Positive Errors Research Articles

Memory trace imbalance in reinforcement and punishment systems can reinforce implicit choices leading to obsessive-compulsive behavior.

We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we model obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the reinforcement learning framework show that agents implicitly learn to respond to intrusive thoughts when the memory trace signal for past actions decays differently for positive and negative prediction errors. Moreover, this model extends our understanding of therapeutic effects of behavioral therapy in OCD. Using empirical data, we confirm that patients with OCD show extremely imbalanced traces, which are normalized by serotonin enhancers. We find that healthy participants also vary in their obsessive-compulsive tendencies, consistent with the degree of imbalanced traces. These behavioral characteristics can be generalized to variations in the healthy population beyond the spectrum of clinical phenotypes.

Patient-based real-time quality control for quantitative hepatitis B virus DNA test using moving rate of positive and negative patient results.

Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of laboratory quality management in recent years. However, PBRTQC has not been reported for use in molecular diagnostics. This study introduces PBRTQC to quantitative hepatitis B virus (HBV) DNA test using moving rate (MR) of positive and negative patient results. In contrast to the MR protocols described in other literature, MR protocol for HBV-DNA test has an additional logarithmic transformation and binary conversion steps before using a common statistical process control algorithm, such as the MR. We used all patient test results of HBV-DNA assay from August 2018 to August 2021 at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, for parameters setting, optimization, and performance validation. The false rejection rate, error detection curves and validation charts were used to assess the MR protocols. The false rejection rates of two MR protocols were both <0.7%. The optimal block sizes for positive and negative errors in each cut-off value were not the same, so we first proposed a combined protocol that used different block size to detect negative and positive errors. It turned out that the combined protocols outperformed the simple protocols for each cut-off value, especially detecting positive errors. The performances of MR protocols using positive or negative patient results to detect constant errors of HBV-DNA test could meet laboratory requirements. Therefore, we have provided an effective alternative toolfor internal quality control in the field of molecular diagnostics.

Measurement of Near infrared Spectroscopy based Biomarkers under in-vitro Ischemic stroke condition.

Near infrared spectroscopy (NIRS) is an optical method which has been used widely to measure the change in the blood chromophores. Abrupt change in blood chromophore viz, oxyhemoglobin [hbo2] and deoxyhemoglobin [hb] happens in ischemia stroke condition, which occurs due to impairment of the oxygenated compounds viz, oxygen and glucose. The current study presents measurement and analysis of near infrared spectroscopy (NIRS) based regional oxygenation level biomarkers Viz., change in regional blood volume index [Formula: see text] and change in regional oxygen saturation [Formula: see text]. To measure these NIRS biomarkers dynamic phantom has been designed in such a way that, it mimics the ischemic stroke condition in-vitro by continuously changing (decrease, increase) the oxygenation level of human blood sample in the closed loop arterial system of dynamic phantom. Continuous decline of the [Formula: see text] % and [Formula: see text] 5% has been observed of the average reading of the samples in an ischemic stroke condition, while 15% and 2% increment has been recorded in reversal of ischemic stroke condition, by increasing the oxygenation level of the arterial system. Correlation between biomarkers in ischemic stroke condition has been found strongly positive (r=56%) and regular residual percentage error in measurement has been measured which has found under 10%. Clinical Relevance- This help in understanding and characterizing the ischemic stroke. Biomarker's trend can be use for classification of stroke types.

Optimization of patient-based real-time quality control based on the Youden index

AimThis study sets out to investigate the utility of exponentially weighted moving average (EWMA) as patient-based real-time quality control (PBRTQC) by conducting a simulation study and subsequent real-patient data implementation to determine optimal EWMA features (weighting factors, control limits, and truncation methods) based on the Youden index. MethodsA simulation experiment was conducted in the first stage to investigate optimal EWMA features for the tests, including aspartate aminotransferase, blood urea nitrogen, and glucose, calcium, creatinine, potassium, sodium, triglycerides, thyroid - stimulating hormone (TSH), and vitamin B12 tests. In the second stage of the study, EWMA was applied to real patient data to elucidate practical utility and achieve final optimal EWMA features. Different degrees of systematic errors (SE) including total allowable error (TEa) as a maximum error level were added to both simulation and patient results, and then the EWMA performance was assessed for different EWMA features. We calculated Youden’s index for each combination of EWMA features to find their optimal features to achieve minimum false positive rate (FPR) and maximum error detection rate at the SE level corresponding to TEa. ResultsEWMA implementation on real patient data revealed optimal EWMA features for each test. FPR values of creatinine and glucose were 18.48% and 10.17%, respectively, which exceeded the acceptable criteria for FPR (10%). The remaining six analytes showed acceptable FPR. ConclusionsWe showed the implementation of EWMA as PBRTQC, and optimization of its features based on the Youden index by conducting extensive performance evaluations and simulations in this study.

