New Content From Perspectives on Psychological Science

A Challenge to Orthodoxy in Psychology: Thomas Sowell and Social Justice
William O’Donohue, Nina Silander, Craig Frisby, and Jane Fisher  

Psychologists address social-justice problems in their research and applied work, and their scholarly efforts have been influenced by assumptions, constructs, and hypotheses from the political left. Recently, some psychologists have called for increased intellectual and political diversity in psychology, particularly as such diversity may lead to improved problem-solving. As an attempt to increase intellectual diversity in psychology, we review here the scholarship of Thomas Sowell. His work represents a rich source of hypotheses for psychologists’ future research. We focus on his views on the importance of freedom; the extent to which reforms can reduce freedom; the importance of free markets to human flourishing; the role of free markets in producing costs for discrimination; the way spontaneously ordered systems can contain knowledge that can be overlooked in reforms; and the importance of culture and cultural capital. We will also discuss Sowell’s more thoroughgoing economic analyses of problems and solutions and his analyses of contingencies operating on politicians and reformers, as well as his views on conflicts in fundamental visions about human nature and the pivotal role of improvements in minority education.  

Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet)
Eunice Yiu, Eliza Kosoy, and Alison Gopnik  

Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skills, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce.  

Understanding Sensory-Motor Disorders in Autism Spectrum Disorders by Extending Hebbian Theory: Formation of a Rigid-Autonomous Phase Sequence
Eiichi Nojiri and Kenkichi Takase  

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. 

Diminished State-Space Theory of Human Aging
Ben Eppinger, Alexa Ruel, and Florian Bolenz  

Many new technologies, such as smartphones, computers, or public-access systems (like ticket-vending machines), are a challenge for older adults. One feature that these technologies have in common is that they involve underlying, partially observable, structures (state spaces) that determine the actions that are necessary to reach a certain goal (e.g., to move from one menu to another, to change a function, or to activate a new service). In this work we provide a theoretical, neurocomputational account to explain these behavioral difficulties in older adults. Based on recent findings from age-comparative computational- and cognitive-neuroscience studies, we propose that age-related impairments in complex goal-directed behavior result from an underlying deficit in the representation of state spaces of cognitive tasks. Furthermore, we suggest that these age-related deficits in adaptive decision-making are due to impoverished neural representations in the orbitofrontal cortex and hippocampus.  

Toward an Integrative Approach to the Study of Positive-Affect Related Aggression 
Joyce Quansah and Jean Gagnon  

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. 

Repositioning Construct Validity Theory: From Nomological Networks to Pragmatic Theories and Their Evaluation by Explanatory Means
Brian Haig  

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.  

Special Collection: Discussion Forum on Cecilia Heyes’ “Rethinking Norm Psychology” 

Social norms are ubiquitous in all human cultures, whether they are codified in law or largely unspoken, universally applicable or group specific. However, uncertainty remains as to what degree “norm psychology”—the set of cognitive and motivational mechanisms for processing social rules and standards—is innate versus acquired. Convened by guest editor Daniel Kelly, this Discussion Forum centers around Cecilia Heyes’s article, “Rethinking Norm Psychology,” which criticizes the emphasis on domain-specificity and genetic inheritance in norm psychology and argues in favor of a cultural-evolutionary, domain-generalist perspective. In 14 accompanying commentaries, authors clarify and examine Heyes’s proposed model and its depiction of the interaction of implicit and explicit processes, and some discuss methodological proposals for how to best test the two main competing hypotheses against each other.  

Toward a General Framework of Biased Reasoning: Coherence-Based Reasoning
Dan Simon and Stephen Read  

A considerable amount of experimental research has been devoted to uncovering biased forms of reasoning. Notwithstanding the richness and overall empirical soundness of the bias research, the field can be described as disjointed, incomplete, and undertheorized. In this article, we seek to address this disconnect by offering “coherence-based reasoning” as a parsimonious theoretical framework that explains a sizable number of important deviations from normative forms of reasoning. Represented in connectionist networks and processed through constraint-satisfaction processing, coherence-based reasoning serves as a ubiquitous, essential, and overwhelmingly adaptive apparatus in people’s mental toolbox. This adaptive process, however, can readily be overrun by bias when the network is dominated by nodes or links that are incorrect, overweighted, or otherwise nonnormative. We apply this framework to explain a variety of well-established biased forms of reasoning, including confirmation bias, the halo effect, stereotype spillovers, hindsight bias, motivated reasoning, emotion-driven reasoning, ideological reasoning, and more. 

