Clinical judgment in forensic neuropsychology: A comment on the risks of claiming more than can be delivered

There is a vast and growing literature in psychology demonstrating the general limits of human judgment and clinical inference. These findings clearly apply in the new specialty of clinical neuropsychology, and there is little empirical research to support the widespread belief that judgmental accuracy correlates substantially with experience, professional stature, or reputation as a neuropsychologist. However, the demand characteristics of expert testimony in the forensic arena may encourage individual neuropsychologists to state or intimate that they have unique or special expertise in understanding brain-behavior relationships, or in predicting outcomes following cerebral insult or injury. These claims will be increasingly difficult to substantiate as attorneys become more conversant with the literature on human judgment.     

Bayesian inference for finite mixtures of generalized linear models with random effects

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a finite mixture of normal distributions. This approach combines the flexibility of semiparametric, latent class models that assume common parameters for each sub-population and the parsimony of random effects models that assume normal distributions for the regression parameters. The number of subpopulations is selected to maximize the posterior probability of the model being true. Simulations are presented which document the performance of the methodology for synthetic data with known heterogeneity and number of sub-populations. An application is presented concerning preferences for various aspects of personal computers.     

Towards end-to-end likelihood-free inference with convolutional neural networks

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fully convolutional neural networks. The method enables users of ABC to derive simultaneously the posterior mean and variance of multidimensional posterior distributions directly from raw simulated data. Once trained on simulated data, the convolutional neural network is able to map real data samples of variable size to the first two posterior moments of the relevant parameter's distributions. Thus, in contrast to other machine learning approaches to ABC, our approach allows us to generate reusable models that can be applied by different researchers employing the same model. We verify the utility of our method on two common statistical models (i.e., a multivariate normal distribution and a multiple regression scenario), for which the posterior parameter distributions can be derived analytically. We then apply our method to recover the parameters of the leaky competing accumulator (LCA) model and we reference our results to the current state-of-the-art technique, which is the probability density estimation (PDA). Results show that our method exhibits a lower approximation error compared with other machine learning approaches to ABC. It also performs similarly to PDA in recovering the parameters of the LCA model.     

Troubles with Theoretical Virtues: Resisting Theoretical Utility Arguments in Metaphysics

In this paper we examine theoretical utility arguments in metaphysics. While philosophers claim a procedural continuity with science when using such arguments, we argue that examining famous instances from the history of science expose their fundamental flaws. We find that arguments from theoretical utility invoke considerations that are not truth conducive and that justifications for claims that a theory possesses theoretical virtues often assume the truth of the theory such virtues are supposed to support. We conclude that theoretical utility arguments provide no epistemic grounds for metaphysical inquiry.     

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes‐optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes‐optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6‐ to 7‐year‐old children when tasked with localizing a ‘hidden’ target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6‐ to 7‐year‐olds learn task‐relevant statistics at a rate on par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young children's behavior to a greater extent than that of adults, leading their behavior to look sub‐optimal. Our findings have important implications for how we should interpret failures in young children's ability to carry out sophisticated computations. These ‘failures’ need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.     

计算模型在道德认知研究中的应用

道德认知关注道德心理背后的信息加工。近年来, 研究者开始将计算模型应用于道德认知研究, 以探索道德认知如何在大脑中实现。但目前研究者对道德认知进行计算建模的研究处于起步阶段。计算模型(漂移扩散模型、效用模型、强化学习模型和分层高斯过筛器模型)在道德认知行为和生理研究上的运用量化了道德决策、道德判断和道德推理背后的认知过程和神经机制。此外, 这一新进展对理解反社会行为和精神障碍等有所助益。最后, 计算建模有待完善, 未来研究需要关注其潜在的问题。     

Argument Structure and Disciplinary Perspective

Many in the informal logic tradition distinguish convergent from linked argument structure. The pragma-dialectical tradition distinguishes multiple from co-ordinatively compound argumentation. Although these two distinctions may appear to coincide, constituting only a terminological difference, we argue that they are distinct, indeed expressing different disciplinary perspectives on argumentation. From a logical point of view, where the primary evaluative issue concerns sufficient strength of support, the unit of analysis is the individual argument, the particular premises put forward to support a given conclusion. Structure is internal to this unit. From a dialectical point of view, where the focus concerns how well a critical discussion comes to a reasoned conclusion of some disputed question, the argumentation need not constitute a single unit of argument. The unit of dialectical analysis will be the entire argumentation made up of these several arguments. The multiple/co-ordinatively compound distinction is dialectical, while the linked/convergent distinction is logical. Keeping these two pairs of distinctions separate allows us to see certain attempts to characterize convergent versus linked arguments as rather characterizing multiple versus co-ordinatively compound arguments, in particular attempts of Thomas, Nolt, and Yanal, and to resolve straightforwardly conflicts, tensions, or anomalies in their accounts. Walton's preferred Suspension/Insufficient Proof test to identify linked argument structure correctly identifies co-ordinatively compound structure. His objection to using the concept of relevance to explicate the distinction between linked and convergent structure within co-ordinatively compound argumentation can be met through explicating relevance in terms of inference licenses. His counterexample to the Suspension/No Support test for identifying linked structure which this approach supports can itself be straightforwardly dealt with when the test is explicated through inference licenses.     

Carroll’s Regress and the Epistemology of Logic

On an internalist account of logical inference, we are warranted in drawing conclusions from accepted premises on the basis of our knowledge of logical laws. Lewis Carroll’s regress challenges internalism by purporting to show that this kind of warrant cannot ground the move from premises to conclusion. Carroll’s regress vindicates a repudiation of internalism and leads to the espousal of a standpoint that regards our inferential practice as not being grounded on our knowledge of logical laws. Such a standpoint can take two forms. One can adopt either a broadly externalist model of inference or a sceptical stance. I will attempt, in what follows, to defend a version of internalism which is not affected by the regress. The main strategy will be to show that externalism and scepticism are not satisfying standpoints to adopt with regard to our inferential practice, and then to suggest an internalist alternative.     

Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a doubly robust-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B), 187-30406.