The computational roots of positivity and confirmation biases in reinforcement learning.

Humans do not integrate new information objectively: outcomes carrying a positive affective value and evidence confirming one's own prior belief are overweighed. Until recently, theoretical and empirical accounts of the positivity and confirmation biases assumed them to be specific to 'high-level' belief updates. We present evidence against this account. Learning rates in reinforcement learning (RL) tasks, estimated across different contexts and species, generally present the same characteristic asymmetry, suggesting that belief and value updating processes share key computational principles and distortions. This bias generates over-optimistic expectations about the probability of making the right choices and, consequently, generates over-optimistic reward expectations. We discuss the normative and neurobiological roots of these RL biases and their position within the greater picture of behavioral decision-making theories.

Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning

Food waste reduction represents a potential opportunity to enhance environmental sustainability. This is especially important in fresh products such as fresh seafood, where waste levels are substantially higher than those of other food products. In this particular case, reducing waste is also vital to meet demand while conserving fisheries. This paper aims to promote more sustainable supply chains by proposing daily fresh fish demand forecasting models that can be used by grocery retailers to align supply and demand, and hence prevent the production of food waste. To accomplish this goal, we explored the potential of different machine learning models, namely Long Short-Term Memory networks, Feedforward neural networks, Support Vector Regression, and Random Forests, as well as a Holt-Winters statistical model. Demand censorship was considered to capture real demand. To validate the proposed methodology, we estimated the demand for fresh fish in a representative store of a large European retailing company used as a case study. The results revealed that the machine learning models provided accurate forecasts in comparison to the baseline models and the statistical model, with the Long Short-Term Memory networks model yielding, in general, the best results in terms of root mean squared error (27.82), mean absolute error (20.63) and mean positive error (17.86). Thus, the implementation of these types of models can thus have a positive impact on the sustainability of fresh fish species and customer satisfaction.

Sticky me: Self-relevance slows reinforcement learning

A prominent facet of social-cognitive functioning is that self-relevant information is prioritized in perception, attention, and memory. What is not yet understood, however, is whether similar effects arise during learning. In particular, compared to other people (e.g., best friend) is information about the self acquired more rapidly? To explore this matter, here we used a probabilistic selection task in combination with computational modeling (i.e., Reinforcement Learning Drift Diffusion Model analysis) to establish how self-relevance influences learning under conditions of uncertainty (i.e., choices are based on the perceived likelihood of positive and negative outcomes). Across two experiments, a consistent pattern of effects was observed. First, learning rates for both positive and negative prediction errors were slower for self-relevant compared to friend-relevant associations. Second, self-relevant (vs. friend-relevant) learning was characterized by the exploitation (vs. exploration) of choice selections. That is, in a complex (i.e., probabilistic) decision-making environment, previously rewarded self-related outcomes were selected more often than novel — but potentially riskier — alternatives. The implications of these findings for accounts of self-function are considered.

Daily precipitation dataset at 0.1° for the Yarlung Zangbo River basin from 2001 to 2015

In order to obtain higher precision regional precipitation dataset in the Yarlung Zangbo River basin, two different schemes were proposed on the basis of the two most application potential satellite-based precipitation products, IMERG and CMORPH_BLD. The first method aimed to correct the positive error of IMERG based on high correlation (CC > 0.9) between IMERG and gauges. The second algorithm was developed to merge IMERG with CMORPH_BLD by the stepwise linear regression. As the reference, IMERG played a key role in correction of precipitation ratio determination and precipitation event detection. Two daily datasets with 0.1° resolution (BRD_IMERG and IGREA_IMERG-CMORPH) performed better than IMERG in CC, RMSE, ME, FAR and CSI, and streamflow simulation in the whole basin (NS: 0.86 and 0.87; RBIAS: −19% and −11%) and sub-basins. The two proposed methods were relatively simple and efficient for reconstructing higher precision regional precipitation, and the datasets provided a good application demonstration in the alpine region.

