Anita Tusche

Neural Responses to Unattended Products Predict Later Consumer Choices

Imagine you are standing at a street with heavy traffic watching someone on the other side of the road. Do you think your brain is implicitly registering your willingness to buy any of the cars passing by outside your focus of attention? To address this question, we measured brain responses to consumer products (cars) in two experimental groups using functional magnetic resonance imaging. Participants in the first group (high attention) were instructed to closely attend to the products and to rate their attractiveness. Participants in the second group (low attention) were distracted from products and their attention was directed elsewhere. After scanning, participants were asked to state their willingness to buy each product. During the acquisition of neural data, participants were not aware that consumer choices regarding these cars would subsequently be required. Multivariate decoding was then applied to assess the choice-related predictive information encoded in the brain during product exposure in both conditions. Distributed activation patterns in the insula and the medial prefrontal cortex were found to reliably encode subsequent choices in both the high and the low attention group. Importantly, consumer choices could be predicted equally well in the low attention as in the high attention group. This suggests that neural evaluation of products and associated choice-related processing does not necessarily depend on attentional processing of available items. Overall, the present findings emphasize the potential of implicit, automatic processes in guiding even important and complex decisions.

Classifying the wandering mind:Revealing the affective content of thoughts during task-free rest periods

Many powerful human emotional thoughts are generated in the absence of a precipitating event in the environment. Here, we tested whether we can decode the valence of internally driven, self-generated thoughts during task-free rest based on neural similarities with task-related affective mental states. We acquired functional magnetic resonance imaging (fMRI) data while participants generated positive and negative thoughts as part of an attribution task (Session A) and while they reported the occurrence of comparable mental states during task-free rest periods (Session B). With the use of multivariate pattern analyses (MVPA), we identified response patterns in the medial orbitofrontal cortex (mOFC) that encode the affective content of thoughts that are generated in response to an external experimental cue. Importantly, these task driven response patterns reliably predicted the occurrence of affective thoughts generated during unconstrained rest periods recorded one week apart. This demonstrates that at least certain elements of task-cued and task-free affective experiences rely on a common neural code. Furthermore, our findings reveal the role that the mOFC plays in determining the affective tone of unconstrained thoughts. More generally, our results suggest that MVPA is an important methodological tool for attempts to understand unguided subject driven mental states such as mind-wandering and daydreaming based on neural similarities with task-based experiences.

Medial prefrontal and anterior cingulate cortical thickness predicts shared individual differences in self-generated thought and temporal discounting

When deprived of compelling perceptual input, the mind is often occupied with thoughts unrelated to the immediate environment. Previous behavioral research has shown that this self-generated task-unrelated thought (TUT), especially under non-demanding conditions, relates to cognitive capacities such as creativity, planning, and reduced temporal discounting. Despite the frequency and importance of this type of cognition, little is known about its structural brain basis. Using MRI-based cortical thickness measures in 37 participants, we were able to show that individuals with a higher tendency to engage in TUT under low-demanding conditions (but not under high-demanding conditions) show an increased thickness of medial prefrontal cortex (mPFC) and anterior/midcingulate cortex. Thickness of these regions also related to less temporal discounting (TD) of monetary rewards in an economic task, indicative of more patient decision-making. The findings of a shared structural substrate in mPFC and anterior/midcingulate cortex underlying both TUT and TD suggest an important role of these brain regions in supporting the self-generation of information that is unrelated to the immediate environment and which may be adaptive in nature.

Exploring the use of thermal infrared imaging in human stress research

High resolution thermal infrared imaging is a pioneering method giving indices of sympathetic activity via the contact-free recording of facial tissues (thermal imprints). Compared to established stress markers, the great advantage of this method is its non-invasiveness. The goal of our study was to pilot the use of thermal infrared imaging in the classical setting of human stress research. Thermal imprints were compared to established stress markers (heart rate, heart rate variability, finger temperature, alpha-amylase and cortisol) in 15 participants undergoing anticipation, stress and recovery phases of two laboratory stress tests, the Cold Pressor Test and the Trier Social Stress Test. The majority of the thermal imprints proved to be change-sensitive in both tests. While correlations between the thermal imprints and established stress markers were mostly non-significant, the thermal imprints (but not the established stress makers) did correlate with stress-induced mood changes. Multivariate pattern analysis revealed that in contrast to the established stress markers the thermal imprints could not disambiguate anticipation, stress and recovery phases of both tests. Overall, these results suggest that thermal infrared imaging is a valuable method for the estimation of sympathetic activity in the stress laboratory setting. The use of this non-invasive method may be particularly beneficial for covert recordings, in the study of special populations showing difficulties in complying with the standard instruments of data collection and in the domain of psychophysiological covariance research. Meanwhile, the established stress markers seem to be superior when it comes to the characterization of complex physiological states during the different phases of the stress cycle.

