Metaphor priming in sentence production: Concrete pictures affect abstract language production

People speak metaphorically about abstract concepts—for instance, a person can be “full of love” or “have a lot of love to give.” Over the past decade, research has begun to focus on how metaphors are processed during language comprehension. Much of this work suggests that understanding a metaphorical expression involves activating brain and body systems involved in perception and motor control. However, no research to date has asked whether the same is true while speakers produce language. We address this gap using a sentence production task. Its results demonstrate that visually activating a concrete source domain can trigger the use of metaphorical language drawn from that same concrete domain, even in sentences that are thematically unrelated to the primes, a metaphorical priming effect. This effect suggests that conceptual metaphors play a part in language production. It also shows that activation in the perceptual system that is not part of an intended message can nevertheless influence sentence formulation.     

Low-level orientation information for social evaluation in face images

Observers make a range of social evaluations based on facial appearance, including judgments of trustworthiness, warmth, competence, and other aspects of personality. What visual information do people use to make these judgments? While links have been made between perceived social characteristics and other high-level properties of facial appearance (e.g., attractiveness, masculinity), there has been comparatively little effort to link social evaluations to low-level visual features, like spatial frequency and orientation sub-bands, known to be critically important for face processing. We explored the extent to which different social evaluations depended critically on horizontal orientation energy vs. vertical orientation energy, as is the case for face identification and emotion recognition. We found that while trustworthiness judgments exhibited this bias for horizontal orientations, competence and dominance did not, suggesting that social evaluations may depend on a multi-channel representation of facial appearance at early stages of visual processing.     

Explanation can cause Forgetting: Memory Dynamics in the Generation of New Arguments

Retrieval-induced forgetting is observed when the retrieval of target information causes the forgetting of nontarget information. The present study investigated whether similar dynamics occur in the context of generating arguments in the process of explanation. Participants studied arguments associated with several issues before attempting to think of new arguments pertaining to a subset of those issues. When given a later memory test, participants were less likely to recall the studied arguments if they had attempted to think of new arguments than if they had not. This argument-induced forgetting effect was observed regardless of whether participants attempted to generate arguments that either agreed or disagreed with the position of the arguments they studied. The effect was significantly reduced, however, and even numerically reversed, when participants generated arguments that were highly related to the studied arguments. This finding fits well with previous research on retrieval-induced forgetting, which has shown that the retrieval or generation of new information fails to cause the forgetting of old information when the two types of information are well integrated or semantically associated.     

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)—a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery’s payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategic-misrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias.     

Speaking two “Languages” in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently

In this article we report a computational semantic analysis of the presidential candidates’ speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts. Our models further showed that the 2016 US presidential nominees had distinct conceptual representations from those of previous election years, and these patterns did not necessarily align with their respective political parties’ average representation of the key political concepts. In Study Two, structural equation modeling demonstrated that reported political engagement among voters differentially predicted reported likelihoods of voting for Clinton versus Trump in the 2016 presidential election. Study Three indicated that Republicans and Democrats showed distinct, systematic word association patterns for the same concepts/terms, which could be reliably distinguished using machine learning methods. These studies suggest that given an individual’s political beliefs, we can make reliable predictions about how they understand words, and given how an individual understands those same words, we can also predict an individual’s political beliefs. Our study provides a bridge between semantic space models and abstract representations of political concepts on the one hand, and the representations of political concepts and citizens’ voting behavior on the other.     

Moral Responsibility and the Moral Community: Another Reply to Zimmerman

Michael Zimmerman has recently argued against the twofold Strawsonian claim that there can be no moral responsibility without a moral community and that, as a result, moral responsibility is essentially interpersonal. I offered a number of objections to Zimmerman’s view, to which Zimmerman responded. In this article, I respond to Zimmerman’s responses to my criticisms.