Psychological constructivist models of emotion propose that emotions arise from the combinations of multiple processes, many of which are not emotion specific. These models attempt to describe both the homogeneity of instances of an emotional “kind” (why are fears similar?) and the heterogeneity of instances (why are different fears quite different?). In this article, we review the iterative reprocessing model of affect, and suggest that emotions, at least in part, arise from the processing of dynamical unfolding representations of valence across (...) time. Critical to this model is the hypothesis that affective trajectories—over time—provide important information that helps build emotional states. (shrink)
We review the psychological literature on the organization of valence, discussing theoretical perspectives that favor a single dimension of valence, multiple valence dimensions, and positivity and negativity as dynamic and flexible properties of mental experience that are contingent upon context. Turning to the neuroscience literature that spans three levels of analysis, we discuss how positivity and negativity can be represented in the brain. We show that the evidence points toward both separable and overlapping brain systems that support affective processes depending (...) on the level of resolution studied. We move from large-scale brain networks that underlie generalized processing, to functionally specific subcircuits, finally to intraregional neuronal distributions, where the organization and interaction across levels allow for multiple types of valence and mixed evaluations. (shrink)
The scientific study of emotion faces a potentially serious problem: after over a hundred years of psychological study, we lack consensus regarding the very definition of emotion. We propose that part of the problem may be the tendency to define emotion in contrast to cognition, rather than viewing both “emotion” and “cognition” as being comprised of more elemental processes. We argue that considering emotion as a type of cognition (viewed broadly as information processing) may provide an understanding of the mechanisms (...) underlying domains that are traditionally thought to be qualitatively distinct. (shrink)
Although rationalization about one's own beliefs and actions can improve an individual's future decisions, beliefs can provide other benefits unrelated to their epistemic truth value, such as group cohesion and identity. A model of resource-rational cognition that accounts for these benefits may explain unexpected and seemingly irrational thought patterns, such as belief polarization.
Research using economic decision-making tasks has established that direct reciprocity plays a role in prosocial decision-making: people are more likely to help those who have helped them in the past. However, less is known about how considerations of mutual exchange influence decisions even when the other party’s actions are unknown and direct reciprocity is therefore not possible. Using a two-party economic task in which the other’s actions are unknown, study 1 shows that prosociality critically depends on the potential for mutual (...) exchange; when the other person has no opportunity to help the participant, prosocial behavior is drastically reduced. In study 2, we find that theories regarding the other person’s intentions influence the degree of prosociality that participants exhibit, even when no opportunity for direct reciprocity exists. Further, beliefs about the other’s intentions are closely related to one’s own motivations in the task. Together, the results support a model in which prosociality depends on both the social conditions for mutual exchange and a mental model of how others will behave within these conditions, which is closely related to knowledge of the self. (shrink)
Activating relevant responses is a key function of automatic processes in De Neys's model; however, what determines the order or magnitude of such activation is ambiguous. Focusing on recently developed sequential sampling models of choice, we argue that proactive control shapes response generation but does not cleanly fit into De Neys's automatic-deliberative distinction, highlighting the need for further model development.
Discovering the taxonomies that best describe emotional experience has been surprisingly challenging. Clore and Huntsinger propose that by exploring the objects of emotion, such as standards or actions, we may better understand differences in emotion that emerge for similarly valenced reactions. We are sympathetic to this idea, although we suggest here that greater attention should be given to the computations that accompany affective processing, such as the discrepancy between different hedonic states, rather than the object per se.
Pietraszewski contends that group representations that rely on a “containment metaphor” fail to adequately capture phenomena of group dynamics such as shifts in allegiances. We argue, in contrast, that social categories allow for computationally efficient, richly structured, and flexible group representations that explain some of the most intriguing aspects of social group behaviour.