Emilio Ferrer - US grants

The purpose of this project is to develop mathematical and statistical models for examining the dynamics of dyadic interactions. Although most theories of social relations describe interactions as dynamic processes, many of their theoretical hypotheses remain untested because of a lack of statistical models that are capable of capturing such processes over time. The proposed research addresses this shortcoming by developing and comparing alternative state-of-the-art mathematical and statistical models that can capture the dynamics of interactions in dyads, as these interactions evolve over time. These models will be applied to data collected from couples at multiple time points to study affective processes and emotion regulation, to test hypotheses derived from psychological and sociological theories regarding relationship quality and instability over time. Finally, the developed models will be applied to other types of dyads (e.g., infant-caregiver), other systems of two elements (e.g., cognition-emotion), and more complex social systems (e.g., infant-mother-father triad). The use of the developed models to evaluate hypotheses derived from both psychological and sociological theories should enhance the understanding of dyadic interactions and will broaden the development of theory in this area. In addition to the advancement in models for dyadic interactions, the proposed work presents several broader impacts. These include research training of graduate and undergraduate students (including underrepresented minorities), dissemination of research through publications in scientific journals, integration of methodological and substantive results into the academic curriculum, and the possibility of applying some of the developed models to clinical settings, for example in the evaluation and prediction of relationship quality and stability over time.

DHB Collaborative Research: Developing Non-Stationary and Network-based Methods for Modeling the Perception and Physiology of Emotion

PI: Sy-Miin Chow, University of North Carolina (lead)
Collaborating institution: UC Davis

Recent developments in dynamic systems modeling have led to new conceptualizations of emotions as dynamic processes. This new paradigm has created exciting research venues to extend our understanding of the perception and physiology of emotions. It is clear that, given the complexity of both the experiential and measurement aspects of emotions, such venues can only be pursued through interdisciplinary collaborations. This project brings together researchers in the fields of psychometrics, emotion/psychophysiology, statistics, bioinformatics/biostatistics and financial econometrics to develop techniques for studying the dynamics of emotions and affective processes. Multiple measures of emotions will be collected over different time scales, with the aims of using these data to (1) develop methods for estimating and diagnosing differential equation models with time-varying parameters and random effects, (2) develop methods for analyzing the dynamics of emotions as indicated by facial electromyography (EMG) data, (3) use network-based methods to represent within-individual transitions among discrete affective states and (4) develop and organize tools for studying complex, non-stationary processes and disseminate these tools to audiences across a variety of disciplines. In addition to introducing novel methodologies for testing existing theories of emotions, this project also provides new opportunities for methodologists to refine and develop new techniques for studying dynamic systems. Beyond emotions, the tools developed in this project can be used to examine other dynamic processes such as lifespan development, disease propagation, and the emergence and disaggregation of social networks.

Every day, infants encounter an enormous amount of new information. Every new object, person, or place introduces new sights, sounds, and experiences to interpret, learn, and remember. Infants are remarkably good at learning new information, especially when it occurs during interactions with parents or caregivers. Many times each day, adults point out and name new objects in books or in the environment, talk to infants about those objects, and describe how objects are similar or different. Research has shown that this type of interaction helps older children learn about the world around them. This project is aimed at understanding the kind of support for learning (often referred to as scaffolding) that parents provide for their infants, and how this scaffolding actually helps infants learn.

Eight- to fourteen-month-old infants and their parents will engage in a picture-book reading task that involves introducing two novel object categories. The instructional strategies used by parents and the dynamics of these interactions will be analyzed. Infant category learning will be subsequently tested using a paired-comparison novelty preference paradigm. In a follow-up study, instructional strategies will be experimentally manipulated to test predictions regarding optimal learning conditions. Thus, this project will allow understanding of how parents' strategies facilitate infants' learning of specific features of the world.

