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The present work examined the extent to which evaluations of actions could be biased by the strategic use of euphemistic (agreeable) and dysphemistic (disagreeable) terms. Across multiple studies, we demonstrate that evaluations of actions are made more favorable by replacing a disagreeable term (e.g., torture) with a semantically related agreeable term (e.g., enhanced interrogation) in an act’s description. Notably, the influence of these terms was reduced (but not eliminated) when actions were described in a less ambiguous (i.e., more detailed) manner. Despite their influence, participants judged both agreeable and disagreeable action descriptions as largely truthful and distinct from lies, and judged agents using such descriptions as more trustworthy and moral than liars. Overall, the present findings suggest that a strategic speaker can, through the careful use of language, sway the opinions of others while avoiding many of the reputational costs associated with less subtle forms of linguistic manipulation (e.g., lying).

First demonstrated by Staw (1976), escalation of commitment is the tendency for an individual to increase their commitment to a failing course of action when they are personally responsible for the negative consequences. An attempt was made to replicate this finding and test whether individual differences in numeracy and cognitive reflection could help explain such an effect. No evidence for escalation of commitment amongst the participants was found (N = 365). Participants simply invested more in more promising projects. Also, no evidence was found that numeracy or cognitive reflection predict escalation behaviour. The validity of escalation of commitment behaviour is discussed which suggests that future work should look to explore the boundary conditions of such an effect.

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries. [ABSTRACT FROM AUTHOR]

Across three studies (N = 659), we present evidence that engaging in explanatory reflection reduces receptivity to pseudo‐profound bullshit but not scientific bullshit or fake news. Additionally, ratings for pseudo‐profound and scientific bullshit attributed to authoritative sources were significantly inflated compared to bullshit from anonymous sources. These findings provide initial evidence that asking people to reflect on why they find certain statements meaningful (or not) helps reduce receptivity to some types of misinformation but not others. Moreover, the appeal of misleading claims spread by perceived experts may be largely immune to the putative benefits of interventions that rely solely on reflective thinking. Taken together, our results suggest that while encouraging the public to be more reflective can certainly be helpful as a general rule, the effectiveness of this strategy in reducing the persuasiveness of misleading or otherwise epistemically‐suspect claims is limited by the type of claims being evaluated. [ABSTRACT FROM AUTHOR]

Human lives are radically uncertain. Making sense of such uncertainties is the hallmark of wisdom. Sense-making requires narratives, putting them in the center stage of human everyday decision-making. Yet what if radical uncertainty is a narrative itself? Moreover, do laypeople always consider such narratives irrational? Here we pose these questions to enrich a theory of choice under uncertainty. [ABSTRACT FROM AUTHOR]

People often overestimate their understanding of how things work. For instance, people believe that they can explain even ordinary phenomena such as the operation of zippers and speedometers in greater depth than they really can. This is called the illusion of explanatory depth. Fortunately, a person can expose the illusion by attempting to generate a causal explanation for how the phenomenon operates (e.g., how a zipper works). This might be because explanation makes salient the gaps in a person's knowledge of that phenomenon. However, recent evidence suggests that people might be able to expose the illusion by instead explaining a different phenomenon. Across three preregistered experiments, we tested whether the process of explaining one phenomenon (e.g., how a zipper works) would lead someone to report knowing less about a completely different phenomenon (e.g., how snow forms). In each experiment, we found that attempting to explain one phenomenon led participants to report knowing less about various phenomena. For example, participants reported knowing less about how snow forms after attempting to explain how a zipper works. We discuss alternative accounts of the illusion of explanatory depth that might better fit our results. We also consider the utility of explanation as an indirect, non-confrontational debiasing method in which a person generalizes a feeling of ignorance about one phenomenon to their knowledge base more generally. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, public health guidance (e.g., regarding the use of non-medical masks) changed over time. Although many revisions were a result of gains in scientific understanding, we nonetheless hypothesized that making changes in guidance salient would negatively affect evaluations of experts and health-protective intentions. In Study 1 (N = 300), we demonstrate that describing COVID-19 guidance in terms of inconsistency (versus consistency) leads people to perceive scientists and public health authorities less favorably (e.g., as less expert). For participants in Canada (n = 190), though not the U.S. (n = 110), making guidance change salient also reduced intentions to download a contact tracing app. In Study 2 (N = 1399), we show that a brief forewarning intervention mitigates detrimental effects of changes in guidance. In the absence of forewarning, emphasizing inconsistency harmed judgments of public health authorities and reduced health-protective intentions, but forewarning eliminated this effect. [ABSTRACT FROM AUTHOR]

