm employs a rhythmic sequence made up of a pattern of loud (on all odd, “beat” positions and, a small portion of even, “offbeat” positions) and soft (in most even, “offbeat” positions) percussive sounds (Fig. 1A), such that the acoustic stimulus could induce a simple binary metrical structure (“duple meter”). The presence of timbre and intensity differences arguably creates an ecological way of inducing a beat (Ladinig, Honing, Háden, & Winkler, 2009). Within the context of this clearly beat-inducing sequence of alternating loud and soft sounds, in a proportion of the patterns, the offbeat positions are also filled with loud sounds. These patterns are used to probe beats and offbeats with identical acoustic properties. These rhythmic sequences are presented in two conditions: an isochronous condition, and a jittered condition (Fig. 1B). In the isochronous condition, sounds are presented with a constant inter-onset interval (IOI), allowing a beat to be induced (i.e., one metrical level of a duple meter). In the jittered condition, the IOIs are irregular (i.e., not isochronous), thus disabling the perception of a regular beat. However, the jittered sequences still contain the same order-based statistical regularity (alternation) between the louder and softer sounds as the isochronous ones. As such, the jittered condition serves as a control for statistical learning of the succession of sounds with different timbres.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
การรับรู้จังหวะในทารกแรกเกิดสัมพันธ์กับความสามารถทางดนตรีในภายหลังอย่างไร

Processing sound sequences is crucial for both music and speech perception (Kotz, Ravignani, & Fitch, 2018, Patel, 2008). Studying the development of sound processing in infants can provide insights into the bootstrapping of speech and music acquisition during a period in which learning and brain maturation go hand in hand (Finlay & Uchiyama, 2020). In sound sequences, several types of structure can be discerned that contribute to efficient processing, including regularities in the temporal aspects of a sequence (i.e., the ‘beat’), regularities in order of events (i.e., statistical regularities), and hierarchical temporal stress patterns (i.e., the meter; Langus, Mehler, & Nespor, 2017). While both the processing of temporal (Winkler, Háden, Ladinig, Sziller, & Honing, 2009) and statistical (Bosseler, Teinonen, Tervaniemi, & Huotilainen, 2016) regularities have been suggested to be present in neonates, the relationship between these remains unclear, since sequences used to probe statistical regularities often also contain temporal regularities and vice versa (Bouwer, Van Zuijen, & Honing, 2014). Here we aim to disentangle the ability of neonates to detect regularities in time (the beat or pulse), and regularities in order (statistical learning). Temporal structure in rhythm often comes in the form of a regular beat: a series of perceived regularly recurring salient events (Cooper & Meyer, 1963, Honing, 2012, Honing and Bouwer, 2019). The ability to synchronize and coordinate movement with a regular beat is called rhythmic entrainment. This coordination is thought to result from the coupling of internal low frequency oscillations in auditory and motor areas with the external rhythmic signal (neural entrainment; see Large, Herrera, & Velasco, 2015). Beat-based timing has been suggested to be somewhat separate from other timing processes, like the perception of (a sequence of) single absolute temporal intervals (Bouwer, Fahrenfort, Millard, Kloosterman and Slagter, 2023, Bouwer, Honing and Slagter, 2020, Breska & Deouell, 2017, Honing & Merchant, 2014, Teki, Grube, Kumar, & Griffiths, 2011). Note that we will refer to the detection of beat as shown by brain responses as “beat perception” to keep the terminology compatible with previous literature, although sleeping infants cannot describe their experience and no overt behavior is involved here, making this term incorrect in the literal. In humans, we have previously suggested that beat perception is already functional at birth (Winkler et al., 2009). However, the results of this study could have been biased by comparing between responses to acoustically different sounds and contexts (detailed in Bouwer et al., 2014). Hence, the presence of beat perception at birth remains to be confirmed. Specifically, beat perception needs to be dissociated from the detection of statistical regularities in the order of events, such as transitional/conditional probabilities of item succession, as was described for language-like (Saffran, Newport, & Aslin, 1996) and in tonal stimuli (Saffran, Johnson, Aslin, & Newport, 1999). Statistical learning in its narrowest sense is the extraction of these regularities from the order of the elements of the input without explicit feedback or even awareness after prolonged exposure (Conway, 2020). Statistical learning is thought to be a domain general mechanism manifest in different domains, including audition (Saffran et al., 1996), music (Pearce, 2018; Saffran et al., 1999), and vision (Duncan & Theeuwes, 2020; Fiser & Aslin, 2001), and most prominently in language. There is evidence that statistical learning is already functional at birth (Bosseler et al., 2016; Bulf, Johnson, & Valenza, 2011; Teinonen, Fellman, Näätänen, Alku, & Huotilainen, 2009). However, there is an ongoing debate whether statistical learning in children is an unchanging domain general mechanism or it shows developmental changes that differ between the visual and the auditory modality (e.g., Saffran, Newport, Aslin, Tunick, & Barrueco, 1997; Raviv & Arnon, 2018; reviewed in Conway, 2020). Understanding the processing of sound sequences in infants requires one to dissociate the processing of the beat, absolute temporal intervals, and statistical regularities in the order of sounds. Firstly, to show beat perception, the rhythmic stimulus needs to be acoustically and/or temporally varied, so that these can be distinguished from interval-based perception (Bouwer et al., 2020; Bouwer, Werner, Knetemann, & Honing, 2016; Honing, Bouwer, Prado, & Merchant, 2018). Secondly, while the presence of acoustic variation in a sequence (e.g., differences in pitch or intensity between events) can aid the detection of a beat, especially in musical novices (Bouwer, Burgoyne, Odijk, Honing, & Grahn, 2018), it also creates possible confounds when probing beat perception, as differences in responses related to the temporal structure (e.g., different responses to events on and off the beat) may in fact be caused by differences in acoustic and sequential order properties when using acoustically rich and varied rhythmic sequences (Bouwer et al., 2014). Thus, to show beat perception, it must be carefully dissociated from learning transitional probabilities. To disentangle beat perception from statistical learning and the perception of interval-based temporal structure (e.g., learning an absolute interval), an auditory oddball paradigm was proposed by Bouwer et al. (2016), also used in Honing et al. (2018). The paradigm employs a rhythmic sequence made up of a pattern of loud (on all odd, “beat” positions and, a small portion of even, “offbeat” positions) and soft (in most even, “offbeat” positions) percussive sounds (Fig. 1A), such that the acoustic stimulus could induce a simple binary metrical structure (“duple meter”). The presence of timbre and intensity differences arguably creates an ecological way of inducing a beat (Ladinig, Honing, Háden, & Winkler, 2009). Within the context of this clearly beat-inducing sequence of alternating loud and soft sounds, in a proportion of the patterns, the offbeat positions are also filled with loud sounds. These patterns are used to probe beats and offbeats with identical acoustic properties. These rhythmic sequences are presented in two conditions: an isochronous condition, and a jittered condition (Fig. 1B). In the isochronous condition, sounds are presented with a constant inter-onset interval (IOI), allowing a beat to be induced (i.e., one metrical level of a duple meter). In the jittered condition, the IOIs are irregular (i.e., not isochronous), thus disabling the perception of a regular beat. However, the jittered sequences still contain the same order-based statistical regularity (alternation) between the louder and softer sounds as the isochronous ones. As such, the jittered condition serves as a control for statistical learning of the succession of sounds with different timbres.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
ภาวะที่ไม่ต่อเนื่องในการศึกษาทางการได้ยินมักเกี่ยวข้องกับอะไร?

