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Relational Matching to Sample Concept Learning in Children

Relational Matching to Sample Concept Learning in Children

With such advantage, subjects may feel different level of familiarity between the sample and the matching stimuli. Against such backdrop, relational matching-to-sample (RMTS) tasks, first invented by Premack [8] to explore the representation same and different in nonhuman primates, might be considered. In RMTS tasks, at first, a pair of sample stimulus (AA) appeared and then two pair of comparison stimuli (BB and CD) appeared. Subjects require choosing one (BB) of the two comparison stimuli (BB & CD) that matches the sample (AA). On the contrary, if the sample is AB, the correct choice is CD rather than BB.As compared to MTS and non MTS (to choose one of the two comparison stimuli that is different from the sample), summary mental symbols of the representation of same and different is in operation in RMTS tasks. In addition, RMTS tasks follow the strategy of a structure mapping analogy in which a correct choice requires building an understanding between two items in the array rather than responding to the items based on the stimuli features making it an array of analogical reasoning (Premack [8]). The RMTS has the potentiality to test an early relational ability. More particularly, it has an ability to explore arguably the simplest and the most basic relation. Furthermore, it is easily perceptually available to infants and young children (Ferry, Hespos & Gentner, unpublished data; Smith [9]) and to other species (Fagot & Thompson [10]; Wright & Katz [11]). It gives birth another merit, that is, no specific experimental knowledge such as causal knowledge is needed to study the identity relation. There is no room for competing similiarity choices in RMTS tasks. A number of researches (e.g., Martinho & Kacelnik [12]; Thompson, oden, & Boysen [13]) were carried on with these procedures. Recently, Martinho and Kacelnik [12] employed relational matching-to-sample (RMTS) method, a second-order relationship, to examine same/different
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Patterns of eye movement in matching-to-sample tasks

Patterns of eye movement in matching-to-sample tasks

The present study evaluated whether during a matching-to-sample procedure (MTS), the time spent observing stimuli is related to the establishment of selection or rejection controlling relationships in human participants. It also evaluated whether different response topographies (i.e., participants using the keyboard or mouse) would influence the duration of eye fixations. Ten college students participated. The procedure established conditional relationships among six sets of abstract stimuli. Five participants selected the comparison stimuli using a computer mouse and five used a keyboard. An eye-scan device recorded eye movements throughout the training procedure. After participants completed training, probes verified whether the conditional relationships learned were controlled by selection (e.g., if A1, select B1), by rejection (e.g., if A1, reject B2), or both. All participants displayed a similar pattern of stimuli observation. Time spent observing the sample stimulus (e.g., A1) was longer than observing the comparison stimuli (e.g., B1 and B2). Time spent observing S+ (positive stimuli; e.g., B1) was longer than observing S- (negative stimuli; e.g., B2). Duration of eye fixation was not related to selection or rejection controlling relationships, but different response topographies appeared to modulate the amount of time spent observing stimuli.
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Acquisition and Transfer of English as a Second Language through the Constructional Response Matching to Sample Procedure for Students with Developmental Disabilities

Acquisition and Transfer of English as a Second Language through the Constructional Response Matching to Sample Procedure for Students with Developmental Disabilities

Sugasawara and Yamamoto (2007) showed that two students with developmental disabilities could acquire reading skills through the CRMTS with the differential feedback procedure even though they were not given reading training for each character. For example, a picture of a cat was the sample stimulus and the Japanese phonograms (Hiragana) characters “ ね (/ne/)” and “ こ (/ko/)” were the comparison stimuli in the CRMTS procedure. When the student chose the character ‘ね’ as a comparison stimulus, the sound of “ne” followed as the differential feedback. When the student subsequently chose the character “ こ ” as a comparison stimulus, the sound of “ko” followed as the differential feedback. When the student con- structed the correct word “ねこ,” a fanfare and a circle was presented as a reinforcer and the sound of “ne-ko” was pre- sented as the differential feedback. This procedure could facili- tate the acquisition of the specific relationship between the sample stimulus, the selected comparison stimulus, and the reinforcing stimulus (Dube & McIlvane, 1995; Sidman, 2000; Yamamoto & Shimizu, 2001). Although these studies showed that the students could acquire reading skill through MTS or CRMTS with the differential feedback procedure, the results
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Fasting in Ramadan Affects Cognitive and Physiological Function in Normal Subjects (Pilot Study)