The perceived duration of expected events depends on how the expectation is formed

Repeated events can seem shortened. It has been suggested that this results from an inverse relationship between predictability and perceived duration, with more predictable events seeming shorter. Some evidence disputes this generalisation, as there are cases where this relationship has been nullified, or even reversed. This study sought to combine different factors that encourage expectation into a single paradigm, to directly compare their effects. We find that when people are asked to declare a prediction (i.e., to predict which colour sequence will ensue), guess-confirming events can seem relatively protracted. This augmented a positive time-order error, with the first of two sequential presentations already seeming protracted. We did not observe a contraction of perceived duration for more probable or for repeated events. Overall, our results are inconsistent with a simple mapping between predictability and perceived duration. Whether the perceived duration of an expected event will seem relatively contracted or expanded seems to be contingent on the causal origin of expectation.

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.

Cardiac Cycle Affects the Asymmetric Value Updating in Instrumental Reward Learning.

This study aimed to investigate whether instrumental reward learning is affected by the cardiac cycle. To this end, we examined the effects of the cardiac cycle (systole or diastole) on the computational processes underlying the participants’ choices in the instrumental learning task. In the instrumental learning task, participants were required to select one of two discriminative stimuli (neutral visual stimuli) and immediately receive reward/punishment feedback depending on the probability assigned to the chosen stimuli. To manipulate the cardiac cycle, the presentation of discriminative stimuli was timed to coincide with either cardiac systole or diastole. We fitted the participants’ choices in the task with reinforcement learning (RL) models and estimated parameters involving instrumental learning (i.e., learning rate and inverse temperature) separately in the systole and diastole trials. Model-based analysis revealed that the learning rate for positive prediction errors was higher than that for negative prediction errors in the systole trials; however, learning rates did not differ between positive and negative prediction errors in the diastole trials. These results demonstrate that the natural fluctuation of cardiac afferent signals can affect asymmetric value updating in instrumental reward learning.

DESC-1, a prospective study on epidemiologic factors influencing general population to engage with liquid biopsy based cancer screening.

e13630 Background: Liquid biopsy technology has delivered promising results for therapy monitoring and disease relapse detection. The simplicity of the procedure makes it attractive for early cancer detection despite its clinical value in this setting being as yet unknown. The objective of the study is to evaluate the acceptance of this new screening technology by the general population. Methods: Participants in a digital health study originally selected randomly from the general population of the canton of Geneva (Switzerland) were invited to participate in a survey investigating (1) their motivation and (2) what factors might affect their decision regarding whether to proceed to a liquid biopsy test for cancer screening. Results: 2’898 participants responded to the survey (participation rate: 35%), 1’568 are &gt; 50yo. Two thirds are female, 7.6% have a history of cancer, 80.8% have a family relative with a history of cancer. In the whole population: 97.7% are ready to use liquid biopsy as a cancer screening test. In the &gt; 50yo, 97.3% of women and 98.5% of men would do the test. In the &lt; 50yo, 97.2% of women and 98.6% of men would take it with X² = 0.018 (p = 0.89) for independence with sex. Age category, education level, income band, professional status, self-rated health, tobacco status, chronic disease, personal or familial history of cancer and COVID pandemic have no statistical impact on the incentive to do a liquid biopsy test. In the whole population, 94.6% would take the test knowing that there is a risk of a false positive. The median accepted false positive percentage error rate is 10% with interquartile rate (IQR) Q1: 5% and Q3: 15%. Ninety-six percent of respondents would take the test knowing that there is a risk of a false negative, accepting a median false negative percentage error rate of 10% with IQR Q1: 5% and Q3 : 20%. Most respondents (97.6%) will do the test knowing that additional tests or procedures could be required in case of positivity and 58.8% would take it yearly, while 24.6% only every two years. Fifty-nine percent believe that taking the test could influence their lifestyle, regardless of the result. Conclusions: This study shows that the surveyed population is ready to engage with liquid biopsy as a cancer screening tool despite its limitations and drawbacks. Willingness to take the test might depend on sex only in the &lt; 50yo population. Knowing the lack of demonstrated benefit, it is urgent to conduct trials assessing the clinical value, psychological impact and financial burden of liquid biopsy tests before incorporating this technology into cancer screening programs. Due to the very high level of interest demonstrated in our study, the possibility of direct-to-consumer availability should prompt health authorities and accreditation bodies to carefully weigh the impact of authorizing market access to liquid biopsy technologies for this purpose.

