Perspectives on Psychological Science最新文献

In this article, I argue for a number of important changes to the conceptual foundations of construct validity theory. I begin by suggesting that construct validity theorists should shift their attention from the validation of constructs to the process of evaluating scientific theories. This shift in focus is facilitated by distinguishing construct validation (understood as theory evaluation) from test validation, thereby freeing it from its long-standing focus on psychological measurement. In repositioning construct validity theory in this way, researchers should jettison the outmoded but superficially popular notion that theories are nomological networks in favor of a more plausible pragmatic view of their natures, such as the idea that theories are explanatorily coherent networks. Consistent with this shift in understanding the nature of theories, my recommendation is that construct validation should embrace an explanationist perspective on the theory evaluation process to complement its focus on hypothetico-deductive theory testing. On this view, abductive research methods have an important role to play. The revisionist perspective on construct validity proposed here is discussed in light of relevant developments in scientific methodology and is applied to an influential account of the validation process that has shaped research practice.

Autism spectrum disorder is a neuropsychiatric disorder characterized by persistent deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. The symptoms invariably appear in early childhood and cause significant impairment in social, occupational, and other important functions. Various abnormalities in the genetic, neurological, and endocrine systems of patients with autism spectrum disorder have been reported as the etiology; however, no clear factor leading to the onset of the disease has been identified. Additionally, higher order cognitive dysfunctions, which are represented by a lack of theory of mind, sensorimotor disorders, and memory-related disorders (e.g., flashbacks), have been reported in recent years, but no theoretical framework has been proposed to explain these behavioral abnormalities. In this study, we extended Hebb's biopsychology theory to provide a theoretical framework that comprehensively explains the various behavioral abnormalities observed in autism spectrum disorder. Specifically, we propose that a wide range of symptoms in autism spectrum disorder may be caused by the formation of a rigid-autonomous phase sequence (RAPS) in the brain. Using the RAPS formation theory, we propose a biopsychological mechanism that could be a target for the treatment of autism spectrum disorders.

Much of the information people encounter in everyday life is not factual; it originates from fictional sources, such as movies, novels, and video games, and from direct experience such as pretense, role-playing, and everyday conversation. Despite the recent increase in research on fiction, there is no theoretical account of how memory of fictional information is related to other types of memory or of which mechanisms allow people to separate fact and fiction in memory. We present a theoretical framework that places memory of fiction in relation to other cognitive phenomena as a distinct construct and argue that it is an essential component for any general theory of human memory. We show how fictionality can be integrated in an existing memory model by extending Rubin's dimensional conceptual memory model. By this means, our model can account for explicit and implicit memory of fictional information of events, places, characters, and objects. Further, we propose a set of mechanisms involving various degrees of complexity and levels of conscious processing that mostly keep fact and fiction separated but also allow information from fiction to influence real-world attitudes and beliefs: content-based reasoning, source monitoring, and an associative link from the memory to the concept of fiction.

Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., "The cat was chased by the mouse"). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays an essential role in resolving conflict during language comprehension. How does cognitive control accomplish this task? Psycholinguistic proposals have conspicuously failed to address this question. We introduce an account in which cognitive control aids language processing when cues conflict by sending top-down biasing signals that strengthen the interpretation supported by the most reliable evidence available. We also provide a computationally plausible model that solves the critical problem of how cognitive control "knows" which way to direct its biasing signal by allowing linguistic knowledge itself to issue crucial guidance. Such a mental architecture can explain a range of experimental findings, including how moment-to-moment shifts in cognitive-control state-its level of activity within a person-directly impact how quickly and successfully language comprehension is achieved.

Loneliness is an important health risk, which is why it is important to understand what can cause persistent or severe loneliness. Previous research has identified numerous personal or relational risk factors for loneliness. Cultural predictors, however, have been considered less. The new framework of norm deviations and loneliness (NoDeL) proposes that social norms, which are defining features of culture, can help explain loneliness within and across cultural contexts. Specifically, people who deviate from social norms are suggested to be at an increased risk for feeling lonely because they are more likely to experience alienation, inauthenticity, lower self-worth, social rejection, relationship dissatisfaction, and/or unfulfilled relational needs. Given that social norms vary by social, geographical, and temporal context, they can furthermore be considered cultural moderators between individual-level risk factors and loneliness: Personal or relational characteristics, such as shyness or being single, may increase the risk for loneliness particularly if they do not fit social norms in a specific environment. Integrating previous quantitative and qualitative findings, I hence offer a framework (NoDeL) to predict loneliness and cultural differences in risk factors for it. Thus, the NoDeL framework may help prepare culture-sensitive interventions against loneliness.

