Higher Confidence Ratings Research Articles

Reflector variables in augmented reality lineups: Assessing eyewitness identification reliability in children and adults with confidence, response time, and proximity to the lineup.

Attending to the behaviors of eyewitnesses at police lineups could help to determine whether an eyewitness identification is accurate or mistaken. Eyewitness identification decision processes were explored using augmented reality holograms. Children (n = 143; Mage = 10.79, SD = 1.12 years) and adults (n = 152; Mage = 22.12, SD = 7.47 years) viewed staged crime videos and made identification decisions from sequential lineups. The lineups were presented in augmented reality. Children were less accurate than adults on the lineup task. For adults, fast response times and high post-identification confidence ratings were both reflective of identification accuracy. Fast response times were also reflective of accuracy for children; however, children's confidence ratings did not reflect the likely accuracy of their identifications. A new additional measure, the witness' proximity to the augmented reality lineup, revealed that children who made mistaken identifications moved closer to the lineup than children who correctly identified the person from the crime video. Adults who moved any distance towards the lineup were less accurate than adults who did not move at all, but beyond that, adults' proximity to the lineup was not reflective of accuracy. The findings give further evidence that behavioral indicators of deliberation and information-seeking by eyewitnesses are signals of low lineup identification reliability. The findings also suggest that when assessing the reliability of children's lineup identifications, behavioral measures are more useful than metacognitive reports.

The effects of public health and social measures (PHSM) implemented during the COVID‐19 pandemic: An overview of systematic reviews

AbstractIntroductionTo systematically review the effectiveness and unintended health and socioeconomic consequences of public health and social measures (PHSM) aimed at reducing the scale and risk of transmission of coronavirus disease 2019 (COVID‐19).MethodsThis review followed guidance about overviews of reviews in the Cochrane handbook for systematic reviews of interventions and used the Epistemonikos database's COVID‐19 Living Overview of Evidence repository as a primary search source. Methodological quality was evaluated using the Measurement Tool to Assess Systematic Reviews (AMSTAR 2) checklist.ResultsA total of 94 reviews were included, of which eight (9%) had “moderate” to “high” confidence ratings on the AMSTAR 2. Of 16 reviews (17%) reporting applying the GRADE framework, none found high certainty evidence for any of our outcomes of interest. Across the 94 reviews, the most frequently examined PHSM were personal protection (n = 18, 19%). Within multicomponent interventions, so‐called “lockdown” was the most frequently examined component (n = 39, 41%). The most frequently reported outcome category was non‐COVID‐19‐related health outcomes (n = 58, 62%). Only five (5%) reviews reported on socioeconomic outcomes. Findings from the eight reviews with moderate or high confidence ratings on AMSTAR 2 are narratively summarized. There is low‐certainty evidence that multicomponent interventions may reduce the transmission of COVID‐19 in different settings. For active surveillance and response measures, low‐certainty evidence suggests that routine testing of residents and staff in long‐term care facilities may reduce the number of infections, hospitalizations, and deaths among residents. We found very low‐certainty evidence about the effectiveness of personal protection measures, travel‐related control measures, and environmental measures. Unintended consequences were rarely examined by those eight reviews.ConclusionWe found predominantly low‐ to very low‐certainty evidence regarding the effectiveness and unintended consequences of PHSM in controlling the risk and scale of COVID‐19 transmission. There is a need to improve the conduct and reporting of systematic reviews.

Developing digital academic skills in preservice teachers: performance and metacognitive perspectives

ABSTRACT The purpose of this research was to evaluate an instructional change in an academic literacy course that transitioned from traditional print- to digital-based instruction. The learning outcomes of 71 preservice teachers who attended the digitally-modified course were compared to the learning outcomes of 55 students who attended the same course with print-based instruction. Results showed that the print and digital cohorts developed comparable academic literacy skills, with higher confidence ratings in the digital cohort for both reading and writing performance. Interestingly, the digital cohort was calibrated in their self-evaluation of writing quality, but not in their assessment of reading comprehension. Taken together, this study emphasises the importance of incorporating strategies for deep processing of digital materials and tools to facilitate digital academic writing skills in teacher education. In addition, future teachers should be made aware of the metacognitive biases that accompany digital learning.

