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Publication Metadata only CRAFT: a benchmark for causal reasoning about forces and inTeractions(Assoc Computational Linguistics-Acl, 2022) Ates, Tayfun; Atesoglu, M. Samil; Yigit, Cagatay; N/A; N/A; Department of Computer Engineering; Department of Psychology; Department of Computer Engineering; Kesen, İlker; Kobaş, Mert; Erdem, Aykut; Göksun, Tilbe; Yüret, Deniz; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Psychology; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Social Sciences and Humanities; College of Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; N/A; 20331; 47278; 179996Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT1, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.Publication Open Access Enhancing local linear models using functional connectivity for brain state decoding(IGI Global, 2013) Fırat, Orhan; Özay, Mete; Önal, Itır; Vural, Fatoş T. Yarman; Department of Psychology; Öztekin, İlke; PhD Student; Department of Psychology; College of Social Sciences and HumanitiesThe authors propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Mesh Arc Descriptors (FC-MAD) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.Publication Metadata only Functional mesh learning for pattern analysis of cognitive processes(IEEE Computer Society, 2013) Firat, Orhan; Özay, Mete; Önal, Itir; Vural, Fatoş T. Yarman; Department of Psychology; Öztekin, İlke; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; N/AWe propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.Publication Metadata only Learning deep temporal representations for fMRI brain decoding(Springer International Publishing Ag, 2015) Firat, Orhan; Aksan, Emre; Fatos T. Yarman; Department of Psychology; Öztekin, İlke; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; N/AFunctional magnetic resonance imaging (fMRI) produces low number of samples in high dimensional vector spaces which is hardly adequate for brain decoding tasks. In this study, we propose a combination of autoencoding and temporal convolutional neural network architecture which aims to reduce the feature dimensionality along with improved classification performance. The proposed network learns temporal representations of voxel intensities at each layer of the network by leveraging unlabeled fMRI data with regularized autoencoders. Learned temporal representations capture the temporal regularities of the fMRI data and are observed to be an expressive bank of activation patterns. Then a temporal convolutional neural network with spatial pooling layers reduces the dimensionality of the learned representations. By employing the proposed method, raw input fMRI data is mapped to a low-dimensional feature space where the final classification is conducted. In addition, a simple decorrelated representation approach is proposed for tuning the model hyper-parameters. The proposed method is tested on a ten class recognition memory experiment with nine subjects. Results support the efficiency and potential of the proposed model, compared to the baseline multi-voxel pattern analysis techniques.Publication Metadata only Modeling the brain connectivity for pattern analysis(IEEE Computer Soc, 2014) Onal, Itir; Aksan, Emre; Velioğlu, Burak; Fırat, Orhan; Ozay, Mete; Vural, Fatoş T. Yarman; Department of Psychology; Öztekin, İlke; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; N/AAn information theoretic approach is proposed to estimate the degree of connectivity for each voxel with its neighboring voxels. The neighborhood system is defined by spatial and functional connectivity metrics. Then, a local mesh of variable size is formed around each voxel using spatial or functional neighborhood. The mesh arc weights, called Mesh Arc Descriptors (MAD), are estimated by a linear regression model fitted to the voxel intensity values of the functional Magnetic Resonance Images (fMRI). Finally, the error term of the linear regression equation is used to estimate the mesh size for a voxel by optimizing Akaike's information Criterion, Bayesian Information Criterion and Rissanen's Minimum Description Length. fMRI measurements are obtained during a memory encoding and retrieval experiment performed on a subject who is exposed to the stimuli from 10 semantic categories. For each sample, a k-NN classifier is trained using the Mesh Arc Descriptors (MAD) having the variable mesh sizes. The classification performances reflect that the suggested variable-size Mesh Arc Descriptors represents the mental states better than the classical multi-voxel pattern representation. Moreover, we observe that the degree of connectivities in the brain greatly varies for each voxel.Publication Metadata only Robo2Box: a toolkit to elicit children's design requirements for classroom robots(Springer-Verlag Berlin, 2016) Barendregt, Wolmet; Department of Mechanical Engineering; Department of Media and Visual Arts; N/A; Department of Psychology; Obaid, Mohammad; Yantaç, Asım Evren; Kırlangıç, Güncel; Göksun, Tilbe; Undergraduate Student; Faculty Member; Master Student; Faculty Member; Department of Mechanical Engineering; Department of Media and Visual Arts; Department of Psychology; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); College of Engineering; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 52621; N/A; 47278We describe the development and first evaluation of a robot design toolkit (Robo2Box) aimed at involving children in the design of classroom robots. We first describe the origins of the Robo2Box elements