What attention is. The priority structure account.

'Everyone knows what attention is' according to William James. Much work on attention in psychology and neuroscience cites this famous phrase only to quickly dismiss it. But James is right about this: 'attention' was not introduced into psychology and neuroscience as a theoretical concept. I argue that we should therefore study attention with broadly the same methodology that David Marr has applied to the study of perception. By focusing more on Marr's Computational Level of analysis, we arrive at a unified answer to the question of what attention is, what role it plays in the mind, and why organisms like us have that capacity. I propose a methodology for studying attention at Marr's Computational Level that optimizes in a three-dimensional space: it should capture core aspects of our first-person experience of attention, be explanatorily powerful in psychology and neuroscience, and fertile in an interdisciplinary context. I show how this methodology leads to what I call the priority structure account of attention. Attention is what organizes current information to make it more useful for the organism. We can identify it by four features. Attention, in this way, helps a cognitive system to integrate its informational state with its current motivational state. I describe how this account improves on alternatives and shows why attention is a useful concept in many disciplines and for connecting them. This article is categorized under: Philosophy > Psychological Capacities Psychology > Attention Philosophy > Foundations of Cognitive Science.

The cognitive science of COVID-19: Acceptance, denial, and belief change.

Because the spread of pandemics depends heavily on human choices and behaviors, dealing with COVID-19 requires insights from cognitive science which integrates psychology, neuroscience, computer modeling, philosophy, anthropology, and linguistics. Cognitive models can explain why scientists adopt hypotheses about the causes and treatments of disease based on explanatory coherence. Irrational deviations from good reasoning are explained by motivated inference in which conclusions are influenced by personal goals that contribute to emotional coherence. Decisions about COVID-19 can also be distorted by well-known psychological and neural mechanisms. Cognitive science provides advice about how to improve human behavior in pandemics by changing beliefs and by improving behaviors that result from intention-action gaps.

Toward a principled Bayesian workflow in cognitive science.

Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise. Even with powerful software, the analyst is responsible for verifying the utility of their model. To demonstrate this point, we introduce a principled Bayesian workflow (Betancourt, 2018) to cognitive science. Using a concrete working example, we describe basic questions one should ask about the model: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks. The running example for demonstrating the workflow is data on reading times with a linguistic manipulation of object versus subject relative clause sentences. This principled Bayesian workflow also demonstrates how to use domain knowledge to inform prior distributions. It provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model. Given the increasing use of Bayesian methods, we aim to discuss how these methods can be properly employed to obtain robust answers to scientific questions. All data and code accompanying this article are available from https://osf.io/b2vx9/. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.

There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques-even simple ones that are straightforward to use-can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

Variational Bayesian methods for cognitive science.

Bayesian inference has become a powerful and popular technique for understanding psychological phenomena. However, compared with frequentist statistics, current methods employing Bayesian statistics typically require time-intensive computations, often hindering our ability to evaluate alternatives in a thorough manner. In this article, we advocate for an alternative strategy for performing Bayesian inference, called variational Bayes (VB). VB methods posit a parametric family of distributions that could conceivably contain the target posterior distribution, and then attempt to identify the best parameters for matching the target. In this sense, acquiring the posterior becomes an optimization problem, rather than a complex integration problem. VB methods have enjoyed considerable success in fields such as neuroscience and machine learning, yet have received surprisingly little attention in fields such as psychology. Here, we identify and discuss both the advantages and disadvantages of using VB methods. In our consideration of possible strategies to make VB methods appropriate for psychological models, we develop the differential evolution variational inference algorithm, and compare its performance with a widely used VB algorithm. As test problems, we evaluate the algorithms on their ability to recover the posterior distribution of the linear ballistic accumulator model and a hierarchical signal detection model. Although we cannot endorse VB methods in their current form as a complete replacement for conventional methods, we argue that their accuracy and speed warrant inclusion within the cognitive scientist's toolkit. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Yes, no, maybe so: the importance of cognitive interviewing to enhance structured surveys on respectful maternity care in northern India.

Quantitative survey findings are important in measuring health-related phenomena, including on sensitive topics such as respectful maternity care (RMC). But how well do survey results truly capture respondent experiences and opinions? Quantitative tool development and piloting often involve translating questions from other settings and assessing the mechanics of implementation, which fails to deeply explore how respondents understand survey questions and response options. To address this gap, we conducted cognitive interviews on survey questions (n = 88) adapted from validated RMC instruments used in Ethiopia, Kenya and elsewhere in India. Cognitive interviews with rural women (n = 21) in Madhya Pradesh, India involved asking the respondent the survey question, recording her response, then interviewing her about what the question and response options meant to her. We analysed the interviews to revise the tool and identify question failures, which we grouped into six areas: issues with sequencing, length and sensitivity; problematic response options; inappropriate vocabulary; temporal and spatial confusion; accessing different cognitive domains; and failure to resonate with the respondent's worldview and reality. Although women tended to provide initial answers to the survey questions, cognitive interviews revealed widespread mismatch between respondent interpretation and question intent. Likert scale response options were generally incomprehensible and questions involving hypothetical scenarios could be interpreted in unexpected ways. Many key terms and concepts from the international RMC literature did not translate well and showed low resonance with respondents, including consent and being involved in decisions about one's care. This study highlights the threat to data quality and the validity of findings when translating quantitative surveys between languages and cultures and showcases the value of cognitive interviews in identifying question failures. While survey tool revision can address many of these issues, further critical discussion is needed on the use of standardized questions to assess the same domains across contexts.