Fasting in Ramadan Affects Cognitive and Physiological Function in Normal Subjects (Pilot Study)

Neuropsychological testing was performed using Cambridge Neuropsychological Test Automated Battery (CANTAB) research suite software (version 6. 0.37, Cambridge cognition). The entire battery required 25 - 30 min to complete the tests. Fist task was Delayed Matching to Sample (DMS), which assessed forced choice rec- ognition memory for non-verbalis able patterns, testing both simultaneous matching and short-term visual mem- ory. The participant was shown a complex visual pattern and then, after a brief delay, four similar patterns. The participant must touch the pattern, which exactly matches the sample. Second task was chosen from attention test, attention-switching task (AST). The test displayed an arrow, which can appear on either side of the screen (right or left) and can point in either direction (to the right or to the left). Each trial displayed a cue at the top of the screen that indicated to the participant whether they had to press the right or left button according to the “side on which the arrow appeared” or the “direction in which the arrow was pointing”.
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Effectiveness of a Computer Based Syntax Program in Improving the Morphosyntax of Students Who are Deaf/Hard of Hearing

Effectiveness of a Computer Based Syntax Program in Improving the Morphosyntax of Students Who are Deaf/Hard of Hearing

because of its dependence on elevated levels of entropy. Entropy, as Wasserman (2002) defines it, is a measure of randomness or disarray within a visual field. It is the detection of this entropy that is thought to underlie same and different conceptualization for pigeons and baboons. The degree to which an animal can detect lower levels of entropy predicts well the success in the utilization of the concepts for reasoning tasks such as analogies. Baboons were presented with displays of up to 16 identical or nonidential icons in a relational matching-to-sample (RMTS) paradigm. Their acquisition of the relational matching rule was slow and gradual, taking as many as 7,000 trials until an 84% criterion was reached and subsequently transferred to new sets of exemplars. While success was high with these displays of 16 icons, failure to meet criterion even after thousands of trials began when the displays were composed of 8 icons or less. That success on the RMTS task could not be garnered with fewer than 8 icons within a display reveals a perceptually-grounded understanding of the same and different concepts (as in Flemming et al., 2007). Without a more rich interpretation of these concepts (i.e., symbolic encoding), one may argue that their rudimentary matching behavior was more or less implicit, driven by perceptual mechanisms.
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<p>A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment</p>

<p>A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment</p>

CANTAB as key cognitive tests for aMCI, including SWM, Rapid Visual information Processing (RVP), Pattern Delayed Matching to Sample (DMS), Pattern Recognition Memory (PRM), and Spatial Span Length (SSP). SWM probes the abil- ity to retain spatial information and manipulate remembered items in working memory by playing a self-ordered task. We used SWM Between Error (SWM_BER) and SWM Strategy (SWM_STR), which are probes that measure the efficient strategy for completing the task. PRM probes visual PRM. PRM Correct (PRM_COR) is the number of correct answers of the subject. DMS probes the ability to perform simultaneous and delayed matching to sample, and probes short term visual memory and visual recognition memory. A complex visual pattern is displayed on the screen and then, after a brief delay, the subject is required to select the matched pattern from four patterns. DMS_COR (Total Correct) is the total number of trials in which the subjects select the correct stimulus on their first response. DMS_MdCOR (Median Correct Latency) is the median latency in all trials where the subject selected the correct stimulus. DMS_PER (Percent Correct) is the percent- age of occasions upon which the subject selected the correct stimulus in trials. RVP probes visual sustained attention capa- bility. RVP_A ′ assesses how effective the subject is at detect- ing target sequences, while RVP_MdL (Median Latency) is a measure of the median time taken to respond. SSP probes the subject’s ability to assess working memory and attention span. We used the SSP_SPAN (Span Length), namely the longest sequence successfully recalled by the subject.
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Impact of off-farm activities on technical efficiency: evidence from maize producers of eastern Ethiopia

