Correct Rejection Rate Research Articles

Transparency improves the accuracy of automation use, but automation confidence information does not.

Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence information, and their potential interacting effect on the accuracy of automation use and other outcomes. An uninhabited vehicle (UV) management task was completed where participants selected the optimal UV to complete missions. Low or high automation transparency was provided, and participants agreed/disagreed with automated advice on each mission. We manipulated between participants whether automated advice was accompanied by confidence information. This information indicated on each trial whether automation was "somewhat" or "highly" confident in its advice. Higher transparency improved the accuracy of automation use, led to faster decisions, lower perceived workload, and increased trust and perceived usability. Providing participant automation confidence information, as compared with not, did not have an overall impact on any outcome variable and did not interact with transparency. Despite no benefit, participants who were provided confidence information did use it. For trials where lower compared to higher confidence information was presented, hit rates decreased, correct rejection rates increased, decision times slowed, and perceived workload increased, all suggestive of decreased reliance on automated advice. Such trial-by-trial shifts in automation use bias and other outcomes were not moderated by transparency. These findings can potentially inform the design of automated decision-support systems that are more understandable by humans in order to optimise human-automation interaction.

Effects of long-term exposure to high-altitude on episodic memory: The moderating role of daytime dysfunction

Chronic exposure to high-altitude hypoxic environments may influence short-term memory and working memory. However, its impact on long-term memory, specifically episodic memory, remains understudied. In this study, we systematically investigated the effects of long-term exposure to high altitude (3650 m) on episodic memory, including item memory and source memory. Moreover, we examined the moderating role of daytime dysfunction on the relationship between altitude and episodic memory. In total, 97 participants were enrolled in the study: 49 were from the high-altitude (HA) group, comprising those born and raised in low altitude (LA) areas (< 500 m) and had migrated to HA for 2∼3 years after turning 18; and 48 were from the LA group, who had never lived at high altitudes. Episodic memory was evaluated using a what-when-where task, whereas daytime dysfunction was measured by the Pittsburgh Sleep Quality Index. For item memory, hit rate and correct rejection rate were significantly lower in the HA group relative to the LA group. For source binding memory, the performance in what-where binding and what-when-where binding were decreased in the HA group, but the what-when binding did not differ between the two groups. Furthermore, the effects of altitude on hit rate, what-where binding, and what-when-where binding were significantly pronounced in individuals with higher levels of daytime dysfunction. These findings suggest that long-term exposure to high-altitude hypoxic environments influences episodic memory performance, including item recognition and source-binding memory. Specifically, spatial source memory is affected by high-altitude exposure, whereas temporal source memory remains unaffected. Moreover, these results highlight the importance of considering sleep quality, especially good daytime function, in maintaining optimal episodic memory function following chronic exposure to high altitudes.

Applying the Robust Chi-Square Goodness-of-Fit Test to Multilevel Multitrait-Multimethod Models: A Monte Carlo Simulation Study on Statistical Performance

As the robust maximum likelihood χ2 goodness-of-fit test had been found to yield inflated type-I error rates for certain two-level confirmatory factor analysis (CFA) models, a new correction for the test was implemented in Mplus version 8.7. In this simulation study, we inspected whether the corrected test statistics follow the expected χ2 distributions when applying more complex two-level models for multitrait-multimethod data with varying sample sizes and correlations within trait factors. Investigating rejection rates and probability-probability plots, we found that the new correction markedly and sufficiently reduced previously inflated rejection rates in conditions with within-trait correlations equal to 1, 100 between-level units, and 10 or 20 within-level units. In other conditions, rejection rates were hardly affected or not sufficiently reduced by the new correction. While in most conditions, 2 within-level units did not suffice, 5 within-level units and 250 between-level units were enough to yield correct rejection rates given within-trait correlations did not exceed 0.80. Correlations above 0.80 required larger sample sizes. In planning studies with multilevel CFA models, researchers should be aware that sample size requirements for likelihood-based model fit evaluations can depend on several different factors and might consider conducting Monte Carlo simulations tailored to their specific modeling conditions.

Chinese character unitization enhances recollection-based associative recognition: Evidence from fMRI.