Processing sound sequences is crucial for both music and speech perception (Kotz, Ravignani, & Fitch, 2018, Patel, 2008). Studying the development of sound processing in infants can provide insights into the bootstrapping of speech and music acquisition during a period in which learning and brain maturation go hand in hand (Finlay & Uchiyama, 2020). In sound sequences, several types of structure can be discerned that contribute to efficient processing, including regularities in the temporal aspects of a sequence (i.e., the ‘beat’), regularities in order of events (i.e., statistical regularities), and hierarchical temporal stress patterns (i.e., the meter; Langus, Mehler, & Nespor, 2017). While both the processing of temporal (Winkler, Háden, Ladinig, Sziller, & Honing, 2009) and statistical (Bosseler, Teinonen, Tervaniemi, & Huotilainen, 2016) regularities have been suggested to be present in neonates, the relationship between these remains unclear, since sequences used to probe statistical regularities often also contain temporal regularities and vice versa (Bouwer, Van Zuijen, & Honing, 2014). Here we aim to disentangle the ability of neonates to detect regularities in time (the beat or pulse), and regularities in order (statistical learning). Temporal structure in rhythm often comes in the form of a regular beat: a series of perceived regularly recurring salient events (Cooper & Meyer, 1963, Honing, 2012, Honing and Bouwer, 2019). The ability to synchronize and coordinate movement with a regular beat is called rhythmic entrainment. This coordination is thought to result from the coupling of internal low frequency oscillations in auditory and motor areas with the external rhythmic signal (neural entrainment; see Large, Herrera, & Velasco, 2015). Beat-based timing has been suggested to be somewhat separate from other timing processes, like the perception of (a sequence of) single absolute temporal intervals (Bouwer, Fahrenfort, Millard, Kloosterman and Slagter, 2023, Bouwer, Honing and Slagter, 2020, Breska & Deouell, 2017, Honing & Merchant, 2014, Teki, Grube, Kumar, & Griffiths, 2011). Note that we will refer to the detection of beat as shown by brain responses as “beat perception” to keep the terminology compatible with previous literature, although sleeping infants cannot describe their experience and no overt behavior is involved here, making this term incorrect in the literal. In humans, we have previously suggested that beat perception is already functional at birth (Winkler et al., 2009). However, the results of this study could have been biased by comparing between responses to acoustically different sounds and contexts (detailed in Bouwer et al., 2014). Hence, the presence of beat perception at birth remains to be confirmed. Specifically, beat perception needs to be dissociated from the detection of statistical regularities in the order of events, such as transitional/conditional probabilities of item succession, as was described for language-like (Saffran, Newport, & Aslin, 1996) and in tonal stimuli (Saffran, Johnson, Aslin, & Newport, 1999). Statistical learning in its narrowest sense is the extraction of these regularities from the order of the elements of the input without explicit feedback or even awareness after prolonged exposure (Conway, 2020). Statistical learning is thought to be a domain general mechanism manifest in different domains, including audition (Saffran et al., 1996), music (Pearce, 2018; Saffran et al., 1999), and vision (Duncan & Theeuwes, 2020; Fiser & Aslin, 2001), and most prominently in language. There is evidence that statistical learning is already functional at birth (Bosseler et al., 2016; Bulf, Johnson, & Valenza, 2011; Teinonen, Fellman, Näätänen, Alku, & Huotilainen, 2009). However, there is an ongoing debate whether statistical learning in children is an unchanging domain general mechanism or it shows developmental changes that differ between the visual and the auditory modality (e.g., Saffran, Newport, Aslin, Tunick, & Barrueco, 1997; Raviv & Arnon, 2018; reviewed in Conway, 2020). Understanding the processing of sound sequences in infants requires one to dissociate the processing of the beat, absolute temporal intervals, and statistical regularities in the order of sounds. Firstly, to show beat perception, the rhythmic stimulus needs to be acoustically and/or temporally varied, so that these can be distinguished from interval-based perception (Bouwer et al., 2020; Bouwer, Werner, Knetemann, & Honing, 2016; Honing, Bouwer, Prado, & Merchant, 2018). Secondly, while the presence of acoustic variation in a sequence (e.g., differences in pitch or intensity between events) can aid the detection of a beat, especially in musical novices (Bouwer, Burgoyne, Odijk, Honing, & Grahn, 2018), it also creates possible confounds when probing beat perception, as differences in responses related to the temporal structure (e.g., different responses to events on and off the beat) may in fact be caused by differences in acoustic and sequential order properties when using acoustically rich and varied rhythmic sequences (Bouwer et al., 2014). Thus, to show beat perception, it must be carefully dissociated from learning transitional probabilities. To disentangle beat perception from statistical learning and the perception of interval-based temporal structure (e.g., learning an absolute interval), an auditory oddball paradigm was proposed by Bouwer et al. (2016), also used in Honing et al. (2018). The paradigm employs a rhythmic sequence made up of a pattern of loud (on all odd, “beat” positions and, a small portion of even, “offbeat” positions) and soft (in most even, “offbeat” positions) percussive sounds (Fig. 1A), such that the acoustic stimulus could induce a simple binary metrical structure (“duple meter”). The presence of timbre and intensity differences arguably creates an ecological way of inducing a beat (Ladinig, Honing, Háden, & Winkler, 2009). Within the context of this clearly beat-inducing sequence of alternating loud and soft sounds, in a proportion of the patterns, the offbeat positions are also filled with loud sounds. These patterns are used to probe beats and offbeats with identical acoustic properties. These rhythmic sequences are presented in two conditions: an isochronous condition, and a jittered condition (Fig. 1B). In the isochronous condition, sounds are presented with a constant inter-onset interval (IOI), allowing a beat to be induced (i.e., one metrical level of a duple meter). In the jittered condition, the IOIs are irregular (i.e., not isochronous), thus disabling the perception of a regular beat. However, the jittered sequences still contain the same order-based statistical regularity (alternation) between the louder and softer sounds as the isochronous ones. As such, the jittered condition serves as a control for statistical learning of the succession of sounds with different timbres.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
จุดประสงค์หลักของการใช้ EEG ในการศึกษาการประมวลผลการได้ยินในทารกแรกเกิดคืออะไร