¿Por qué necesitamos una Psicología Crítica en el Deporte? / Why do we need Critical Psychology on Sport?

La psicología del deporte posee una tradición marcada por la perspectiva cognitiva conductual de la disciplina que ha sido considerada como la versión hegemónica en el campo. Sin embargo, en los últimos años se han hecho visibles varias propuestas que han logrado posicionarse como versiones válidas para trabajar desde la psicología en el amplio campo deportivo. Estas versiones, a su vez, han generado nuevos temas de investigación para resolver diferentes problemas que habían sido invisibilizados por la Psicología Tradicional del Deporte. Con el objetivo de difundir entre las redes latinoamericanas, caribeñas e iberoamericanas la existencia de diferentes perspectivas en el campo de la psicología del deporte, este número especial incluye diez artículos, 5 de ellos en español, 3 en portugués y 2 en inglés, que colaboran con el avance y la amplitud teórica en el campo

Sports psychology has a tradition marked by the behavioral cognitive perspective of the disci-pline, which has been considered the hegemonic version on the field. Nevertheless, in recent years several proposals have become visible and have managed as valid versions to work from psychology in the broad field of sports. These versions have generated new research topics to solve different problems that had been invisible in Traditional Sports Psychology. With the aim of communicate the existence of different perspectives in the field of Psychology of Sport among Latin American, Caribbean and Ibero-American networks, this special issue includes ten articles, 5 of them in Spanish, 3 in Portuguese and 2 in English, which collaborate with ad-vancement and theoretical amplitude in the field

Immoral Professors and Malfunctioning Tools: Counterfactual Relevance Accounts Explain the Effect of Norm Violations on Causal Selection.

Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have been interpreted as showing that prescriptive norm violations only affect causal reasoning about intentional agents, but not the use of inanimate artifacts, thereby providing evidence that the effect of prescriptive norm violations arises from mechanisms specific to reasoning about intentional agents, and thus casting doubt on a unified counterfactual analysis of causal reasoning. Four experiments explore this recent finding and provide clear support for a unified counterfactual analysis. Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely mirrored by judgments of counterfactual relevance. Experiment 2 shows that the relationship between causal and counterfactual judgments is moderated by causal structure, as uniquely predicted by counterfactual accounts. Experiment 3 directly manipulates the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiment 4 shows that prescriptive norm violations (in which artifacts malfunction) affect causal judgments of inanimate artifacts in much the same way that prescriptive norm violations (in which agents act immorally) affect causal judgments of intentional agents.

Deep Neural Networks as Scientific Models.

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in turn cognitive scientists have started using DNNs as models to investigate biological cognition and its neural basis, creating heated debate. Here, we reflect on the case from the perspective of philosophy of science. After putting DNNs as scientific models into context, we discuss how DNNs can fruitfully contribute to cognitive science. We claim that beyond their power to provide predictions and explanations of cognitive phenomena, DNNs have the potential to contribute to an often overlooked but ubiquitous and fundamental use of scientific models: exploration.

A Semantic Network Cartography of the Creative Mind.

The role of semantic memory in creativity is theoretically assumed, but far from understood. In recent years, computational network science tools have been applied to investigate this role. These studies shed unique quantitative insights on the role of semantic memory structure in creativity, via measures of connectivity, distance, and structure.

Social Transmission of False Memory in Small Groups and Large Networks.

Sharing information and memories is a key feature of social interactions, making social contexts important for developing and transmitting accurate memories and also false memories. False memory transmission can have wide-ranging effects, including shaping personal memories of individuals as well as collective memories of a network of people. This paper reviews a collection of key findings and explanations in cognitive research on the transmission of false memories in small groups. It also reviews the emerging experimental work on larger networks and collective false memories. Given the reconstructive nature of memory, the abundance of misinformation in everyday life, and the variety of social structures in which people interact, an understanding of transmission of false memories has both scientific and societal implications.

ACT-R: A cognitive architecture for modeling cognition.