based on previous research with children and interaction designers drawing their preferred classroom robots. Then we describe a study in which 31 children created their own classroom robot using the toolkit. We present children’s preferences based on their use of the different elements of the toolkit, compare their designs with the drawings presented in previous research, and suggest changes for improvement of the toolkit.Publication Metadata only Sensation: Measuring the effects of a human-to-human social touch based controller on the player experience(Assoc Computing Machinery, 2016) N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Department of Mechanical Engineering; Department of Computer Engineering; Department of Psychology; N/A; Department of Psychology; Department of Media and Visual Arts; Canat, Mert; Tezcan, Mustafa Ozan; Yurdakul, Celalettin; Tiza, Eran; Sefercik, Buğra Can; Bostan, İdil; Buruk, Oğuz Turan; Göksun, Tilbe; Özcan, Oğuzhan; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; PhD Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Mechanical Engineering; Department of Computer Engineering; Department of Psychology; Department of Media and Visual Arts; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); College of Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; N/A; N/A; N/A; N/A; N/A; 47278; 12532We observe an increasing interest on usage of full-body interaction in games. However, human-to-human social touch interaction has not been implemented as a sophisticated gaming apparatus. To address this, we designed the Sensation, a device for detecting touch patterns between players, and introduce the game, Shape Destroy, which is a collaborative game designed to be played with social touch. To understand if usage of social touch has a meaningful contribution to the overall player experience in collaborative games we conducted a user study with 30 participants. Participants played the same game using i) the Sensation and ii) a gamepad, and completed a set of questionnaires aimed at measuring the immersion levels. As a result, the collected data and our observations indicated an increase in general, shared, ludic and affective involvement with significant differences. Thus, human-to-human touch can be considered a promising control method for collaborative physical games.Publication Metadata only Synchrony and complexity in state-related EEG networks: an application of spectral graph theory(MIT Press, 2020) Ghaderi, Amir Hossein; Baltaretu, Bianca R.; Andevari, Masood Nemati; Bharmauria, Vishal; Department of Psychology; Balcı, Fuat; Faculty Member; Department of Psychology; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Social Sciences and Humanities; 51269The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.Publication Open Access The effects of odor and body posture on perceived duration(Frontiers, 2014) Schreuder, Eliane; Hoeksma, Marco R.; Smeets, Monique A. M.; Department of Psychology; Semin, Gün Refik; Researcher; Department of Psychology; College of Social Sciences and Humanities; 58066This study reports an examination of the internal clock model, according to which subjective time duration is influenced by attention and arousal state. In a time production task, we examine the hypothesis that an arousing odor and an upright body posture affect perceived duration. The experimental task was performed while participants were exposed to an odor and either sitting upright (arousing condition) or lying down in a relaxing chair (relaxing condition). They were allocated to one of three experimental odor conditions: rosemary (arousing condition), peppermint (relaxing condition), and no odor (control condition). The predicted effects of the odors were not borne out by the results. Self-reported arousal (SRA) and pleasure (PL) states were measured before, during (after each body posture condition) and postexperimentally. Heart rate (HR) and skin conductance were measured before and during the experiment. As expected, odor had an effect on perceived duration. When participants were exposed to rosemary odor, they produced significantly shorter time intervals than in the no odor condition. This effect, however, could not be explained by increased arousal. There was no effect of body posture on perceived duration, even though body posture did induce arousal. The results do not support the proposed arousal mechanism of the internal clock model.Publication Metadata only The increase in the rate of publications originating from Turkey(Springer, 1999) Department of Chemistry; Department of Psychology; Yurtsever, İsmail Ersin; Gülgöz, Sami; Faculty Member; Faculty Member; Department of Chemistry; Department of Psychology; College of Sciences; College of Social Sciences and Humanities; 7129; 49200The scientific publications of 231 chemistry professors employed at Turkish Universities are studied for a period of 10 years. The quantitative as well as the qualitative aspects of the trends in the scientific information output of this group are analyzed in order to evaluate the underlying facts of the recent increase in the number of publications coming from Turkey. The selected group is a fairly good representative of the Turkish scientific community and our observations could be generalized to describe the development of basic sciences in Turkey. We conclude that even though there exists a serious increase in the scientific output from Turkey, a rather small portion of the studied group is responsible both for high number of publications and for higher quality.