Previous research has suggested that familiarity can enhance associative memory after unitization, but the cognitive mechanisms underlying unitization remain debated. To explore the neural mechanisms of associative memory after unitization in the absence of semantic relations, we used Chinese characters as stimuli and recorded participants' blood oxygen level-dependent signals during recognition. Behavioral results showed that after Chinese character unitization, not only the associative performance of recognition (Pr, hit rate minus false alarm rate) and general Pr but also the hit rate and correct rejection rate increased. Neuroimaging results revealed activation of the hippocampus and parahippocampal gyrus during associative recognition in both the unitized and the non-unitized condition, and hippocampal activation increased after unitization. However, activation of the perirhinal cortex was not observed in either condition. These findings, in contrast to those from previous studies on unitization, suggest that Chinese character unitization enhances recollection-based, rather than familiarity-based, associative recognition. This suggests that the encoding of semantic relations during unitization is critical for subsequent familiarity-based associative recognition.

0186 Continuous Theta Burst Transcranial Magnetic Stimulation and Signal Detection Performance on a Military Go/No-Go Task

Abstract Introduction Repetitive transcranial Magnetic Stimulation (rTMS) is an emerging non-pharmacological treatment method utilized to treat multiple clinical diagnoses to include anxiety, depression, and now insomnia. As part of a larger project, we showed that a single rTMS stimulation with continuous theta burst stimulation (cTBS) to the default mode network (DMN) was associated with improved sleep. However, there is little known about potential effects on attention and vigilance because of cTBS to the DMN. We hypothesized that active cTBS would have no significant impact on signal detection tasks post-TMS administration. Methods Twenty adults with clinical insomnia completed this study (Mage=26.90, SD=6.56). Participants completed two overnight visits in total; one where they received active cTBS and one where they received sham cTBS over an easily accessible node of the DMN (left angular gyrus). We analyzed signal detection performance utilizing a Go-No-Go (GNG) task. Three variables were calculated as a result: hit rate, correct rejection rate, and false alarm rate. Higher scores on hit rate and correct rejection rate indicate higher correct responses. Higher scores on false alarm rate indicates a higher number of incorrect responses. We conducted a repeated measures ANOVA with factors of condition, administration, and reversal. Results There was no main effect of condition on GNG hit rate (F(1,17)=1.365, p=.259, ηp2 =.074), GNG false alarm rate (F(1,17)=.210, p=.091, ηp2 =.244), or GNG correct rejection rate (F(1, 17)=1.677, p=.213, ηp2 =.090). We also found there were no significant interaction effects of TMS condition on performance on reversal conditions for GNG hit rate (F(1,17)=0.45, p=.834, ηp2 =.003), GNG false alarm rate (F(1,17)=1.572, p=.227, ηp2 =.085), or GNG correct rejection rate (F(1, 17)=1.510, p=.236, ηp2 =.082). Posthoc pairwise comparisons demonstrated that participants did not significantly differ on GNG hit rate, false alarm rate, or correct rejection rate between their active cTBS and sham cTBS session (p&amp;gt;.05). Conclusion Active cTBS was not significantly associated with any decline in response inhibition or signal detection. It is helpful for future recipients to know they are not adversely affected in attention or vigilance after receiving cTBS rTMS of the DMN for treatment of insomnia. Support (if any) W81XWH2010173

