Two core motivational systems have been conceptualized as underlying emotion and behavior. The approach system drives the organism toward stimuli or events in the environment, and the avoidance system instead deters the organism away from these stimuli or events. This approach—avoidance dichotomy has been central to theories of emotion. Advances in neuroscience complementing well-designed behavioral experiments have begun to elucidate the neural mechanisms underlying approach—avoidance motivation, suggesting that these two systems exist in parallel and are lateralized in the brain. This (...) review explores the notion of approach—avoidance and the cerebral lateralization of these motivational tendencies. (shrink)

Researchers have wondered how the brain creates emotions since the early days of psychological science. With a surge of studies in affective neuroscience in recent decades, scientists are poised to answer this question. In this target article, we present a meta-analytic summary of the neuroimaging literature on human emotion. We compare the locationist approach (i.e., the hypothesis that discrete emotion categories consistently and specifically correspond to distinct brain regions) with the psychological constructionist approach (i.e., the hypothesis that discrete emotion categories (...) are constructed of more general brain networks not specific to those categories) to better understand the brain basis of emotion. We review both locationist and psychological constructionist hypotheses of brain–emotion correspondence and report meta-analytic findings bearing on these hypotheses. Overall, we found little evidence that discrete emotion categories can be consistently and specifically localized to distinct brain regions. Instead, we found evidence that is consistent with a psychological constructionist approach to the mind: A set of interacting brain regions commonly involved in basic psychological operations of both an emotional and non-emotional nature are active during emotion experience and perception across a range of discrete emotion categories. (shrink)

Individual differences in emotional reactivity or affective style can be decomposed into more elementary constituents. Several separable of affective style are identified such as the threshold for reactivity, peak amplitude of response, the rise time to peak and the recovery time. latter two characteristics constitute components of affective chronometry The circuitry that underlies two fundamental forms of motivation and and withdrawal-related processes-is described. Data on differences in functional activity in certain components of these are next reviewed, with an emphasis on (...) the nomological network of surrounding individual differences in asymmetric prefrontal The relevance of such differences for understanding the nature affective dysfunction in affective disorders is then considered. The ends by considering what the prefrontal cortex “does” in certain of affective style and highlights some of the important questions for future research. (shrink)

Rutherford and Lindell (2011) review an extensive literature on lateralization of emotion. As they note, an important issue surrounding this question is the nature of emotion, which bears on what, precisely, is lateralized. The present comments are intended to broaden the context of the review, by considering lateralization from the standpoint of a bivariate model of evaluative processes and a neuroevolutionary perspective.

Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion-specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that (...) central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system. (shrink)