This work not only provides insight into how infants learn, but may also inform ways in which parents or caregivers can better structure learning contexts for infants at risk for later problems. The results of this work will be broadly disseminated, not only in the scientific community, but also to parents and childcare professionals through social and traditional media, lectures given to parenting groups, and participation in the Yolo County Child Development Conference.

Understanding the patterns of communication between brain regions, and how they develop across childhood, is critical for understanding the development of the neural mechanisms that implement complex cognitive operations such as reasoning. Functional connectivity, or correlations in patterns of brain activation (measured via fMRI), is thought to reflect this inter-regional communication. But communication between brain regions ultimately depends on structural connectivity: the white matter tracts that, either directly or indirectly, connect them. This proposal is aimed at resolving two open questions: 1) What are the dynamic relationships between structural and functional connectivity, for the key networks known to be involved in reasoning and other higher cognitive processes, as these develop together across childhood?, and 2) How do these dynamic relationships affect developmental improvements in reasoning ability? The answers that we obtain will provide both fundamental insight into the development of brain connectivity and mechanistic insight into the development of reasoning ability. This knowledge could impact future research both on educational and training programs designed to teach reasoning ability, and on interventions or medical programs designed to correct problems in reasoning.

To address these questions, we will combine fMRI, DTI, and behavioral data from three longitudinal developmental datasets, collected at UC Berkeley, UC Davis, and Vanderbilt University. By combining datasets, we are able to examine longitudinal data from 400 children and young adults between the ages of 6 and 22. Our primary measures of interest include a) intrinsic functional connectivity between specific regions of interest; b) structural connectivity, measured via fractional anisotropy along tracts that connect these regions; and c) reasoning ability, measured via standard cognitive tests. Our main analyses will focus on connectivity within and between two brain networks, the fronto-parietal network and cingulo-opercular network, which are most closely associated with reasoning and other higher cognitive functions. Mixed-model regression analyses will be employed to examine concurrent relationships among structural and functional connectivity in these networks and reasoning ability. Multivariate latent difference score models will be employed to examine lead-lag relationships. The outcome of this research will be a model of interacting structural and functional connectivity development across childhood, and of how the co-development of these two indicators of neural communication relates to developmental improvements in reasoning ability.

How do people learn large-scale spaces, like new towns and cities that they visit, as they navigate? Addressing this question poses surprising obstacles, such as the difficulty in optimizing large-scale spaces for experimental testing and controlling for pre-existing knowledge. Desktop virtual reality offers one possible way to address this question, although such testing offers an incomplete rendition of the full-body, immersive experience that is real-world navigation. Researchers will develop a 2-D treadmill coupled with a head-mounted display to allow free ambulation of large-scale virtual spaces. Successful development of this device has important societal applications. For example, pre-training with enriched body-based cues has the potential to increase knowledge transfer to real world environments, which could be helpful for training individuals such as first-responders and navigation in wilderness environments. Also, the device and proposed experiments will provide a completely novel understanding of the neural basis of human spatial navigation with body-based cues, fundamental to accurately modeling spatial cognition and understanding why we often get lost when we visit new cities.

Almost all theories of the neural basis of spatial navigation, largely developed in freely navigating rodents, assume the critical importance of importance of body-based cues to this code. Yet the vast majority of studies in humans involve navigation in desktop virtual reality. The novel device that will be developed will permit 2-D locomotion-based VR navigation, allowing a full range of body/head rotations and ambulation. The experiments will determine 1) the contributions of body-based input to human spatial navigation and how navigation in VR with body-based can enhance subsequent knowledge of real world environments 2) how the brain codes spatial distance by employing simultaneous EEG recordings 3) how the brain codes the relative directions of landmarks in the environment by modeling the underlying multidimensional brain networks using high-resolution functional magnetic imaging (fMRI). The outcomes from these experiments will be important to testing models of spatial navigation and advancing our understanding of the extent to which we employ visual vs. body-based cues to represent spatial environments, currently an issue of significant debate in the field.