Independence and dependence in human causal reasoning

Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult’s causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned “associatively,” that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects’ assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.     

The origins of probabilistic inference in human infants

Reasoning under uncertainty is the bread and butter of everyday life. Many areas of psychology, from cognitive, developmental, social, to clinical, are interested in how individuals make inferences and decisions with incomplete information. The ability to reason under uncertainty necessarily involves probability computations, be they exact calculations or estimations. What are the developmental origins of probabilistic reasoning? Recent work has begun to examine whether infants and toddlers can compute probabilities; however, previous experiments have confounded quantity and probability—in most cases young human learners could have relied on simple comparisons of absolute quantities, as opposed to proportions, to succeed in these tasks. We present four experiments providing evidence that infants younger than 12 months show sensitivity to probabilities based on proportions. Furthermore, infants use this sensitivity to make predictions and fulfill their own desires, providing the first demonstration that even preverbal learners use probabilistic information to navigate the world. These results provide strong evidence for a rich quantitative and statistical reasoning system in infants.     

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.