Kalyan Raman

The relationship between self-report of depression and media usage

Depression is a debilitating condition that adversely affects many aspects of a persons life and general health. Earlier work has supported the idea that there may be a relationship between the use of certain media and depression. In this study, we tested if self-report of depression (SRD), which is not a clinically based diagnosis, was associated with increased internet, television, and social media usage by using data collected in the Media Behavior and Influence Study (MBIS) database (N = 19,776 subjects). We further assessed the relationship of demographic variables to this association. These analyses found that SRD rates were in the range of published rates of clinically diagnosed major depression. It found that those who tended to use more media also tended to be more depressed, and that segmentation of SRD subjects was weighted toward internet and television usage, which was not the case with non-SRD subjects, who were segmented along social media use. This study found that those who have suffered either economic or physical life setbacks are orders of magnitude more likely to be depressed, even without disproportionately high levels of media use. However, among those that have suffered major life setbacks, high media users—particularly television watchers—were even more likely to report experiencing depression, which suggests that these effects were not just due to individuals having more time for media consumption. These findings provide an example of how Big Data can be used for medical and mental health research, helping to elucidate issues not traditionally tested in the fields of psychiatry or experimental psychology.

Age-related striatal BOLD changes without changes in behavioral loss aversion.

Loss aversion (LA), the idea that negative valuations have a higher psychological impact than positive ones, is considered an important variable in consumer research. The literature on aging and behavior suggests older individuals may show more LA, although it is not clear if this is an effect of aging in general (as in the continuum from age 20 and 50 years), or of the state of older age (e.g., past age 65 years). We also have not yet identified the potential biological effects of aging on the neural processing of LA. In the current study we used a cohort of subjects with a 30 year range of ages, and performed whole brain functional MRI (fMRI) to examine the ventral striatum/nucleus accumbens (VS/NAc) response during a passive viewing of affective faces with model-based fMRI analysis incorporating behavioral data from a validated approach/avoidance task with the same stimuli. Our a priori focus on the VS/NAc was based on (1) the VS/NAc being a central region for reward/aversion processing; (2) its activation to both positive and negative stimuli; (3) its reported involvement with tracking LA. LA from approach/avoidance to affective faces showed excellent fidelity to published measures of LA. Imaging results were then compared to the behavioral measure of LA using the same affective faces. Although there was no relationship between age and LA, we observed increasing neural differential sensitivity (NDS) of the VS/NAc to avoidance responses (negative valuations) relative to approach responses (positive valuations) with increasing age. These findings suggest that a central region for reward/aversion processing changes with age, and may require more activation to produce the same LA behavior as in younger individuals, consistent with the idea of neural efficiency observed with high IQ individuals showing less brain activation to complete the same task.

A stochastic differential equation analysis of cerebrospinal fluid dynamics

BackgroundClinical measurements of intracranial pressure (ICP) over time show fluctuations around the deterministic time path predicted by a classic mathematical model in hydrocephalus research. Thus an important issue in mathematical research on hydrocephalus remains unaddressed--modeling the effect of noise on CSF dynamics. Our objective is to mathematically model the noise in the data.MethodsThe classic model relating the temporal evolution of ICP in pressure-volume studies to infusions is a nonlinear differential equation based on natural physical analogies between CSF dynamics and an electrical circuit. Brownian motion was incorporated into the differential equation describing CSF dynamics to obtain a nonlinear stochastic differential equation (SDE) that accommodates the fluctuations in ICP.ResultsThe SDE is explicitly solved and the dynamic probabilities of exceeding critical levels of ICP under different clinical conditions are computed. A key finding is that the probabilities display strong threshold effects with respect to noise. Above the noise threshold, the probabilities are significantly influenced by the resistance to CSF outflow and the intensity of the noise.ConclusionsFluctuations in the CSF formation rate increase fluctuations in the ICP and they should be minimized to lower the patients risk. The nonlinear SDE provides a scientific methodology for dynamic risk management of patients. The dynamic output of the SDE matches the noisy ICP data generated by the actual intracranial dynamics of patients better than the classic model used in prior research.

