Spontaneous discovery of novel task solutions in children

Li, Amy X.; Wenke, Dorit; Ay-Bryson, Destina S.; Loewe, Anika T.; Gaschler, Robert; Shing, Yee Lee

doi:10.1371/journal.pone.0266253

Cited by 7 publications

(8 citation statements)

References 45 publications

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“…In comparison, a study investigating adults found high accuracy of the thigh-worn accelerometer for predicting time spent in each PA intensity category, as seen by sensitivities and specificities > 99% for correctly classifying each PA intensity category [ 68 ]. One possible explanation for the differences between children and adults could be the inconsistent performance of activities in children whereas adults could more consequently perform activities over a certain time period [ 76 ].…”

Section: Discussionmentioning

confidence: 99%

Determination of cut-off points for the Move4 accelerometer in children aged 8–13 years

Beck,

Marzi,

Eisenreich

et al. 2023

BMC Sports Sci Med Rehabil

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Background To assess physical activity (PA) there is a need of objective, valid and reliable measurement methods like accelerometers. Before these devices can be used for research, they need to be calibrated and validated for specific age groups as the locomotion differs between children and adults, for instance. Therefore, the aim of the present study was the calibration and validation of the Move4 accelerometer for children aged 8–13 years. Methods 53 normal weighted children (52% boys, 48%girls) aged 8–13 years (mean age = 10.69 ± 1.46, mean BMI = 17.93 kg/m− 2, 60th percentile), wore the Move4 sensor at four different body positions (thigh, hip, wrist and the Move4ecg including heart rate measurement at the chest). They completed nine activities that considered the four activity levels (sedentary behavior (SB), light PA (LPA), moderate PA (MPA) and vigorous PA (VPA)) within a test-retest design. Intensity values were determined using the mean amplitude deviation (MAD) as well as the movement acceleration intensity (MAI) metrics. Determination of activities and energy expenditure was validated using heart rate. After that, cut-off points were determined in Matlab by using the Classification and Regression Trees (CART) method. The agreement for the cut-off points between T1 and T2 was analyzed. Results MAD and MAI accelerometer values were lowest when children were lying on the floor and highest when running or doing jumping jacks. The mean correlation coefficient between acceleration values and heart rate was 0.595 (p = 0.01) for MAD metric and 0.611 (p = 0.01) for MAI metric, indicating strong correlations. Further, the MAD cut-off points for SB-LPA are 52.9 mg (hip), 62.4 mg (thigh), 86.4 mg (wrist) and 45.9 mg (chest), for LPA-MPA they are 173.3 mg (hip), 260.7 mg (thigh), 194.4 mg (wrist) and 155.7 mg (chest) and for MPA-VPA the cut-off points are 543.6 mg (hip), 674.5 mg (thigh), 623.4 mg (wrist) and 545.5 mg (chest). Test-retest comparison indicated good values (mean differences = 9.8%). Conclusion This is the first study investigating cut-off points for children for four different sensor positions using raw accelerometer metrics (MAD/MAI). Sensitivity and specificity revealed good values for all positions. Nevertheless, depending on the sensor position, metric values differ according to the different involvement of the body in various activities. Thus, the sensor position should be carefully chosen depending on the research question of the study.

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Section: Discussionmentioning

confidence: 99%

Determination of cut-off points for the Move4 accelerometer in children aged 8–13 years

Beck,

Marzi,

Eisenreich

et al. 2023

BMC Sports Sci Med Rehabil

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“…In addition, research on covariation-learning has shown that other procedures can be learned even in situations where the participants know the procedure that can solve the problem (Schuck et al, 2015 , 2022 ; Gaschler et al, 2019 ). These studies deal with situations in which participants are required to make a response corresponding to a stimulus with covariant two features.…”

Section: Discussionmentioning

confidence: 99%

Differences in the distribution of attention to trained procedure between finders and non-finders of the alternative better procedure

Ninomiya¹,

Terai

Miwa³

2022

Front. Psychol.

