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.     

The optimal experimental design for multiple alternatives perceptual search

Perceptual bias is inherent to all our senses, particularly in the form of visual illusions and aftereffects. However, many experiments measuring perceptual biases may be susceptible to nonperceptual factors, such as response bias and decision criteria. Here, we quantify how robust multiple alternative perceptual search (MAPS) is for disentangling estimates of perceptual biases from these confounding factors. First, our results show that while there are considerable response biases in our four-alternative forced-choice design, these are unrelated to perceptual biases estimates, and these response biases are not produced by the response modality (keyboard vs. mouse). We also show that perceptual bias estimates are reduced when feedback is given on each trial, likely due to feedback enabling observers to partially (and actively) correct for perceptual biases. However, this does not impact the reliability with which MAPS detects the presence of perceptual biases. Finally, our results show that MAPS can detect actual perceptual biases and is not a decisional bias towards choosing the target in the middle of the candidate stimulus distribution. In summary, researchers conducting a MAPS experiment should use a constant reference stimulus, but consider varying the mean of the candidate distribution. Ideally, they should not employ trial-wise feedback if the magnitude of perceptual biases is of interest.     

Active Listening Delays Attentional Disengagement and Saccadic Eye Movements

Successful goal-directed visual behavior depends on efficient disengagement of attention. Attention must be withdrawn from its current focus before being redeployed to a new object or internal process. Previous research has demonstrated that occupying cognitive processes with a secondary cellular phone conversation impairs attentional functioning and driving behavior. For example, attentional processing is significantly impacted by concurrent cell phone use, resulting in decreased explicit memory for on-road information. Here, we examined the impact of a critical component of cell-phone use—active listening—on the effectiveness of attentional disengagement. In the gap task—a saccadic manipulation of attentional disengagement—we measured saccade latencies while participants performed a secondary active listening task. Saccadic latencies significantly increased under an active listening load only when attention needed to be disengaged, indicating that active listening delays a disengagement operation. Simple dual-task interference did not account for the observed results. Rather, active cognitive engagement is required for measurable disengagement slowing to be observed. These results have implications for investigations of attention, gaze behavior, and distracted driving. Secondary tasks such as active listening or cell-phone conversations can have wide-ranging impacts on cognitive functioning, potentially impairing relatively elementary operations of attentional function, including disengagement.     

The relationship of three measures of childhood depression to academic underachievement

Clinical features of depression (short attention span, lethargy, poor memory and shortened task persistence) appear to be incompatible with effective learning. This has led several investigators to suggest that depression is a significant cause of academic underachievement. The most adequate test of this hypothesis (Tesiny, Lefkowitz, & Gordon, 1980), however, found only small correlations between depression and academic achievement when neither IQ nor socioeconomic status were controlled. In the present study, three measures of childhood depression (Peer Nomination Inventory for Depression, Children's Depression Inventory, and an ad hoc teacher rating) were correlated with scores from the Peabody Individual Achievement Test and the Stanford Achievement Test. IQ was statistically controlled to provide a direct measure of underachievement and analyses were conducted separately for males, females, and the combined sample in a sample that was homogeneous for socioeconomic status. Although intercorrelations among the variables indicated that the sample was appropriate to test the hypothesis, only a few significant correlations were found. These findings suggest that if depression causes academic underachievement, it is a weak or uncommon effect.