scholarly journals The effect of perceptual organization on risky choice and numerical decisions shows inter-subject correlation

2021 ◽  
Author(s):  
Moshe Glickman ◽  
Sela Tal ◽  
Marius Usher ◽  
Levy Dino
Keyword(s):  
Individual Differences ◽  
Cognitive Processing ◽  
Perceptual Organization ◽  
Computational Models ◽  
Numerical Cognition ◽  
Risky Choice ◽  
Local Processing ◽  
Processing Level ◽  
Wide Range ◽  
Novel Method

Individual differences in cognitive processing have been the subject of intensive research. An important type of such individual differences is the tendency for global vs. local processing, which was shown to correlate with a wide range of processing differences in fields such as decision making, social judgments and creativity. Yet, whether these global/local processing tendencies are correlated within a subject across different domains is still an open question. To address this question, we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local and global information instead of instructing subjects to specifically attend to one processing level. We apply our novel method to two different domains: i) a numerical cognition task, and ii) a preference task. Using computational models, we accounted for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.

Information Processing in Posttraumatic Stress Disorder

2012 ◽  
pp. 190-218 ◽  
Author(s):  
Anke Ehlers ◽  
Thomas Ehring ◽  
Birgit Kleim
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Information Processing ◽  
Individual Differences ◽  
Posttraumatic Stress ◽  
Cognitive Processing ◽  
Stress Disorder ◽  
Cognitive Vulnerability ◽  
Cognitive Strategies ◽  
Future Research ◽  
Wide Range

The chapter reviews the contribution of information processing models to understanding the development and maintenance of posttraumatic stress disorder. Individual differences in cognitive processing during the trauma and basic memory mechanism, such as priming and associative learning, may help explain why people with PTSD involuntarily re-experience parts of the trauma in a wide range of situations. Individual differences in how people remember traumatic events may influence the likelihood of developing PTSD. Attentional bias to trauma-related cues and threatening interpretations of the trauma or its aftermath help explain why people with PTSD have many symptoms of anxiety even though the trauma is over. Cognitive strategies people use to deal with memories of the trauma, such as effortful suppression of trauma memories and rumination, help explain why some develop chronic PTSD whereas many recover from trauma. Finally, there may be cognitive vulnerability factors that increase the probability of developing PTSD in response to trauma. Directions for future research are outlined.

Group Experiences and Individual Differences in Stuttering

2019 ◽  
Vol 62 (12) ◽  
pp. 4335-4350 ◽  
Author(s):  
Seth E. Tichenor ◽  
J. Scott Yaruss
Keyword(s):  
Individual Differences ◽  
Support Groups ◽  
Support Group ◽  
Affective States ◽  
Life Outcomes ◽  
Self Help ◽  
Wide Range ◽  
Group Experiences ◽  
History Of ◽  
Adults Who Stutter

Purpose This study explored group experiences and individual differences in the behaviors, thoughts, and feelings perceived by adults who stutter. Respondents' goals when speaking and prior participation in self-help/support groups were used to predict individual differences in reported behaviors, thoughts, and feelings. Method In this study, 502 adults who stutter completed a survey examining their behaviors, thoughts, and feelings in and around moments of stuttering. Data were analyzed to determine distributions of group and individual experiences. Results Speakers reported experiencing a wide range of both overt behaviors (e.g., repetitions) and covert behaviors (e.g., remaining silent, choosing not to speak). Having the goal of not stuttering when speaking was significantly associated with more covert behaviors and more negative cognitive and affective states, whereas a history of self-help/support group participation was significantly associated with a decreased probability of these behaviors and states. Conclusion Data from this survey suggest that participating in self-help/support groups and having a goal of communicating freely (as opposed to trying not to stutter) are associated with less negative life outcomes due to stuttering. Results further indicate that the behaviors, thoughts, and experiences most commonly reported by speakers may not be those that are most readily observed by listeners.

Variable Precision Depth Encoding for 3D Range Geometry Compression

2020 ◽  
Vol 2020 (17) ◽  
pp. 34-1-34-7
Author(s):  
Matthew G. Finley ◽  
Tyler Bell
Keyword(s):  
Normal Distribution ◽  
Arbitrary Distribution ◽  
File Size ◽  
Geometry Compression ◽  
Variable Precision ◽  
Wide Range ◽  
Novel Method ◽  
Encoding Method ◽  
Rgb Image ◽  
Color Channels

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications.

Individual differences in white and grey matter structure associated with verbal habits of thought

2019 ◽  
Author(s):  
Justin C. Hayes ◽  
Katherine L Alfred ◽  
Rachel Pizzie ◽  
Joshua S. Cetron ◽  
David J. M. Kraemer
Keyword(s):  
Individual Differences ◽  
White Matter ◽  
Cognitive Processing ◽  
Grey Matter ◽  
Structural Changes ◽  
Structural Integrity ◽  
Diffusion Tensor ◽  
Self Report ◽  
White Matter Tracts

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Expressing the Self

2018 ◽  
Keyword(s):  
Philosophy Of Mind ◽  
The Self ◽  
Epistemic Status ◽  
First Person ◽  
Semantic Categories ◽  
Wide Range ◽  
Person Reference ◽  
De Se ◽  
Novel Method ◽  
Cultural Pragmatics