Trends in optimization models of sales force management

In the last half-century, significant advances have been made in directing sales force behavior with the use of optimization and decision models. The present paper both presents the current state-of-the art in sales force decision modeling, and also discusses key issues and trends in contemporary modeling of relevance to sales force researchers. The paper begins by exploring critical concepts regarding the estimation of the sales response function, and then discusses critical problems of endogeneity, heterogeneity, and temporal variation that are faced by modelers in this task. Modern approaches to dealing with these issues are presented. We then discuss areas of importance concerning finding model solutions, including closed form versus simulation, and optimization versus heuristic solutions. The paper next moves to areas of practical importance where models can help, including call planning, sales force size, territory allocation, and compensation design. Finally, we discuss trends that will likely impact on sales force modeling in coming years, including the use of big data and data mining, the possible breakdown of rationality, the rise of the Internet and social media, and the potential of agent-based modeling.

Detecting price thresholds in choice models using a semi-parametric approach

The semi-parametric methodology, underutilized in marketing, can be applied to distinguish between competing models of price response and to estimate the model that most validly describes consumer response to price. The methodology is robust and flexible, thereby making it applicable to a wide spectrum of models of consumer response. In the specific context of reference prices, we show that the semi-parametric methodology helps the manager develop price promotions that most effectively capitalize on the nature of consumer price response.

A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior

This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher “loss aversion.” Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans.

Using fMRI Analysis to Unpack a Portion of Prospect Theory for Advertising/Marketing Understanding

One of the key elements being used today to support/reject/enhance marketing/advertising theory is Kahneman and Tversky’s prospect theory (1979). Interest has been growing on how that concept might support/explain how advertising “works” based on Kahneman’s later concepts as found in his text “Thinking Fast and Slow” (2011). All have spawned and supported the field of behavioral economics (Kahneman, American Economic Review, 93: 1449–1475, 2003). Literally thousands of discussions, speculations, hypotheses, and applications of these concepts can now be found in the advertising literature. Yet, in spite of its broad industry and practitioner acceptance, the basic fundamentals of prospect theory, as Kahneman and Tversky outlined them in their original paper, “Prospect Theory: An Analysis of Decision Under Risk” (1979), and their follow-on book, “Choices, Values and Frames” (2000) still rely mostly on support from small scale, academic, laboratory experiments based on questionnaires and researcher interpretations. We employ the new tools of fMRI in an age-related experiment. Loss Aversion has a long history in marketing and communication theory and the ability to connect or refute that concept to aging in marketing theory would seem a major aid to marketers going forward.

A quantitative interaction between signal detection in attention and reward/aversion behavior

This study examines how processes such as reward/aversion and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize attention with a continuous performance task and variables from signal detection theory, and reward/aversion with a keypress task using variables from relative preference theory. We find that while the relationship between reward/aversion and attention is functionally invariant, a power law formulation akin to the Cobb-Douglas production function in economics provides the best model fit and theoretical explanation for the interaction. These results indicate that a decreasing signal-to-noise with signal detection results in higher loss aversion. Furthermore, the estimated exponents for the multiplicative power law suggest capacity constraints to processing for attention and reward/aversion. These results demonstrate a systemic interaction of attention and reward/aversion across subjects, with a quantitative schema raising the hypothesis that mechanistic inference may be possible at the level of behavior alone.

Modelling, estimation and optimal control issues in cerebrospinal fluid dynamics

Background Mathematical models have proved useful in studying the pressure-volume compensation of patients suffering from hydrocephalus, a condition caused by excessive accumulation of cerebrospinal fluid (CSF) in the brain. A standard approach to managing hydrocephalus is through implantation of a shunt. Two important issues in mathematical research on hydrocephalus remain unaddressed-the effect of noise on the nonlinear CSF dynamics and the optimal shunt design for managing hydrocephalus.