Transcranial magnetic stimulation effects support an oscillatory model of ERP genesis

Whether prestimulus oscillatory brain activity contributes to the generation of post-stimulus-evoked neural responses has long been debated, but findings remain inconclusive. We first investigated the hypothesized relationship via EEG recordings during a perceptual task with this correlational evidence causally probed subsequently by means of online rhythmic transcranial magnetic stimulation. Both approaches revealed a close link between prestimulus individual alpha frequency (IAF) and P1 latency, with faster IAF being related to shorter latencies, best explained via phase-reset mechanisms. Moreover, prestimulus alpha amplitude predicted P3 size, best explained via additive (correlational and causal evidence) and baseline shift mechanisms (correlational evidence), each with distinct prestimulus alpha contributors. Finally, in terms of performance, faster prestimulus IAF and shorter P1 latencies were both associated with higher task accuracy, while lower prestimulus alpha amplitudes and higher P3 amplitudes were associated with higher confidence ratings. Our results are in favor of the oscillatory model of ERP genesis and modulation, shedding new light on the mechanistic relationship between prestimulus oscillations and functionally relevant evoked components.

Evolving interpretable decision trees for reinforcement learning

In recent years, reinforcement learning (RL) techniques have achieved great success in many different applications. However, their heavy reliance on complex deep neural networks makes most RL models uninterpretable, limiting their application in domains where trust and security are important. To address this challenge, we propose MENS-DT-RL, an algorithm capable of constructing interpretable models for RL via the evolution of decision tree (DT) models. MENS-DT-RL uses a multi-method ensemble algorithm to evolve univariate DTs, guiding the process with a fitness metric that prioritizes interpretability and consistent high performance. Three different initializations for the MENS-DT-RL are proposed, including the use of Imitation Learning (IL) techniques, and a novel pruning approach that reduces solution size without compromising performance. To evaluate the proposed approach, we compare it with other models from the literature on three benchmark tasks from the OpenAI Gym library, as well as on a fertilization problem inspired by real-world crop management. To the best of our knowledge, the proposed scheme is the first to solve the Lunar Lander benchmark with both interpretability and a high confidence rate (90% of episodes are successful), as well as the first to solve the Mountain Car environment with a tree of only 7 nodes. On the real-world task, the proposed MENS-DT-RL is able to produce solutions with the same quality as deep RL policies, with the added bonus of interpretability. We also analyze the best solutions found by the algorithm and show that they are not only interpretable but also diverse in their behavior, empowering the end user with the choice of which model to apply. Overall, the findings show that the proposed approach is capable of producing high-quality transparent models for RL, achieving interpretability without losing performance.

Adversarial Defensive Framework for State-of-Health Prediction of Lithium Batteries

Neural Networks are subject to malicious data poisoning attacks affecting the ability of the model to make accurate predictions. The attacks are generated using adversarial techniques imperceptible to the human eye since they use minimal noise to alter features which end up affecting boundary decisions of the prediction Model. Predicting lithium-ion batteries' State of Health (SOH) in a adversarial context becomes a challenging task especially if the model is expected to always predict at a very high accuracy level. Our article presents three novel contributions. The first contribution is a SOH prediction model that shows one of the best accuracy rates in the literature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R2=99.82\%$</tex-math></inline-formula> ) and yet uses the simplest LSTM model configuration compared to literature. The second contribution is the implementation of three state-of-the-art adversarial data poisoning attacks at decision time, knowingly Fast Gradient Method (FGSM), Momentum Iterative Method (MIM) and Basic Iterative Method (BIM) and the assessment of their impact on the original prediction accuracy. Most of literature use the attacks in a classification context while we are applying it to a time series prediction context. The third and most important contribution of this article is presenting a generic defense strategy combined with a feature engineering method that can be genrelized to prevent any potential adversarial attack attempt on any prediction model in a time-series prediction context using the same suggested feature engineering approach as suggested in this paper. The accuracy of the model is assessed using error estimators Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the R square (R2).The results show that adversarial data poisoning attacks are lethal to a time-series prediction model and our proposed defense strategy is able to detect and flag the existence of malicious data using a Simple Vector Machine (SVM) classifier with a very high confidence rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$area\,uder\,curve=0.996$</tex-math></inline-formula> ) which allow our model to defend against any potential unseen adversarial attack.

Neural retrieval processes occur more rapidly for visual mental images that were previously encoded with high-vividness.

