This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is contrasted with two other (...) accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed. (shrink)

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.

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)

In rebellion against low‐dimensional (e.g., two‐factor) theories in psychology, the authors make the case for high‐dimensional theories. This change in perspective requires a shift towards a focus on computation and quantitative reasoning.

Recent behavioral experiments, along with imaging experiments and neuropsychological studies appear to support the hypothesis that emotions play a causal or constitutive role in moral judgment. Those who resist this hypothesis tend to suggest that affective mechanisms are better suited to play a modulatory role in moral cognition. But I argue that claims about the role of emotion in moral cognition frame the debate in ways that divert attention away from other plausible hypotheses. I suggest that the available data may (...) be more plausibly explained by appeal to predictive and evaluative mechanisms, which are neither wholly affective nor straightforwardly cognitive. By recognizing this fact, we can begin to see why questions about the role of emotion in moral psychology are likely to be empirically misguided. (shrink)

A theoretical framework for the reinforcement learning of aesthetic biases was recently proposed based on brain circuitries revealed by neuroimaging. A model grounded on that framework accounted for interesting features of human aesthetic biases. These features included individuality, cultural predispositions, stochastic dynamics of learning and aesthetic biases, and the peak-shift effect. However, despite the success in explaining these features, a potential weakness was the linearity of the value function used to predict reward. This linearity meant that the learning process employed (...) a value function that assumed a linear relationship between reward and sensory stimuli. Linearity is common in reinforcement learning in neuroscience. However, linearity can be problematic because neural mechanisms and the dependence of reward on sensory stimuli were typically nonlinear. Here, we analyze the learning performance with models including optimal nonlinear value functions. We also compare updating the free parameters of the value functions with the delta rule, which neuroscience models use frequently, vs. updating with a new Phi rule that considers the structure of the nonlinearities. Our computer simulations showed that optimal nonlinear value functions resulted in improvements of learning errors when the reward models were nonlinear. Similarly, the new Phi rule led to improvements in these errors. These improvements were accompanied by the straightening of the trajectories of the vector of free parameters in its phase space. This straightening meant that the process became more efficient in learning the prediction of reward. Surprisingly, however, this improved efficiency had a complex relationship with the rate of learning. Finally, the stochasticity arising from the probabilistic sampling of sensory stimuli, rewards, and motivations helped the learning process narrow the range of free parameters to nearly optimal outcomes. Therefore, we suggest that value functions and update rules optimized for social and ecological constraints are ideal for learning aesthetic biases. (shrink)

The Iowa Gambling Task (IGT) is a popular method for examining real-life decision-making. Research has shown gender related differences in performance, in that men consistently outperform women. It has been suggested that these performance differences are related to decreased emotional control in women compared to men. Given the likely role of emotion in these gender differences, in the present study we examine the effect of a humor induction on IGT performance and whether the effect of humor is moderated by gender. (...) IGT performance and parameters from the Expectancy Valence Model were measured in 68 university students (34 men; mean age 22.02, SD = 4.3 and 34 women; mean age 22.3, SD = 4.1) during a 100 trial-IGT task. Participants were exposed to a brief video before each of the IGT decisions available; one half of the sample (17 men and 17 women) was exposed to 100 humor videos, while the other half was exposed to 100 non-humor videos during the task. We observed a significant interaction between gender and humor, such that under humor, women’s performance during the last block (trials 80-100) improved (compared to women under non-humor), whereas men’s performance during the last block was worse (compared to men under non-humor). Consistent with previous work, under non-humor, men outperformed women in the last block. Lastly, our EVM results show that humor impacts the learning mechanisms of decision-making differently in men and women. Humor impaired men’s ability to acquire knowledge about the payoff structure of the decks, and as a consequence they were stuck in suboptimal performance. On the other hand, humor facilitated women’s ability to explore and to learn from experience, improving performance. These findings deepen our understanding of the mechanisms underlying IGT decision-making and differential effects of humor in men and women. (shrink)