Learning Landscape in Gamification: The Need for a Methodological Protocol in Research Applications
Matteo Orsoni, Adam Dubé, Catia Prandi, Sara Giovagnoli, Mariagrazia Benassi, Elvis Mazzoni, and Martina Benvenuti  

In education, the term “gamification” refers to of the use of game-design elements and gaming experiences in the learning processes to enhance learners’ motivation and engagement. Despite researchers’ efforts to evaluate the impact of gamification in educational settings, several methodological drawbacks are still present. Indeed, the number of studies with high methodological rigor is reduced and, consequently, so are the reliability of results. In this work, we identified the key concepts explaining the methodological issues in the use of gamification in learning and education, and we exploited the controverses identified in the extant literature. Our final goal was to set up a checklist protocol that will facilitate the design of more rigorous studies in the gamified-learning framework. The checklist suggests potential moderators explaining the link between gamification, learning, and education identified by recent reviews, systematic reviews, and meta-analyses: study design, theory foundations, personalization, motivation and engagement, game elements, game design, and learning outcomes.   

Individuals, Collectives, and Individuals in Collectives: The Ineliminable Role of Dependence
Ulrike Hahn  

Our beliefs are inextricably shaped through communication with others. Furthermore, even conversation we conduct in pairs may itself be taking place across a wider, connected social network. Our communications, and with that our thoughts, are consequently typically those of individuals in collectives. This has fundamental consequences with respect to how our beliefs are shaped. This article examines the role of dependence on our beliefs and seeks to demonstrate its importance with respect to key phenomena involving collectives that have been taken to indicate irrationality. It is argued that (with the benefit of hindsight) these phenomena no longer seem surprising when one considers the multiple dependencies that govern information acquisition and the evaluation of cognitive agents in their normal (i.e., social) context. 

A Normative Framework for Assessing the Information Curation Algorithms of the Internet
David Lazer, Briony Swire-Thompson, and Christo Wilson  

It is critical to understand how algorithms structure the information people see and how those algorithms support or undermine society’s core values. We offer a normative framework for the assessment of the information curation algorithms that determine much of what people see on the internet. The framework presents two levels of assessment: one for individual-level effects and another for systemic effects. With regard to individual-level effects we discuss whether (a) the information is aligned with the user’s interests, (b) the information is accurate, and (c) the information is so appealing that it is difficult for a person’s self-regulatory resources to ignore (“agency hacking”). At the systemic level we discuss whether (a) there are adverse civic-level effects on a system-level variable, such as political polarization; (b) there are negative distributional or discriminatory effects; and (c) there are anticompetitive effects, with the information providing an advantage to the platform. The objective of this framework is both to inform the direction of future scholarship as well as to offer tools for intervention for policymakers. 

The Spread of Beliefs in Partially Modularized Communities
Robert Goldstone, Marina Dubova, Rachith Aiyappa, and Andy Edinger  

Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore–exploit continuum are usefully paired. 

Do COVID-19 Vaccination Policies Backfire? The Effects of Mandates, Vaccination Passports, and Financial Incentives on COVID-19 Vaccination
Bita Fayaz-Farkhad and Haesung Jung  

Faced with the challenges of motivating people to vaccinate, many countries have introduced policy-level interventions to encourage vaccination against COVID-19. For example, mandates were widely imposed requiring individuals to vaccinate to work and attend school, and vaccination passports required individuals to show proof of vaccination to travel and access public spaces and events. Furthermore, some countries also began offering financial incentives for getting vaccinated. One major criticism of these policies was the possibility that they would produce reactance and thus undermine voluntary vaccination. This article therefore reviews relevant empirical evidence to examine whether this is indeed the case. Specifically, we devote separate sections to reviewing and discussing the impacts of three major policies that were implemented during the COVID-19 pandemic: vaccination mandates, vaccination passports, and the provision of financial incentives. A careful analysis of the evidence provides little support that these policies backfire but instead can effectively promote vaccination at the population level. The policies are not without limitations, however, such as their inability to mobilize those that are strongly hesitant to vaccines. Finally, we discuss how policy-level interventions should be designed and implemented to address future epidemics and pandemics.   

How Social Media Algorithms Shape Offline Civic Participation: A Framework of Social Psychological Processes
Haesung Jung, Wenhao Dai, and Dolores Albarracín  

Even though social media platforms have created opportunities for more efficient and convenient civic participation, they are unlikely to bring about social change if the online actions do not propagate to offline civic participation. This article begins by reviewing the meta-analytic evidence on the relation between social media use and offline civic participation. Following this discussion, we present a theoretical framework that incorporates the attitudinal, motivational, and relational processes that may mediate the effect of social media use on offline civic participation. The framework highlights how social media algorithms may shape attitudes on important societal issues, promote generalized action goals among habitual users, and build social capital. We further discuss factors that may strengthen or undermine each of these processes, suggest ways to design and implement algorithms that may promote offline civic participation, and propose questions for future research. 

