Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
THE JOINT EFFECT OF WORD FREQUENCY AND OUTPUT INTERFERENCE IN RECOGNITION MEMORY: TEST OF A MODEL PREDICTION
Download
hatice_dedetas_sbe.pdf
Date
2022-5-10
Author
Dedetaş, Hatice
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
141
views
145
downloads
Cite This
Output interference (OI) is defined as a decline in memory performance throughout the test. Different models have separate explanations for OI. For item noise models, OI results from encoding during the test. When items are presented at the test, they are compared to all items in the memory trace and if a match occurs, the matched study item is updated, and if a match does not occur, a new memory trace is added. These updates during the test phase lead to confusion through the end of the test. Another hotly debated effect is the word frequency effect (WFE) which is better recognition performance for low frequency (LF) items than high frequency (HF) items. Item noise approach explains this effect through the feature distinction approach. Since LF words have distinctive features, the matching procedure will be easier during the test leading to higher performance. The current project aims to simulate and test the prediction of Retrieving Effectively from Memory Model (REM) on the joint effect of WF and OI.
Subject Keywords
Output Interference
,
Test Position Effect
,
Word Frequency Effect
,
REM
,
Recognition Memory
URI
https://hdl.handle.net/11511/96813
Collections
Graduate School of Social Sciences, Thesis
Suggestions
OpenMETU
Core
Effects of temporal aggregation on cointegration tests
Yozgatlıgil, Ceylan (null; 2005-08-01)
In time series analysis, data are often available in the form of temporal aggregation or systematical sampling, and they are routinely used to test cointegration of nonstationary variables. We first derive the vector error correction representation of vector autoregressive moving average model for aggregate variables. It is known that the cointegration is unchanged under temporal aggregation for stock variables. We proved the cointegration also remains unchanged under temporal aggregation for flow variables...
The Effects of Gate-Connected Field Plates on Hotspot Temperatures of AlGaN/GaN HEMTs
Dundar, Canberk; Kara, Dogacan; Donmezer, Nazli (Institute of Electrical and Electronics Engineers (IEEE), 2020-01-01)
To increase the reliability and the maximum performance of AlGaN/GaN high electron mobility transistors (HEMTs), gate field plates are frequently used with surface passivation. Although significant research has been done to understand the electrical effects of gate field plates on devices, their thermal effects are still not fully understood. For this purpose, electrothermal simulations are performed on devices with and without gate field plates having different thicknesses of Si3N4 surface passivation at t...
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
Temizel, Alptekin; Taşkaya Temizel, Tuğba (2018-03-01)
Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data. Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems. To be immune to such attacks, it is desirable to have a pre-processing mechanism which remove...
A diversity and coding gain analysis for the cooperative wireless communication channel under fading using sampling property of the Q-function İşbi̇rli̇kli̇ kablosuz haberleşmede sönümlemeli̇ kanal i̇çi̇n Q-fonksi̇yonunun örnekleme özelli̇ǧi̇nden yararlanan bi̇r çeşi̇tleme ve kodlama kazanci anali̇zi̇
Aktaş, Tuǧcan; Yılmaz, Ali Özgür; AKTAŞ, EMRE (2012-07-09)
This work presents approximation methods that are used to identify Bit Error Rate (BER) expressions which are frequently utilized in investigation and comparison of performance for wireless communication systems in the literature. In this group of approximation methods, some expectation integrals, which are complicated to analyze and time-consuming to evaluate through Monte Carlo simulations, are handled. For this group of integrals, by using the sampling property of the Q-function under mid- and high- Sign...
On the profile of frequency dependent interface states and series resistance in Au/p-InP SBDs prepared with photolithography technique
Korucu, D.; Turut, A.; Turan, Raşit; Altindal, Ş. (Springer Science and Business Media LLC, 2012-6-25)
The frequency dependent of the forward and reverse bias capacitance-voltage (C-V) and conductance-voltage (G/w-V) characteristics of Au/p-InP SBDs have been investigated in the frequency range of 20 kHz-10 MHz and voltage range of -5 - 5 V at room temperature. The effects of surface states (N (ss)) and series resistance (R (s)) on C-V and G/w-V characteristics have been investigated in detail. The frequency dependent N (ss) and R (s) profiles were obtained for various applied bias voltages. The experimental...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
H. Dedetaş, “THE JOINT EFFECT OF WORD FREQUENCY AND OUTPUT INTERFERENCE IN RECOGNITION MEMORY: TEST OF A MODEL PREDICTION,” M.S. - Master of Science, Middle East Technical University, 2022.