Normality and actual causal strength

Icard, Thomas; Kominsky, Jonathan F.; Knobe, Joshua

doi:10.31234/osf.io/g7cqk

Cited by 46 publications

(270 citation statements)

References 17 publications

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“…Making these data available is important for a variety of disciplines, such as philosophy, psychology, and linguistics. Results from x-phi have been used to object to an exclusive reliance on intuition as a source of justification for philosophical arguments, but also as relevant psychological evidence concerning central concepts such as knowledge and belief [9][10], intentional action [11][12], the meaning of proper names [13][14], freedom and determinism [15][16], consciousness [17][18], and causal and moral responsibility [19][20].…”

Section: Introductionmentioning

confidence: 99%

Statistical Reporting Inconsistencies in Experimental Philosophy

Colombo¹,

Duev²,

Nuijten³

et al. 2017

Preprint

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Statistical reporting inconsistencies in experimental philosophyColombo, Matteo; Duev, Georgi; Nuijten, M.B.; Sprenger, Jan General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractExperimental philosophy (x-phi) is a young field of research in the intersection of philosophy and psychology. It aims to make progress on philosophical questions by using experimental methods traditionally associated with the psychological and behavioral sciences, such as null hypothesis significance testing (NHST). Motivated by recent discussions about a methodological crisis in the behavioral sciences, questions have been raised about the methodological standards of x-phi. Here, we focus on one aspect of this question, namely the rate of inconsistencies in statistical reporting. Previous research has examined the extent to which published articles in psychology and other behavioral sciences present statistical inconsistencies in reporting the results of NHST. In this study, we used the R package statcheck to detect statistical inconsistencies in x-phi, and compared rates of inconsistencies in psychology and philosophy. We found that rates of inconsistencies in x-phi are lower than in the psychological and behavioral sciences. From the point of view of statistical reporting consistency, x-phi seems to do no worse, and perhaps even better, than psychological science. IntroductionExperimental philosophy (x-phi) is a young field at the intersection of philosophy and psychology that aims to make progress on philosophical questions by using experimental methods traditionally associated with the psychological and behavioral sciences [1][2]. Those sciences are, however, undergoing a methodological crisis regarding the reproducibility and statistical correctness of experimental research [3][4][5][6];. This raises the question of whether x-phi is equally affected by this crisis, or whether notable differences can be found.One aspect of the methodological crisis in psychology is the high rate of reporting inconsistencies in the statistical analysis of data; roughly half of the published papers in psychology contain at least one inconsistent result where the reported p-value does not match the reported value and degrees of freedom of the test statistic. Around one in eight papers contain at least one gross inconsi...

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Section: Introductionmentioning

confidence: 99%

Statistical Reporting Inconsistencies in Experimental Philosophy

Colombo¹,

Duev²,

Nuijten³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…These counterfactual relevance accounts (CFR accounts hereafter) propose that when a norm violation occurs, it increases the relevance of counterfactual alternatives wherein the norm violations are replaced by norm-conforming events (e.g., Halpern & Hitchcock, 2015;Icard et al, 2017). In support of this account, recent work demonstrated that norm violations affect explicit assessments of counterfactual relevance in precisely the same way that they affect causal judgments (Phillips, Luguri, & Knobe, 2015).…”

Section: Introductionmentioning

confidence: 63%

“…Previous work has mostly focused on moral norm violations and statistical or descriptive norm violations (e.g., Alicke, 2001;Gerstenberg et al, 2015;Icard et al, 2017). As noted previously, moral prescriptions only apply to moral agents, and so it is unsurprising that they do not affect judgments of artifacts (Experiment 1).…”

Section: Methodsmentioning

confidence: 99%

“…The key idea of CFR accounts is that norm violations lead people to consider counterfactual alternatives that are more "normal" (Kahneman & Miller, 1986;Phillips & Knobe, 2018), and that people evaluate whether the outcome would still obtain in these counterfactual possibilities (Hitchcock & Knobe, 2009;Icard et al, 2017). Put differently, deviations from 'normality' that are necessary for a given outcome to occur lead to increased causal attribution to the abnormal event.…”