0055 Effect of acute sleep restriction on sleep physiology and emotional imagery memory in schizophrenia

Abstract Introduction Cognitive dysfunction in individuals with schizophrenia (SZ) represents a major determinant of poor functioning and may arise in part due to deficits in sleep quality that have been reported in in this population. In the current project, we investigate the effect of acute sleep restriction on a sleep-dependent emotional memory task. Methods Six individuals with medication-controlled SZ encoded consensus-rated emotionally valent and neutral pictures from the International Affective Picture System in the evening before 2 counterbalanced, polysomnographically-recorded nights containing either ad libitum sleep (~11pm to 7am) or sleep restriction (SR) to 4 hours between 3 and 7 am. Image recall occurred within 1 hour of wake time. We compared memory performance for negative emotional/neutral memory and PSG measures across the two sleep conditions with paired signed-rank tests. Results Mean age was 38 years and 50% were women. Acute sleep restriction resulted in reduced total sleep time (Adlib: median 372 min. (interquartile-range 53) vs SR: 209 (16) min., p=0.03), including reduction in duration of N1 (Ad lib: 44 min. (44) vs. SR: 29 min. (33), p=0.03), N2 (Ad lib: 158 min. (86) vs. SR: 60 min. (33), p=0.03) and REM sleep (Ad lib: 18 min. (5) vs. SR: 13 min. (13), p=0.03). We did not observe differences in N3 duration, sleep efficiency, and arousal index. Measures of breathing such as AHI4%, AHI3A, and SpO2 nadir were not significantly different between conditions. Compared to SR, the false alarm rate for emotional images was increased in ad lib sleep (n=6, p=0.03) with no difference in false alarm rate for neutral images (p=0.19). Correct rejection rate for emotional images was increased in SR compared to ad lib sleep (p=0.03) with no difference in neutral images (p=0.19). No other change was observed in accuracy of recall for emotional or neutral, miss rate for emotional or neutral and d-prime for emotional or neutral images. Conclusion In subjects with SZ, unrestricted sleep was associated with a greater false alarm rate for emotional, but not neutral scenes, suggesting a deficit in veridical memory or generalization of emotionally encoded information. Support (if any) R21MH126357, R01AG066870, R01AG056682, K24HL109156, AASMF Focus award/AARG-D

Police trainees versus laypeople: Identification performance and confidence–accuracy relationship for facial and body lineups

AbstractCourt instructions and public perception endorse that eyewitness evidence provided by police should weight more heavily than laypeople's in court. Evidence is inconsistent. The current experiment provides a nuanced analysis of identification performance of police and laypeople at different levels of confidence. Laypeople and advanced police trainees (N = 192) viewed portrait, profile, and body‐only lineups for central and peripheral targets. Police trainees displayed higher hit and correct rejection rates than laypeople for portrait lineups, and higher correct rejection rates in profile lineups for central targets. Calibration was similar for both groups, although police trainees had an advantage at low target presence base rates. Calibration was best for central targets' portrait and profile lineups. Participants displayed poor calibration and strong overconfidence for body‐only lineups and peripheral target lineups. We conclude that experience and specialization of police might be important when investigating a possible superiority of police who serve as eyewitness.

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds.

Simple SummaryThis cross-sectional study assessed the sensitivity and specificity of clinical mastitis detection by automated milking systems on Bavarian dairy herds in southern Germany. Clinical mastitis affects animal health and food safety, and therefore, its detection is an important task of any automatic milking system. Different manufacturers have different approaches to detecting clinical mastitis, with detection rates (sensitivity) ranging between 31% and 78% and correct rejection rate (specificity) between 79% and 97%. In multivariable models, some cow-level factors were shown to influence these rates.In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%–72.8%)), GEA (75.9% (62.4%–86.5%)), Lely (78.2% (67.4%–86.8%)), and Lemmer-Fullwood (67.6% (50.2%–82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%–90.7%)), GEA (79.2% (77.1%–81.2%)), Lely (86.2% (84.6%–87.7%)), and Lemmer-Fullwood (92.2% (90.8%–93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

The protocol for assessing olfactory working memory capacity in mice.

BackgroundWorking memory capacity (WMC) is the ability to maintain information over a few seconds. Although it has been extensively studied in healthy subjects and neuropsychiatric patients, few tasks have been developed to measure such changes in rodents. Many procedures have been used to measure WM in rodents, including the radial arm maze, the WM version of the Morris swimming task, and various delayed matching and nonmatching‐to‐sample tasks. It should be noted, however, that the memory components assessed in these procedures do not include memory capacity.MethodsWe developed an olfactory working memory capacity (OWMC) paradigm to assess the WMC of 3‐month‐old 5×FAD mice, a mouse model of Alzheimer's disease. The task is divided into five phases: context adaptation, digging training, rule learning for nonmatching to a single sample odor (NMSS), rule learning for nonmatching to multiple sample odors (NMMS), and capacity testing.ResultsIn the NMSS rule‐learning phase, there was no difference between wild‐type (WT) mice and 5×FAD mice in the performance correct rate, correct option rate, and correct rejection rate. The WT mice and 5×FAD mice showed similar memory capacity in the NMMS rule‐learning phase. After capacity test, we found that the WMC was significantly diminished in 5×FAD mice. As the memory load increased, 5×FAD mice also made significantly more errors than WT mice.ConclusionThe OWMC task, based on a nonmatch‐to‐sample rule, is a sensitive and robust behavioral assay that we validated as a reliable method for measuring WMC and exploring different components of memory in mice.

