The Experts below are selected from a list of 89055 Experts worldwide ranked by ideXlab platform
Igor I Stepanov - One of the best experts on this subject based on the ideXlab platform.
-
the use of the first order System Transfer Function in the analysis of proboscis extension learning of honey bees apis mellifera l exposed to pesticides
Bulletin of Environmental Contamination and Toxicology, 2012Co-Authors: Charles I Abramson, Igor I StepanovAbstract:No attempts have been made to apply a mathematical model to the learning curve in honey bees exposed to pesticides. We applied a standard Transfer Function in the form Y = B3*exp(− B2 * (X − 1)) + B4 * (1 − exp(− B2 * (X − 1))), where X is the trial number; Y is proportion of correct responses, B2 is the learning rate, B3 is readiness to learn and B4 is ability to learn. Reanalyzing previously published data on the effect of insect growth regulators tebufenozide and diflubenzuron on the classical conditioning of proboscis extension, the model revealed additional effects not detected with standard statistical tests of significance.
-
assessment of the learning curve from the california verbal learning test children s version with the first order System Transfer Function
Child Neuropsychology, 2011Co-Authors: Igor I Stepanov, Charles I Abramson, Seth WarschauskyAbstract:A mathematical model is proposed to measure the learning curve in the California Verbal Learning Test-Children's Version. The model is based on the first-order System Transfer Function in the form Y = B3*exp[-B2*(X-1)]+B4*{1-exp[-B2*(X-1)]}, where X is the trial number, Y is the number of recalled correct words, B2 is the learning rate, B3 is interpreted as readiness to learn and B4 as the ability to learn. Children's readiness to learn and ability to learn were lower than adults. Modeling revealed that girls had greater readiness to learn and ability to learn than boys.
-
the application of the first order System Transfer Function for fitting the california verbal learning test learning curve
Journal of The International Neuropsychological Society, 2010Co-Authors: Igor I Stepanov, Charles I Abramson, Oliver T Wolf, Antonio ConvitAbstract:Very few attempts have been made to apply a mathematical model to the learning curve in the California Verbal Learning Test list A immediate recall. Our rationale was to fiout whether modeling of the learning curve can add additional information to the standard CVLT-II measures. We applied a standard Transfer Function in the form Y = B3*exp(-B2*(X-1))+B4*(1-exp(-B2*(X-1))), where X is the trial number; Y is the number of recalled correct words, B2 is the learning rate, B3 is readiness to learn and B4 is ability to learn. The coeffi cients of the model were found to be independent measures not duplicating standard CVLT-II measures. Regression analysis revealed that readiness to learn (B3) and ability to learn (B4) were signifi cantly ( p .2). The proposed model is appropriate for clinical application and as a guide for research and may be used as a good supplemental tool for the CVLT-II and similar memory tests. ( JINS , 2010, 16 , 443–452.)
-
the application of the first order System Transfer Function for fitting the 3 arm radial maze learning curve
Journal of Mathematical Psychology, 2008Co-Authors: Igor I Stepanov, Charles I AbramsonAbstract:Abstract A mathematical model is described based on the first order System Transfer Function in the form Y = B 3 ∗ exp ( − B 2 ∗ ( X − 1 ) ) + B 4 ∗ ( 1 − exp ( − B 2 ∗ ( X − 1 ) ) ) , where X is the learning session number; Y is the quantity of errors, B 2 is the learning rate, B3 is resistance to learning and B 4 is ability to learn. The model is tested in a light–dark discrimination learning task in a 3-arm radial maze using Wistar and albino rats. The model provided good fits of experimental data under acquisition and reacquisition, and was able to detect strain differences among Wistar and albino rats. The model was compared to Rescorla–Wagner, and was found to be mutually complementary. Comparisons with Tulving’s logarithmic Function and Valentine’s hyperbola and the arc cotangent Functions are also provided. Our model is valid for fitting averaged group data, if averaging is applied to a subgroup of subjects possessing individual learning curves of an exponential shape.
Charles I Abramson - One of the best experts on this subject based on the ideXlab platform.