Sometimes, we must choose between obtaining an immediate reward or foregoing it in favor of searching for a better reward elsewhere. Such decisions have been characterized as involving exploration‐exploitation trade‐offs. Here, we studied the reliability and basis of individual differences in tasks involving choices between exploration and exploitation. In Studies 1, 2, and 4, we found little evidence for a stable individual difference in tendency to explore (vs. exploit). Additionally, we tested delay discounting as a potential predictor of individual differences in exploration. In Studies 3 and 4, we found that delay discounting was inconsistently predictive of exploration behavior. Our results support the claim that people adapt their exploration behavior to the environment in which they find themselves. This adaptation overrides any general preference to explore environments more or less than other people. Our results also suggest that predictors of exploration may be exclusively restricted to the particular environment in which they were observed. Implications for past and future research of exploration‐exploitation decision making are discussed. [ABSTRACT FROM AUTHOR]

While they usually should, people do not revise their beliefs more to expert (economist) opinion than to lay opinion. The present research sought to better understand the factors that make it more likely for an individual to change their mind when faced with the opinions of expert economists versus the general public. Across five studies we examined the role that overestimation of knowledge plays in this behavior. We replicated the finding that people fail to privilege the opinion of experts over the public across two different (Study 1) and five different (Study 5) economic issues. We further find that undermining an illusion of both topic-relevant (Studies 2–4) and -irrelevant knowledge (Studies 3 and 4) leads to greater normative belief revision in response to expert rather than lay opinion. We suggest one reason that people fail to revise their beliefs more in response to experts is because people think they know more than they really do. [ABSTRACT FROM AUTHOR]

When people have invested resources into an endeavor, they typically persist in it, even when it becomes obvious that it will fail. Here we show this bias extends to people's moral decision-making. Across two preregistered experiments (N = 1592) we show that people are more willing to proceed with a futile, immoral action when costs have been sunk (Experiment 1A and 1B). Moreover, we show that sunk costs distort people's perception of morality by increasing how acceptable they find actions that have received past investment (Experiment 2). We find these results in contexts where continuing would lead to no obvious benefit and only further harm. We also find initial evidence that the bias has a larger impact on judgment in immoral compared to non-moral contexts. Our findings illustrate a novel way that the past can affect moral judgment. Implications for rational moral judgment and models of moral cognition are discussed. [ABSTRACT FROM AUTHOR]

For over 45 years, neuroscientists have conducted experiments aimed at understanding the neural basis of working memory. Early results examining individual neurons highlighted that information is stored in working memory in persistent sustained activity where neurons maintained elevated firing rates over extended periods of time. However, more recent work has emphasized that information is often stored in working memory in dynamic population codes, where different neurons contain information at different periods in time. In this paper, I review findings that show that both sustained activity as well as dynamic codes are present in the prefrontal cortex and other regions during memory delay periods. I also review work showing that dynamic codes are capable of supporting working memory and that such dynamic codes could easily be "readout" by downstream regions. Finally, I discuss why dynamic codes could be useful for enabling animals to solve tasks that involve working memory. Although additional work is still needed to know definitively whether dynamic coding is critical for working memory, the findings reviewed here give insight into how different codes could contribute to working memory, which should be useful for guiding future research. [ABSTRACT FROM AUTHOR]

The article discusses a problem that the health professionals do not withdraw treatment from a patient, even though they would withhold that treatment from a new patient with same current symptoms. Topics discussed include comparison of withdrawal aversion with sunk cost bias; freeing the decision maker from consequences; and decision to withdraw treatment that should be taken by health professional.

