scholarly journals Side- and similarity-biases during confidence conformity

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253577
Author(s):  
Winny W. Y. Yue ◽  
Kiyofumi Miyoshi ◽  
Wendy W. S. Yue
Keyword(s):  
Confidence Rating ◽  
Confidence Level ◽  
Human Motion ◽  
Social Situations ◽  
High Confidence ◽  
Confidence Levels ◽  
Memory Accuracy ◽  
Memory Conformity ◽  
Subjective Confidence ◽  
Gradual Accumulation

Memory conformity may develop when people are confronted with distinct memories reported by others in social situations and knowingly/unknowingly adhere to these exogenous memories. Earlier research on memory conformity suggests that (1) subjects were more likely to conform to confederate with high confidence; (2) subjects with low confidence on their memory accuracy were more likely to conform, and; (3) this subjective confidence could be adjusted by social manipulations. Nonetheless, it remains unclear how the confidence levels of ours and others may interact and produce a combined effect on our degree of conformity. More importantly, is memory conformity, defined by a complete adoption of the opposite side, the result of a gradual accumulation of subtler changes at the confidence level, i.e., a buildup of confidence conformity? Here, we followed participant’s confidence transformation quantitatively over three confederate sessions in a memory test. After studying a set of human motion videos, participants had to answer simultaneously whether a target or lure video had appeared before by indicating their side (i.e., Yes/No) and their associated confidence rating. Participants were allowed to adjust their responses as they were being shown randomly-generated confederates’ answers and confidence values. Results show that participants indeed demonstrated confidence conformity. Interestingly, they tended to become committed to their side early on and gain confidence gradually over subsequent sessions. This polarizing behaviour may be explained by two kinds of preferences: (1) Participant’s confidence enhancement towards same-sided confederates was greater in magnitude compared to the decrement towards an opposite-sided confederate; and (2) Participants had the most effective confidence boost when the same-sided confederates shared similar, but not considerably different, confidence level to theirs. In other words, humans exhibit side- and similarity-biases during confidence conformity.

scholarly journals Side- and similarity-biases during confidence conformity

2021 ◽  
Author(s):  
Winny Wing Yin Yue ◽  
Kiyofumi Miyoshi ◽  
Wendy Wing Sze YUE
Keyword(s):  
Confidence Rating ◽  
Confidence Level ◽  
Human Motion ◽  
Memory Test ◽  
Social Situations ◽  
Confidence Levels ◽  
Memory Accuracy ◽  
Memory Conformity ◽  
Subjective Confidence

Memory conformity may develop when people are confronted with some divergent memories of others in social situations and knowingly/unknowingly incorporate these exogenous memories into their owns. Earlier research suggests that memory conformity is more prominent in subjects who bear low confidence towards their memory accuracy. Nonetheless, it is unclear whether and how this subjective confidence may likewise be influenced by the confidence levels of others. Here, we followed participant’s confidence transformation quantitatively over three confederate sessions in a memory test. After studying a set of human motion videos, participants had to answer whether a particular video had appeared before by indicating their side (i.e. Yes/No) and the associated confidence rating simultaneously. Participants were allowed to adjust their responses as they were being shown randomly-generated confederates’ answers and confidence values. Overall, we found that participants tended to become committed to their side early on and gain confidence gradually over subsequent sessions. This polarizing behavior may be explained by two kinds of preferences: (1) Participant’s confidence enhancement towards same-sided confederates was greater in magnitude compared to the decrement towards an opposite-sided confederate; and (2) Participants had the most effective confidence boost when the same-sided confederates shared similar, but not considerably different, confidence level to theirs. In other words, humans exhibit side- and similarity-biases during confidence conformity. Interestingly, among our participants, those who built up their confidence upon others’ retained a higher level of flexibility to change than those who had strong initial confidence. Thus, confidence polarization may not be a totally irreversible trend.

scholarly journals Study on the Confidence and Reliability of the Mean Seismic Probability Risk Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Xiao-Lei Wang ◽  
Da-Gang Lu
Keyword(s):  
Seismic Design ◽  
Confidence Level ◽  
Risk Model ◽  
Safety Evaluation ◽  
Design Parameters ◽  
Critical Infrastructures ◽  
High Confidence ◽  
Confidence Levels ◽  
The Mean ◽  
Mean Risk

