Separating kindhood from naturalness: Kinds are diverse in causal structure

2019 ◽  
Author(s):  
Alexander Noyes ◽  
Yarrow Dunham
Keyword(s):  
Factor Analysis ◽  
Predictive Validity ◽  
Real World ◽  
Natural Kinds ◽  
Causal Structure ◽  
Social Categories ◽  
Psychological Essentialism ◽  
Socially Constructed ◽  
Nominal Categories ◽  
Consistent Support

[UPDATED 3/11/2020] This paper proposes that the richness of a category (i.e., high inductive potential, non-accidental properties, and generalizable causal structure) is conceptually distinct from its being natural or socially constructed. To test this account, we explore beliefs related to the classic distinction between natural kinds and nominal categories. Specifically, we subjected these beliefs, across diverse categories, to exploratory factor analysis (Studies 1 and 2), examined the inferential connections between these beliefs using experimental manipulations of novel categories (Study 3), and tested the discriminant and predictive validity of these beliefs in the context of real-world social categories (Studies 4 and 5). We find consistent support that rich structure (kindhood) is conceptually distinct from that structure being natural or social (naturalness). We argue that ‘psychological essentialism’ is best understood as a circumscribed set of beliefs related to naturalness, and that referring to kindhood as a type of essentialist belief is inaccurate.

What Can Experimental Studies of Bias Tell Us About Real-World Group Disparities?

2021 ◽  
pp. 1-82
Author(s):  
Joseph Cesario
Keyword(s):  
Real World ◽  
Experimental Studies ◽  
White Men ◽  
Human Mind ◽  
Behavioral Differences ◽  
Social Categories ◽  
Systematic Testing ◽  
Black And White ◽  
Research Tradition ◽  
The Social

Abstract This article questions the widespread use of experimental social psychology to understand real-world group disparities. Standard experimental practice is to design studies in which participants make judgments of targets who vary only on the social categories to which they belong. This is typically done under simplified decision landscapes and with untrained decision makers. For example, to understand racial disparities in police shootings, researchers show pictures of armed and unarmed Black and White men to undergraduates and have them press "shoot" and "don't shoot" buttons. Having demonstrated categorical bias under these conditions, researchers then use such findings to claim that real-world disparities are also due to decision-maker bias. I describe three flaws inherent in this approach, flaws which undermine any direct contribution of experimental studies to explaining group disparities. First, the decision landscapes used in experimental studies lack crucial components present in actual decisions (Missing Information Flaw). Second, categorical effects in experimental studies are not interpreted in light of other effects on outcomes, including behavioral differences across groups (Missing Forces Flaw). Third, there is no systematic testing of whether the contingencies required to produce experimental effects are present in real-world decisions (Missing Contingencies Flaw). I apply this analysis to three research topics to illustrate the scope of the problem. I discuss how this research tradition has skewed our understanding of the human mind within and beyond the discipline and how results from experimental studies of bias are generally misunderstood. I conclude by arguing that the current research tradition should be abandoned.

scholarly journals Validation of the Hedonic and Eudaimonic Motives for Activities-Revised Scale in Chinese Adults

2021 ◽  
Vol 18 (8) ◽  
pp. 3959
Author(s):  
Wenjie Li ◽  
Linting Zhang ◽  
Ning Jia ◽  
Feng Kong
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Measurement Invariance ◽  
Predictive Validity ◽  
Factor Model ◽  
Well Being ◽  
Smartphone Addiction ◽  
Chinese Adults ◽  
Confirmatory Factor ◽  
Chinese Undergraduates

The Hedonic and Eudaimonic Motives for Activities-Revised scale (HEMA-R) is one of the most extensively used instruments to assess how people pursue well-being. The main aims of the present research were to translate HEMA-R into Chinese and test its construct and predictive validity as well as measurement invariance across gender. In Study 1, we conducted confirmatory factor analysis with data containing 1090 Chinese undergraduates, and replicated the two-factor model which has been found in other studies. Furthermore, the measurement invariance across gender was supported throughout the multi-group confirmatory factor analysis. Study 2 replicated these results and further found the HEMA-R had satisfactory predictive validity in measures of well-being, social support and smartphone addiction. All the findings indicate that the HEMA-R is reliable and valid to measure hedonic and eudaimonic motives, and it could be applied generally across gender in Chinese adults.

