You Make Your Own Luck

This essay takes up two questions. First, what does it mean to say that someone creates her own luck? At least colloquially speaking, luck is conceived as something out of an agent's control. So how could an agent increase or decrease the likelihood that she'll be lucky? Building on some recent work on the metaphysics of luck, the essay argues that there is a sense in which agents can create their own luck because people with more skill tend to have more opportunities to benefit from luck. Second, what implications does this conception of luck have for related topics such as how we evaluate performances (like shooting an arrow), including coming to know something? The ubiquitous presence of luck in our actions is often underappreciated. The essay argues that when we combine an expected outcomes view of luck with a counterfactual view of causation, the distinction between environmental and intervening veritic luck seems to disappear. We need a more nuanced view of how luck sometimes undermines credit for success in agents' actions. The upshot of this view is that while luck may undermine the creditworthiness of an agent's success, it only partially undermines creditworthiness.     

Salience Not Status: How Category Labels Influence Feature Inference

Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special‐mechanism hypothesis and the label super‐salience hypothesis. The special‐mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a tendency for prototype‐compatible inferences because the labels trigger a special mechanism rather than because of any influences they have on similarity evaluation. The super‐salience hypothesis assumes that the large label influence is due to their high salience and corresponding impact on similarity without any need for a special mechanism. Application of the two models to a feature inference task based on a family resemblance category structure yields strong support for the label super‐salience hypothesis and in particular does not support the need for a special mechanism based on prototypes.     

The rise and fall of the Pacquiao Effect: Contrastive priming and national identification

We demonstrate in a series of field and controlled experiments that assimilative and contrastive priming effects can be observed in the pattern of self‐concept change in response to a major cultural event. Study 1 used the brief implicit association test (BIAT) to measure national identification of Filipinos online across a period of time that encompassed a national sporting event. The pattern of scores support the hypothesis that while people who were ambivalent about identifying with Filipino concepts exhibited an assimilation effect (i.e. a slight rise in identification after the fight), people who already highly identified with being Filipino experienced a contrast effect (i.e. a slight drop in identification). Study 2 replicated this result five months later with a new sample and ruled out several possible alternative hypotheses. A subsequent controlled experiment and a qualitative investigation consistently supported the hypothesis that the pattern observed in the previous studies is more consistent with assimilative and contrastive priming effects rather than disidentification.     

There aren't plenty more fish in the sea: A causal network approach

The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two‐way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology.     

Calibrated Bayes Factors Should Not Be Used: A Reply to Hoijtink,van Kooten,and Hulsker

Hoijtink, Kooten, and Hulsker (2016 Hoijtink, H., van Kooten, P., &; Hulsker, K. (2016). Why Bayesian psychologists should change the way they use the Bayes factor. Multivariate Behavioral Research, 51, 1--9. doi: 10.1080/00273171.2014.969364.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) present a method for choosing the prior distribution for an analysis with Bayes factor that is based on controlling error rates, which they advocate as an alternative to our more subjective methods (Morey &; Rouder, 2014 Morey, R.D., &; Rouder, J.N. (2014). Bayesfactor: Computation of Bayes factors for common designs. R package version 0.9.9. Retrieved from http://CRAN.R-project.org/package=BayesFactor [Google Scholar]; Rouder, Speckman, Sun, Morey, &; Iverson, 2009 Rouder, J.N., Speckman, P.L., Sun, D., Morey, R.D., &; Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin and Review, 16, 225–237. doi: 10.3758/PBR.16.2.225[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Wagenmakers, Wetzels, Borsboom, &; van der Maas, 2011 Wagenmakers, E.-J., Wetzels, R., Borsboom, D., &; van der Maas, H. (2011). Why psychologists must change the way they analyze their data: The case of psi. A comment on Bem (2011). Journal of Personality and Social Psychology, 100, 426–432. doi: 10.1037/a0022790[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). We show that the method they advocate amounts to a simple significance test, and that the resulting Bayes factors are not interpretable. Additionally, their method fails in common circumstances, and has the potential to yield arbitrarily high Type II error rates. After critiquing their method, we outline the position on subjectivity that underlies our advocacy of Bayes factors.     

Using Marginal Structural Modeling for Grade Retention Effects

Vandecandelaere, Vansteelandt, De Fraine, and Van Damme (this issue) described marginal structural modeling (MSM) and used it to estimate the effects of a time-varying intervention, retention (holding back) in school grades, on students' math achievement. This commentary supplements Vandecandelaere et al. (this issue) and discusses several topics in retention studies and MSM. First, we discuss the importance of equating time-varying confounders in retention studies. Second, we discuss same-grade and same-age comparisons in retention studies. Third, we discuss one important section in the authors' overview of MSM: why standard methods (e.g., ANCOVA, propensity score analysis) cannot properly adjust for time-varying confounders. Finally, using the grade retention analyses in Vandecandelaere et al. (this issue) as an example, we provide our insights on four aspects of MSM: (a) covariate selection, (b) estimation of weights, (c) evaluation of balance properties, and (d) missing data handling.     

The body as laboratory: Prediction-error minimization,embodiment, and representation

In his (2014) paper, Jakob Hohwy outlines a theory of the brain as an organ for prediction-error minimization (PEM), which he claims has the potential to profoundly alter our understanding of mind and cognition. One manner in which our understanding of the mind is altered, according to PEM, stems from the neurocentric conception of the mind that falls out of the framework, which portrays the mind as “inferentially-secluded” from its environment. This in turn leads Hohwy to reject certain theses of embodied cognition. Focusing on this aspect of Hohwy’s argument, we first outline the key components of the PEM framework such as the “evidentiary boundary,” before looking at why this leads Hohwy to reject certain theses of embodied cognition. We will argue that although Hohwy may be correct to reject specific theses of embodied cognition, others are in fact implied by the PEM framework and may contribute to its development. We present the metaphor of the “body as a laboratory” in order to highlight what we believe is a more significant role for the body than Hohwy suggests. In detailing these claims, we will expose some of the challenges that PEM raises for providing an account of representation.     

Gaussian variational estimation for multidimensional item response theory

Multidimensional item response theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying a functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that the likelihood involves intractable multidimensional integrals due to the latent variable structure. Various methods have been proposed that involve either direct numerical approximations to the integrals or Monte Carlo simulations. However, these methods are known to be computationally demanding in high dimensions and rely on sampling data points from a posterior distribution. We propose a new Gaussian variational expectation--maximization (GVEM) algorithm which adopts variational inference to approximate the intractable marginal likelihood by a computationally feasible lower bound. In addition, the proposed algorithm can be applied to assess the dimensionality of the latent traits in an exploratory analysis. Simulation studies are conducted to demonstrate the computational efficiency and estimation precision of the new GVEM algorithm compared to the popular alternative Metropolis–Hastings Robbins–Monro algorithm. In addition, theoretical results are presented to establish the consistency of the estimator from the new GVEM algorithm.