The mean seismic probability risk model has widely been used in seismic design and safety evaluation of critical infrastructures. In this paper, the confidence levels analysis and error equations derivation of the mean seismic probability risk model are conducted. It has been found that the confidence levels and error values of the mean seismic probability risk model are changed for different sites and that the confidence levels are low and the error values are large for most sites. Meanwhile, the confidence levels of ASCE/SEI 43-05 design parameters are analyzed and the error equation of achieved performance probabilities based on ASCE/SEI 43-05 is also obtained. It is found that the confidence levels for design results obtained using ASCE/SEI 43-05 criteria are not high, which are less than 95%, while the high confidence level of the uniform risk could not be achieved using ASCE/SEI 43-05 criteria and the error values between risk model with target confidence level and mean risk model using ASCE/SEI 43-05 criteria are large for some sites. It is suggested that the seismic risk model considering high confidence levels instead of the mean seismic probability risk model should be used in the future.

scholarly journals Cosmic Ray Anisotropy above 1015 eV

1987 ◽  
Vol 40 (3) ◽  
pp. 423 ◽  
Author(s):  
RW Clay
Keyword(s):  
Internal Consistency ◽  
Confidence Level ◽  
Cosmic Ray ◽  
Published Data ◽  
High Confidence ◽  
Real Effect ◽  
Confidence Levels ◽  
High Confidence Level

An examination is made of published data on cosmic ray anisotropy at energies above about 1015 eV. Both amplitude and phase results are examined in an attempt to assess the confidence which can be placed in the observations as a whole. It is found that whilst many published results individually may suggest quite high confidence levels of real measured anisotropy, the data taken as a whole are less convincing. Some internal consistency in the phase results suggests that a real effect may have been measured but, again, this is not at a high confidence level.

scholarly journals A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 115-120
Author(s):  
Dejin Tang ◽  
Xiaoming Zhou ◽  
Jie Jiang ◽  
Caiping Li
Keyword(s):  
Confidence Level ◽  
Spatial Location ◽  
Point Clouds ◽  
Airborne Lidar ◽  
High Confidence ◽  
Confidence Levels ◽  
Segment Distribution ◽  
Adjacent Segments ◽  
Airborne Lidar Data ◽  
High Confidence Level

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

scholarly journals A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 115-120
Author(s):  
Dejin Tang ◽  
Xiaoming Zhou ◽  
Jie Jiang ◽  
Caiping Li
Keyword(s):  
Confidence Level ◽  
Spatial Location ◽  
Point Clouds ◽  
Airborne Lidar ◽  
High Confidence ◽  
Confidence Levels ◽  
Segment Distribution ◽  
Adjacent Segments ◽  
Airborne Lidar Data ◽  
High Confidence Level

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

Dynamic Response and Buckling Failure Measures for Structures With Bounded and Random Imperfections

1991 ◽  
Vol 58 (4) ◽  
pp. 1092-1095 ◽  
Author(s):  
H. E. Lindberg
Keyword(s):  
Standard Deviation ◽  
Dynamic Response ◽  
Confidence Level ◽  
Maximum Response ◽  
Statistical Distributions ◽  
High Confidence ◽  
Confidence Levels ◽  
Buckling Failure ◽  
Failure Avoidance ◽  
To Come

Comparisons between an unknown-but-bounded imperfection model and a random imperfection model show that for simple pointwise failure measures, at least, the two models give the same expressions for their measures of response, but each measure has a distinctly different interpretation. The former gives the maximum possible response for any imperfection within a specified bound. The latter gives the standard deviation of response, which, together with the statistical distribution, can be used to specify the maximum response at a specified confidence level. However, since the statistical distributions of imperfections, and hence of the response are often unknown, confidence levels are difficult to define, especially in the tail of the distribution at high confidence levels. The unknown-but-bounded model requires less information about the imperfections to come to a well-defined bound on response. It is further shown that, while the maximum possible response might seem to be a severe failure avoidance criterion, it can be less constricting than having to impose artificially high confidence levels with poorly known statistical distributions.

scholarly journals The internship dentists self confidence levels during root canal treatment procedures

2017 ◽  
Vol 5 (2) ◽  
pp. 121
Author(s):  
Mazen Doumani ◽  
Adnan Habib ◽  
Abrar Alhababi ◽  
Ahmad Bashnakli ◽  
Enass Shamsy ◽  
...  
Keyword(s):  
Confidence Level ◽  
Root Canal ◽  
Scoring System ◽  
Dental School ◽  
Root Canal Treatment ◽  
Confidence Levels ◽  
Self Confidence ◽  
Treatment Procedures