Impact of off-farm activities on technical efficiency: evidence from maize producers of eastern Ethiopia

Finally, we examined which of the two groups (participants vs. nonparticipants) has higher output after controlling for biases from observed and unobserved variables. For this purpose, we compared the predicted frontier output of the two groups at three dif- ferent input levels: (1) at the average for the smallest matched pair of farms, (2) at the average for the entire sample, and (3) at the average for the largest matched pair. The result is indicated along with a test of the mean difference in Table 7. As it is indicated in the table, households who are participating in off-farm activities are significantly more productive than the non-participant farmers. Thus, the analysis suggests that par- ticipants do not only exhibit higher TE but also higher total output.
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Genome wide matching of genes to cellular roles using guilt by association models derived from single sample analysis

Genome wide matching of genes to cellular roles using guilt by association models derived from single sample analysis

As with all computationally-derived guilt-by-associ- ation correlations, we also point out limitations to this type of analysis which include 1) the trends may be dependent on the expression data used, 2) the analysis may be affected by low quality gene sets or gene sets with ambiguous biological underpinnings, and 3) the en- richment statistic can be influenced by expression of a gene if it resides within the actual gene set (i.e. self- fulfilling associations). However, in the latter case the calculation used for the enrichment statistic should at least partially compensate for this effect. Though we used tissue-matched normal tissues to standardize the tumor tissues in our analysis, we also conducted similar analyses using only normal tissues and using only tumor tissues. Many of the gene expression-by-gene set enrich- ment correlations were similar to our initial findings when we used these smaller, median-standardized sub- sets of expression data. However, there tended to be less expression variability for a given gene across the restricted data sets and genes with low expression variability tended to produce incorrect gene set asso- ciations. We also noted this effect when using a smal- ler set of tumor-normal gene expression data as described in [73]. Perhaps intuitively, a sample set with sufficient expression variability in the gene of interest should be used. Two obvious sources of expression variability may come from using a greater number of samples or increasing the heterogeneity of the tissue types used.
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Simultaneous detection of BRCA mutations and large genomic rearrangements in germline DNA and FFPE tumor samples

Simultaneous detection of BRCA mutations and large genomic rearrangements in germline DNA and FFPE tumor samples

tools (Torrent Suite Software in the case of Ion Torrent and MiSeq Reporter software for Illumina). However, when it became clear that these tools do not fulfil all our expectations we developed our own bioinformatics pipeline, the NGSeXplorer. The software contains an accurate alignment module, a coverage generator, and a variant calling unit with several variant filtering algorithms for Ion Torrent PGM sequencing data. The software uses the Breast Cancer Information Core [32], the UMD, and the Ensembl.org databases for variant annotation. The most important factor for accurate mutation detection during NGS is the complete coverage of the target region as well as the optimal depth of coverage inside the target region. Coverage results of the training sample set clearly demonstrated that 100% of the target region was successfully covered by the PCR fragments amplified in multiplex setting. A major challenge in the multiplex PCR enrichment method is the uniform representation of different amplicons in the reactions and later in the coverage data. This was successfully overcome here by adjusting the primer concentrations and PCR conditions (Figure 1). Regarding the minimal depth of coverage for germline mutation detection, we took into account the range of 50-fold coverage, as suggested by several studies on BRCA analysis [7, 26]. In addition to fulfilling these conditions, all the pathogenic mutations in the 24 training samples were accurately identified without any false-positive alterations, including the large indels that were identified in two independent sequencing runs (Supplementary Table S1).
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Miniaturized embedded stereo vision system (MESVS)

Miniaturized embedded stereo vision system (MESVS)

5.1 A sample screenshot of the calibration results obtained using Caltech toolbox 70 5.2 The effect of calibration accuracy on performance of the stereo matching engine 71 5.3 Comparis[r]

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On the reconstraction of the matching polynomial and the reconstruction conjecture

On the reconstraction of the matching polynomial and the reconstruction conjecture

Matching, perfect matching, matching polynomial, matching matrix, Reconstruction Conjecture, edge reconstruction, node reconstruction.. 1980 AMS SUBJECT CLASSIFICATION CODE.[r]