Contextual Data Cleaning with Ontology Functional Dependencies

Functional Dependencies define attribute relationships based on syntactic equality, and when used in data cleaning, they erroneously label syntactically different but semantically equivalent values as errors. We explore dependency-based data cleaning with Ontology Functional Dependencies (OFDs), which express semantic attribute relationships such as synonyms defined by an ontology. We study the theoretical foundations of OFDs, including sound and complete axioms and a linear-time inference procedure. We then propose an algorithm for discovering OFDs (exact ones and ones that hold with some exceptions) from data that uses the axioms to prune the search space. Toward enabling OFDs as data quality rules in practice, we study the problem of finding minimal repairs to a relation and ontology with respect to a set of OFDs. We demonstrate the effectiveness of our techniques on real datasets and show that OFDs can significantly reduce the number of false positive errors in data cleaning techniques that rely on traditional Functional Dependencies.

The Effects of Teachers’ Error Orientations on Students’ Mathematics Learning: The Role of Teacher Emotions

Several attempts have been made to explore the factors influencing teacher emotions, most of which focus on external factors such as student behaviors and classroom teaching. However, research on the links between internal factors and teacher emotions is scant. Based on the control value theory, this article explored the influence of junior secondary mathematics teachers’ error orientations on their emotions, and how teachers’ error orientations and emotions were related to students’ mathematics learning strategies. A sample of 70 junior high school mathematics teachers and their students (N = 2453) in mainland China participated in this study. Confirmatory factor analysis and multilevel structural equation modeling were used to analyze the data. The results showed that teachers’ positive error orientation increased their positive emotions and reduced their negative emotions, whereas teachers’ negative error orientation increased their negative emotions and reduced their positive emotions. Regarding the effects of teacher emotions, teachers’ positive emotions increased students’ positive mathematics achievement emotions and reduced their negative emotions. Meanwhile, students’ negative mathematics achievement emotions significantly reduced their adoption of desirable mathematics learning strategies. The findings highlight the importance of teachers’ positive error orientation and positive emotion for students’ mathematics learning.

Disentangling performance-monitoring signals encoded in feedback-related EEG dynamics

The feedback-related negativity (FRN) is a well-established electrophysiological correlate of feedback-processing. However, there is still an ongoing debate whether the FRN is driven by negative or positive reward prediction errors (RPE), valence of feedback, or mere surprise. Our study disentangles independent contributions of valence, surprise, and RPE on the feedback-related neuronal signal including the FRN and P3 components using the statistical power of a sample of N = 992 healthy individuals. The participants performed a modified time-estimation task, while EEG from 64 scalp electrodes was recorded. Our results show that valence coding is present during the FRN with larger amplitudes for negative feedback. The FRN is further modulated by surprise in a valence-dependent way being more positive-going for surprising positive outcomes. The P3 was strongly driven by both global and local surprise, with larger amplitudes for unexpected feedback and local deviants. Behavioral adaptations after feedback and FRN just show small associations. Results support the theory of the FRN as a representation of a signed RPE. Additionally, our data indicates that surprising positive feedback enhances the EEG response in the time window of the P3. These results corroborate previous findings linking the P3 to the evaluation of PEs in decision making and learning tasks.

Gaze as an Indicator of Input Recognition Errors

Input recognition errors are common in gesture- and touch-based recognition systems, and negatively affect user experience and performance. When errors occur, systems are unaware of them, but the user's gaze following an error may provide valuable cues for error detection. A study was conducted using a manual serial selection task to investigate whether gaze could be used to discriminate user-initiated selections from injected false positive selection errors. Logistic regression models of gaze dynamics could successfully identify injected selection errors as early as 50 milliseconds following a selection, with performance peaking at 550 milliseconds. A two-phase gaze pattern was observed in which users exhibited high gaze motion immediately following errors, and then decreased gaze motion as the error was noticed. Together, these results provide the first demonstration that gaze dynamics can be used to detect input recognition errors, and open new possibilities for systems that can assist with error recovery.

Sluggish retrieval of positive memories in depressed adults.