-
the use of the first order System Transfer Function in the analysis of proboscis extension learning of honey bees apis mellifera l exposed to pesticides
Bulletin of Environmental Contamination and Toxicology, 2012Co-Authors: Charles I Abramson, Igor I StepanovAbstract:No attempts have been made to apply a mathematical model to the learning curve in honey bees exposed to pesticides. We applied a standard Transfer Function in the form Y = B3*exp(− B2 * (X − 1)) + B4 * (1 − exp(− B2 * (X − 1))), where X is the trial number; Y is proportion of correct responses, B2 is the learning rate, B3 is readiness to learn and B4 is ability to learn. Reanalyzing previously published data on the effect of insect growth regulators tebufenozide and diflubenzuron on the classical conditioning of proboscis extension, the model revealed additional effects not detected with standard statistical tests of significance.
-
assessment of the learning curve from the california verbal learning test children s version with the first order System Transfer Function
Child Neuropsychology, 2011Co-Authors: Igor I Stepanov, Charles I Abramson, Seth WarschauskyAbstract:A mathematical model is proposed to measure the learning curve in the California Verbal Learning Test-Children's Version. The model is based on the first-order System Transfer Function in the form Y = B3*exp[-B2*(X-1)]+B4*{1-exp[-B2*(X-1)]}, where X is the trial number, Y is the number of recalled correct words, B2 is the learning rate, B3 is interpreted as readiness to learn and B4 as the ability to learn. Children's readiness to learn and ability to learn were lower than adults. Modeling revealed that girls had greater readiness to learn and ability to learn than boys.
-
the application of the first order System Transfer Function for fitting the california verbal learning test learning curve
Journal of The International Neuropsychological Society, 2010Co-Authors: Igor I Stepanov, Charles I Abramson, Oliver T Wolf, Antonio ConvitAbstract:Very few attempts have been made to apply a mathematical model to the learning curve in the California Verbal Learning Test list A immediate recall. Our rationale was to fiout whether modeling of the learning curve can add additional information to the standard CVLT-II measures. We applied a standard Transfer Function in the form Y = B3*exp(-B2*(X-1))+B4*(1-exp(-B2*(X-1))), where X is the trial number; Y is the number of recalled correct words, B2 is the learning rate, B3 is readiness to learn and B4 is ability to learn. The coeffi cients of the model were found to be independent measures not duplicating standard CVLT-II measures. Regression analysis revealed that readiness to learn (B3) and ability to learn (B4) were signifi cantly ( p .2). The proposed model is appropriate for clinical application and as a guide for research and may be used as a good supplemental tool for the CVLT-II and similar memory tests. ( JINS , 2010, 16 , 443–452.)
-
the application of the first order System Transfer Function for fitting the 3 arm radial maze learning curve
Journal of Mathematical Psychology, 2008Co-Authors: Igor I Stepanov, Charles I AbramsonAbstract:Abstract A mathematical model is described based on the first order System Transfer Function in the form Y = B 3 ∗ exp ( − B 2 ∗ ( X − 1 ) ) + B 4 ∗ ( 1 − exp ( − B 2 ∗ ( X − 1 ) ) ) , where X is the learning session number; Y is the quantity of errors, B 2 is the learning rate, B3 is resistance to learning and B 4 is ability to learn. The model is tested in a light–dark discrimination learning task in a 3-arm radial maze using Wistar and albino rats. The model provided good fits of experimental data under acquisition and reacquisition, and was able to detect strain differences among Wistar and albino rats. The model was compared to Rescorla–Wagner, and was found to be mutually complementary. Comparisons with Tulving’s logarithmic Function and Valentine’s hyperbola and the arc cotangent Functions are also provided. Our model is valid for fitting averaged group data, if averaging is applied to a subgroup of subjects possessing individual learning curves of an exponential shape.
Seth Warschausky - One of the best experts on this subject based on the ideXlab platform.
-
assessment of the learning curve from the california verbal learning test children s version with the first order System Transfer Function
Child Neuropsychology, 2011Co-Authors: Igor I Stepanov, Charles I Abramson, Seth WarschauskyAbstract:A mathematical model is proposed to measure the learning curve in the California Verbal Learning Test-Children's Version. The model is based on the first-order System Transfer Function in the form Y = B3*exp[-B2*(X-1)]+B4*{1-exp[-B2*(X-1)]}, where X is the trial number, Y is the number of recalled correct words, B2 is the learning rate, B3 is interpreted as readiness to learn and B4 as the ability to learn. Children's readiness to learn and ability to learn were lower than adults. Modeling revealed that girls had greater readiness to learn and ability to learn than boys.