Visual mental imagery refers to our ability to experience visual images in the absence of sensory stimulation. Studies have shown that visual mental imagery can improve episodic memory. However, we have limited understanding of the neural mechanisms underlying this improvement. Using electroencephalography, we examined the neural processes associated with the retrieval of previously generated visual mental images, focusing on how the vividness at generation can modulate retrieval processes. Participants viewed word stimuli referring to common objects, forming a visual mental image of each word and rating the vividness of the mental image. This was followed by a surprise old/new recognition task. We compared retrieval performance for items rated as high- versus low-vividness at encoding. High-vividness items were retrieved with faster reaction times and higher confidence ratings in the memory judgment. While controlling for confidence, neural measures indicated that high-vividness items produced an earlier decrease in alpha-band activity at retrieval compared with low-vividness items, suggesting an earlier memory reinstatement. Even when low-vividness items were remembered with high confidence, they were not retrieved as quickly as high-vividness items. These results indicate that when highly vivid mental images are encoded, the speed of their retrieval occurs more rapidly, relative to low-vivid items.

Neural patterns of conscious visual awareness in the Riddoch syndrome

The Riddoch syndrome is one in which patients blinded by lesions to their primary visual cortex can consciously perceive visual motion in their blind field, an ability that correlates with activity in motion area V5. Our assessment of the characteristics of this syndrome in patient ST, using multimodal MRI, showed that: 1. ST’s V5 is intact, receives direct subcortical input, and decodable neural patterns emerge in it only during the conscious perception of visual motion; 2. moving stimuli activate medial visual areas but, unless associated with decodable V5 activity, they remain unperceived; 3. ST’s high confidence ratings when discriminating motion at chance levels, is associated with inferior frontal gyrus activity. Finally, we report that ST’s Riddoch Syndrome results in hallucinatory motion with hippocampal activity as a correlate. Our results shed new light on perceptual experiences associated with this syndrome and on the neural determinants of conscious visual experience.

Insights from coherence in students’ scientific reasoning skills

Improving scientific reasoning enables students to navigate the challenges of learning science. Teachers use Lawson's classroom test of scientific reasoning (LCTSR) to measure scientific reasoning. The LCTSR is a two-tiered assessment that uses content-based questions and explanation statements. Researchers have found that if a student answers a knowledge-based question correctly but selects an incorrect explanation statement, there may be an element of guessing or an established misconception. Misconceptions are beliefs that students hold that are not based on scientific evidence. The present study added a confidence variable to the LCTSR, which measures how confident students regarded their responses in both tiers. Selecting a correct response to a knowledge-based question while providing an incorrect explanation and having a high confidence rating indicates an established misconception. The confidence variable is, therefore, a measure of an established scientific misconception and is the basis of the present study. The present study analyzed the responses of 71 first-year university students enrolled in an introductory physics course. The LCTSR results indicate that students performed the best in the conservation reasoning dimension and the worst in the proportional reasoning dimension. In all scientific reasoning dimensions, more than half the students chose the incorrect explanation for each context question. Students' confidence responses surpassed their performance in three of the 14 LCTSR items. The low frequency of correct answers and the statistically significant correlation between LCTSR items and confidence suggest possible misconceptions in students' scientific reasoning skills.

Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis

BackgroundThe health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality. MethodsThe Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis. ResultsThirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates—including smoking, body mass index, and individual and area-level socioeconomic status—and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter—pollutants with more than 10 studies—were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m3, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure–response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well. ConclusionsThe overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Fault detection and isolation of gas turbine: Hierarchical classification and confidence rate computation

In this paper, a Fault Detection and Isolation (FDI) system based on an ensemble-based hierarchical classifier is devised to detect and isolate twelve typical turbine faulty scenarios and a healthy scenario. The hierarchical classifier is designed based on eight studied classifiers' best-practice confusion patterns, ranging from classical to state-of-the-art methods. The proposed hierarchical classifier discriminates five faulty classes with almost perfect accuracy while increasing the accuracy of healthy scenario discrimination by 10%, which is particularly important since it significantly reduces the false alarm rate for any form of fault. The overall accuracy of classification is improved by 2%, as well. Along with the classifier, an appropriate notion of confidence rate helps evaluate the classifier's decisions, especially when unlabeled data are involved. This motivation leads to devising a confidence rate concept that suits the devised classifier well. The obtained results show that the introduced confidence rate changes monotonically with classification accuracy. In the case of the high confidence rate of unlabeled data, this property is utilized to label the data with high accuracy. The proposed confidence rate is particularly effective in decision-making in the labeling of challenging mixed-up scenarios. Obtained results show that the classifier's decisions are reliable, with a confidence rate greater than 0.7. The developed FDI system is examined with simulated and real data, where obtained results verify the satisfactory performance of the system.