Section: What Is the Moral Thing For An Artifact To Do?mentioning

confidence: 99%

“…Most researchers take the impact of statistical norms on causal judgments to reveal part of the basic underlying processes that support causal reasoning (e.g., Alicke, Rose, & Bloom 2011;Gerstenberg et al, 2015;Icard, Kominsky, & Knobe, 2017;Samland & Waldmann, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Immoral professors and malfunctioning tools: Counterfactual relevance accounts explain the effect of norm violations on causal selection

Phillips¹,

Kominsky²

2018

Preprint

Self Cite

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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 the 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 inanimate artifacts, and therefore contradicting a unified counterfactual analysis of causal reasoning about agents and artifacts. Four experiments provide evidence against this conclusion.Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely correlated with judgments of counterfactual relevance. Experiment 2 then more directly manipulated the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiments 3 and 4show that, across variations in causal structure, 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.

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The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Lapidow

Walker

2020

Language and Concept Acquisition From Infancy Through Childhood

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To appear in Childers, J.B., Graham, S.A. & Namy, L. (Eds). (2019). Learning Language and Concepts from Multiple Examples in Infancy and Childhood What is invariant does not emerge unequivocally except with a flux. The essentials become evident in the context of changing nonessentials.-James Gibson, 1979 Human learners are intuitively exploratory: We acquire new knowledge from the outcomes of our actions. However, in order for exploration to support learning, at least some of these actions must serve to evaluate our existing knowledge. Despite this need for informative 'hypothesis testing' in everyday learning, decades of research examining self-directed experimentation suggests that learners rarely choose informative tests. That is, instead of selecting actions to test whether their current hypothesis is correct, both children and adults tend to prefer 'positive tests': actions that will produce an effect assuming their current hypothesis is correct (see Klayman, 1995; Zimmerman, 2007). To illustrate, suppose you drop an ice cube on the floor and it shatters. As a learner, you might form an initial hypothesis that 'impact with an unyielding surface causes ice to shatter.' This hypothesis is also a causal explanation for your observation: indicating how one variable (X) makes a difference to the state of another variable (Y). According to traditional interpretations of Popper's (1959) falsificationist approach, testing this hypothesis would require disconfirming its alternatives. That is, assessing whether 'X is the cause of Y,' requires 'negative tests,' or actions to determine whether Y occurs in the absence of X. Here, since Y is 'shattering' and X is 'impacting an unyielding surface,' you should drop an ice cube on a yielding surface (not-X), like rubber or cotton, to determine whether it will shatter. However, learners rarely choose this kind of disconfirming action during their exploration. Instead, they are much more likely to repeat the initial observation: e.g., to pick up another ice-cube and drop it on the same surface, or a similar one. This tendency to generate multiple positive examples is a puzzling characteristic of self-directed learning, since it does not initially appear to be informative. After all, these repeated demonstrations often produce the same evidence, and do not distinguish between the current hypothesis (i.e., impacting an unyielding surface) and potential alternatives (e.g., impacting any surface at a particular speed), since they are consistent with both. Why then, do self-directed learners consistently and repeatedly conduct positive tests? In this chapter, we propose a novel answer to this question: the Search for Invarinace (SI) hypothesis, which suggests that observing muliple, positive examples may facilitate learning by allowing us to assess the invariance of our causal theories. That is, by repeatedly activating a hypothesized cause and checking if its anticipated effect occurs, positive tests generate information about the degree to which this relationship holds across tim...

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Normality and actual causal strength

Cited by 46 publications

References 17 publications

Statistical Reporting Inconsistencies in Experimental Philosophy

Statistical Reporting Inconsistencies in Experimental Philosophy

Immoral professors and malfunctioning tools: Counterfactual relevance accounts explain the effect of norm violations on causal selection

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

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