The Neural Representation of Voluntary Task-Set Selection in Dynamic Environments

When choosing actions, humans have to balance carefully between different task demands. On the one hand, they should perform tasks repeatedly to avoid frequent and effortful switching between different tasks. On the other hand, subjects have to retain their flexibility to adapt to changes in external task demands such as switching away from an increasingly difficult task. Here, we developed a difficulty-based choice task to investigate how subjects voluntarily select task-sets in predictably changing environments. Subjects were free to choose 1 of the 3 task-sets on a trial-by-trial basis, while the task difficulty changed dynamically over time. Subjects self-sequenced their behavior in this environment while we measured brain responses with functional magnetic resonance imaging (fMRI). Using multivariate decoding, we found that task choices were encoded in the medial prefrontal cortex (dorso-medial prefrontal cortex, dmPFC, and dorsal anterior cingulate cortex, dACC). The same regions were found to encode task difficulty, a major factor influencing choices. Importantly, the present paradigm allowed us to disentangle the neural code for task choices and task difficulty, ensuring that activation patterns in dmPFC/dACC independently encode these 2 factors. This finding provides new evidence for the importance of the dmPFC/dACC for task-selection and motivational functions in highly dynamic environments.

The Structure of Human Prosociality Differentiating Altruistically Motivated, Norm Motivated, Strategically Motivated, and Self-Reported Prosocial Behavior

Prosocial behavior is crucial for functioning societies. However, its reliable scientific assessment and the understanding of its underlying structure are still a challenge. We integrated 14 paradigms from diverse disciplines to identify reliable and method-independent subcomponents of human prosociality; 329 participants performed game theoretical paradigms and hypothetical distribution tasks commonly used in behavioral economics and completed interactive computer tasks and self-reports typically employed in psychology. Four subcomponents of prosociality were identified by exploratory factor analysis and verified by confirmatory factor analysis in an independent sample: altruistically motivated prosocial behavior, norm motivated prosocial behavior, strategically motivated prosocial behavior, and self-reported prosocial behavior. Altruistically motivated behavior was related to gender, to enhanced cognitive skills, and to reduced negative affect. Our study provides a crucial step toward an overarching framework on prosocial behavior that will benefit future research on predictors, neural underpinnings, and plasticity of human cooperation and prosociality.

From Faces to Prosocial Behavior: Cues, Tools, and Mechanisms

In this review, we ask how looking at people’s faces can influence prosocial behaviors toward them. Components of this process have been studied in two disparate literatures: one focused on the perception of faces and judgments based on them, using both psychological and neuroscience approaches, and a second focused on actual social behaviors as studied in behavioral economics and decision science. Bridging these disciplines requires a mechanistic account of how processing of particular face attributes or features influences social judgments and behaviors. We review these two lines of research and suggest that combining some of their methodological tools may reveal the bridging mechanistic explanations.

Cognitive regulation alters social and dietary choice by changing attribute representations in domain-general and domain-specific brain circuits

Are some people generally more successful using cognitive regulation or does it depend on the choice domain? Why? We combined behavioral computational modeling and multivariate decoding of fMRI responses to identify neural loci of regulation-related shifts in value representations across goals and domains (dietary or altruistic choice). Surprisingly, regulatory goals did not alter integrative value representations in the ventromedial prefrontal cortex, which represented all choice-relevant attributes across goals and domains. Instead, the dorsolateral prefrontal cortex (DLPFC) flexibly encoded goal-consistent values and predicted regulatory success for the majority of choice-relevant attributes, using attribute-specific neural codes. We also identified domain-specific exceptions: goal-dependent encoding of prosocial attributes localized to precuneus and temporo-parietal junction (not DLPFC). Our results suggest that cognitive regulation operated by changing specific attribute representations (not integrated values). Evidence of domain-general and domain-specific neural loci reveals important divisions of labor, explaining when and why regulatory success generalizes (or doesn’t) across contexts and domains.

The Structure of Human Prosociality Revisited: Corrigendum and Addendum to Böckler, Tusche, and Singer (2016)

In a recent publication, we employed factor analyses to integrate 14 measures of prosocial behavior, proposing four subcomponents of human prosociality: altruistically motivated, norm motivated, strategically motivated, and self-reported prosocial behavior. However, the reported confirmatory factor analysis (CFA) yielded standardized regression weights above 1, resulting from an improper solution (Heywood cases), which precludes straightforward interpretation of results. Here, we present two adjusted CFA models that rectify this problem. Model 1 resolves the issue of Heywood cases by implementing equality constraints, yielding a four-factor structure that is largely similar to the original model. Model 2 accommodates additional methodological considerations and presents a revised structure of prosociality with three subcomponents: altruistically motivated, norm motivated, and self-reported prosocial behavior. We also report minor corrections of descriptive results, none of which alter the pattern of results and interpretations of the original publication.

Distinct mental trainings differentially affect altruistically motivated, norm motivated, and self-reported prosocial behaviour

Global challenges such as climate change or the refugee crises emphasize the necessity of altruism and cooperation. In a large-scale 9-month intervention study, we investigated the malleability of prosociality by three distinct mental trainings cultivating attention, socio-affective, or socio-cognitive skills. We assessed numerous established measures of prosociality that capture three core facets: Altruistically motivated behaviours, norm motivated behaviours, and self-reported prosociality. Results of multiple time point confirmatory factor analyses support the validity and temporal stability of this model. Furthermore, linear mixed effects models reveal differential effects of mental trainings on the subcomponents of prosociality: Only training care and compassion effectively boosted altruistically motivated behaviour. No effects were revealed for norm-based behaviour. Self-reported prosociality increased with all training modules; this increase was, however, unrelated to changes in task-based measures of altruistic behaviour. These findings corroborate our motivation-based framework of prosociality, challenge economic views of fixed preferences by showing that socio-affective training boosts altruism, and inform policy makers and society about how to increase global cooperation.