Researcher:
Mızrak, Eda

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PhD Student

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Eda

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Mızrak

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Mızrak, Eda

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2016 - 20186

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Researcher Of Publications: search.filters.isAuthorOfPublication.bae32c98-93fb-444a-9a24-32d0c41428e4

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Now showing 1 - 6 of 6
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    Working memory capacity and controlled serial memory search
    (Elsevier, 2016) N/A; Department of Psychology; Mızrak, Eda; Öztekin, İlke; PhD Student; Faculty Member; Department of Psychology; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A
    The speed-accuracy trade-off (SAT) procedure was used to investigate the relationship between working memory capacity (WMC) and the dynamics of temporal order memory retrieval. High- and low-span participants (HSs, LSs) studied sequentially presented five-item lists, followed by two probes from the study list. Participants indicated the more recent probe. Overall, accuracy was higher for HSs compared to LSs. Crucially, in contrast to previous investigations that observed no impact of WMC on speed of access to item information in memory (e.g., Oztekin & McElree, 2010), recovery of temporal order memory was slower for LSs. While accessing an item's representation in memory can be direct, recovery of relational information such as temporal order information requires a more controlled serial memory search. Collectively, these data indicate that WMC effects are particularly prominent during high demands of cognitive control, such as serial search operations necessary to access temporal order information from memory. (C) 2016 Elsevier B.V. All rights reserved.
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    Decoding cognitive states using the bag of words model on fMRI time series
    (Institute of Electrical and Electronics Engineers (IEEE), 2016) Sucu, Gunes; Akbas, Emre; Vural, Fatos Yarman; Department of Psychology; N/A; Öztekin, İlke; Mızrak, Eda; Faculty Member; PhD Student; Department of Psychology; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; N/A; N/A
    Bag-of-words (BoW) modeling has yielded successful results in document and image classification tasks. In this paper, we explore the use of BoW for cognitive state classification. We estimate a set of common patterns embedded in the fMRI time series recorded in three dimensional voxel coordinates by clustering the BOLD responses. We use these common patterns, called the code-words, to encode activities of both individual voxels and group of voxels, and obtain a BoW representation on which we train linear classifiers. Our experimental results show that the BoW encoding, when applied to individual voxels, significantly improves the classification accuracy (an average 7.2% increase over 13 different datasets) compared to a classical multi voxel pattern analysis method. This preliminary result gives us a clue to generate a code-book for fMRI data which may be used to represent a variety of cognitive states to study the human brain.
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    Relationship between emotion and forgetting
    (Amer Psychological Assoc, 2016) N/A; N/A; Department of Psychology; Mızrak, Eda; Öztekin, İlke; PhD Student; Faculty Member; Department of Psychology; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A
    A major determinant of forgetting in memory is the presence of interference in the retrieval context. Previous research has shown that proactive interference has less impact for emotional than neutral study material (Levens & Phelps, 2008). However, it is unclear how emotional content affects the impact of interference in memory. Emotional content could directly affect the buildup of interference, leading to reduced levels of interference. Alternatively, emotional content could affect the controlled processes that resolve interference. The present study employed the response deadline speed-accuracy trade-off procedure to independently test these hypotheses. Participants studied 3-item lists consisting of emotional or neutral images, immediately followed by a recognition probe. Results indicated a slower rate of accrual for interfering material (lures from previous study list) and lower levels of interference for emotional than neutral stimuli, suggesting a direct impact of emotion on the buildup of interference. In contrast to this beneficiary effect, resolution of interference for emotional material was less effective than neutral material. These findings can provide insight into the interactions of emotion and memory processes.
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    A robust normalization method for fMRI data for brain decoding
    (Institute of Electrical and Electronics Engineers (IEEE), 2016) Sucu, Gunes; Akbas, Emre; Vural, Fatos Yarman; Department of Psychology; N/A; Öztekin, İlke; Mızrak, Eda; Faculty Member; PhD Student; Department of Psychology; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; N/A; N/A
    Functional Magnetic Resonance Imaging (fMRI) methods produce high dimensional representation of cognitive processes under heavy noise due to the limitations of hardware and measurement techniques. In order to reduce the noise and extract useful information from the fMRI data, a sequence of pre-processing techniques, such as smoothing with spatial filters and z-scoring, are used. In this study, we suggest an additional normalization technique based upon a statistical property of fMRI data. We, first, define a random variable V(t) as the average voxel intensity value of a brain volume measured at a time instant t. Then, we measure the Pearson correlation between V(t) and 1/V(t) for all time instances. We observe that the Pearson correlation values are very close to -1, indicating that V(t) and 1/V(t) have a strong negative correlation. We show that one explanation for this property is V(t) being almost surely constant and the small fluctuations on V(t) caused by noise. The proposed method removes these fluctuations on the data resulting in almost surely constant brain volumes V(t) for all values of t. The effectiveness of the proposed normalization method is tested with well-known brain decoding algorithms and voxel selection methods. It is observed that the suggested normalization method improves the performance 1-2 percent on the average. The method also improves the signal to noise ratio.
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    A new representation of fMRI signal by a set of local meshes for brain decoding
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017) Önal, Itır; Özay, Mete; Vural, Fatoş T. Yarman; N/A; Department of Psychology; Mızrak, Eda; Öztekin, İlke; PhD Student; Faculty Member; Department of Psychology; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A
    How neurons influence each other's firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measurements recorded during a cognitive stimulus by a set of local meshes. Then, we represent the corresponding cognitive state by the edge weights of these meshes each of which is estimated assuming a regularized linear relationship among voxel time series in a predefined locality. The estimated mesh edge weights provide a better representation of information in the brain for cognitive state or task classification. We examine the representative power of ourmesh edge weights on visual recognition and emotional memory retrieval experiments by training a support vector machine classifier. Also, we use mesh edge weights as feature vectors of inter-subject classification onHuman Connectome Project task fMRI dataset, and test their performance. We observe that mesh edge weights perform better than the popular fMRI features, such as, raw voxel intensity values, pairwise correlations, features extracted using PCA and ICA, for classifying the cognitive states.
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    PublicationOpen Access
    Forgetting emotional material in working memory
    (Oxford University Press (OUP), 2018) Singmann, Henrik; Department of Psychology; Mızrak, Eda; Öztekin, İlke; PhD Student; PhD Student; Department of Psychology; Graduate School of Social Sciences and Humanities
    Proactive interference (PI) is the tendency for information learned earlier to interfere with more recently learned information. In the present study, we induced PI by presenting items from the same category over several trials. This results in a build-up of PI and reduces the discriminability of the items in each subsequent trial. We introduced emotional (e.g. disgust) and neutral (e.g. furniture) categories and examined how increasing levels of PI affected performance for both stimulus types. Participants were scanned using functional magnetic resonance imaging (fMRI) performing a 5-item probe recognition task. We modeled responses and corresponding response times with a hierarchical diffusion model. Results showed that PI effects on latent processes (i.e. reduced drift rate) were similar for both stimulus types, but the effect of PI on drift rate was less pronounced PI for emotional compared to neutral stimuli. The decline in the drift rate was accompanied by an increase in neural activation in parahippocampal regions and this relationship was more strongly observed for neutral stimuli compared to emotional stimuli.