Emre Demiralp

Facebook Use Predicts Declines in Subjective Well-Being in Young Adults

Over 500 million people interact daily with Facebook. Yet, whether Facebook use influences subjective well-being over time is unknown. We addressed this issue using experience-sampling, the most reliable method for measuring in-vivo behavior and psychological experience. We text-messaged people five times per day for two-weeks to examine how Facebook use influences the two components of subjective well-being: how people feel moment-to-moment and how satisfied they are with their lives. Our results indicate that Facebook use predicts negative shifts on both of these variables over time. The more people used Facebook at one time point, the worse they felt the next time we text-messaged them; the more they used Facebook over two-weeks, the more their life satisfaction levels declined over time. Interacting with other people “directly” did not predict these negative outcomes. They were also not moderated by the size of peoples Facebook networks, their perceived supportiveness, motivation for using Facebook, gender, loneliness, self-esteem, or depression. On the surface, Facebook provides an invaluable resource for fulfilling the basic human need for social connection. Rather than enhancing well-being, however, these findings suggest that Facebook may undermine it.

Neural and behavioral effects of interference resolution in depression and rumination

Individuals diagnosed with major depressive disorder (MDD) often ruminate about their depression and their life situations, impairing their concentration and performance on daily tasks. We examined whether rumination might be due to a deficit in the ability to expel negative information from short-term memory (STM), and fMRI was used to examine the neural structures involved in this ability. MDD and healthy control (HC) participants were tested using a directed-forgetting procedure in a short-term item recognition task. As predicted, MDD participants had more difficulty than did HCs in expelling negative, but not positive, words from STM. Overall, the neural networks involved in directed forgetting were similar for both groups, but the MDDs exhibited more spatial variability in activation in the left inferior frontal gyrus (a region critical for inhibiting irrelevant information), which may contribute to their relative inability to inhibit negative information.

Feeling Blue or Turquoise? Emotional Differentiation in Major Depressive Disorder

Some individuals have very specific and differentiated emotional experiences, such as anger, shame, excitement, and happiness, whereas others have more general affective experiences of pleasure or discomfort that are not as highly differentiated. Considering that individuals with major depressive disorder (MDD) have cognitive deficits for negative information, we predicted that people with MDD would have less differentiated negative emotional experiences than would healthy people. To test this hypothesis, we assessed participants’ emotional experiences using a 7-day experience-sampling protocol. Depression was assessed using structured clinical interviews and the Beck Depression Inventory-II. As predicted, individuals with MDD had less differentiated emotional experiences than did healthy participants, but only for negative emotions. These differences were above and beyond the effects of emotional intensity and variability.

A probabilistic foundation for dynamical systems: theoretical background and mathematical formulation

In this paper we describe a probabilistic framework for describing dynamical systems. The approach is inspired by quantum dynamical expectation dynamics. Specifically, an abstract evolution operator corresponding to the Hamiltonian in quantum dynamics is constructed. The evolution of this operator defining PDE’s solution is isomorphic to the functional structure of the wave function as long as its initial form permits. This operator enables us to use one of the most important probabilistic concepts, namely expectations. The expectation dynamics are governed by equations which are constructed via commutator algebra. Based on inspiration from quantum dynamics, we have used both the independent variables and the symmetric forms of their derivatives. For construction of the expectation dynamics, the algebraic independent variable operators which multiply their operands by the corresponding independent variable suffice. In our descriptions, we remain at the conceptual level in a self-consistent manner. The phenomenological implications and the tremendous potential of this approach for scientific discovery and advancement is described in the companion to this paper.

A probabilistic foundation for dynamical systems: phenomenological reasoning and principal characteristics of probabilistic evolution

This paper is the second in a series of two. The first paper has been devoted to the detailed explanation of the mathematical formulation of the underlying theoretical framework. Specifically, the first paper shows that it is possible to construct an infinite linear ODE set, which describes a probabilistic evolution. The evolution is probabilistic because the unknowns are expectations, with appropriate initial conditions. These equations, which we name, Probabilistic Evolution Equations (PEE) are linear at the level of ODEs and initial conditions. In this paper, we first focus on the phenomenological reasoning that lead us to the derivation of PEE. Second, the aspects of the PEE construction is revisited with a focus on the spectral nature of the probabilistic evolution. Finally, we postulate fruitful avenues of research in the fields of dynamical causal modeling in human neuroimaging and effective connectivity analysis. We believe that this final section is a prime example of how the rigorous methods developed in the context of mathematical chemistry can be influential in other fields and disciplines.

