Approaching archetypes: reconsidering innateness

The question of innateness has hounded Jungian psychology since Jung originally postulated the archetype as an a priori structure within the psyche. During his life and after his death he was continually accused of Lamarckianism and criticized for his theory that the archetypes existed as prior structures. More recently, with the advent of genetic research and the human genome project, the idea that psychological structures can be innate has come under even harsher criticism even within Jungian thought. There appears to be a growing consensus that Jung's idea of innate psychological structures was misguided, and that perhaps the archetype-as-such should be abandoned for more developmental and 'emergent' theories of the psyche. The purpose of this essay is to question this conclusion, and introduce some literature on psychological innateness that appears relevant to this discussion.     

IRT模型参数估计的新方法——MCMC算法

本研究主要探讨MCMC算法在IRT模型参数估计中的实现及其估计精度.通过模拟多种实验条件(人少题少、人题适中、人多题多、被试数及其参数固定情况下项目数变化、项目数及其参数固定情况下人数变化),考察两参数和叁参数Logistic模型的MCMC算法对其参数估计的精度,并与国际通用测量程序-Bilog程序(E-M算法)进行比较研究.模拟实验研究表明,上述各种实验条件下,MCMC算法均可用于IRT模型参数估计,且其估计的精度均较Bilog程序(E-M算法)高,值得推广.     

缺失数据处理的期望-极大化算法与马尔可夫蒙特卡洛方法

缺失数据普遍存在于心理学研究中, 影响着统计推断。极大似然估计(MLE)与基于贝叶斯的多重借补(MI)是处理缺失数据的两类重要方法。期望-极大化算法(EM)是寻求MLE的一种强有力的方法。马尔可夫蒙特卡洛方法(MCMC)可以相对简易地实现MI, 而且可以适用于复杂情况下的缺失数据处理。结合研究的需要讨论了实现这两类方法的适用软件。     

Temporalising Tableaux

As a remedy for the bad computational behaviour of first-order temporal logic (FOTL), it has recently been proposed to restrict the application of temporal operators to formulas with at most one free variable thereby obtaining so-called monodic fragments of FOTL. In this paper, we are concerned with constructing tableau algorithms for monodic fragments based on decidable fragments of first-order logic like the two-variable fragment or the guarded fragment. We present a general framework that shows how existing decision procedures for first-order fragments can be used for constructing a tableau algorithm for the corresponding monodic fragment of FOTL. Some example instantiations of the framework are presented.     

An EM algorithm for fitting two-level structural equation models

Maximum likelihood is an important approach to analysis of two-level structural equation models. Different algorithms for this purpose have been available in the literature. In this paper, we present a new formulation of two-level structural equation models and develop an EM algorithm for fitting this formulation. This new formulation covers a variety of two-level structural equation models. As a result, the proposed EM algorithm is widely applicable in practice. A practical example illustrates the performance of the EM algorithm and the maximum likelihood statistic.We are thankful to the reviewers for their constructive comments that have led to significant improvement on the first version of this paper. Special thanks are due to the reviewer who suggested a comparison with the LISREL program in the saturated means model, and provided its setup and output. This work was supported by National Institute on Drug Abuse grants DA01070, DA00017, and a UNH 2002 Summer Faculty Fellowship.     

A marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data

Abstract: At least two types of models, the vector model and the unfolding model can be used for the analysis of dichotomous choice data taken from, for example, the pick any/ n method. The previous vector threshold models have a difficulty with estimation of the nuisance parameters such as the individual vectors and thresholds. This paper proposes a new probabilistic vector threshold model, where, unlike the former vector models, the angle that defines an individual vector is a random variable, and where the marginal maximum likelihood estimation method using the expectation-maximization algorithm is adopted to avoid incidental parameters. The paper also attempts to discuss which of the two models is more appropriate to account for dichotomous choice data. Two sets of dichotomous choice data are analyzed by the model.     

Expectation creates something out of nothing: The role of attention in iconic memory reconsidered

Conscious experience is modulated by attention and expectation, yet is believed to be independent of attention. The experiments on iconic memory (IM) are usually taken as support for this claim. However, a recent experiment demonstrated that when attention is diverted away from the IM letter display subjects fail to see the absence of IM letters. Here we contribute to the ongoing debate by overcoming experimental shortcomings of this previous experiment, by measuring subjective visibility and by testing the effect of the post-cue. We were able to replicate these earlier findings and extend them by demonstrating that subjects who do not realize the absence of letters perceive illusory letters. This result means that there is still phenomenal consciousness, even when attention is diverted. Expectation creates illusory content that overwrites valid IM content. Taken together these findings suggest that the present experimental paradigm is not appropriate to make claims about IM content.     

Does Wittgenstein Actually Undermine the Foundation of Artificial Intelligence?

Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence (AI) thesis,namely,that it is in principle possible to build a programmed machine which can achieve real intelligence.Stuart Shanker has provided the most systematic reconstruction of the Wittgensteinian argument against AI,building on Wittgenstein's own statements,the "rule-following" feature of language-games,and the putative alliance between AI and psychologism.This article will attempt to refute this reconstruction and its constituent arguments,thereby paving the way for a new and amicable rather than agonistic conception of the Wittgensteinian position on AI.     

The Modulation of Operant Variation by the Probability, Magnitude, and Delay of Reinforcement

Recent studies have demonstrated that the expectation of reward delivery has an inverse relationship with operant behavioral variation (e.g., Stahlman, Roberts, & Blaisdell, 2010). Research thus far has largely focused on one aspect of reinforcement – the likelihood of food delivery. In two experiments with pigeons, we examined the effect of two other aspects of reinforcement: the magnitude of the reward and the temporal delay between the operant response and outcome delivery. In the first experiment, we found that a large reward magnitude resulted in reduced spatiotemporal variation in pigeons’ pecking behavior. In the second experiment, we found that a 4-s delay between response-dependent trial termination and reward delivery increased variation in behavior. These results indicate that multiple dimensions of the reinforcer modulate operant response variation.     

Demand response management system with discrete time window using supervised learning algorithm

Demand Response (DR) is a key attribute to enhance the operation of smart grid. Demand response improves the performance of the electric power systems and also deals with peak demand issues. Demand Response (DR) implementation for residential consumers is potentially accredited by Home Energy Management System (HEMS). This paper presents an algorithm for home energy management system to shift the schedulable loads in a residential home, that neglects consumer discomfort and minimizes electricity bill of energy consumption using Hourly-Time-Of-Use (HTOU) pricing scheme. Supervised learning algorithm is used in this paper to learn the usage patterns of consumers to allow schedulable appliances at a residential home to autonomously overcome consumer discomfort. Simulation results confirms that the proposed algorithm effectively decreases consumer electricity bill, decreases peak load demand and also avoids consumer discomfort.