Many mechanisms of social bonding are common to all primates, but humans seemingly have developed some that are unique to the species. These involve various kinds of interactive experiences-from taking a walk together to having a conversation-whose common feature is the triadic sharing of experience. Current theories of social bonding have no explanation for why humans should have these unique bonding mechanisms. Here we propose a shared intentionality account of uniquely human social bonding. Humans evolved to participate with others in unique forms of cooperative and communicative activities that both depend on and create shared experience. Sharing experience in these activities causes partners to feel closer because it allows them to assess their partner's cooperative competence and motivation toward them and because the shared representations created during such interactions make subsequent cooperative interactions easier and more effective.

There is growing interest in the field of cooperative artificial intelligence (AI), that is, settings in which humans and machines cooperate. By now, more than 160 studies from various disciplines have reported on how people cooperate with machines in behavioral experiments. Our systematic review of the experimental instructions reveals that the implementation of the machine payoffs and the information participants receive about them differ drastically across these studies. In an online experiment (N = 1,198), we compare how these different payoff implementations shape people's revealed social preferences toward machines. When matched with machine partners, people reveal substantially stronger social preferences and reciprocity when they know that a human beneficiary receives the machine payoffs than when they know that no such "human behind the machine" exists. When participants are not informed about machine payoffs, we found weak social preferences toward machines. Comparing survey answers with those from a follow-up study (N = 150), we conclude that people form their beliefs about machine payoffs in a self-serving way. Thus, our results suggest that the extent to which humans cooperate with machines depends on the implementation and information about the machine's earnings.

Humans now understand the world as multilevel in nature. For example, societies emerge from individuals, and general experiences of life consist of specific aspects and momentary episodes. A critical feature of multilevel phenomena is between-level incongruences. Applied to human positivity, this means that positive higher-level units are not simply composed of positive lower-level units and that what is good for lower-level units may not be good for higher-level units (and vice versa). For example, killjoys may improve societal well-being, personal achievement may require giving up on certain goals, and a happy life may not arise from simply happy moments. In this article, I provide examples (organized by the positive outcome of well-being and performance and by the social, structural, and temporal forms of multilevel phenomena) to show that such between-level incongruences are ubiquitous. Next, I analyze a few mechanisms that may govern the diverse instantiations of between-level incongruences in positivity. Finally, I discuss implications of this perspective, such as why positivity claims should always qualify their level of analysis; how psychological science may benefit from a multilevel, dynamical, and computational perspective; and how to improve human positivity in light of between-level incongruences.

Political polarization is a barrier to enacting policy solutions to global issues. Social psychology has a rich history of studying polarization, and there is an important opportunity to define and refine its contributions to the present political realities. We do so in the context of one of the most pressing modern issues: climate change. We synthesize the literature on political polarization and its applications to climate change, and we propose lines of further research and intervention design. We focus on polarization in the United States, examining other countries when literature was available. The polarization literature emphasizes two types of mechanisms of political polarization: (1) individual-level psychological processes related to political ideology and (2) group-level psychological processes related to partisan identification. Interventions that address group-level processes can be more effective than those that address individual-level processes. Accordingly, we emphasize the promise of interventions leveraging superordinate identities, correcting misperceived norms, and having trusted leaders communicate about climate change. Behavioral interventions like these that are grounded in scientific research are one of our most promising tools to achieve the behavioral wedge that we need to address climate change and to make progress on other policy issues.

We propose an account of individual differences in risk preferences called "reference-point theory" for choices between sure things and gambles. Like most descriptive theories of risky choice, preferences depend on two drivers-hedonic sensitivities to change and beliefs about risk. But unlike most theories, these drivers are estimated from judged feelings about choice options and gamble outcomes. Furthermore, the reference point is assumed to be the less risky option (i.e., sure thing). Loss aversion (greater impact of negative change than positive change) and pessimism (belief the worst outcome is likelier) predict risk aversion. Gain seeking (greater impact of positive change than negative change and optimism (belief the best outcome is likelier) predict risk seeking. But other combinations of hedonic sensitivities and beliefs are possible, and they also predict risk preferences. Finally, feelings about the reference point predict hedonic sensitivities. When decision makers feel good about the reference point, they are frequently loss averse. When they feel bad about it, they are often gain seeking. Three studies show that feelings about reference points, feelings about options and feelings about outcomes predict risky choice and help explain why individuals differ in their risk preferences.