Is the preference of natural versus man-made scenes driven by bottom-up processing of the visual features of nature?

Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention, and mood. In this study, we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom–up processing of the low-level visual features of nature. We used participants’ ratings of perceived naturalness as well as esthetic preference for 307 images with varied natural and urban content. We then quantified 10 low-level image features for each image (a combination of spatial and color properties). These features were used to predict esthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modeled by the low-level visual features) and non-modeled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom–up). On the other hand, perceived naturalness that was not modeled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modeled and non-modeled) was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modeled naturalness, and the remaining unmodeled naturalness in predicting esthetic preference. In summary, bottom–up (color and spatial) properties of natural images captured by our features and the non-modeled naturalness are important to esthetic judgments of natural and man-made scenes, with each predicting unique variance.

A contemporary linear representation theory for ordinary differential equations: probabilistic evolutions and related approximants for unidimensional autonomous systems

In this paper, we build on our previous research on probabilistic foundations of dynamical systems and introduce a theory of linear representation for ordinary differential equations. The theory is developed for explicit ODEs and can be further extended to cover implicit cases. In this report, we investigate the case of a canonical single unknown autonomous system. First we construct a linear representation to get an infinite linear ODE set with a constant coefficient matrix which can be transformed into an upper triangular form. Then we find its approximate truncated solutions. We describe a number of properties of the theory using this framework. The companion of this paper expands this canonical approach to cover multidimensional cases using the theory of folded arrays which is another line of research established by our research group.

A contemporary linear representation theory for ordinary differential equations: multilinear algebra in folded arrays (folarrs) perspective and its use in multidimensional case

In this paper, we extend the framework describing the probabilistic evolution of explicit unidimensional ODEs, which is described in the companion of this paper, to multidimensional cases. We show that an infinite set of linear ODEs accompanied by an initial condition (represented with an infinite vector) can also be constructed for the multidimensional cases. The principles that underly the construction of the equations and the truncated approximants of the solutions are the same. The crucial addition of this paper is the use of multiindex, folded and unfolded vectors and matrices. Unlike our earlier work on folded arrays, which relied on probabilistic principles of construction, in this work, we use a purely mathematical approach for the construction of the multidimensional structures. We provide a procedural description of how such constructions can be made.

The development of abstract letter representations for reading: evidence for the role of context.

We review evidence that in the course of reading, the visual system computes abstract letter identities (ALIs): a representation of letters that encodes their identity but that abstracts away from their visual appearance. How could the visual system learn such a seemingly nonvisual representation? We propose that different forms of the same letter tend to appear in similar distributions of contexts (in the same words written in different ways) and that this environmental correlation interacts with correlation-based learning mechanisms in the brain to lead to the formation of ALIs. We review a neural network model that demonstrates the feasibility of this common contexts hypothesis and present two experiments confirming some novel predictions: (a) repeatedly presenting arbitrary visual stimuli in common contexts leads those stimuli to be confusable with each other, and (b) different forms of the same letter are more confusable with each other in word-like contexts than in nonword-like contexts. We then extend the model to use real pictures of letters as input and simulate some of the novel empirical findings from the experiments.

An Orthonormal Decomposition Method for Multidimensional Matrices

The utilization of arrays with more than two indices which which are also called also multidimensional matrices has noticably increased in recent years. This brought the need for their decompositions to be used in practical applications. Especially signal processing, computer vision and neuroscience studies are relevant to this issue. This work aims at the construction of an orthogonal decomposition. It is formed by multidimensional outer products each of which is composed of a one and one less than the original input array type components. These components are obtained via a semi optimization type algorithm. Each outer product is constructed as a unit norm entity and its proportionality constant can be considered as an eigenvalue or a scalar measuring contribution. Certain illustrative numerical implementations are also reported.