ACT-R is a hybrid cognitive architecture. It is comprised of a set of programmable information processing mechanisms that can be used to predict and explain human behavior including cognition and interaction with the environment. We start by reviewing its history, which shapes its current form, contrasts and relates it to other architectures, and helps readers to anticipate where it is going. Based on this history, we then describe it as a theory of cognition that is realized as a computer program. After this, we briefly discuss tools for working with ACT-R, and also note several major accomplishments that have been gained by working with ACT-R in both basic and applied science, including summarizing some of the insights about human behavior. We conclude by discussing its future, which we believe will include adding emotions and physiology, increasing usability, and the use of nongenerative models. This article is categorized under: Computer Science > Artificial Intelligence Psychology > Reasoning and Decision Making Psychology > Theory and Methods.

Distinct mental trainings differentially affect altruistically motivated, norm motivated, and self-reported prosocial behaviour.

Global challenges such as climate change or the refugee crises emphasize the necessity of altruism and cooperation. In a large-scale 9-month intervention study, we investigated the malleability of prosociality by three distinct mental trainings cultivating attention, socio-affective, or socio-cognitive skills. We assessed numerous established measures of prosociality that capture three core facets: Altruistically motivated behaviours, norm motivated behaviours, and self-reported prosociality. Results of multiple time point confirmatory factor analyses support the validity and temporal stability of this model. Furthermore, linear mixed effects models reveal differential effects of mental trainings on the subcomponents of prosociality: Only training care and compassion effectively boosted altruistically motivated behaviour. No effects were revealed for norm-based behaviour. Self-reported prosociality increased with all training modules; this increase was, however, unrelated to changes in task-based measures of altruistic behaviour. These findings corroborate our motivation-based framework of prosociality, challenge economic views of fixed preferences by showing that socio-affective training boosts altruism, and inform policy makers and society about how to increase global cooperation.

Automatic imitation: A meta-analysis.

Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of gz = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record

Reporte de las diferencias confiables en el perfil del ACE-III / A report of reliable differences in the profile of the ACE-III

No disponible

The effect of research method type on stereotypes' content: A brief research report.

Stereotype Content Model (SCM) emphasizes the content rather than the underlying processes of the stereotypes and the content might be influenced by several cultural dimensions (e.g., individualism vs. collectivism). The main dimensions of SCM-namely warmth and competence-underlying various contents are assumed to be universal. However, from a cognitive science paradigm, we argue that different research methods (i.e., data collections and data analysis) might also yield different stereotype contents that might impact the universality versus specificity problem in the SCM. Indeed, using a sample from a collectivistic country (i.e., Romania), we found that using different methods in data collection (i.e., unstructured vs. semi-structured vs. structured interview) and different methods of data analysis (i.e., availability vs. accessibility scores) might be an important research strategy to counter artefacts and confusions in the universality versus specificity problem related to the SCM. Theoretical and practical implications are discussed.

Online webcam-based eye tracking in cognitive science: A first look.

Online experimentation is emerging in many areas of cognitive psychology as a viable alternative or supplement to classical in-lab experimentation. While performance- and reaction-time-based paradigms are covered in recent studies, one instrument of cognitive psychology has not received much attention up to now: eye tracking. In this study, we used JavaScript-based eye tracking algorithms recently made available by Papoutsaki et al. (International Joint Conference on Artificial Intelligence, 2016) together with consumer-grade webcams to investigate the potential of online eye tracking to benefit from the common advantages of online data conduction. We compared three in-lab conducted tasks (fixation, pursuit, and free viewing) with online-acquired data to analyze the spatial precision in the first two, and replicability of well-known gazing patterns in the third task. Our results indicate that in-lab data exhibit an offset of about 172 px (15% of screen size, 3.94° visual angle) in the fixation task, while online data is slightly less accurate (18% of screen size, 207 px), and shows higher variance. The same results were found for the pursuit task with a constant offset during the stimulus movement (211 px in-lab, 216 px online). In the free-viewing task, we were able to replicate the high attention attribution to eyes (28.25%) compared to other key regions like the nose (9.71%) and mouth (4.00%). Overall, we found web technology-based eye tracking to be suitable for all three tasks and are confident that the required hard- and software will be improved continuously for even more sophisticated experimental paradigms in all of cognitive psychology.

Philosophical aesthetics and cognitive science.

Philosophical aesthetics is the branch of philosophy which explores issues having to do with art, beauty, and related phenomena. Philosophers have often been skeptical about the place of empirical investigation in aesthetics. However, in recent years many philosophical aestheticians have turned to cognitive science to enrich their understanding of their subject matter. Cognitive scientists have, in turn, been inspired by work in philosophical aesthetics. This essay focuses on a representative subset of the areas in which there has been fruitful dialog between philosophical aestheticians and cognitive scientists. We start with some general topics in philosophical aesthetics-the definition of art and the epistemic status of aesthetic judgments. We then move on to discussing research concerning the roles that imagination and perception play in our aesthetic engagement. We conclude with a discussion of the emerging field of experimental philosophical aesthetics. WIREs Cogn Sci 2018, 9:e1445. doi: 10.1002/wcs.1445 This article is categorized under: Philosophy > Value.