Processing sound sequences is crucial for both music and speech perception (Kotz, Ravignani, & Fitch, 2018, Patel, 2008). Studying the development of sound processing in infants can provide insights into the bootstrapping of speech and music acquisition during a period in which learning and brain maturation go hand in hand (Finlay & Uchiyama, 2020). In sound sequences, several types of structure can be discerned that contribute to efficient processing, including regularities in the temporal aspects of a sequence (i.e., the ‘beat’), regularities in order of events (i.e., statistical regularities), and hierarchical temporal stress patterns (i.e., the meter; Langus, Mehler, & Nespor, 2017). While both the processing of temporal (Winkler, Háden, Ladinig, Sziller, & Honing, 2009) and statistical (Bosseler, Teinonen, Tervaniemi, & Huotilainen, 2016) regularities have been suggested to be present in neonates, the relationship between these remains unclear, since sequences used to probe statistical regularities often also contain temporal regularities and vice versa (Bouwer, Van Zuijen, & Honing, 2014). Here we aim to disentangle the ability of neonates to detect regularities in time (the beat or pulse), and regularities in order (statistical learning). Temporal structure in rhythm often comes in the form of a regular beat: a series of perceived regularly recurring salient events (Cooper & Meyer, 1963, Honing, 2012, Honing and Bouwer, 2019). The ability to synchronize and coordinate movement with a regular beat is called rhythmic entrainment. This coordination is thought to result from the coupling of internal low frequency oscillations in auditory and motor areas with the external rhythmic signal (neural entrainment; see Large, Herrera, & Velasco, 2015). Beat-based timing has been suggested to be somewhat separate from other timing processes, like the perception of (a sequence of) single absolute temporal intervals (Bouwer, Fahrenfort, Millard, Kloosterman and Slagter, 2023, Bouwer, Honing and Slagter, 2020, Breska & Deouell, 2017, Honing & Merchant, 2014, Teki, Grube, Kumar, & Griffiths, 2011). Note that we will refer to the detection of beat as shown by brain responses as “beat perception” to keep the terminology compatible with previous literature, although sleeping infants cannot describe their experience and no overt behavior is involved here, making this term incorrect in the literal. In humans, we have previously suggested that beat perception is already functional at birth (Winkler et al., 2009). However, the results of this study could have been biased by comparing between responses to acoustically different sounds and contexts (detailed in Bouwer et al., 2014). Hence, the presence of beat perception at birth remains to be confirmed. Specifically, beat perception needs to be dissociated from the detection of statistical regularities in the order of events, such as transitional/conditional probabilities of item succession, as was described for language-like (Saffran, Newport, & Aslin, 1996) and in tonal stimuli (Saffran, Johnson, Aslin, & Newport, 1999). Statistical learning in its narrowest sense is the extraction of these regularities from the order of the elements of the input without explicit feedback or even awareness after prolonged exposure (Conway, 2020). Statistical learning is thought to be a domain general mechanism manifest in different domains, including audition (Saffran et al., 1996), music (Pearce, 2018; Saffran et al., 1999), and vision (Duncan & Theeuwes, 2020; Fiser & Aslin, 2001), and most prominently in language. There is evidence that statistical learning is already functional at birth (Bosseler et al., 2016; Bulf, Johnson, & Valenza, 2011; Teinonen, Fellman, Näätänen, Alku, & Huotilainen, 2009). However, there is an ongoing debate whether statistical learning in children is an unchanging domain general mechanism or it shows developmental changes that differ between the visual and the auditory modality (e.g., Saffran, Newport, Aslin, Tunick, & Barrueco, 1997; Raviv & Arnon, 2018; reviewed in Conway, 2020). Understanding the processing of sound sequences in infants requires one to dissociate the processing of the beat, absolute temporal intervals, and statistical regularities in the order of sounds. Firstly, to show beat perception, the rhythmic stimulus needs to be acoustically and/or temporally varied, so that these can be distinguished from interval-based perception (Bouwer et al., 2020; Bouwer, Werner, Knetemann, & Honing, 2016; Honing, Bouwer, Prado, & Merchant, 2018). Secondly, while the presence of acoustic variation in a sequence (e.g., differences in pitch or intensity between events) can aid the detection of a beat, especially in musical novices (Bouwer, Burgoyne, Odijk, Honing, & Grahn, 2018), it also creates possible confounds when probing beat perception, as differences in responses related to the temporal structure (e.g., different responses to events on and off the beat) may in fact be caused by differences in acoustic and sequential order properties when using acoustically rich and varied rhythmic sequences (Bouwer et al., 2014). Thus, to show beat perception, it must be carefully dissociated from learning transitional probabilities. To disentangle beat perception from statistical learning and the perception of interval-based temporal structure (e.g., learning an absolute interval), an auditory oddball paradigm was proposed by Bouwer et al. (2016), also used in Honing et al. (2018). The paradigm employs a rhythmic sequence made up of a pattern of loud (on all odd, “beat” positions and, a small portion of even, “offbeat” positions) and soft (in most even, “offbeat” positions) percussive sounds (Fig. 1A), such that the acoustic stimulus could induce a simple binary metrical structure (“duple meter”). The presence of timbre and intensity differences arguably creates an ecological way of inducing a beat (Ladinig, Honing, Háden, & Winkler, 2009). Within the context of this clearly beat-inducing sequence of alternating loud and soft sounds, in a proportion of the patterns, the offbeat positions are also filled with loud sounds. These patterns are used to probe beats and offbeats with identical acoustic properties. These rhythmic sequences are presented in two conditions: an isochronous condition, and a jittered condition (Fig. 1B). In the isochronous condition, sounds are presented with a constant inter-onset interval (IOI), allowing a beat to be induced (i.e., one metrical level of a duple meter). In the jittered condition, the IOIs are irregular (i.e., not isochronous), thus disabling the perception of a regular beat. However, the jittered sequences still contain the same order-based statistical regularity (alternation) between the louder and softer sounds as the isochronous ones. As such, the jittered condition serves as a control for statistical learning of the succession of sounds with different