There is a widespread interest among scientists in understanding a specific and well defined form of intelligence, that is human intelligence. For this reason we propose a stronger version of the original Turing test. In particular, we describe here an open-ended set of Turing++ questions that we are developing at the Center for Brains, Minds, and Machines at MIT -- that is questions about an image. For the Center for Brains, Minds, and Machines the main research goal is the science of intelligence rather than the engineering of intelligence -- the hardware and software of the brain rather than just absolute performance in face identification. Our Turing++ questions reflect fully these research priorities. [ABSTRACT FROM AUTHOR]

Faces are a behaviorally important class of visual stimuli for primates. Recent work in macaque monkeys has identified six discrete face areas where most neurons have higher firing rates to images of faces compared with other objects (Tsao et al., 2006). While neurons in these areas appear to have different tuning (Freiwald and Tsao, 2010; Issa and DiCarlo, 2012), exactly what types of information and, consequently, which visual behaviors neural populations within each face area can support, is unknown. Here we use population decoding to better characterize three of these face patches (ML/MF, AL, and AM). We show that neural activity in all patches contains information that discriminates between the broad categories of face and nonface objects, individual faces, and nonface stimuli. Information is present in both high and lower firing rate regimes. However, there were significant differences between the patches, with the most anterior patch showing relatively weaker representation of nonface stimuli. Additionally, we find that pose-invariant face identity information increases as one moves to more anterior patches, while information about the orientation of the head decreases. Finally, we show that all the information we can extract from the population is present in patterns of activity across neurons, and there is relatively little information in the total activity of the population. These findings give new insight into the representations constructed by the face patch system and how they are successively transformed. [ABSTRACT FROM AUTHOR]

The human visual system can rapidly recognize objects despite transformations that alter their appearance. The precise timing of when the brain computes neural representations that are invariant to particular transformations, however, has not been mapped in humans. Here we employ magnetoencephalography decoding analysis to measure the dynamics of size- and positioninvariant visual information development in the ventral visual stream. With this method we can read out the identity of objects beginning as early as 60 ms. Size- and position-invariant visual information appear around 125 ms and 150 ms, respectively, and both develop in stages, with invariance to smaller transformations arising before invariance to larger transformations. Additionally, the magnetoencephalography sensor activity localizes to neural sources that are in the most posterior occipital regions at the early decoding times and then move temporally as invariant information develops. These results provide previously unknown latencies for key stages of human-invariant object recognition, as well as new and compelling evidence for a feedforward hierarchical model of invariant object recognition where invariance increases at each successive visual area along the ventral stream. [ABSTRACT FROM AUTHOR]

Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (NDT) which is a Matlab package that makes it easy to apply population decoding analyses to neural activity. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capable of analyzing data from many different types of recording modalities, and we give examples of how it can be used to decode basic visual information from neural spiking activity and how it can be used to examine how invariant the activity of a neural population is to stimulus transformations. Overall this toolbox will make it much easier for neuroscientists to apply population decoding analyses to their data, which should help increase the pace of discovery in neuroscience. [ABSTRACT FROM AUTHOR]

Human visual recognition processes are remarkably robust and can function effectively even under highly degraded viewing conditions. Contextual information may play a critical role in such circumstances. Here, we provide neurophysiological evidence that contextual cues can elicit object-specific neural responses, which have hitherto been believed to be based on intrinsic cues alone. Specifically, we find that the "fusiform face area" (FFA) maintains its selectivity for faces without regard to whether the faces are defined intrinsically or contextually. This finding further elucidates the role of the FFA and reveals neural correlates of contextual processing in the service of robust object recognition. [ABSTRACT FROM AUTHOR]

To further understand how to combat COVID-19 vaccination hesitancy, we examined the effects of pro-vaccine expert consensus messaging on lay attitudes about vaccine safety and intention to get a COVID-19 vaccine. We surveyed 729 unvaccinated individuals from four countries in the early stages of the pandemic and 472 unvaccinated individuals from two countries after 2 years of the pandemic. We found belief of vaccine safety strongly correlated with intention to vaccinate in the first sample and less strongly in the second. We also found that consensus messaging improved attitudes toward vaccination even for participants who did not believe the vaccine is safe nor intended to get it. The persuasiveness of expert consensus was unaffected by exposing participants' lack of knowledge about vaccines. We conclude that highlighting expert consensus may be a way to increase support toward COVID-19 vaccination in those hesitant or skeptical. (PsycInfo Database Record (c) 2023 APA, all rights reserved).