Electrophysiological correlates of confidence differ across correct and erroneous perceptual decisions

Every decision we make is accompanied by an estimate of the probability that our decision is accurate or appropriate. This probability estimate is termed our degree of decision confidence. Recent work has uncovered event-related potential (ERP) correlates of confidence both during decision formation and after a decision has been made. However, the interpretation of these findings is complicated by methodological issues related to ERP amplitude measurement that are prevalent across existing studies. To more accurately characterise the neural correlates of confidence, we presented participants with a difficult perceptual decision task that elicited a broad range of confidence ratings. We identified a frontal ERP component within an onset prior to the behavioural response, which exhibited more positive-going amplitudes in trials with higher confidence ratings. This frontal effect also biased measures of the centro-parietal positivity (CPP) component at parietal electrodes via volume conduction. Amplitudes of the error positivity (Pe) component that followed each decision were negatively associated with confidence for trials with decision errors, but not for trials with correct decisions, with Bayes factors providing moderate evidence for the null in the latter case. We provide evidence for both pre- and post-decisional neural correlates of decision confidence that are observed in trials with correct and erroneous decisions, respectively. Our findings suggest that certainty in having made a correct response is associated with frontal activity during decision formation, whereas certainty in having committed an error is instead associated with the post-decisional Pe component. These findings also highlight the possibility that some previously reported associations between decision confidence and CPP/Pe component amplitudes may have been a consequence of ERP amplitude measurement-related confounds. Re-analysis of existing datasets may be useful to test this hypothesis more directly.

Learning specialized vocabulary in the second language: Does transfer from the first language help?

Learners entering higher education may learn specialized vocabulary of new disciplines in their second language (L2) with or without support from their first language (L1). However, these learners cannot rely on an established conceptual representation in L1 when learning L2 specialized vocabulary. The effects of learning a new concept in L1 prior to learning and testing the same concept in L2 are yet unclear. The present study compared the effectiveness of L1-mediation and L2-repetition word learning methods randomly assigned to 75 bilingual adults. Participants learned subject-specific specialized vocabulary through reading a series of definition sentences in two learning episodes a week apart. The L1L2 group ( N = 31) first learned L1 words on Day 1 and then their L2 translation equivalents on Day 2, while the L2L2 group ( N = 44) learned the words in L2 on both days. In each learning session, participants provided self-ratings on their confidence in acquiring word meaning after reading each sentence and completed a word meaning recognition test afterward. Results using linear mixed-effects modeling showed that L1-mediation learning may ease the start of L2 word learning as shown in higher confidence ratings and less reading time, leading to a faster establishment of high-quality conceptual representations. In contrast, L2-repetition learning may strengthen lexical representations and L2-concept connections through increased exposure to L2 words. However, the two learning methods produced comparable results on L2 meaning recognition, so flexible adoption of the learning methods according to the learning goal is suggested.

Improved recommender systems by denoising ratings in highly sparse datasets through individual rating confidence

Collaborative filtering (CF) is the most successful approach of Recommender Systems that has been applied in a wide range of applications. In this approach, historical rating data is exploited to calculate similarity between pairs of users and predict user preference over unseen items. The similarity is calculated using users' global preferences estimated from their previous interactions with items. To this end, commonly rated items by a given pair of users are utilized to calculate the similarity. In a sparse dataset, there are limited co-rated items. In addition, inconsistent behaviours of users in rating items may add some noise to data, which makes the ratings more untrustworthy leading to less accuraterecommendations. In this manuscript, we introduce an individual ratings confidence measure (IRC) to calculate the confidence of a given rating to each item by the target user. IRC consists of 5 factors that help to have more information about the interest and boredom of users. User ratings are denoised by prioritizing extreme and high-confidence ratings, and finally, the denoised ratings are used to obtain similarity values. This approach leads to a more accurate neighbourhood selection and rating prediction. We show the superiority of the proposed method when compared with state-of-the-art recommender algorithms over some well-known benchmark datasets.

The application of AMSTAR2 in 32 overviews of systematic reviews of interventions for mental and behavioural disorders: A cross-sectional study.