timbres.Fig. 1. Schematic diagram of the rhythmic stimulus patterns used in the experiment. (A) The two standard (S1 and S2) and two deviant patterns (D1 and D2) are made up of three different sounds (A = accented, U = unaccented, and T = attenuated). An accented sound could occur either on the beat or offbeat, an unaccented sound was restricted to the offbeat position. Attenuated sounds were used as deviants in both positions (beat and offbeat) and conditions (isochronous and jittered). (B) Standard and deviant sound patterns were concatenated into a single rhythmic stream in a random order (see main text for details). Sequences in the isochronous condition had an inter-onset interval (IOI) of 225 ms, in the jittered condition these were randomly chosen from the range 150 to 300 ms using a uniform distribution. Deviants were always preceded and followed by an accented sound, with a fixed IOI of 225 ms in both conditions. Note that while we use the labels Beat and Offbeat in both conditions to refer to the position of the deviants in the rhythmic sequence,but a Beat (or Offbeat) can, of course, only be sensed in the isochronous condition, and not in the jittered condition. (Adapted from Honing et al., 2018). Bouwer et al. (2016) presented these sequences to adults, and measured event-related potentials (ERPs) to rare, unexpected intensity decrements (i.e., deviants) to assess the formation of beat-based expectations. When the prediction of incoming stimuli based on previous stimuli fails, that is, the regularity extracted from the sound sequence is violated, the mismatch negativity (MMN) ERP component is elicited in adults (for a recent review, see Fitzgerald & Todd, 2020). The amplitude of the MMN increases together with the specificity of the prediction (Southwell & Chait, 2018) and the amount of deviation from the predicted sound (e.g., Novitski, Tervaniemi, Huotilainen, & Näätänen, 2004). Bouwer et al. (2016) found larger MMN responses for deviants in odd (beat) than even (offbeat) positions, suggesting that the prediction was stronger on the odd position. This difference between predictions for beat and offbeat positions was only evident in the isochronous condition under unattended conditions, in which the MMN was measured. The authors argue that this was due to beat perception contributing to differences between positions only in the isochronous, but not the jittered condition because in the latter, no beat should emerge. Of note, in the jittered condition, significant difference in the amplitude between beat and offbeat positions did appear in the P3a range under unattended conditions, as well as in the N2 range when the sounds were attended. These ERP amplitude differences suggest that adults picked up on the statistical differences between beat and offbeat positions even in jittered sequences under both attended and unattended conditions, albeit to a lesser extent than when the sequences were isochronous and beat perception additionally contributed to position differences. Moreover, in both beat and offbeat positions, the P3a (an ERP component indexing post-processing following the detection of an unattended regularity violation; for a review, see Polich, 2007) was larger in the isochronous than the jittered condition, indicating that the isochrony of the sequence increased the amount of processing needed after a regularity violation. This held true for both attended and unattended blocks. A mismatch response (MMR) similar to MMN can be measured in young infants in response to regularity violations, including sleeping neonates (Alho, Sainio, Sajaniemi, Reinikainen, & Näätänen, 1990; Háden, Németh, Török, & Winkler, 2016). In our previous study in newborn infants (Winkler et al., 2009) stimulus omissions (Háden, Honing, Török, & Winkler, 2015) at different metrical positions of a repetitive rhythmic sequence were used to test beat and meter processing. However, as was noted before, the different metrical positions had different acoustic and sequential properties. This makes it possible that differences in omission MMR responses found for different metrical positions were based on learning the statistical properties of the order of different tones, that is, by statistical learning (Bouwer et al., 2014). Here, we presented a variant of Bouwer et al. (2016) stimulus paradigm (same stimuli, but no active condition, no silent movie presented, fewer but longer stimulus blocks, cf. Procedure sections) to sleeping newborn infants to separate the effects of beat perception from those of statistical learning (Fig. 1) in order to provide converging evidence to the notion that newborns brains can process the beat in rhythm, as suggested in our previous study (Winkler et al., 2009), while controlling for the effects of statistical learning. Response differences (MMR) between rare deviant and corresponding standard sounds were calculated at beat (odd) and offbeat (even) positions, separately for the isochronous and the jittered sequences (note that we refer to odd and even positions as “beat” and “offbeat” even for the jittered sequences, to make the terminology more consistent). In line with the effects in adults, firstly we expected newborns to learn the statistically predictable alternation between louder and softer sounds. Since beat perception does not occur in the jittered condition and the MMR is calculated by comparing the responses to sounds with identical acoustic properties and context (i.e., no acoustic difference between the tones in the compared nor in the preceding position), differences in the MMR responses between beat and offbeat positions should result from learning the sequential statistical regularities of the sound sequence (i.e., the alternation). Secondly, we expected to find evidence for beat perception, to corroborate our previous results (c.f. Bouwer et al., 2016 as well as Winkler et al., 2009). Based on Bouwer et al. (2016) results, beat perception should make the MMR difference between beat and offbeat position larger in the isochronous than in the jittered condition, as it would additionally contribute to the beat-offbeat difference.In the current experiment we have established that newborn infants are capable of beat based processing, providing converging evidence for the conclusions of Winkler et al. (2009). Importantly, the paradigm of Bouwer et al. (2016) used here allowed for the separation of beat processing and statistical learning of transition probabilities in neonates. Although the results suggest the presence of beat perception in newborns, we could not show the presence of statistical learning of transition probabilities when sequence timing was not isochronous. Current results, and previous results that show better statistical learning for sequences of regular temporal structure, bring up the possibility that extracting the temporal structure and statistical learning work in a complementary fashion

