Two causal theories of counterfactual conditionals

Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data.     

The need for central resources in answering questions in different domains: Folk psychology,biology, and economics

This study examined whether, when lay people reason about everyday domains, they do so in a slow, serial, deliberate process or using an automatic, effortless, associative process, and whether there are differences in this respect between content domains. The cognitive resources used by lay persons in reasoning about everyday domains were investigated using a dual task methodology. Fifty-eight adult participants answered computer-administered questions on folk biology (ToB), folk economics (ToE), and folk psychology, where the latter included questions requiring participants to infer an emotion from a verbally presented situation (ToP) and false-belief tasks (ToM). Each domain was tested with questionnaires at two levels of difficulty. Half the participants concurrently performed the 2-back task. Cognitive load reduced accuracy on the main task for ToM tasks (especially for second-order false-belief items) but not for the other domains. Performing the main tasks reduces accuracy and increases latencies on the secondary task for all domains. These findings suggest that the appropriate generalisation does not relate to content domains (such as folk psychology vs. folk biology), but rather to the nature of the task under consideration. For ToE, ToB, and ToP, the relevant inference questions appear to be processed mainly by the associative systems in LTM, and the process is not affected by a concurrent secondary task. For ToM, the response generation processing occurs in the analytic subsystem, and is therefore affected by cognitive load. Further, the more difficult set of questions impaired the secondary task more than the simpler set, in every domain and in terms of both accuracy and latency. This suggests that the processing involved in the associative system consumes domain-general resources too.     

儿童科学概念的认知发展

综述了作者在儿童科学概念认知领域的研究,其中包括儿童的朴素生物学、朴素物理学认知及其与心理理论发展关系的研究。作者研究了儿童在生命科学领域的自发概念,包括对生长、衰老、疾病和死亡等生命现象的认知,探查儿童对日常生活中物理学概念（如力等）的朴素认知,力图发现儿童早期认知中的潜力,同时探明他们相应的错误概念,并采用教育干预实验,促进儿童科学概念的转化。还探查影响儿童科学概念发展的因素,包括外部教育条件以及个体认知能力（如心理理论发展水平,推理能力等）。研究发现,儿童在接受正规的科学教育前就对人类重要的科学领域形成了自己的“朴素理论”,他们用这种理论来解释现实世界的现象。作者主张儿童的科学教育（包括健康教育）应该以儿童的“朴素理论”为基础,从而真正做到因材施教     

A tutorial on Bayes factor estimation with the product space method

The Bayes factor is an intuitive and principled model selection tool from Bayesian statistics. The Bayes factor quantifies the relative likelihood of the observed data under two competing models, and as such, it measures the evidence that the data provides for one model versus the other. Unfortunately, computation of the Bayes factor often requires sampling-based procedures that are not trivial to implement. In this tutorial, we explain and illustrate the use of one such procedure, known as the product space method (Carlin & Chib, 1995). This is a transdimensional Markov chain Monte Carlo method requiring the construction of a “supermodel” encompassing the models under consideration. A model index measures the proportion of times that either model is visited to account for the observed data. This proportion can then be transformed to yield a Bayes factor. We discuss the theory behind the product space method and illustrate, by means of applied examples from psychological research, how the method can be implemented in practice.     

Constructing informative model priors using hierarchical methods

Despite their negative reputation, informative priors are very useful in inference. Priors that express psychologically meaningful intuitions damp out random fluctuations in the data due to sampling variability, without sacrificing flexibility. This article focuses on how an intuitively satisfying informative prior distribution can be constructed. In particular, it demonstrates how the hierarchical introduction of a parameterized generative account of the set of models under consideration naturally imposes a non-uniform prior distribution over the models, encoding existing intuitions about the models. The hierarchical approach for constructing informative model priors is made concrete using a worked example, the Varying Abstraction Model (VAM), a family of categorization models including and expanding the exemplar and prototype models. It is shown how psychological intuitions about the relative plausibilities of the models in the VAM can be formally captured in an informative prior distribution over these models, by specifying a theoretically informed process for generating the models in the VAM. The smoothing effect of the informative prior in estimation is demonstrated by considering ten previously published data sets from the category learning literature.     

Subliminal or not? Comparing null-hypothesis and Bayesian methods for testing subliminal priming

A difficulty for reports of subliminal priming is demonstrating that participants who actually perceived the prime are not driving the priming effects. There are two conventional methods for testing this. One is to test whether a direct measure of stimulus perception is not significantly above chance on a group level. The other is to use regression to test if an indirect measure of stimulus processing is significantly above zero when the direct measure is at chance. Here we simulated samples in which we assumed that only participants who perceived the primes were primed by it. Conventional analyses applied to these samples had a very large error rate of falsely supporting subliminal priming. Calculating a Bayes factor for the samples very seldom falsely supported subliminal priming. We conclude that conventional tests are not reliable diagnostics of subliminal priming. Instead, we recommend that experimenters calculate a Bayes factor when investigating subliminal priming.     

Is Theism a Simple,and hence Probable,Explanation for the Universe?

Abstract

Richard Swinburne, in his The Existence of God (2004), presents a cosmological argument in defence of theism (Swinburne 1991: 119, 135). God, Swinburne argues, is more likely to bring about an ordered universe than other states (ibid.: 144, 299). To defend this view, Swinburne presents the following arguments: (1) That this ordered universe is a priori improbable (2004: 49, 150, 1991: 304 et seq.), given the stringent requirements for life (cf. also Leslie 2000: 12), and the Second Law of Thermodynamics (Giancoli 1990: 396); (2) That it seems as if this ordered universe can be explained by theism; (3) A theistic explanation for the universe is more probable because it is a simple explanation. To this end, Swinburne makes use of Bayes’ Theorem. Symbolically, this claim can be represented as (e) for the evidence of the existence of a complex universe, and (h) for a hypothesis. Swinburne’s argument is that theism has a higher prior probability, P(h_theism) > P(h_materialism), since theism is simpler than materialism. He concludes that P(e|h_theism) > P(e|h_materialism). In this paper I will address only this argument (3) above, and defend the view that it is false: theism is not simpler than materialism, nor it is more probably true. I conclude that theism is less probable than materialism, expressed by P(h_theism) < P(h_materialism) : 2/N(2n+1) < 1/n, where N is the number of possible universes and n the number of entities in existence.