Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research

Synthese ◽  
2015 ◽  
Vol 193 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
York Hagmayer
Keyword(s):  
Causal Reasoning ◽  
Psychological Research ◽  
Bayes Nets ◽  
Psychological Theories ◽  
Causal Bayes Nets

Intersectionality as a Framework for Theory and Research in Feminist Psychology

2018 ◽  
Author(s):  
Leah R. Warner ◽  
Stephanie A. Shields
Keyword(s):  
Social Activism ◽  
Data Gathering ◽  
Psychological Research ◽  
Social Identities ◽  
Future Directions ◽  
Science Practices ◽  
Intersectionality Theory ◽  
Psychological Science ◽  
Psychological Theories ◽  
History Of

Intersectionality theory concerns the interdependence of systems of inequality and implications for psychological research. Social identities cannot be studied independently of one another nor separately from the societal processes that maintain inequality. In this chapter we provide a brief overview of the history of intersectionality theory and then address how intersectionality theory challenges the way psychological theories typically conceive of the person, as well as the methods of data gathering and analysis customarily used by many psychologists. We specifically address two concerns often expressed by feminist researchers. First, how to reconcile the use of an intersectionality framework with currently-valued psychological science practices. Second, how intersectionality transforms psychology’s concern with individual experience by shifting the focus to the individual’s position within sociostructural frameworks and their social and political underpinnings. In a concluding section we identify two future directions for intersectionality theory: how psychological research on intersectionality can facilitate social activism, and current developments in intersectionality theory.

Causal Models and Conditional Reasoning

2017 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater
Keyword(s):  
Model Theory ◽  
Causal Model ◽  
Recent Model ◽  
Causal Models ◽  
Conditional Reasoning ◽  
Cognitive System ◽  
Markov Condition ◽  
Bayes Nets ◽  
Causal Bayes Nets ◽  
Conditional Statements

There are deep intuitions that the meaning of conditional statements relate to probabilistic law-like dependencies. In this chapter it is argued that these intuitions can be captured by representing conditionals in causal Bayes nets (CBNs) and that this conjecture is theoretically productive. This proposal is borne out in a variety of results. First, causal considerations can provide a unified account of abstract and causal conditional reasoning. Second, a recent model (Fernbach & Erb, 2013) can be extended to the explicit causal conditional reasoning paradigm (Byrne, 1989), making some novel predictions on the way. Third, when embedded in the broader cognitive system involved in reasoning, causal model theory can provide a novel explanation for apparent violations of the Markov condition in causal conditional reasoning (Ali et al, 2011). Alternative explanations are also considered (see, Rehder, 2014a) with respect to this evidence. While further work is required, the chapter concludes that the conjecture that conditional reasoning is underpinned by representations and processes similar to CBNs is indeed a productive line of research.

scholarly journals Norms and high-level cognition: Consequences, trends, and antidotes

2011 ◽  
Vol 34 (5) ◽  
pp. 260-261
Author(s):  
Simon McNair ◽  
Aidan Feeney
Keyword(s):  
Bayes Nets ◽  
Psychological Science ◽  
Causal Bayes Nets ◽  
High Level

AbstractWe are neither as pessimistic nor as optimistic as Elqayam & Evans (E&E). The consequences of normativism have not been uniformly disastrous, even among the examples they consider. However, normativism won't be going away any time soon and in the literature on causal Bayes nets new debates about normativism are emerging. Finally, we suggest that to concentrate on expert reasoners as an antidote to normativism may limit the contribution of research on thinking to basic psychological science.

scholarly journals The Flatland Fallacy: Moving Beyond Low Dimensional Thinking

2017 ◽  
Author(s):  
Eshin Jolly ◽  
Luke J. Chang
Keyword(s):  
Short Story ◽  
Psychological Research ◽  
Dimensional Problem ◽  
Psychological Phenomena ◽  
Mathematical Proofs ◽  
Psychological Theories ◽  
Higher Dimensional ◽  
Complex Landscape ◽  
Low Dimensional ◽  
Complex Phenomena

