Steven Tompson

Neural mechanisms of dissonance: an fMRI investigation of choice justification.

Cognitive dissonance theory proposes that difficult choice produces negatively arousing cognitive conflict (called dissonance), which motivates the chooser to justify her decision by increasing her preference for the chosen option while decreasing her preference for the rejected option. At present, however, neural mechanisms of dissonance are poorly understood. To address this gap of knowledge, we scanned 24 young Americans as they made 60 choices between pairs of popular music CDs. As predicted, choices between CDs that were close (vs. distant) in attractiveness (referred to as difficult vs. easy choices) resulted in activations of the dorsal anterior cingulate cortex (dACC), a brain region associated with cognitive conflict, and the left anterior insula (left aINS), a region often linked with aversive emotional arousal. Importantly, a separate analysis showed that choice-justifying attitude change was predicted by the in-choice signal intensity of the posterior cingulate cortex (PCC), a region that is linked to self-processing. The three regions identified (dACC, left aINS, and PCC) were correlated, within-subjects, across choices. The results were interpreted to support the hypothesis that cognitive dissonance plays a key role in producing attitudes that justify the choice.

Connectivity between mPFC and PCC predicts post-choice attitude change: The self-referential processing hypothesis of choice justification.

Prior research shows that after making a choice, decision makers shift their attitudes in a choice‐congruous direction. Although this post‐choice attitude change effect is robust, the neural mechanisms underlying it are poorly understood. Here, we tested the hypothesis that decision makers elaborate on their choice in reference to self‐knowledge to justify the choice they have made. This self‐referential processing of the choice is thought to play a pivotal role in the post‐choice attitude change. Twenty‐four young American adults made a series of choices. They also rated their attitudes toward the choice options before and after the choices. In support of the current hypothesis, we found that changes in functional connectivity between two putative self‐regions (medial prefrontal cortex and posterior cingulate cortex/precuneus]) during the post‐choice (vs. pre‐choice) rating of the chosen options predicted the post‐choice shift of the attitudes toward the chosen options. This finding is the first to suggest that cognitive integration of various self‐relevant cognitions is instrumental in fostering post‐choice attitude change. Hum Brain Mapp 37:3810–3820, 2016.

Individual Differences in Learning Social and Non-Social Network Structures

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between nonsocial bits of information? Here, the authors use a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. They examined individual differences in the ability to learn community structure of networks composed of social versus nonsocial stimuli. Although participants were able to learn community structure of both social and nonsocial networks, their performance in social network learning was uncorrelated with their performance in nonsocial network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of nonsocial community structure. Taken together, the results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in nonsocial networks. The study design provides a promising approach to identify neurophysiological drivers of social network versus nonsocial network learning, extending knowledge about the impact of individual differences on these learning processes.

Associations between coherent neural activity in the brain’s value system during antismoking messages and reductions in smoking.

Objective: Worldwide, tobacco use is the leading cause of preventable death and illness. One common strategy for reducing the prevalence of cigarette smoking and other health risk behaviors is the use of graphic warning labels (GWLs). This has led to widespread interest from the perspective of health psychology in understanding the mechanisms of GWL effectiveness. Here we investigated differences in how the brain responds to negative, graphic warning label-inspired antismoking ads and neutral control ads, and we probed how this response related to future behavior. Method: A group of smokers (N = 45) viewed GWL-inspired and control antismoking ads while undergoing fMRI, and their smoking behavior was assessed before and one month after the scan. We examined neural coherence between two regions in the brain’s valuation network, the medial prefrontal cortex (MPFC) and ventral striatum (VS). Results: We found that greater neural coherence in the brain’s valuation network during GWL ads (relative to control ads) preceded later smoking reduction. Conclusions: Our results suggest that the integration of information about message value may be key for message influence. Understanding how the brain responds to health messaging and relates to future behavior could ultimately contribute to the design of effective messaging campaigns, as well as more broadly to theories of message effects and persuasion across domains.

Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from noninvasive neuroimaging techniques such as functional magnetic resonance imaging. An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.

Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly. Author Summary How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? In this report, we assess brain network dynamics by using fMRI while smokers view antismoking messages, and relate these metrics to smoking behavior and intentions to quit smoking 1 month following the scan. Smokers who showed reduced allegiance (less consistent network communities) among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking. Furthermore, the degree to which the ventromedial prefrontal cortex flexibly changed its community assignment over time was positively associated with later smoking reduction. These data show that metrics of functional network dynamics can provide new information about individual differences in responsiveness to anti-smoking messaging.