Vis-NIR hyperspectral imaging combined with incremental learning for open world maize seed varieties identification

There are many maize seed varieties circulating on the Chinese market, some varieties will be added and eliminated by the government every year, and fake varieties cannot be exhaustively listed. Furthermore, the updating of some varieties requires retraining the entire model, which is time-consuming and laborious. This poses a huge challenge for the authenticity identification of varieties and online model updating. Herein, hyperspectral imaging (HSI) combined with incremental learning (IL) was used to solve this problem. A novel radial basis function-biomimetic pattern recognition (RBF-BPR) model for IL was proposed and compared with one-class support vector machine (OCSVM) and BPR models under two test schemes. Hyperspectral images of five varieties of maize seeds were collected, and convolutional autoencoder (CAE) was used to extract features to remove redundant information and improve the generalization ability of models. A unique hybrid model was designed for each variety respectively, and the IL process was simulated. In two test schemes, the overall correct acceptance rate (CAR) for the known varieties and the overall correct rejection rate (CRR) for the unknown varieties of CAE-RBF-BPR model both reached 100%, which was superior to CAE-OCSVM and CAE-BPR models. Especially after RBF was used to map data, the performance of RBF-BPR model had a qualitative improvement compared with the original BPR model. In summary, the proposed method can realize IL without accessing the old classes data, while meeting the requirements of identifying known varieties and rejecting unknown varieties. In addition, if some varieties are eliminated by the government in the future, the corresponding models can also be removed form the whole system. The combination of such method and HSI has a broad application prospect in the identification of maize seed varieties, thus avoiding the trouble of retraining entire model when some maize seed varieties need to be updated.

Can signal detection theory explain everyday amnesia (high confident misses)?

Levi et al. (2021) critique the concept of everyday amnesia (high confident misses) by arguing that these are simply due to criterion shifts within a signal detection framework. We agree that signal detection figures can be drawn to conceptualize the results, but we argue such efforts merely provide a re-description of the phenomenon without explaining it. For that, one would need a process theory. Signal detection theory represents an elegant framework for conceiving of issues in decision making, but not for explaining mechanisms underlying them. A signal detection figure can be created for any possible recognition memory result; any pair of hit rates and false alarm rates (and hence miss rates and correct rejection rates) is amenable to such a depiction. If we were to cast the issue we raised in terms of signal detection theory, we might ask: Why do some subjects place their most liberal criterion in such a way that they miss, with high confidence, items that they recently studied? Signal detection theory provides no answer.

Is Novelty Detection Important in Long-Term Odor Memory?

Memory for odors is believed to be longer-lasting than memory for visual stimuli, as is evidenced by flat forgetting curves. However, performance on memory tasks is typically weaker in olfaction than vision. Studies of odor memory that use forced-choice methods confound responses that are a result of a trace memory and responses that can be obtained through process of elimination. Moreover, odor memory is typically measured with common stimuli, which are more familiar and responses may be confounded by verbal memory, and measure memory in intentional learning conditions, which are ecologically questionable. Here we demonstrate the value of using tests of memory in which hit rate and correct rejection rate are evaluated separately (i.e., not using forced-choice methods) and uncommon stimuli are used. This study compared memory for common and uncommon odors and pictures that were learned either intentionally (Exp. 1) or incidentally (Exp. 2) and tested with either a forced-choice or a one-stimulus-at-a-time (“monadic”) recognition task after delays of 15 min, 48 h or 1 week. As expected, memory declined with delay in most conditions, but depended upon the particular measure of memory and was better for pictures than odors and for common than uncommon stimuli. For common odors, hit rates decreased with delay but correct rejection rates remained constant with delay. For common pictures, we found the opposite result, constant hit rates and decreased correct rejection rates. Our results support the ‘misfit theory of conscious olfactory perception’, which highlights the importance of the detection of novelty in olfactory memory and suggests that olfactory memory should be studied using more ecologically valid methods.

The Impact of Transparency and Decision Risk on Human-Automation Teaming Outcomes.