Antonio Convit - One of the best experts on this subject based on the ideXlab platform.
-
the application of the first order System Transfer Function for fitting the california verbal learning test learning curve
Journal of The International Neuropsychological Society, 2010Co-Authors: Igor I Stepanov, Charles I Abramson, Oliver T Wolf, Antonio ConvitAbstract:Very few attempts have been made to apply a mathematical model to the learning curve in the California Verbal Learning Test list A immediate recall. Our rationale was to fiout whether modeling of the learning curve can add additional information to the standard CVLT-II measures. We applied a standard Transfer Function in the form Y = B3*exp(-B2*(X-1))+B4*(1-exp(-B2*(X-1))), where X is the trial number; Y is the number of recalled correct words, B2 is the learning rate, B3 is readiness to learn and B4 is ability to learn. The coeffi cients of the model were found to be independent measures not duplicating standard CVLT-II measures. Regression analysis revealed that readiness to learn (B3) and ability to learn (B4) were signifi cantly ( p .2). The proposed model is appropriate for clinical application and as a guide for research and may be used as a good supplemental tool for the CVLT-II and similar memory tests. ( JINS , 2010, 16 , 443–452.)
Herbert Kostler - One of the best experts on this subject based on the ideXlab platform.
-
field camera versus phantom based measurement of the gradient System Transfer Function gstf with dwell time compensation
Magnetic Resonance Imaging, 2020Co-Authors: M Stich, Tobias Wech, Ralf Ringler, Thorsten A Bley, Herbert Kostler, Julian A J Richter, Adrienne E CampbellwashburnAbstract:PURPOSE The gradient System Transfer Function (GSTF) can be used to describe the dynamic gradient System and applied for trajectory correction in non-Cartesian MRI. This study compares the field camera and the phantom-based methods to measure the GSTF and implements a compensation for the difference in measurement dwell time. METHODS The self-term GSTFs of a MR System were determined with two approaches: 1) using a dynamic field camera and 2) using a spherical phantom-based measurement with standard MR hardware. The phantom-based GSTF was convolved with a box Function to compensate for the dwell time dependence of the measurement. The field camera and phantom-based GSTFs were used for trajectory prediction during retrospective image reconstruction of 3D wave-CAIPI phantom images. RESULTS Differences in the GSTF magnitude response were observed between the two measurement methods. For the wave-CAIPI sequence, this led to deviations in the GSTF predicted trajectories of 4% compared to measured trajectories, and residual distortions in the reconstructed phantom images generated with the phantom-based GSTF. Following dwell-time compensation, deviations in the GSTF magnitudes, GSTF-predicted trajectories, and resulting image artifacts were eliminated (< 0.5% deviation in trajectories). CONCLUSION With dwell time compensation, both the field camera and the phantom-based GSTF self-terms show negligible deviations and lead to strong artifact reduction when they are used for trajectory correction in image reconstruction.
-
gradient waveform pre emphasis based on the gradient System Transfer Function
Magnetic Resonance in Medicine, 2018Co-Authors: Manuel Stich, Tobias Wech, Anne Slawig, Ralf Ringler, Andrew Dewdney, Andreas Greiser, Gudrun Ruyters, Thorsten A Bley, Herbert KostlerAbstract:Purpose The gradient System Transfer Function (GSTF) has been used to describe the distorted k-space trajectory for image reconstruction. The purpose of this work was to use the GSTF to determine the pre-emphasis for an undistorted gradient output and intended k-space trajectory. Methods The GSTF of the MR System was determined using only standard MR hardware without special equipment such as field probes or a field camera. The GSTF was used for trajectory prediction in image reconstruction and for a gradient waveform pre-emphasis. As test sequences, a gradient-echo sequence with phase-encoding gradient modulation and a gradient-echo sequence with a spiral read-out trajectory were implemented and subsequently applied on a structural phantom and in vivo head measurements. Results Image artifacts were successfully suppressed by applying the GSTF-based pre-emphasis. Equivalent results are achieved with images acquired using GSTF-based post-correction of the trajectory as a part of image reconstruction. In contrast, the pre-emphasis approach allows reconstruction using the initially intended trajectory. Conclusion The artifact suppression shown for two sequences demonstrates that the GSTF can serve for a novel pre-emphasis. A pre-emphasis based on the GSTF information can be applied to any arbitrary sequence type.