'A measurement tool to assess systematic reviews, version 2' (AMSTAR2) is a 16-item tool to critically appraise systematic reviews (SRs) of healthcare interventions. This study aimed to assess the methods and outcomes of AMSTAR2 appraisals in overviews of SRs of interventions for mental and behavioural disorders. The cross-sectional study was conducted using 32 overviews of SRs selected from three electronic databases in January 2021. Data items included overview and SR characteristics and AMSTAR2 appraisal methods and outcomes. Data were extracted by two authors independently and narratively synthesised using descriptive statistics (means ± SD and relative frequencies). SR characteristics were compared based on AMSTAR2 appraisal outcomes using chi-square tests. The 32 overviews appraised SRs of predominantly non-pharmacological interventions for mental disorders. AMSTAR2 appraisals were reported as confidence ratings in 25/32 overviews or individual item scores in 24/32 overviews. Most SRs/overview were non-Cochrane (mean=94%), included RCTs only (mean=77%) and were published before AMSTAR2 release (mean=79%). The confidence ratings derived in 25 overviews for 349 SRs were predominantly critically low (68%). Confidence ratings were similar for SRs with RCTs only versus RCTs+non-RCTs or SRs published before versus after AMSTAR2 release, while Cochrane SRs received more high+moderate than low+critically low confidence ratings (p < 0.01). Confidence ratings derived based on AMSTAR2 do not differentiate among SRs of healthcare interventions except for Cochrane SRs that fulfil the criteria for high confidence ratings. AMSTAR2 items should be consulted to avoid common weaknesses in future SRs.

Spatial Patterns of ‘Ōhi‘a Mortality Associated with Rapid ‘Ōhi‘a Death and Ungulate Presence

Effective forest management, particularly during forest disturbance events, requires timely and accurate monitoring information at appropriate spatial scales. In Hawai‘i, widespread ‘ōhi‘a (Metrosideros polymorpha Gaud.) mortality associated with introduced fungal pathogens affects forest stands across the archipelago, further impacting native ecosystems already under threat from invasive species. Here, we share results from an integrated monitoring program based on high resolution (&lt;5 cm) aerial imagery, field sampling, and confirmatory laboratory testing to detect and monitor ‘ōhi‘a mortality at the individual tree level across four representative sites on Hawai‘i island. We developed a custom imaging system for helicopter operations to map thousands of hectares (ha) per flight, a more useful scale than the ten to hundreds of ha typically covered using small, unoccupied aerial systems. Based on collected imagery, we developed a rating system of canopy condition to identify ‘ōhi‘a trees suspected of infection by the fungal pathogens responsible for rapid ‘ōhi‘a death (ROD); we used this system to quickly generate and share suspect tree candidate locations with partner agencies to rapidly detect new mortality outbreaks and prioritize field sampling efforts. In three of the four sites, 98% of laboratory samples collected from suspect trees assigned a high confidence rating (n = 50) and 89% of those assigned a medium confidence rating (n = 117) returned positive detections for the fungal pathogens responsible for ROD. The fourth site, which has a history of unexplained ‘ōhi‘a mortality, exhibited much lower positive detection rates: only 6% of sampled trees assigned a high confidence rating (n = 16) and 0% of the sampled suspect trees assigned a medium confidence rating (n = 20) were found to be positive for the pathogen. The disparity in positive detection rates across study sites illustrates challenges to definitively determine the cause of ‘ōhi‘a mortality from aerial imagery alone. Spatial patterns of ROD-associated ‘ōhi‘a mortality were strongly affected by ungulate presence or absence as measured by the density of suspected ROD trees in fenced (i.e., ungulate-free) and unfenced (i.e., ungulate present) areas. Suspected ROD tree densities in neighboring areas containing ungulates were two to 69 times greater than those found in ungulate-free zones. In one study site, a fence line breach occurred during the study period, and feral ungulates entered an area that was previously ungulate-free. Following the breach, suspect ROD tree densities in this area rose from 0.02 to 2.78 suspect trees/ha, highlighting the need for ungulate control to protect ‘ōhi‘a stands from Ceratocystis-induced mortality and repeat monitoring to detect forest changes and resource threats.

Quality appraisal of systematic reviews of interventions for children with cerebral palsy reveals critically low confidence.