Examine the effects of decision risk and automation transparency on the accuracy and timeliness of operator decisions, automation verification rates, and subjective workload. Decision aids typically benefit performance, but can provide incorrect advice due to contextual factors, creating the potential for automation disuse or misuse. Decision aids can reduce an operator's manual problem evaluation, and it can also be strategic for operators to minimize verifying automated advice in order to manage workload. Participants assigned the optimal unmanned vehicle to complete missions. A decision aid provided advice but was not always reliable. Two levels of decision aid transparency were manipulated between participants. The risk associated with each decision was manipulated using a financial incentive scheme. Participants could use a calculator to verify automated advice; however, this resulted in a financial penalty. For high- compared with low-risk decisions, participants were more likely to reject incorrect automated advice and were more likely to verify automation and reported higher workload. Increased transparency did not lead to more accurate decisions and did not impact workload but decreased automation verification and eliminated the increased decision time associated with high decision risk. Increased automation transparency was beneficial in that it decreased automation verification and decreased decision time. The increased workload and automation verification for high-risk missions is not necessarily problematic given the improved automation correct rejection rate. The findings have potential application to the design of interfaces to improve human-automation teaming, and for anticipating the impact of decision risk on operator behavior.

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.

Artificial intelligence assisted false alarm detection and diagnosis system development for reducing maintenance cost of chillers at the data centre

According to a literature survey, artificial intelligence (AI)-assisted fault detection and diagnosis (FDD) for heat, ventilation, and air conditioning equipment can only reach a correct diagnosis rate of 97%–98.8% Yet, for industrial application, even 1% uncertainty may cause serious problems. Therefore, with AI-assisted FDD (AI-FDD), it is difficult to satisfy high-reliability requirements. This study proposed an innovative AI-assisted false alarm detection and diagnosis (AI-FADD) system. The proposed AI-FADD system combines signal preprocessing, a fault-free classifier, and a fault classifier to meet the requirement of Six Sigma and 3.4 defective parts per million opportunities (DPMO); the developed system achieved 14.48 DPMO, approaching the Six Sigma standard; for 1 million opportunities for alarm, the system only guaranteed 14.48 incorrect false alarm rejections. The developed system can be used for chillers at data centres, which have stringent reliability requirements. Operational data for 14 chillers distributed across north, central, and south Taiwan in 2019 indicated that chiller malfunction alarms were triggered 122 times, but the AI-FADD system determined that only 57 were actual malfunctions; the others were false alarms. According to data verification, the system reached a 100% correct rejection rate, yielding a maintenance savings of up to 260 person-hours. These substantial labour cost savings indicate that commercial application of the AI-FADD system can result in financial benefits.

The Association Between the Level of General Trust and the Judgment Accuracy of Group Members’ Cooperation in a Social Dilemma

The current study sought to examine the association between the level of general trust and the judgment accuracy of others’ cooperativeness. Based on data collected from 107 female first-year undergraduate students, we demonstrated that a high level of general trust was associated with a high level of judgment accuracy of group members’ cooperation in a social dilemma game. Additional analysis suggested that the association was present even when the judgment accuracy was divided into hit rate (i.e., the rate of correct judgment on the cooperator as a cooperative) and correct rejection rate (i.e., the rate of correct judgment on the non-cooperator as a non-cooperative) by controlling the participants’ judgment bias, Big Five personality traits, and the proportion of cooperators in the group. These results are in accordance with previous studies insofar as they suggest that high trusters are more skilled at discerning others’ trustworthiness. The current study adds to the evidence that high trusters have increased cognitive skills and supports Yamagishi’s emancipation theory of trust.

Testing a new lineup procedure with children: The elimination with wildcard

Preschoolers' (4- to 7-year-olds) recall and correct rejection rates using the simultaneous, wildcard, elimination, and a new lineup; the elimination with wildcard were compared. The new lineup uses the traditional two judgement procedure inherent to the elimination lineup but modifies Judgement 2 by providing a wildcard rejection. Participants (N = 116) watched a 4-min video of a male target. Each participant was asked to free recall the details of the video and to answer cued recall questions about the target's appearance. Participants were then given one of the four target-absent lineups. Separating by older and younger preschoolers, the 4/5-year-olds reported an average of 1.18 descriptors with an 87% accuracy rate, while the 6/7-year-olds reported an average of 1.93 descriptors with a 91% accuracy rate. For both the older and younger preschoolers, correct rejection rates were highest for the new elimination with wildcard lineup, followed by the simultaneous lineup.

