Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion.

In the present study, we used an unsupervised classification algorithm to reveal both consistency and degeneracy in neural network connectivity during anger and anxiety. Degeneracy refers to the ability of different biological pathways to produce the same outcomes. Previous research is suggestive of degeneracy in emotion, but little research has explicitly examined whether degenerate functional connectivity patterns exist for emotion categories such as anger and anxiety.

Initial validation of the trust of automated systems test (TOAST).

Trust is a key determinant of whether people rely on automated systems in the military and the public. However, there is currently no standard for measuring trust in automated systems. In the present studies, we propose a scale to measure trust in automated systems that is grounded in current research and theory on trust formation, which we refer to as the Trust in Automated Systems Test (TOAST).

Uncovering general, shared, and unique temporal patterns in ambulatory assessment data.

Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, a subset of the sample, and a given individual.

Unsupervised Classification During Time-Series Model Building.

Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects.

The separation of between-person and within-person components of individual change over time: a latent curve model with structured residuals.

Objective: Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful to varying degrees. However, none provide the unambiguous separation over time of between-person and within-person components of stability and change, components that are often hypothesized to exist in the psychological sciences.

Clarifying the nature of startle habituation using latent curve modeling.

Startle habituation is present in all startle studies, whether as a dependent variable, discarded habituation block, or ignored nuisance. However, there is still much that remains unknown about startle habituation, including the following: (1) what is the nature of the startle habituation curve?; (2) at what point does startle habituation approach an asymptote?; and (3) are there gender differences in startle habituation? The present study investigated these three questions in a sample of 94 undergraduates using both traditional means-based statistical methods and latent curve modeling. Results provided new information about the nature of the startle habituation curve, indicated that the optimal number of habituation trials with a 100dB startle stimulus is 13, and showed that females display greater startle reactivity but habituate toward the same level as males.