The Sunny Side of Negative Feedback: Negative Feedback Enhances One’s Motivation to Win in Another Activity

Negative feedback has been widely reported to be a demotivator that could frustrate the recipient’s need for competence and erode his intrinsic motivation in the same activity. Nevertheless, little attention has been devoted to the intertemporal effect of negative feedback on one’s intrinsic motivation in another activity. To fill this gap, we arranged participants in a game with two sessions and manipulated the content of feedback as a between-subject factor. In session 1, participants had to complete a time estimation task with moderate difficulty, during which half of the participants received normal performance feedback and the other half received negative performance feedback. In session 2, all participants were guided to accomplish a moderately difficult stopwatch task that was competence-supportive. A more pronounced win-loss difference wave of reward positivity (RewP) was detected in the experimental (negative performance feedback) group compared to the control (normal performance feedback) group during session 2. This finding indicates that negative feedback in an activity may have a positive impact on one’s intrinsic motivation in a following competence-supportive activity.

Dysnumeria in Sign Language: Impaired Construction of the Decimal Structure in Reading Multidigit Numbers in a Deaf ISL Signer

We report on the first in-depth analysis of a specific type of dysnumeria, number-reading deficit, in sign language. The participant, Nomi, is a 45-year-old signer of Israeli Sign Language (ISL). In reading multidigit numbers (reading-then-signing written numbers, the counterpart of reading aloud in spoken language), Nomi made mainly decimal, number-structure errors– reading the correct digits in an incorrect (smaller) decimal class, mainly in longer numbers of 5–6-digits. A unique property of ISL allowed us to rule out the numeric-visual analysis as the source of Nomi's dysnumeria: In ISL, when the multidigit number signifies the number of objects, it is signed with a decimal structure, which is marked morphologically (e.g., 84 → Eight-Tens Four); but a parallel system exists (e.g., for height, age, bus numbers), in which multidigit numbers are signed non-decimally, as a sequence of number-signs (e.g., 84 → Eight, Four). When Nomi read and signed the exact same numbers, but this time non-decimally, she performed significantly better. Additional tests supported the conclusion that her early numeric-visual abilities are intact: she showed flawless detection of differences in length, digit-order, or identity in same-different tasks. Her decimal errors did not result from a number-structure deficit in the phonological-sign output either (no decimal errors in repeating the same numbers, nor in signing multidigit numbers written as Hebrew words). Nomi had similar errors of conversion to the decimal structure in number comprehension (number-size comparison tasks), suggesting that her deficit is in a component shared by reading and comprehension. We also compared Nomi's number reading to her reading and signing of 406 Hebrew words. Nomi's word reading was in the high range of the normal performance of hearing controls and of deaf signers and significantly better than her multidigit number reading, demonstrating a dissociation between number reading, which was impaired, and word reading, which was spared. These results point to a specific type of dysnumeria in the number-frame generation for written multidigit numbers, whereby the conversion from written multidigit numbers to the abstract decimal structure is impaired, affecting both reading and comprehension. The results support abstract, non-verbal decimal structure generation that is shared by reading and comprehension, and also suggest the existence of a non-decimal number-reading route.

The Accident Network (AcciNet): A new accident analysis method for describing the interaction between normal performance and failure

Accidents continue to create an unacceptable personal, social, and economic burden in many domains. Various accident analysis methods exist; however, key limitations have been identified. This paper describes a new accident analysis method, the Accident Network (AcciNet), that was recently developed as part of an ongoing collaboration between Human Factors and Ergonomics research groups from Australia and the United Kingdom. The method is demonstrated via an analysis of the Uber-Volvo fatal pedestrian collision. The analysis demonstrates how AcciNet goes beyond current state-of-the-art accident analysis methods to consider the role of normal performance in accident causation and identify the interrelations between failures, normal performance, and both human and non-human actors in the system. We describe the implications for accident analysis in practice and outline the next steps of the research program, including formal reliability and validity testing of AcciNet and the development of practical training materials.

Development of Cardiorespiratory Fitness Standards for Working Memory Using Receiver Operating Curves In 15-Year-Old Adolescents

Working memory performance is associated with better academic achievements in children and adolescents, and it is positively related to CRF. However, what level of cardiorespiratory fitness (CRF) discriminates higher working memory performance is not known. The purpose of this study was to identify thresholds of CRF linked to working memory in adolescents. Data of 141 adolescents (53.2% girls) were collected (14.9 years) from a cross-sectional study during the year 2019. CRF was assessed by the 20-m shuttle run test, and maximal oxygen uptake were calculated by Mahar´s equation. Working memory was evaluated by the Corsi blocks test and the performance was classified by percentiles. Receiver operating characteristic (ROC) curve analysis was used to identify CRF thresholds. The results of ROC analysis indicated that CRF could be used to discriminate working memory in adolescents. CRF thresholds of ³45.03 ml.kg-1.min-1for boys and ³36.63 ml.kg-1.min-1for girls were found to be indicative of “normal” performance in working memory. ConclusionCRF could discriminate low and normal working memory performance in 14-16 years old adolescents. These thresholds could allow for earlier identification and intervention of low working memory performance by the CRF.

The use of the Montreal Cognitive Assessment (MoCA) to assess cognitive impairment in Prader-Willi syndrome

Purpose When a comprehensive neuropsychological assessment cannot be carried out, a quick and discriminant tool of good psychometric properties can be useful to practitioners. The purpose of this paper is to examine the use of the Montreal Cognitive Assessment (MoCA) in patients with Prader–Willi syndrome (PWS) and to test its reliability for cognitive assessment in a population with intellectual disabilities. Design/methodology/approach Thirty-seven adults with PWS took the MoCA. Reliability of the battery was tested using Cronbach’s alphas. The performance of PWS adults in each subtest was then compared to that of a normative population of healthy adults. Findings The MoCA was found to be unreliable in PWS. The subtests analyses indicated that the PWS sample underperformed the normative population of healthy adults on most subtests of the MoCA. A sub-sample aged between 17and 29 years showed normal performance on Naming and Memory, and a sub-sample aged from 30 to 39 years showed similar performance on Language, Memory and Orientation relative to age-matched normative healthy adults. Research limitations/implications Results showed that the current version of the MoCA, if taken as a whole test for cognitive assessment, does not present with adequate psychometric properties, which the authors interpret as reflecting the heterogeneity in PWS cognitive profiles. If used in PWS, the MoCA may however be useful in examining cognitive functions separately using subtest-based comparisons to normative data. Originality/value This research contributes to a better assessment of cognitive profile in PWS and people with learning disabilities by arguing that the use of psychometric tests should depend more on the specificity of the population under evaluation.

Examining the relationship between self-actualization and job performance via taking charge

Based on Construal-level theory, this study hypothesized that self-actualization positively relates to employees’ taking charge, and self-actualization affects creative performance and normal performance through taking charge positively, but the effect is different. The authors selected a cross-sectional design to investigate interrelations amongst study variables at two different time points with the interval of one month, and surveyed 417 team members and 186 immediate team leaders in the banking sector in Pakistan. The authors used individual-level data to evaluate the validity and test the proposed relationships by using Mplus. They revealed that self-actualization affects taking charge positively, and taking charge mediated the relationship with the difference in effect e.g. taking charge has high effect on creative performance in comparison to normal performance. The outcomes carry important implications aimed at increasing high-level self-actualization that results in taking charge, and so on leads to creative and normal performance.

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.