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
คุณลักษณะการได้ยินใดที่ไม่ได้รับการศึกษาโดยตรงในการวิจัยการประมวลผลการได้ยินของทารกแรกเกิด

Processing sound sequences is crucial for both music and speech perception (Kotz, Ravignani, & Fitch, 2018, Patel, 2008). Studying the development of sound processing in infants can provide insights into the bootstrapping of speech and music acquisition during a period in which learning and brain maturation go hand in hand (Finlay & Uchiyama, 2020). In sound sequences, several types of structure can be discerned that contribute to efficient processing, including regularities in the temporal aspects of a sequence (i.e., the ‘beat’), regularities in order of events (i.e., statistical regularities), and hierarchical temporal stress patterns (i.e., the meter; Langus, Mehler, & Nespor, 2017). While both the processing of temporal (Winkler, Háden, Ladinig, Sziller, & Honing, 2009) and statistical (Bosseler, Teinonen, Tervaniemi, & Huotilainen, 2016) regularities have been suggested to be present in neonates, the relationship between these remains unclear, since sequences used to probe statistical regularities often also contain temporal regularities and vice versa (Bouwer, Van Zuijen, & Honing, 2014). Here we aim to disentangle the ability of neonates to detect regularities in time (the beat or pulse), and regularities in order (statistical learning). Temporal structure in rhythm often comes in the form of a regular beat: a series of perceived regularly recurring salient events (Cooper & Meyer, 1963, Honing, 2012, Honing and Bouwer, 2019). The ability to synchronize and coordinate movement with a regular beat is called rhythmic entrainment. This coordination is thought to result from the coupling of internal low frequency oscillations in auditory and motor areas with the external rhythmic signal (neural entrainment; see Large, Herrera, & Velasco, 2015). Beat-based timing has been suggested to be somewhat separate from other timing processes, like the perception of (a sequence of) single absolute temporal intervals (Bouwer, Fahrenfort, Millard, Kloosterman and Slagter, 2023, Bouwer, Honing and Slagter, 2020, Breska & Deouell, 2017, Honing & Merchant, 2014, Teki, Grube, Kumar, & Griffiths, 2011). Note that we will refer to the detection of beat as shown by brain responses as “beat perception” to keep the terminology compatible with previous literature, although sleeping infants cannot describe their experience and no overt behavior is involved here, making this term incorrect in the literal. In humans, we have previously suggested that beat perception is already functional at birth (Winkler et al., 2009). However, the results of this study could have been biased by comparing between responses to acoustically different sounds and contexts (detailed in Bouwer et al., 2014). Hence, the presence of beat perception at birth remains to be confirmed. Specifically, beat perception needs to be dissociated from the detection of statistical regularities in the order of events, such as transitional/conditional probabilities of item succession, as was described for language-like (Saffran, Newport, & Aslin, 1996) and in tonal stimuli (Saffran, Johnson, Aslin, & Newport, 1999). Statistical learning in its narrowest sense is the extraction of these regularities from the order of the elements of the input without explicit feedback or even awareness after prolonged exposure (Conway, 2020). Statistical learning is thought to be a domain general mechanism manifest in different domains, including audition (Saffran et al., 1996), music (Pearce, 2018; Saffran et al., 1999), and vision (Duncan & Theeuwes, 2020; Fiser & Aslin, 2001), and most prominently in language. There is evidence that statistical learning is already functional at birth (Bosseler et al., 2016; Bulf, Johnson, & Valenza, 2011; Teinonen, Fellman, Näätänen, Alku, & Huotilainen, 2009). However, there is an ongoing debate whether statistical learning in children is an unchanging domain general mechanism or it shows developmental changes that differ between the visual and the auditory modality (e.g., Saffran, Newport, Aslin, Tunick, & Barrueco, 1997; Raviv & Arnon, 2018; reviewed in Conway, 2020). Understanding the processing of sound sequences in infants requires one to dissociate the processing of the beat, absolute temporal intervals, and statistical regularities in the order of sounds. Firstly, to show beat perception, the rhythmic stimulus needs to be acoustically and/or temporally varied, so that these can be distinguished from interval-based perception (Bouwer et al., 2020; Bouwer, Werner, Knetemann, & Honing, 2016; Honing, Bouwer, Prado, & Merchant, 2018). Secondly, while the presence of acoustic variation in a sequence (e.g., differences in pitch or intensity between events) can aid the detection of a beat, especially in musical novices (Bouwer, Burgoyne, Odijk, Honing, & Grahn, 2018), it also creates possible confounds when probing beat perception, as differences in responses related to the temporal structure (e.g., different responses to events on and off the beat) may in fact be caused by differences in acoustic and sequential order properties when using acoustically rich and varied rhythmic sequences (Bouwer et al., 2014). Thus, to show beat perception, it must be carefully dissociated from learning transitional probabilities. To disentangle beat perception from statistical learning and the perception of interval-based temporal structure (e.g., learning an absolute interval), an auditory oddball paradigm was proposed by Bouwer et al. (2016), also used in Honing et al. (2018). The paradigm employs a rhythmic sequence made up of a pattern of loud (on all odd, “beat” positions and, a small portion of even, “offbeat” positions) and soft (in most even, “offbeat” positions) percussive sounds (Fig. 1A), such that the acoustic stimulus could induce a simple binary metrical structure (“duple meter”). The presence of timbre and intensity differences arguably creates an ecological way of inducing a beat (Ladinig, Honing, Háden, & Winkler, 2009). Within the context of this clearly beat-inducing sequence of alternating loud and soft sounds, in a proportion of the patterns, the offbeat positions are also filled with loud sounds. These patterns are used to probe beats and offbeats with identical acoustic properties. These rhythmic sequences are presented in two conditions: an isochronous condition, and a jittered condition (Fig. 1B). In the isochronous condition, sounds are presented with a constant inter-onset interval (IOI), allowing a beat to be induced (i.e., one metrical level of a duple meter). In the jittered condition, the IOIs are irregular (i.e., not isochronous), thus disabling the perception of a regular beat. However, the jittered sequences still contain the same order-based statistical regularity (alternation) between the louder and softer sounds as the isochronous ones. As such, the jittered condition serves as a control