Although depression is associated with poor memory for positive material, the underlying mechanisms remain unclear. We used the Hierarchical Drift Diffusion Model (HDDM) to determine whether slow evidence accumulation at retrieval contributes to depressed individuals' difficulty remembering positive events. Participants completed the Beck Depression Inventory-II and were stratified into High BDI (HBDI; BDI-II > 20, n = 49) and Low BDI (LBDI; BDI-II < 6, n = 46) groups. Next, participantscompleted an oddball task in which neutral, negative, and positive pictures served as rare targets. One day later, recognition memory was tested by presenting the encoded ("old") pictures along with closely matched ("new") lures. Recognition accuracy was analyzed with a generalized linear model, and choice and response time data were analyzed with the HDDM. Recognition accuracy for old positive pictures was lower in HBDI versus LBDI participants, and the HDDM highlighted slow evidence accumulation during positive memory retrieval in the HBDI group. Impaired memory for positive material in depressed adults was related to slow evidence accumulation at retrieval. Because oddballs should elicit prediction errors that normally strengthen memory formation, these retrieval findings may reflect weak positive prediction errors, at encoding, in depressed adults.

Capnografía en recién nacidos en ventilación mecánica y su relación con la medición de CO2 en muestras sanguíneas

IntroductionMonitoring the partial pressure of CO2 (PCO2) in newborns who require ventilation would allow avoiding hypocapnia and hypercapnia. The measurement of end-tidal carbon dioxide (ETCO2) is an alternative rarely implemented in this population. ObjectiveTo evaluate the relationship between ETCO2 and PCO2 in newborns. MethodsCross-sectional study comparing two PCO2 measurement methods, the conventional one by analysis of blood samples and the one estimated by ETCO2. The study included hospitalized newborns that required conventional mechanical ventilation. The ETCO2 was measured with a Tecme GraphNet® neo, a neonatal ventilator with an integrated capnography, and we obtained the ETCO2-PCO2 gradient. We conducted correlation and Bland-Altman plot analyses to estimate the agreement. ResultsA total of 277 samples (ETCO2/PCO2) from 83 newborns were analyzed. The mean values of ETCO2 and PCO2 were 41.36 mmHg and 42.04 mmHg. There was a positive and significant correlation between ETCO2 and PCO2 in the overall analysis (r = 0.5402; p < .001) and in the analysis of each unit (p < .001). The mean difference was 0.68 mmHg (95% CI, −0.68 to 1.95) and was not significant. We observed a positive systematic error (PCO2 > ETCO2) in 2 of the units, and a negative difference in the third (PCO2 < ETCO2). DiscussionThe correlation between ETCO and PCO2 was significant, although the obtained values were not equivalent, with differences ranging from 0.1 mmHg to 20 mmHg. Likewise, we found systematic errors that differed in sign (positive or negative) between institutions.

Positive error climate promotes learning outcomes through students’ adaptive reactions towards errors

Errors are an integral part of the learning process and an opportunity to increase skills and knowledge, but they are often discouraged, sanctioned and derided in the classroom. This study tests whether students' perceptions of being part of an error-friendly classroom context (i.e., a positive classroom error climate) is positively related to students' learning outcomes via students' adaptive reactions towards errors. A total of 563 Italian middle school students from 32 mathematics classes completed a questionnaire on their perceptions of classroom error climate and their reactions towards errors. Students' math grades were used as indicators of their level of learning outcomes. A multilevel model showed that perceived classroom error climate was positively related to math grades via increased adaptive reactions towards errors. Our findings revealed that an error-friendly classroom context is associated with students’ adaptive adjustment to errors and to better learning outcomes in mathematics.

A Novel Spatio-Temporal Data Redundancy Elimination Approach for RFID Systems with Multiple Readers

In this paper, we propose a Voronoi diagram based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so that every RFID tag will be read by only one reader depending on the distance between the tag and the center of the Minimum Enclosing Circle (MEC) of the Voronoi cell which the reader belongs to. Thus, the spatial redundancy in the gathered data is completely eliminated. Temporal redundancy is also minimized by reading the RFID tags at regular time intervals larger than an appropriately chosen threshold value. Existing methods of reducing data redundancy in RFID systems are often associated with loss of data, false positive errors and false negative errors. In contrast to this, our proposed technique is free from any false positive and false negative errors with no loss of data and every tag being read by only one single reader. Simulation of our proposed approach also establishes its superiority to the existing techniques in terms of these performance parameters.