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Policy evaluation via a statistical control: A non-parametric evaluation of the 'Want2Work' active labour market policy

Policy evaluation via a statistical control: A non-parametric evaluation of the 'Want2Work' active labour market policy

As well as considering simply whether a job is obtained, the analysis also provided some evidence as to the quality of jobs being obtained. In terms of the type of job accepted, the results are not changed greatly when the focus is restricted to only full-time or permanent jobs, though the W2W effect is slightly weaker for obtaining full-time work than for obtaining any job in the case of former IB claimants. Even in this case, however, the W2W effect compared to the control group is strongly pos- itive. In terms of earnings, there is some evidence that W2W participants accept lower wage jobs than members of the control group, though this difference is not statistically signi fi cant. Overall, there is nothing to sug- gest that the success of W2W in terms of getting people into work can be explained by a greater willingness to push clients into low quality jobs. Finally, on a methodological note, this paper shows how evalua- tions of labour market policies can be undertaken, even when policy-makers have not collected data on a control group of non- participants, or on participants before involvement, thus ruling out DiD techniques. Data from national surveys such as Labour Force Surveys can be used to obtain a sample of individuals in non-policy areas, who can then be matched to programme participants using PSM techniques, to ensure the employment probabilities of similar individuals are being compared. Such non-parametric techniques have additional advantages over more traditional evaluation tech- niques, in that they do not impose functional form, and identify any individual for whom there is a lack of common support. Howev- er, these techniques should be seen as a fall-back position, to be re- served for situations when a control group has not been established as part of the design. A properly controlled prospective evaluation is still to be preferred.
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Machine learning for improved data analysis of biological aerosol using the WIBS

Machine learning for improved data analysis of biological aerosol using the WIBS

When evaluating a large number of clusterings, it may be useful to use a statistic to summarize the information in the matching matrix. In a previous study (Ruske et al., 2017), we used percentage of particles correctly classified as a statistic for indicating performance. This is an easy to interpret statis- tic, but can be misleading when used on imbalanced data. In both example A and B, we have two randomly generated clusterings. However in B we have 80 % of the data points placed into the first cluster, whereas in A the data points are approximately equally distributed between the two clusters. The percentage of points which are placed into the same clus- ter for both clusterings are 52.2 % and 68.3 % for A and B, respectively. We can see that the more imbalanced a data set is, the more likely data points are to be placed into the same clusters. It is for this reason we elect to use an alternative statistic: the adjusted rand score. This statistic attains a value of approximately zero for both A and B.
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Effects of Class Size on Achievement of College Students

Effects of Class Size on Achievement of College Students

The administrative data we have at hand provide detailed information on students’ academic results (grades obtained at each exam and whether the exam was passed or failed) and on individual characteristics such as gender, type of high school attended, high school grade, province of residence, year of enrolment etc. We end up with a sample of 1,392 students and a total of 10,205 student-course level observations. Table 1 provides descriptive statistics for the sample of students we use. About 68% of the students were female. Students mainly came from two different types of high school: Lyceums (about 47%) and Technical and Vocational Schools (about 53%). High School Grade ranges from 60 to 100, with a mean of 89.44.
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The Personality matching of mentors and mentees in a youth mentoring program

The Personality matching of mentors and mentees in a youth mentoring program

Client (or mentee) personality may be a better predictor of positive outcomes, however, little can be done about the personality of a mentee. Matching mentors and mentees based on personality may be a way to produce better outcomes in a mentoring program. Coleman (2006) mentioned that the improvement of outcomes in their study may be attributed to the increase of personality similarity over time as the relationship developed. This contention has some merit; however, personality is generally viewed as a stable trait that increases over the course of a lifetime (Ferguson, 2010; Neppl et al., 2010). Researchers have found a high rate of personality stability across the lifespan of adulthood with only slight differentiation in childhood (Ferguson, 2010; Roberts & Delvecchio, 2000). Even with the above mentioned malleability, consistency has been found in three broad dimensions of temperament and personality (positive emotionality, negative emotionality, and constraint) from toddlerhood to middle childhood (Neppl et al., 2010). A meta-analysis from 152 studies found that correlations of personality consistency increased from .31 in childhood to .54 in young adulthood to .64 in adulthood (Roberts & Delvecchio, 2000). Personality consistency reached its peak from ages 50 to 70 with a
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Reviewing Human Machine Interaction through Speech Recognition approaches and Analyzing an approach for Designing an Efficient System