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The human ability to flexibly discover alternatives without fixating on a known solution supports a variety of human creative activities. Previous research has shown that people who discover an alternative procedure relax their attentional bias to information regarding the known solutions just prior to the discovery. This study examined whether the difference in the distribution of attention between the finders and non-finders of the alternative procedure is observed from the phase of solving the problem using the trained procedure. We evaluated the characteristics of the finders' distribution of attention in situations where problem solving using a trained procedure was successful. This aspect has been little examined in previous research. Our study obtained empirical evidence for the fact that, compared to non-finders, finders pay more attention to information unrelated to the trained procedure acquired through knowledge and experience, even time when using a trained procedure. We also confirmed that this difference does not exist from the beginning of the task, but emerges during repeated use of familiar procedures. These findings indicate that in order to find an alternative procedure, one should not only divert attention from a familiar procedure just before the discovery but also pay a certain amount of attention to information unrelated to the familiar procedure even when the familiar procedure is functioning well.

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“…This seems promising as Luo et al (2019) have summarized that reduction of ADHD symptoms over development correlates with the maturation of the prefrontal cortex and related circuitry. Based on reasoning about this maturation, Schuck et al (2022) have recently applied a covariation learning task to compare children and young adolescents. Strategy change was shown by 27.5% of the 8–10 year olds and by 28.2% of the adults.…”

Section: Discussionmentioning

confidence: 99%

“…One approach to target this issue is to compare groups of persons potentially differing in task shielding. For instance, referring to differences in prefrontal cortex maturation among children and adolescents, Schuck et al (2022) tested for differences among children and young adults to deviate from instruction-based task performance. Children and young adults to a similar instead engaged in task-performance controlled by a stimulus feature that was not mentioned in the instructions, but was correlated with the instructed stimulus feature.…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, throughout practice any stimulus in which magenta dominated was positioned in the upper part of the reference frame (while it was positioned in the lower part in case cyan dominated). We developed this setup based on earlier work on covariation-based strategy change with a color-position covariation (Schuck et al, 2015(Schuck et al, , 2022Gaschler et al, 2019). More closely resembling the traffic light setup, in the current work color was the instructed stimulus feature while position was the covarying feature.…”

Section: Introductionmentioning

confidence: 99%

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Using position rather than color at the traffic light – Covariation learning-based deviation from instructions in attention deficit/hyperactivity disorder

Gaschler

Ditsche-Klein²,

Kriechbaumer³

et al. 2022

Front. Psychol.

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Based on instructions people can form task representations that shield relevant from seemingly irrelevant information. It has been documented that instructions can tie people to a particular way of performing a task despite that in principle a more efficient way could be learned and used. Since task shielding can lead to persistence of inefficient variants of task performance, it is relevant to test whether individuals with attention deficit/hyperactivity disorder (ADHD) – characterized by less task shielding – are more likely and quicker to escape a suboptimal instructed variant of performing a task. The paradigm used in this online study builds on the observation that in many environments different covarying features could be used to determine the appropriate response. For instance, as they approach a traffic light, drivers and pedestrians monitor the color (instructed stimulus feature) and/or the position of the signal (covarying stimulus feature, more efficient in case of reduced color sight). Similarly, we instructed participants to respond to the color of a stimulus without mentioning that color covaried with the position of the stimulus. In order to assess whether with practice participants would use the non-instructed feature position to an increasing extent, we compared reaction times and error rates for standard trials to trials in which color was either ambiguous or did not match the usual covariation. Results showed that the covariation learning task can be administered online to adult participants with and without ADHD. Performance differences suggested that with practice ADHD participants (n = 43 out of a total N = 245) might increase attention to non-instructed stimulus features. Yet, they used the non-instructed covarying stimulus feature to a similar extent as other participants. Together the results suggest that participants with ADHD do not lag behind in abandoning instructed task processing in favor of a learned alternative strategy.

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Spontaneous discovery of novel task solutions in children

Cited by 7 publications

References 45 publications

Determination of cut-off points for the Move4 accelerometer in children aged 8–13 years

Determination of cut-off points for the Move4 accelerometer in children aged 8–13 years

Differences in the distribution of attention to trained procedure between finders and non-finders of the alternative better procedure

Using position rather than color at the traffic light – Covariation learning-based deviation from instructions in attention deficit/hyperactivity disorder

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