The reliability and transparency bases of trust in human-swarm interaction: principles and implications

Automation reliability and transparency are key factors for trust calibration and as such can have distinct effects on human reliance behaviour and mission performance. One question that remains unexplored is: what are the implications of reliability and transparency on trust calibration for human-swarm interaction? We investigate this research question in the context of human-swarm interaction, as swarm systems are becoming more popular for their robustness and versatility. Thirty-two participants performed swarm-based tasks under different reliability and transparency conditions. The results indicate that trust, whether it is reliability- or transparency-based, indicates high reliance rates and shorter response times. Reliability-based trust is negatively correlated with correct rejection rates while transparency-based trust is positively correlated with these rates. We conclude that reliability and transparency have distinct effects on trust calibration. Practitioner Summary: Reliability and transparency have distinct effects on trust calibration. Findings from our human experiments suggest that transparency is a necessary design requirement if and when humans need to be involved in the decision-loop of human-swarm systems, especially when swarm reliability is high. Abbreviations: HRI: human-robot interaction; IOS: inter-organisational systems; LMM: liner mixed models; MANOVA: multivariate analysis of variance; UxV: heterogeneous unmanned vehicles; UAV: unmanned aerial vehicle

Faster, sharper, more precise: Automated Cluster-FLIM in preclinical testing directly identifies the intracellular fate of theranostics in live cells and tissue

Fluorescence microscopy is widely used for high content screening in 2D cell cultures and 3D models. In particular, 3D tissue models are gaining major relevance in modern drug development. Enabling direct multiparametric evaluation of complex samples, fluorescence lifetime imaging (FLIM) adds a further level to intensity imaging by the sensitivity of the fluorescence lifetime to the microenvironment. However, the use of FLIM is limited amongst others by the acquisition of sufficient photon numbers without phototoxic effects in live cells. Herein, we developed a new cluster-based analysis method to enhance insight, and significantly speed up analysis and measurement time for the accurate translation of fluorescence lifetime information into pharmacological pathways.Methods: We applied a fluorescently-labeled dendritic core-multishell nanocarrier and its cargo Bodipy as molecules of interest (MOI) to human cells and reconstructed human tissue. Following the sensitivity and specificity assessment of the fitting-free Cluster-FLIM analysis of data in silico and in vitro, we evaluated the dynamics of cellular molecule uptake and intracellular interactions. For 3D live tissue investigations, we applied multiphoton (mp) FLIM. Owing to Cluster-FLIM's statistics-based fitting-free analysis, we utilized this approach for automatization.Results: To discriminate the fluorescence lifetime signatures of 5 different fluorescence species in a single color channel, the Cluster-FLIM method requires only 170, respectively, 90 counts per pixel to obtain 95% sensitivity (hit rate) and 95% specificity (correct rejection rate). Cluster-FLIM revealed cellular interactions of MOIs, representing their spatiotemporal intracellular fate. In a setting of an automated workflow, the assessment of lysosomal trapping of the MOI revealed relevant differences between normal and tumor cells, as well as between 2D and 3D models.Conclusion: The automated Cluster-FLIM tool is fitting-free, providing images with enhanced information, contrast, and spatial resolution at short exposure times and low fluorophore concentrations. Thereby, Cluster-FLIM increases the applicability of FLIM in high content analysis of target molecules in drug development and beyond.

Verbatim editing: A general model of recollection rejection.

Recollection rejection is traditionally defined as using verbatim traces of old items' presentations to reject new similar test cues, in old/new recognition (e.g., rejecting that couch is old by retrieving verbatim traces of sofa's presentation). We broaden this conceptualization to include (a) old as well as new similar test cues, (b) using verbatim traces for acceptance as well as rejection, and (c) using illusory verbatim traces of unpresented items (phantom recollection) as well as actual verbatim traces (true recollection). The expanded model describes how true recollection and phantom recollection generate memory decisions by creating matches and mismatches between comparisons of test cues to the content of retrieved verbatim traces versus comparisons of test cues to the content of test questions. This model generates a series of predictions about verbatim editing. Some are intuitive, such as the prection that performance will be more accurate for old cues than for new similar ones. Others are counterintuitive and conflict with an alternative model, such as correct rejections are easier than hits and that correct rejection rates will be more stable over time than hit rates. Meta-analyses of a corpus of conjoint recognition data sets provided support for the model's predictions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).