for statistical learning of the succession of sounds with different timbres.Fig. 1. Schematic diagram of the rhythmic stimulus patterns used in the experiment. (A) The two standard (S1 and S2) and two deviant patterns (D1 and D2) are made up of three different sounds (A = accented, U = unaccented, and T = attenuated). An accented sound could occur either on the beat or offbeat, an unaccented sound was restricted to the offbeat position. Attenuated sounds were used as deviants in both positions (beat and offbeat) and conditions (isochronous and jittered). (B) Standard and deviant sound patterns were concatenated into a single rhythmic stream in a random order (see main text for details). Sequences in the isochronous condition had an inter-onset interval (IOI) of 225 ms, in the jittered condition these were randomly chosen from the range 150 to 300 ms using a uniform distribution. Deviants were always preceded and followed by an accented sound, with a fixed IOI of 225 ms in both conditions. Note that while we use the labels Beat and Offbeat in both conditions to refer to the position of the deviants in the rhythmic sequence,but a Beat (or Offbeat) can, of course, only be sensed in the isochronous condition, and not in the jittered condition. (Adapted from Honing et al., 2018). Bouwer et al. (2016) presented these sequences to adults, and measured event-related potentials (ERPs) to rare, unexpected intensity decrements (i.e., deviants) to assess the formation of beat-based expectations. When the prediction of incoming stimuli based on previous stimuli fails, that is, the regularity extracted from the sound sequence is violated, the mismatch negativity (MMN) ERP component is elicited in adults (for a recent review, see Fitzgerald & Todd, 2020). The amplitude of the MMN increases together with the specificity of the prediction (Southwell & Chait, 2018) and the amount of deviation from the predicted sound (e.g., Novitski, Tervaniemi, Huotilainen, & Näätänen, 2004). Bouwer et al. (2016) found larger MMN responses for deviants in odd (beat) than even (offbeat) positions, suggesting that the prediction was stronger on the odd position. This difference between predictions for beat and offbeat positions was only evident in the isochronous condition under unattended conditions, in which the MMN was measured. The authors argue that this was due to beat perception contributing to differences between positions only in the isochronous, but not the jittered condition because in the latter, no beat should emerge. Of note, in the jittered condition, significant difference in the amplitude between beat and offbeat positions did appear in the P3a range under unattended conditions, as well as in the N2 range when the sounds were attended. These ERP amplitude differences suggest that adults picked up on the statistical differences between beat and offbeat positions even in jittered sequences under both attended and unattended conditions, albeit to a lesser extent than when the sequences were isochronous and beat perception additionally contributed to position differences. Moreover, in both beat and offbeat positions, the P3a (an ERP component indexing post-processing following the detection of an unattended regularity violation; for a review, see Polich, 2007) was larger in the isochronous than the jittered condition, indicating that the isochrony of the sequence increased the amount of processing needed after a regularity violation. This held true for both attended and unattended blocks. A mismatch response (MMR) similar to MMN can be measured in young infants in response to regularity violations, including sleeping neonates (Alho, Sainio, Sajaniemi, Reinikainen, & Näätänen, 1990; Háden, Németh, Török, & Winkler, 2016). In our previous study in newborn infants (Winkler et al., 2009) stimulus omissions (Háden, Honing, Török, & Winkler, 2015) at different metrical positions of a repetitive rhythmic sequence were used to test beat and meter processing. However, as was noted before, the different metrical positions had different acoustic and sequential properties. This makes it possible that differences in omission MMR responses found for different metrical positions were based on learning the statistical properties of the order of different tones, that is, by statistical learning (Bouwer et al., 2014). Here, we presented a variant of Bouwer et al. (2016) stimulus paradigm (same stimuli, but no active condition, no silent movie presented, fewer but longer stimulus blocks, cf. Procedure sections) to sleeping newborn infants to separate the effects of beat perception from those of statistical learning (Fig. 1) in order to provide converging evidence to the notion that newborns brains can process the beat in rhythm, as suggested in our previous study (Winkler et al., 2009), while controlling for the effects of statistical learning. Response differences (MMR) between rare deviant and corresponding standard sounds were calculated at beat (odd) and offbeat (even) positions, separately for the isochronous and the jittered sequences (note that we refer to odd and even positions as “beat” and “offbeat” even for the jittered sequences, to make the terminology more consistent). In line with the effects in adults, firstly we expected newborns to learn the statistically predictable alternation between louder and softer sounds. Since beat perception does not occur in the jittered condition and the MMR is calculated by comparing the responses to sounds with identical acoustic properties and context (i.e., no acoustic difference between the tones in the compared nor in the preceding position), differences in the MMR responses between beat and offbeat positions should result from learning the sequential statistical regularities of the sound sequence (i.e., the alternation). Secondly, we expected to find evidence for beat perception, to corroborate our previous results (c.f. Bouwer et al., 2016 as well as Winkler et al., 2009). Based on Bouwer et al. (2016) results, beat perception should make the MMR difference between beat and offbeat position larger in the isochronous than in the jittered condition, as it would additionally contribute to the beat-offbeat difference.In the current experiment we have established that newborn infants are capable of beat based processing, providing converging evidence for the conclusions of Winkler et al. (2009). Importantly, the paradigm of Bouwer et al. (2016) used here allowed for the separation of beat processing and statistical learning of transition probabilities in neonates. Although the results suggest the presence of beat perception in newborns, we could not show the presence of statistical learning of transition probabilities when sequence timing was not isochronous. Current results, and previous results that show better statistical learning for sequences of regular temporal structure, bring up the possibility that extracting the temporal structure and statistical learning work in a complementary fashion