scholarly journals Natural kinds of mental disorder

Synthese ◽  
2021 ◽  
Author(s):  
Sander Werkhoven
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Mental Disorder ◽  
Mental Health Problems ◽  
Natural Kinds ◽  
Health Problems ◽  
Abnormal Behaviour ◽  
Diagnostic Categories ◽  
Socially Constructed

AbstractAre mental disorders (autism, ADHD, schizophrenia) natural kinds or socially constructed categories? What is at stake if either of these views prove to be true? This paper offers a qualified defence for the view that there may be natural kinds of mental disorder, but also that the implications of this claim are generally overestimated. Especially concerns about over-inclusiveness of diagnostic categories and medicalisation of abnormal behaviour are not addressed by the debate. To arrive at these conclusions the paper opens with a discussion of kind formation in science, followed by an analysis of natural kinds. Seven principled and empirically informed objections to the possibility of natural kinds of mental disorder are considered and rejected. The paper ends with a reflection on diagnostics of mental health problems that don’t fall into natural kinds. Despite the defence of the possibility of natural kinds of mental disorder, this is likely to be the majority of cases.

scholarly journals Who is it okay to punch?

2021 ◽  
Author(s):  
Frank Gonzalez ◽  
Alexandra McCoy
Keyword(s):  
Real World ◽  
Physical Violence ◽  
Intergroup Conflict ◽  
Intergroup Processes ◽  
The Social ◽  
Psychology Literature ◽  
Group Attributes ◽  
Political Alignment ◽  
Race Ideology ◽  
Consistent Support

A popular contemporary meme involves the social acceptability of “punching Nazis.” This phenomenon raises the question: what characteristics make a group or member of a group more or less “punch-able”? More broadly, what group attributes yield support for physical violence against them? In this study, we build on the extant psychology literature on intergroup processes to look at what leads individuals to find physical violence against a group acceptable, and if the factors that lead to such acceptance differ from those that lead to sheer affective intolerance. We use two experimental tasks to test expectations built on prominent theories. In a “real-world rating task,” participants evaluated a series of real-world groups and individuals with varying characteristics such as race, ideology, intelligence, warmth, and tendency toward violence. Also, in a conjoint experiment, participants chose between two groups in terms of which they would support being punched, as various attributes were manipulated such as race, expressed ideological values, partisanship, income, tendency toward violence, and being described as disgusting or threatening. We find consistent support for effects of political alignment (particularly, expressed ideological values related to dominance) and perceived tendency toward violence, as well as mixed findings for race and several other factors. These findings synthesize theories on intergroup conflict with contemporary findings on affective polarization.

scholarly journals Psychometric evaluation of the Smartphone for Clinical Work Scale to measure nurses’ use of smartphones for work purposes

2018 ◽  
Vol 25 (8) ◽  
pp. 1018-1025
Author(s):  
John Robert Bautista ◽  
Sonny Rosenthal ◽  
Trisha Tsui-Chuan Lin ◽  
Yin-Leng Theng
Keyword(s):  
Factor Analysis ◽  
Construct Validity ◽  
Predictive Validity ◽  
Structural Equation ◽  
Instant Messaging ◽  
Work Productivity ◽  
Psychometric Evaluation ◽  
Clinical Work ◽  
Equation Modeling ◽  
Five Factors

Abstract Objective This study reports the development and psychometric evaluation of the Smartphone for Clinical Work Scale (SCWS) to measure nurses’ use of smartphones for work purposes. Methods Items were developed based on literature review and a preliminary study. After expert consultations and pilot testing, a 20-item scale was administered in January-June 2017 to 517 staff nurses from 19 tertiary-level general hospitals in Metro Manila, Philippines. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to evaluate construct validity. Structural equation modeling (SEM) was used to test the predictive validity of SCWS on perceived work productivity. Results EFA results show that 15 out of 20 items loaded on five factors: communication with clinicians via call and text, communication with doctors via instant messaging, information seeking, communication with nurses via instant messaging, and communication with patients via call and text. CFA results suggest that the five factors that form SCWS have adequate fit to the data, thus supporting construct validity. SEM results suggest predictive validity since SCWS was positively associated with perceived work productivity. Conclusions The 15-item SCWS showed satisfactory psychometric properties for use in future studies. These studies can focus on identifying factors associated with nurses’ use of smartphones for work purposes.