The Emerging Science of Interacting Minds
Thalia Wheatley, Mark Thornton, Arjen Stolk, and Luke Chang  

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction. 

The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Manish Raghavan  

More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden—but serious—problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them “inversion problems”: The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science. 

New Forms of Collaboration Between the Social and Natural Sciences Could Become Necessary for Understanding Rapid Collective Transitions in Social Systems
Stefan Thurner  

Human societies are complex systems and as such have tipping points. They can rapidly transit from one mode of operation to another and thereby change the way they function as a whole. Such transitions appear as financial or economic crises, rapid swings in collective opinion, political regime shifts, or revolutions. In physics collective transitions are known as phase transitions; for example, water exists in states of liquid, ice, and vapor. A few variables determine which state is realized: temperature, pressure, and volume. For social systems it is less clear what determines collective social states. A better understanding of social tipping points would allow us to tackle some of the big challenges more systematically, such as polarization, loss of social cohesion, fragmentation, or the green transition. The physics concept of universality might be key to understanding some tipping points in human societies and why agent-based models (ABMs) might make sense for identifying the transition points. If universality exists in social systems there is hope that relatively simple ABMs will be sufficient for understanding collective social systems in transition; if it does not exist, highly detailed computational models will be unavoidable. Both are possible. Both need new forms of collaboration between the social and natural sciences, and new types of data will be essential.   

A Systematic Review and New Analyses of the Gender-Equality Paradox
Agneta Herlitz, Ida Hönig, Kåre Hedebrant, and Martin Asperholm  

Some studies show that living conditions, such as economy, gender equality, and education, are associated with the magnitude of psychological sex differences. We systematically and quantitatively reviewed 54 articles and conducted new analyses on 27 meta-analyses and large-scale studies to investigate the association between living conditions and psychological sex differences. We found that sex differences in personality, verbal abilities, episodic memory, and negative emotions are more pronounced in countries with higher living conditions. In contrast, sex differences in sexual behavior, partner preferences, and math are smaller in countries with higher living conditions. We also observed that economic indicators of living conditions, such as gross domestic product, are most sensitive in predicting the magnitude of sex differences. Taken together, results indicate that more sex differences are larger, rather than smaller, in countries with higher living conditions. It should therefore be expected that the magnitude of most psychological sex differences will remain unchanged or become more pronounced with improvements in living conditions, such as economy, gender equality, and education. 

AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories
Max Pellert, Clemens Lechner, Claudia Wagner, Beatrice Rammstedt, and Markus Strohmaier  

We illustrate how standard psychometric inventories originally designed for assessing noncognitive human traits can be repurposed as diagnostic tools to evaluate analogous traits in large language models (LLMs). We start from the assumption that LLMs, inadvertently yet inevitably, acquire psychological traits (metaphorically speaking) from the vast text corpora on which they are trained. Such corpora contain sediments of the personalities, values, beliefs, and biases of the countless human authors of these texts, which LLMs learn through a complex training process. The traits that LLMs acquire in such a way can potentially influence their behavior, that is, their outputs in downstream tasks and applications in which they are employed, which in turn may have real-world consequences for individuals and social groups. By eliciting LLMs’ responses to language-based psychometric inventories, we can bring their traits to light. Psychometric profiling enables researchers to study and compare LLMs in terms of noncognitive characteristics, thereby providing a window into the personalities, values, beliefs, and biases these models exhibit (or mimic). We discuss the history of similar ideas and outline possible psychometric approaches for LLMs. We demonstrate one promising approach, zero-shot classification, for several LLMs and psychometric inventories. We conclude by highlighting open challenges and future avenues of research for AI Psychometrics. 

The Sound of Emotional Prosody: Nearly 3 Decades of Research and Future Directions
Pauline Larrouy-Maestri, David Poeppel, and Marc Pell  

Emotional voices attract considerable attention. A search on any browser using “emotional prosody” as a key phrase leads to more than a million entries. Such interest is evident in the scientific literature as well; readers are reminded in the introductory paragraphs of countless articles of the great importance of prosody and that listeners easily infer the emotional state of speakers through acoustic information. However, despite decades of research on this topic and important achievements, the mapping between acoustics and emotional states is still unclear. In this article, we chart the rich literature on emotional prosody for both newcomers to the field and researchers seeking updates. We also summarize problems revealed by a sample of the literature of the last decades and propose concrete research directions for addressing them, ultimately to satisfy the need for more mechanistic knowledge of emotional prosody. 

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