Initial excitatory conditioning with the truly random control procedure in rats: The effects of density of the conditioned stimulus

The present experiments were designed to test whether initial excitatory conditioning to a stimulus was obtained, using a within-subject design of the truly random control (TRC) procedure, and to examine the effects of the density of the conditioned stimulus on it. In Experiment 1, the effect of the three levels of CS density (50%, 25%, 20%) was assessed at six different stages of training, using the conditioned suppression of licking in rats. Although all of the groups given the TRC procedure showed evidence of initial excitatory conditioning, it was more marked in the group with the highest CS density than in the other groups. Experiment 2 confirmed the results of Experiment 1, using a between-subject design. The findings are in line with predictions from the Rescorla-Wagner model, and are discussed with reference to other contemporary theories of associative learning.     

On the Bock-Aitkin procedure—From an EM algorithm perspective

The relationship between the EM algorithm and the Bock-Aitkin procedure is described with a continuous distribution of ability (latent trait) from an EM-algorithm perspective. Previous work has been restricted to the discrete case from a probit-analysis perspective. The author is grateful to Bradley A. Hanson for valuable discussion and comments. Thanks also go to Terry A. Ackerman, Meichu Fan, Subrata Kundu, and Robert K. Tsutakawa for their help and encouragement in this study.     

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.     

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。     

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.     

Post-choice information processing by pigeons

In a conditional discrimination (matching-to-sample), a sample is followed by two comparison stimuli, one of which is correct, depending on the sample. Evidence from previous research suggests that if the stimulus display is maintained following an incorrect response (the so-called penalty-time procedure), acquisition by pigeons is facilitated. The present research tested the hypothesis that the penalty-time procedure allows the pigeons to review and learn from the maintained stimulus display following an incorrect choice. It did so by including a penalty-time group for which, following an incorrect choice, the sample changed to match the incorrect comparison, thus providing the pigeons with post-choice misinformation. This misinformation group acquired the matching task significantly slower than the standard penalty-time group (that had no change in the sample following an error). Furthermore, acquisition of matching by a control group that received no penalty time fell midway between the other two groups, suggesting that the pigeons did not merely take more care in making choices because of the aversiveness of penalty-time. Thus, it appears that in the acquisition of matching-to-sample, when the stimulus display is maintained following an incorrect choice, the pigeons can review or acquire information from the display. This is the first time that such an effect has been reported for a nonhuman species.