Self-confidence level assessment in newly graduated students is very important to evaluate the undergraduate endodontic courses. Objective: The aim of this study was to get information from internship dentists in Alfarabi dental college related to their confidence levels during root canal treatment procedures.Methods: Anonymous survey forms were sent to 150 internship dentists in Alfarbi dental college. They were asked to indicate their self-confidence level by Lickert’s scoring system ranging between 1 and 5.Results: Removal of broken instruments was determined as a procedure that was not experienced by 25.2% of the dentists. (44.6%) of dentists felt confident about taking radiographs during root canal treatment. 1.9 % of them reported as having very little confidence during retreatment. The irrigation was a procedure in which they felt very confident about (59.2%).Conclusion: The non-practiced endodontic procedure was clearly related to levels of self confidence among internship dentists; this means; a lot of studies in dental school should be performed to determine the weakness points or gaps in undergraduate endodontic courses.

scholarly journals Building a high quality data linkage spine using a targeted approach to clerical review

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Nadine Wiggins ◽  
Brian Stokes
Keyword(s):  
Quality Assurance ◽  
Data Linkage ◽  
Quality Data ◽  
High Confidence ◽  
Software Module ◽  
High Quality ◽  
Confidence Levels ◽  
Lower Confidence ◽  
Linkage Quality

ABSTRACTObjectivesThe Tasmanian Data Linkage Unit (TDLU) was established through the University of Tasmania in 2011 with the first dataset imported to its Master Linkage Map (MLM) during 2014. Tasmania an island state of Australia, has a population of approximately 516,000. From the TDLU’s earliest inception, it was deemed important to build a high quality linkage spine comprising key administrative data representative of significant state health and related datasets to support quality population level research.ApproachThe TDLU has embraced a model of continual quality and process enhancement as a determined strategy to support ongoing business improvement. Initial linkage approaches utilised ‘traditional’ methods of reviewing record pairs within an upper and lower confidence range. This approach resulted in false record pairs with high confidence levels being linked (false positives) and true record pairs at lower confidence levels not linked (false negatives). To improve linkage quality, the TDLU has continually refined and modified its clerical review methodology with a specialist software module developed to identify specific record attributes within groups that require the group to be manually reviewed and resolved. A range of SQL queries have also been developed to identify incorrect links and further enhance the linkage quality of the MLM.ResultsThe linkage quality tools implemented have led to improved clerical review and quality assurance processes which in turn have increased the overall quality of the linkage spine. The ‘targeted’ method of clerical review provides easy identification of false positive records, particularly those with high confidence scores such as twins and husband/wife combinations. The review of groups at lower confidence levels has minimised the rate of false negative pairs however further refinement of tools is required to minimise the time spent on reviewing these groups. The clerical review software module has equipped staff with the necessary information to make informed and timely decisions when reviewing groups of records. Detailed documentation is maintained for each linkage project providing continual feedback for system and process improvements as the linkage spine increases in size.ConclusionThe process of clerical review and quality assurance requires a commitment to continual refinement of tools and techniques resulting in a higher quality linkage spine and a reduction in the total time and resource required to link datasets.

scholarly journals Discrete confidence levels revealed by sequential decisions

2017 ◽  
Author(s):  
Matteo Lisi ◽  
Gianluigi Mongillo ◽  
Andrei Gorea
Keyword(s):  
Stimulus Level ◽  
Decision Makers ◽  
Optimal Decision ◽  
Confidence Levels ◽  
Opt Out ◽  
Under Sampling ◽  
Subjective Confidence ◽  
Optimal Decision Making ◽  
The Cost ◽  
Bayesian Strategy

AbstractWhether humans are optimal decision makers is still a debated issue in the realm of perceptual decisions. Taking advantage of the direct link between an optimal decision-making and the confidence in that decision, we offer a new dual-decisions method of inferring such confidence without asking for its explicit valuation. Our method circumvents the well-known miscalibration issue with explicit confidence reports as well as the specification of the cost-function required by ‘opt-out’ or post-decision wagering methods. We show that observers’ inferred confidence in their first decision and its use in a subsequent decision (conditioned upon the correctness of the first) fall short of both the ideal Bayesian strategy, as well as of an under-sampling approximation or a modified Bayesian strategy augmented with an additional bias term to accommodate global miscalibration of confidence. The observed data are instead significantly better fitted by a model positing that observers use only few confidence levels or states, at odds with the continuous confidence function of stimulus level prescribed by a normative behavior. These findings question the validity of normative-Bayesian accounts of subjective confidence and metaperceptual judgments.

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