Causal Feature Selection with Missing Data

2022 ◽  
Vol 16 (4) ◽  
pp. 1-24
Author(s):  
Kui Yu ◽  
Yajing Yang ◽  
Wei Ding
Keyword(s):  
Feature Selection ◽  
Missing Data ◽  
Real World ◽  
Missing Values ◽  
Prediction Models ◽  
Causal Structure ◽  
Data Imputation ◽  
Accurate Data ◽  
Unified Framework ◽  
Class Variable

Causal feature selection aims at learning the Markov blanket (MB) of a class variable for feature selection. The MB of a class variable implies the local causal structure among the class variable and its MB and all other features are probabilistically independent of the class variable conditioning on its MB, this enables causal feature selection to identify potential causal features for feature selection for building robust and physically meaningful prediction models. Missing data, ubiquitous in many real-world applications, remain an open research problem in causal feature selection due to its technical complexity. In this article, we discuss a novel multiple imputation MB (MimMB) framework for causal feature selection with missing data. MimMB integrates Data Imputation with MB Learning in a unified framework to enable the two key components to engage with each other. MB Learning enables Data Imputation in a potentially causal feature space for achieving accurate data imputation, while accurate Data Imputation helps MB Learning identify a reliable MB of the class variable in turn. Then, we further design an enhanced kNN estimator for imputing missing values and instantiate the MimMB. In our comprehensively experimental evaluation, our new approach can effectively learn the MB of a given variable in a Bayesian network and outperforms other rival algorithms using synthetic and real-world datasets.

Tracing the Sociopolitical Reality of Race

What Is Race? ◽  
2019 ◽  
pp. 4-37 ◽  
Author(s):  
Sally Haslanger
Keyword(s):  
Medical Research ◽  
Natural Kinds ◽  
Socially Constructed ◽  
The Social ◽  
Racial Injustice ◽  
Cultural Empowerment ◽  
History Of ◽  
Critical Theorist ◽  
Philosophical Questions

The concept of race has a troublesome history. It has been used to divide societies and subordinate groups in unjust ways. It has also been a source of pride and strength for the subordinate (as well as, unfortunately, for the dominant). Historically it has also carried assumptions of naturalness: races are natural kinds that exist independent of human thought and activity. In recent years, however, the naturalness of race has been challenged and replaced with the idea that race is socially constructed. This raises many important philosophical questions: How should one inquire into the concept of race when there is such broad controversy over what race is? What are the relevant phenomena to be considered? How should such an inquiry take into account the social stakes (e.g. the potential impact of maintaining or rejecting the concept of race)? Is it possible for concepts to evolve, or is conceptual replacement the only option? In Chapter 1, the author takes up these methodological questions and positions herself as a critical theorist considering what role the concept of race has in the sociopolitical domain. She argues that there is a meaningful political conception of race that is important in order to address the history of racial injustice. This is compatible with there being different conceptions of race that are valuable in other contexts and for different purposes (e.g., for medical research, cultural empowerment).

Is Imagination Constrained Enough for Science?

2019 ◽  
pp. 250-261 ◽  
Author(s):  
Deena Skolnick Weisberg
Keyword(s):  
Empirical Evidence ◽  
Real World ◽  
Scientific Inquiry ◽  
Causal Structure ◽  
Thought Experiments ◽  
The Real ◽  
The Future ◽  
Practice Of Science

The imagination is a necessary tool for doing science, because it allows scientists to form hypotheses, make predictions about the future, and consider non-actual possibilities. But some have worried that the imagination is too unconstrained to be used in the service of scientific inquiry, which needs to be tied closely to reality. This chapter reviews these arguments and provides empirical evidence that the imagination is constrained enough for science. Both children and adults base their imagined worlds on the real world, and these worlds rarely stray from the causal structure of reality. And although the imagination may be subject to some biases that make certain kinds of worlds easier to imagine, these biases can be identified and corrected through training and enculturation in science. Finally, the conclusions drawn within an imagined context can be brought to bear appropriately on reality, allowing the results of thought experiments and hypothetical scenarios to inform the practice of science.

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