This book addresses different linguistic and philosophical aspects of referring to the self in a wide range of languages from different language families, including Amharic, English, French, Japanese, Korean, Mandarin, Newari (Sino-Tibetan), Polish, Tariana (Arawak), and Thai. In the domain of speaking about oneself, languages use a myriad of expressions that cut across grammatical and semantic categories, as well as a wide variety of constructions. Languages of Southeast and East Asia famously employ a great number of terms for first-person reference to signal honorification. The number and mixed properties of these terms make them debatable candidates for pronounhood, with many grammar-driven classifications opting to classify them with nouns. Some languages make use of egophors or logophors, and many exhibit an interaction between expressing the self and expressing evidentiality qua the epistemic status of information held from the ego perspective. The volume’s focus on expressing the self, however, is not directly motivated by an interest in the grammar or lexicon, but instead stems from philosophical discussions of the special status of thoughts about oneself, known as de se thoughts. It is this interdisciplinary understanding of expressing the self that underlies this volume, comprising philosophy of mind at one end of the spectrum and cross-cultural pragmatics of self-expression at the other. This unprecedented juxtaposition results in a novel method of approaching de se and de se expressions, in which research methods from linguistics and philosophy inform each other. The importance of this interdisciplinary perspective on expressing the self cannot be overemphasized. Crucially, the volume also demonstrates that linguistic research on first-person reference makes a valuable contribution to research on the self tout court, by exploring the ways in which the self is expressed, and thereby adding to the insights gained through philosophy, psychology, and cognitive science.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alekhya Mandali ◽  
Arjun Sethi ◽  
Mara Cercignani ◽  
Neil A. Harrison ◽  
Valerie Voon
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Computational Models ◽  
Risky Choice ◽  
Risk Tolerance ◽  
Healthy Population ◽  
Hyperactivity Disorder ◽  
Risky Choices ◽  
Cardinal Symptom

AbstractRisk evaluation is a critical component of decision making. Risk tolerance is relevant in both daily decisions and pathological disorders such as attention-deficit hyperactivity disorder (ADHD), where impulsivity is a cardinal symptom. Methylphenidate, a commonly prescribed drug in ADHD, improves attention but has mixed reports on risk-based decision making. Using a double-blinded placebo protocol, we studied the risk attitudes of ADHD patients and age-matched healthy volunteers while performing the 2-step sequential learning task and examined the effect of methylphenidate on their choices. We then applied a novel computational analysis using the hierarchical drift–diffusion model to extract parameters such as threshold (‘a’—amount of evidence accumulated before making a decision), drift rate (‘v’—information processing speed) and response bias (‘z’ apriori bias towards a specific choice) focusing specifically on risky choice preference. Critically, we show that ADHD patients on placebo have an apriori bias towards risky choices compared to controls. Furthermore, methylphenidate enhanced preference towards risky choices (higher apriori bias) in both groups but had a significantly greater effect in the patient population independent of clinical scores. Thus, methylphenidate appears to shift tolerance towards risky uncertain choices possibly mediated by prefrontal dopaminergic and noradrenergic modulation. We emphasise the utility of computational models in detecting underlying processes. Our findings have implications for subtle yet differential effects of methylphenidate on ADHD compared to healthy population.

Sensory Processing Sensitivity Predicts Individual Differences in Resting-State Functional Connectivity Associated with Depth of Processing

2021 ◽  
pp. 1-15
Author(s):  
Bianca P. Acevedo ◽  
Tyler Santander ◽  
Robert Marhenke ◽  
Arthur Aron ◽  
Elaine Aron
Keyword(s):  
Individual Differences ◽  
Cognitive Processing ◽  
Sensory Processing ◽  
Resting State ◽  
Brain Connectivity ◽  
Region Of Interest ◽  
Central Feature ◽  
Resting State Functional Connectivity ◽  
Depth Of Processing ◽  
Sensory Processing Sensitivity

<b><i>Background:</i></b> Sensory processing sensitivity (SPS) is a biologically based temperament trait associated with enhanced awareness and responsivity to environmental and social stimuli. Individuals with high SPS are more affected by their environments, which may result in overarousal, cognitive depletion, and fatigue. <b><i>Method:</i></b> We examined individual differences in resting-state (rs) brain connectivity (using functional MRI) as a function of SPS among a group of adults (<i>M</i> age = 66.13 ± 11.44 years) immediately after they completed a social affective “empathy” task. SPS was measured with the Highly Sensitive Person (HSP) Scale and correlated with rs brain connectivity. <b><i>Results:</i></b> Results showed enhanced rs brain connectivity within the ventral attention, dorsal attention, and limbic networks as a function of greater SPS. Region of interest analyses showed increased rs brain connectivity between the hippocampus and the precuneus (implicated in episodic memory); while weaker connectivity was shown between the amygdala and the periaqueductal gray (important for anxiety), and the hippocampus and insula (implicated in habitual cognitive processing). <b><i>Conclusions:</i></b> The present study showed that SPS is associated with rs brain connectivity implicated in attentional control, consolidation of memory, physiological homeostasis, and deliberative cognition. These results support theories proposing “depth of processing” as a central feature of SPS and highlight the neural processes underlying this cardinal feature of the trait.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

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