Reviewing Human Machine Interaction through Speech Recognition approaches and Analyzing an approach for Designing an Efficient System

word. Technical results are described in the tables below: The results in table 2 shows that features extracted from MFCC are more efficient than the PLP, LPC and HFCC and the WER reached is 94.8%.We remark that among the entire pattern matching techniques, extraction features based on MFCC are the most promising one with the maximum word recognition rate reaching to 94.8 %( highest among all the feature extraction techniques).

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The role of neighborhood characteristics in mortgage default risk: evidence from New York City

The role of neighborhood characteristics in mortgage default risk: evidence from New York City

match LoanPerformance to HMDA. Our data from ACRIS do not include Staten Island and thus we had to drop this borough from our analysis. We merged LoanPerformance loans to ACRIS mortgage deeds using three common fields: origination or deed date, loan amount and zip code, using six stages of hierarchical matching. At the end of each stage, loans and deeds that uniquely matched each other were set aside and considered matched, while all other loans and deeds enter the next stage. Stage 1 matched loans and deeds on the raw values of date, loan amount and zip code. Stage 2 matched the remaining loans and deeds on the raw values of date and zip code, and the loan amount rounded to $1,000. Stage 3 matched on the raw values of date and zip code, and the loan amount rounded to $10,000. Stage 4 matched on the raw values of zip code and loan amount, and allowed dates to differ by up to 90 days. Stage 5 matched on the raw value of zip code, loan amount rounded to $1,000, and allowed dates to differ by up to 90 days. Stage 6 matched on the raw value of zip code, loan amount rounded to $10,000, and allowed dates to differ by up to 90 days. We believe it is valid to introduce a 90-day window because for a good fraction of LoanPerformance loans, the origination date is imputed by backdating the first payment date by one month, and in ACRIS, there may be administrative lags in the recording of the deeds data. The chance of false positive matching is low because we are matching loans to the full universe of deed records, and only considering unique matches.
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Matching facial sketches using ann

Matching facial sketches using ann

Figure 3 shows the functional flow of the0proposed methodology. The computer generated composite0sketches are given as the input images for further processing. The proposed face identification scheme consists of two phases training and testing. In the first phase i.e. training process database images are trained, each image face region is located, pre-processed and texture features like MLBP and Tchebichef moment0invariant features are extracted. The resultent feature vectors are stored in the0database for matching with test images. Next step is testing step, build up with different modules namely pre-processing Module where input images are resized and conversion from RGB0to gray scale0image is performed [10].
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Matching rate improvement on pako

Matching rate improvement on pako

It is necessary that for any vehicle to give better performance with an average speed; nozzle should work with better efficiency. For it pairing of nozzle body and needle should be done accurately; it should be done within given range. Generally it is in range of 50-65 percentage, in form of matching rate in PAKO. But it is not sufficient. We have to increase it. The problems like needle mix up, inlet hole burr, taper parts affect matching rate.

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“Clinical Stability” and Propensity Score Matching in Cardiac Surgery: is the clinical evaluation of treatment efficacy algorithmdependent in small sample size settings?

“Clinical Stability” and Propensity Score Matching in Cardiac Surgery: is the clinical evaluation of treatment efficacy algorithmdependent in small sample size settings?

Evaluating the final matched samples, we found that residual imbalance in baseline characteristics was still present in all reconstructed sets created with different PS. More balanced sets were observed when LR, GBM and RF were used to estimate PS and only a subset of covariates often reached balance in all the final matched samples. Indeed, final outcome analysis should be conducted with proper methods that takes into account the role of TABLE 4. Patient level details with matching results based on each estimation method and covariate information. The column labelled as “Number of inclusions” identifies the number of times the patient was included in the final matched sample created using different techniques to estimate PS.
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