To evaluate the methodological quality of recent systematic reviews of interventions for children with cerebral palsy in order to determine the level of confidence in the reviews' conclusions. A comprehensive search of 22 databases identified eligible systematic reviews with and without meta-analysis published worldwide from 2015 to 2019. We independently extracted data and used A Measurement Tool to Assess Systematic Reviews-2 (AMSTAR-2) to appraise methodological quality. Eighty-three systematic reviews met strict eligibility criteria. Most were from Europe and Latin America and reported on rehabilitative interventions. AMSTAR-2 appraisal found critically low confidence in 88% (n=73) because of multiple and varied deficiencies. Only 7% (n=6) had no AMSTAR-2 critical domain deficiency. The number of systematic reviews increased fivefold from 2015 to 2019; however, quality did not improve over time. Most of these systematic reviews are considered unreliable according to AMSTAR-2. Current recommendations for treating children with CP based on these flawed systematic reviews need re-evaluation. Findings are comparable to reports from other areas of medicine, despite the general perception that systematic reviews are high-level evidence. The required use of current widely accepted guidance for conducting and reporting systematic reviews by authors, peer reviewers, and editors is critical to ensure reliable, unbiased, and transparent systematic reviews. What this paper adds Confidence was critically low in the conclusions of 88% of systematic reviews about interventions for children with cerebral palsy (CP). Quality issues in the sample were not limited to systematic reviews of non-randomized trials, or to those about certain populations of CP or interventions. The inclusion of meta-analysis did not improve the level of confidence in these systematic reviews. Numbers of systematic reviews on this topic increased over the 5 search years but their methodological quality did not improve.

Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy.

Colonoscopic screening and surveillance for colorectal cancer could be made safer and more efficient if endoscopists could predict histology without the need to biopsy and perform histopathology on every polyp. Elastic-scattering spectroscopy (ESS), using fiberoptic probes integrated into standard biopsy tools, can assess, both in vivo and in real time, the scattering and absorption properties of tissue related to its underlying pathology. The objective of this study was to evaluate prospectively the potential of ESS to predict polyp pathology accurately. We obtained ESS measurements from patients undergoing screening/surveillance colonoscopy using an ESS fiberoptic probe integrated into biopsy forceps. The integrated forceps were used for tissue acquisition, following current standards of care, and optical measurement. All measurements were correlated to the index pathology. A machine learning model was then applied to measurements from 367 polyps in 169 patients to prospectively evaluate its predictive performance. The model achieved sensitivity of 0.92, specificity of 0.87, negative predictive value (NPV) of 0.87, and high-confidence rate (HCR) of 0.84 for distinguishing 220 neoplastic polyps from 147 non-neoplastic polyps of all sizes. Among 138 neoplastic and 131 non-neoplastic polyps ≤ 5mm, the model achieved sensitivity of 0.91, specificity of 0.88, NPV of 0.89, and HCR of 0.83. Results show that ESS is a viable endoscopic platform for real-time polyp histology, particularly for polyps ≤ 5mm. ESS is a simple, low-cost, clinically friendly, optical biopsy modality that, when interfaced with minimally obtrusive endoscopic tools, offers an attractive platform for in situ polyp assessment.

Novelty Detection in Memory for Common and Uncommon Odors over Short Delays.

Odor memory is commonly believed to be very strong and long-lasting. The present study examined factors that impact odor recognition memory over short delay intervals (immediately or 30 s after target presentation) with emphasis on memory task (forced-choice vs "monadic"/single stimulus yes/no), odor category, and target/foil relationship. We explored trial-by-trial confidence as well as the effect of target familiarity, pleasantness, and intensity ratings, and odor nameability on memory for odors. Overall odor recognition memory in terms of proportion correct and sensitivity measures did not decline significantly during the 30-s delay interval in either task. However, hit rates were lower at 30 s and correct rejection rates for common odors remained consistently high. Recognition memory was better on trials in which the odor pairs were highly dissimilar, as well as on trials in which the target was an uncommon odor, particularly if it could be named. Familiarity, pleasantness, and intensity had no systematic effect on recognition memory. Whereas the results provide evidence of a fading memory trace, indicated by the decreased hit rates after a 30-s delay, the constant rates of correct rejections and high confidence ratings on those trials, even after delay, suggests that novelty detection (i.e., recognition that an odor is not one that has been encountered previously in that context) may play an important role in the memory for odors over short delays. Whether there is a separate short-term odor memory store is also addressed.

The nature of metacognitive inefficiency in perceptual decision making.

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across 5 different data sets and 4 different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency. (PsycInfo Database Record (c) 2020 APA, all rights reserved).