An Analysis of Generalized Identity Matching-to-Sample Test Procedures

1992 ◽  
Vol 42 (1) ◽  
pp. 17-28 ◽  
Author(s):  
William V. Dube ◽  
William J. McIlvane ◽  
Gina Green
Keyword(s):  
Matching To Sample ◽  
Test Procedures ◽  
Identity Matching ◽  
Sample Test

Reflexivity in Rats: Location Control in Generalized Identity Matching-to-Sample

2017 ◽  
Author(s):  
Sarah Beurms ◽  
Ana Gloria Plaza Jurado ◽  
Ana Sánchez-Kuhn ◽  
Jan De Houwer ◽  
Tom Beckers
Keyword(s):  
Sample Stimulus ◽  
Matching To Sample ◽  
Middle Position ◽  
Direct Training ◽  
Nonhuman Animals ◽  
Identity Matching

Reflexivity entails that an organism can match a stimulus to itself (“A=A”) without direct training. Reflexivity is typically studied in identity matching-to-sample tasks wherein subjects are first presented with a sample stimulus in the middle position and trained to select the same stimulus from two comparison stimuli that are subsequently presented in the side positions. However, when the position of the comparisons is altered, nonhuman animals often revert to responding at chance levels, suggesting that they encode the location of stimuli together with their identity as part of the functional stimulus. This might hamper generalization of the task to novel stimuli (i.e., generalized identity matching-to-sample), which would be an observation of reflexivity. To test whether the use of multiple locations facilitates generalized identity matching-to-sample in rats, we used an olfactory matching-to-sample task. Two rats received training in which the location of the stimuli varied randomly. The speed with which they learned to match identical odors and the generalization to new stimuli was compared with two rats that received standard matching-to-sample training in which the location of the stimuli was fixed. We observed generalized identity matching-to-sample in two rats that could not be explained by reinforcement recency. However, we found no evidence that the use of multiple locations facilitated generalized identity matching-to-sample.

scholarly journals Conditional Discriminations by Preverbal Children in an Identity Matching-to-Sample Task

2011 ◽  
Vol 61 (3) ◽  
pp. 327-339 ◽  
Author(s):  
Maria Stella C. de Alcantara Gil ◽  
Thais Porlan de Oliveira ◽  
William J. McIlvane
Keyword(s):  
Matching To Sample ◽  
Conditional Discriminations ◽  
Identity Matching ◽  
Preverbal Children

On a class of partially sequential two-sample test procedures for multivariate continuous data

Statistics ◽  
2014 ◽  
Vol 49 (2) ◽  
pp. 455-473
Author(s):  
Gopaldeb Chattopadhyay ◽  
Indranil Mukhopadhyay
Keyword(s):  
Continuous Data ◽  
Test Procedures ◽  
Sample Test

High-dimensional Two-sample Test Procedures under the Strongly Spiked Eigenvalue Model

2020 ◽  
Vol 49 (3) ◽  
pp. 109-125
Author(s):  
Aki Ishii ◽  
Kazuyoshi Yata ◽  
Makoto Aoshima
Keyword(s):  
High Dimensional ◽  
Test Procedures ◽  
Sample Test

Identity matching to sample and exclusion performance in elderly with and without neurocognitive disorders

2017 ◽  
Vol 32 (4) ◽  
pp. 326-340 ◽  
Author(s):  
Marina Ribeiro Camara ◽  
Mariana Ducatti ◽  
Andréia Schmidt
Keyword(s):  
Neurocognitive Disorders ◽  
Matching To Sample ◽  
Identity Matching

scholarly journals Evaluation of Sampling Plans to Detect Cry9C Protein in Corn Flour and Meal

2004 ◽  
Vol 87 (4) ◽  
pp. 950-960 ◽  
Author(s):  
Thomas B Whitaker ◽  
Mary W Trucksess ◽  
Francis G Giesbrecht ◽  
Andrew B Slate ◽  
Francis S Thomas
Keyword(s):  
Normal Distribution ◽  
Test Procedure ◽  
Enzyme Linked Immunosorbent Assay ◽  
Sampling Variance ◽  
Regression Equations ◽  
Corn Flour ◽  
Test Procedures ◽  
Sampling Plans ◽  
Sample Test ◽  
Almost All

Abstract StarLink is a genetically modified corn that produces an insecticidal protein, Cry9C. Studies were conducted to determine the variability and Cry9C distribution among sample test results when Cry9C protein was estimated in a bulk lot of corn flour and meal. Emphasis was placed on measuring sampling and analytical variances associated with each step of the test procedure used to measure Cry9C in corn flour and meal. Two commercially available enzyme-linked immunosorbent assay kits were used: one for the determination of Cry9C protein concentration and the other for % StarLink seed. The sampling and analytical variances associated with each step of the Cry9C test procedures were determined for flour and meal. Variances were found to be functions of Cry9C concentration, and regression equations were developed to describe the relationships. Because of the larger particle size, sampling variability associated with cornmeal was about double that for corn flour. For cornmeal, the sampling variance accounted for 92.6% of the total testing variability. The observed sampling and analytical distributions were compared with the Normal distribution. In almost all comparisons, the null hypothesis that the Cry9C protein values were sampled from a Normal distribution could not be rejected at 95% confidence limits. The Normal distribution and the variance estimates were used to evaluate the performance of several Cry9C protein sampling plans for corn flour and meal. Operating characteristic curves were developed and used to demonstrate the effect of increasing sample size on reducing false positives (seller's risk) and false negatives (buyer's risk).

scholarly journals Recognition of stimulus received by recurrent neural network

2021 ◽  
Vol 2094 (3) ◽  
pp. 032041
Author(s):  
S I Bartsev ◽  
G M Markova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Neural Activity ◽  
Activity Patterns ◽  
Matching To Sample ◽  
Minimal Set ◽  
Delayed Matching ◽  
Delayed Matching To Sample ◽  
Sample Test ◽  
Stimulus Recognition

Abstract The study is concerned with the comparison of two methods for identification of stimulus received by artificial neural network using neural activity pattern that corresponds to the period of storing information about this stimulus in the working memory. We used simple recurrent neural networks learned to pass the delayed matching-to-sample test. Neural activity was detected at the period of pause between receiving stimuli. The analysis of neural excitation patterns showed that neural networks encoded variables that were relevant for the task during the delayed matching-to-sample test, and their activity patterns were dynamic. The method of centroids allowed identifying the type of the received stimuli with efficiency up to 75% while the method of neural network-based decoder showed 100% efficiency. In addition, this method was applied to determine the minimal set of neurons whose activity was the most significant for stimulus recognition.

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