Psychology is a complicated science. It has no general axioms or mathematical proofs, is rarely directly observable, and has the privilege of being the only discipline in which the content under investigation (i.e. human psychological phenomena) are the very tools utilized to conduct this investigation. For these reasons, it is easy to be seduced by the idea that our psychological theories, limited by our cognitive capacities, accurately reflect a far more complex landscape. Like the Flatlanders in Edwin Abbot’s famous short story (1884), we may be led to believe that the parsimony offered by our low-dimensional theories reflects the reality of a much higher-dimensional problem. Here we contest that this “Flatland fallacy” leads us to seek out simplified explanations of complex phenomena, limiting our capacity as scientists to build and communicate useful models of human psychology. We suggest that this fallacy can be overcome through (1) the use of quantitative models which force researchers to formalize their theories to overcome this fallacy and (2) improved quantitative training which can build new norms for conducting psychological research.

scholarly journals Dead Science in Live Psychology: A Case Study from Heart Rate Variability (HRV)

2017 ◽  
Author(s):  
James Heathers ◽  
Matthew Goodwin
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Evidence Base ◽  
Psychological Research ◽  
The Body ◽  
Psychological Science ◽  
Psychological Theories ◽  
Publishing Practice ◽  
Physiological Research

Psychological theories often build from theoretically separate fields in the biosciences – physiology, biology, neuroscience, etc. – to situate human behavior within the body. However, these are increasingly sophisticated areas of research which rapidly change and adapt their evidence base. The current paper is a case study examining what happens to psychological research when its foundational biological context is invalidated or superseded. The example we use is heart rate variability (HRV) as a purported measure of cardiac sympathetic outflow. While objections to this technique within physiological research have been established and confirmed for decades, its false status continues to be maintained in applied psychological research. We review a combination of factors within scientific and publishing practice, practical and conceptual barriers to experimental interface, and personal/professional value of the invalidated theory in attempt to understand how dead science can be kept alive in psychological science.

scholarly journals Digital Emotion Regulation

2020 ◽  
Vol 29 (4) ◽  
pp. 412-418
Author(s):  
Greg Wadley ◽  
Wally Smith ◽  
Peter Koval ◽  
James J. Gross
Keyword(s):  
Emotion Regulation ◽  
Digital Technology ◽  
Digital Technologies ◽  
Psychological Research ◽  
Affective States ◽  
Social Situations ◽  
Psychological Theories ◽  
Disciplinary Field ◽  
Behavioral Phenomenon

People routinely regulate their emotions in order to function more effectively at work, to behave more appropriately in social situations, or simply to feel better. Recently, researchers have begun to examine how people shape their affective states using digital technologies, such as smartphones. In this article, we discuss the emergence of digital emotion regulation, both as a widespread behavioral phenomenon and a new cross-disciplinary field of research. This field bridges two largely distinct areas of enquiry: (a) psychological research into how and why people regulate their emotions, which has yet to systematically explore the role of digital technology, and (b) computing research into how digital technologies impact users’ emotions, which has yet to integrate psychological theories of emotion regulation. We argue that bringing these two areas into better contact will benefit both and will facilitate a deeper understanding of the nature and significance of digital emotion regulation.

scholarly journals The Psychology of Causal Perception and Reasoning

2010 ◽  
Author(s):  
David Danks
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Psychological Research ◽  
Causal Learning ◽  
Causal Beliefs ◽  
Causal Knowledge ◽  
Causal Relations ◽  
Causal Perception ◽  
The World ◽  
Causal Cognition

Causal beliefs and reasoning are deeply embedded in many parts of our cognition. We are clearly ‘causal cognizers’, as we easily and automatically (try to) learn the causal structure of the world, use causal knowledge to make decisions and predictions, generate explanations using our beliefs about the causal structure of the world, and use causal knowledge in many other ways. Because causal cognition is so ubiquitous, psychological research into it is itself an enormous topic, and literally hundreds of people have devoted entire careers to the study of it. Causal cognition can be divided into two rough categories: causal learning and causal reasoning. The former encompasses the processes by which we learn about causal relations in the world at both the type and token levels; the latter refers to the ways in which we use those causal beliefs to make further inferences, decisions, predictions, and so on.

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