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

โจทย์ ปรนัย
คำใดที่ใช้อธิบายลักษณะที่ปรากฏของความน่าเชื่อถือทางวิทยาศาสตร์ซึ่งใช้ในการตลาดการบำบัดด้วยเซลล์ที่ไม่ได้รับการพิสูจน์

Although globally there is rising interest in living guidelines, limited research has explored the implications of a living approach for implementation, uptake, and impact [17]. Further work is needed to address these details as more living guidelines are developed. This evaluation highlights the value of living guidelines during a pandemic when the evidence base is rapidly expanding. It presents useful learnings on the ways clinicians and others are using living evidence to inform their clinical practice and decision-making and the diverse impacts the guidelines are having around Australia. Furthermore, it provides useful insight into the benefit of considering a range of policy and jurisdictional and implementation considerations to develop and support the implementation of comprehensive and nuanced living evidence.

Exploring the use and impact of the Australian living guidelines for the clinical care of people with COVID-19: where to from here?

โจทย์ ปรนัย
จากบทความ ข้อใดต่อไปนี้ไม่ใช่กลไกการรายงานที่ได้รับการยอมรับสำหรับผลข้างเคียงจากการบำบัดด้วยเซลล์และยีน

Although globally there is rising interest in living guidelines, limited research has explored the implications of a living approach for implementation, uptake, and impact [17]. Further work is needed to address these details as more living guidelines are developed. This evaluation highlights the value of living guidelines during a pandemic when the evidence base is rapidly expanding. It presents useful learnings on the ways clinicians and others are using living evidence to inform their clinical practice and decision-making and the diverse impacts the guidelines are having around Australia. Furthermore, it provides useful insight into the benefit of considering a range of policy and jurisdictional and implementation considerations to develop and support the implementation of comprehensive and nuanced living evidence.

Exploring the use and impact of the Australian living guidelines for the clinical care of people with COVID-19: where to from here?

โจทย์ ปรนัย
การพิจารณาด้านจริยธรรมประการใดที่ถูกท้าทายโดยการตลาดโดยตรงสู่ผู้บริโภคสำหรับการบำบัดด้วยเซลล์และยีนที่ไม่ได้รับการพิสูจน์

Although globally there is rising interest in living guidelines, limited research has explored the implications of a living approach for implementation, uptake, and impact [17]. Further work is needed to address these details as more living guidelines are developed. This evaluation highlights the value of living guidelines during a pandemic when the evidence base is rapidly expanding. It presents useful learnings on the ways clinicians and others are using living evidence to inform their clinical practice and decision-making and the diverse impacts the guidelines are having around Australia. Furthermore, it provides useful insight into the benefit of considering a range of policy and jurisdictional and implementation considerations to develop and support the implementation of comprehensive and nuanced living evidence.

Exploring the use and impact of the Australian living guidelines for the clinical care of people with COVID-19: where to from here?

โจทย์ ปรนัย
คุณลักษณะหลักใดที่ทำให้ผลิตภัณฑ์ CGT ที่ได้รับการพิสูจน์แล้วแตกต่างจากผลิตภัณฑ์ที่ไม่ผ่านการพิสูจน์ตามมาตรฐานกฎระเบียบ

The measurement results of the motion measurement device showed how to move the workpiece during fitting task based on the worker's experience. In addition, by combining the data obtained from two measurement devices, the force information that triggers the task transition was identified. In a future work, we will clarify the task skills based on experience that can be used for robot motion design by measuring the task performed by skilled workers using the developed assembly motion analysis system. We will also clarify the force information that indicates a sign of failure, which can be perceived only by skilled workers.

Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots

โจทย์ ปรนัย
ข้อใดต่อไปนี้เป็นความเสี่ยงที่เกี่ยวข้องกับผลิตภัณฑ์ CGT ที่ไม่ได้รับการพิสูจน์ซึ่งเน้นไว้ในบทความ

The measurement results of the motion measurement device showed how to move the workpiece during fitting task based on the worker's experience. In addition, by combining the data obtained from two measurement devices, the force information that triggers the task transition was identified. In a future work, we will clarify the task skills based on experience that can be used for robot motion design by measuring the task performed by skilled workers using the developed assembly motion analysis system. We will also clarify the force information that indicates a sign of failure, which can be perceived only by skilled workers.

Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots

โจทย์ ปรนัย
ข้อใดต่อไปนี้ไม่ใช่ลักษณะทั่วไปของผลิตภัณฑ์ CGT ที่ไม่ได้รับการพิสูจน์ตามที่กล่าวไว้ในบทความ

The measurement results of the motion measurement device showed how to move the workpiece during fitting task based on the worker's experience. In addition, by combining the data obtained from two measurement devices, the force information that triggers the task transition was identified. In a future work, we will clarify the task skills based on experience that can be used for robot motion design by measuring the task performed by skilled workers using the developed assembly motion analysis system. We will also clarify the force information that indicates a sign of failure, which can be perceived only by skilled workers.

Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots

โจทย์ ปรนัย
หน่วยงานกำกับดูแล เช่น FDA และ EMA จะรับรองความปลอดภัยของผลิตภัณฑ์ CGT ได้อย่างไร

The measurement results of the motion measurement device showed how to move the workpiece during fitting task based on the worker's experience. In addition, by combining the data obtained from two measurement devices, the force information that triggers the task transition was identified. In a future work, we will clarify the task skills based on experience that can be used for robot motion design by measuring the task performed by skilled workers using the developed assembly motion analysis system. We will also clarify the force information that indicates a sign of failure, which can be perceived only by skilled workers.

Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots

โจทย์ ปรนัย
เป้าหมายหลักของ ISCT ในด้านการบำบัดด้วยเซลล์และยีนตามที่กล่าวไว้ในบทความคืออะไร

The measurement results of the motion measurement device showed how to move the workpiece during fitting task based on the worker's experience. In addition, by combining the data obtained from two measurement devices, the force information that triggers the task transition was identified. In a future work, we will clarify the task skills based on experience that can be used for robot motion design by measuring the task performed by skilled workers using the developed assembly motion analysis system. We will also clarify the force information that indicates a sign of failure, which can be perceived only by skilled workers.

Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots

โจทย์ ปรนัย
อะไรคือผลลัพธ์ที่อาจเกิดขึ้นสำหรับผู้ป่วยที่ใช้ผลิตภัณฑ์ CGT ที่ไม่ได้รับการพิสูจน์?

Although globally there is rising interest in living guidelines, limited research has explored the implications of a living approach for implementation, uptake, and impact [17]. Further work is needed to address these details as more living guidelines are developed. This evaluation highlights the value of living guidelines during a pandemic when the evidence base is rapidly expanding. It presents useful learnings on the ways clinicians and others are using living evidence to inform their clinical practice and decision-making and the diverse impacts the guidelines are having around Australia. Furthermore, it provides useful insight into the benefit of considering a range of policy and jurisdictional and implementation considerations to develop and support the implementation of comprehensive and nuanced living evidence.

Exploring the use and impact of the Australian living guidelines for the clinical care of people with COVID-19: where to from here?

โจทย์ ปรนัย
ISCT มีบทบาทอย่างไรในบริบทของการบำบัดเซลล์และยีน

The measurement results of the motion measurement device showed how to move the workpiece during fitting task based on the worker's experience. In addition, by combining the data obtained from two measurement devices, the force information that triggers the task transition was identified. In a future work, we will clarify the task skills based on experience that can be used for robot motion design by measuring the task performed by skilled workers using the developed assembly motion analysis system. We will also clarify the force information that indicates a sign of failure, which can be perceived only by skilled workers.

Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots