心算加工年老化及其机制研究

以１７２名２０－７９岁成人为被式对心算加工的年老化过程及其机制进行了研究。任务为连续减法心算,依难度分为５种：１０００－１,１０００－３,１０００－１３及１０００－１７。结果表明,年龄与心算难度存在明显的交互作用,且随着年龄增加,不同难度心算效率均以幂函数形式降低。     

心算加工年老化及其机制研究

以172名20－79岁成人为被试对心算加工的年老化过程及其机制进行了研究。任务为连续减法心算,依难度分为5种：1000－1、1000－3、1000－7、1000－13及1000－17。结果表明,年龄与心算难度存在明显的交互作用,且随着年龄增加,不同难度心算效率均以幂函数形式降低。结构方程建模分析发现心算加工年老化过程存在两个中介作用因子,即速度因子和非速度因子,且速度因子对非速度因子存在明显的正效应。     

Array training in a categorization task

Two components of categorization, within-category commonalities and between-category distinctiveness, were investigated in a categorization task. Subjects learned three prototype categories composed of moderately high distortions, by observing arrays containing patterns that belonged either to a common prototype category or to three different categories; a third group learned patterns presented one at a time, mirroring the standard paradigm. Following 6 learning blocks, subjects transferred to old patterns and new patterns at low-, medium-, and high-level distortions of the category prototype. The results showed that array training facilitated learning, especially when patterns in the array belonged to the same category. Transfer results showed a strong gradient effect across pattern distortion level for all conditions, with the highest performance obtained following array training on different category patterns and worst in the control condition. Interestingly, the old training patterns were classified worse than new low and no better than medium distortions. Neither this ordering nor the steepness of the gradient across prototype similarity for each condition could be predicted by the generalized context model. A prototype model better captured the steep gradient and ordinal pattern of results, although the overall fits were only slightly better than the exemplar model. The crucial role played by category commonalities and distinctiveness on categorical representations is addressed.     

Modelling Cognitive Bias in Crowdsourcing Systems

Crowdsourcing is the process of obtaining input or enlisting the services of a crowd of people with the aim of finding the best solution to a proposed problem. The method with which the collected input is aggregated affects the outcome of the crowdsourcing process. In this paper, we introduce a modelling framework through which we compare several aggregation methods for crowdsourcing systems. The work reveals a surprising result where confidence-related approaches lack in performance when compared to other approaches such as simple plurality voting or approaches which consider respondent competence. This inadequacy stems from a psychological phenomenon brought forth by David Dunning and Justin Kruger related to people’s bias in assessing their own cognitive abilities.     

Supporting the formal verification of mathematical texts

The formal verification of mathematical texts is one of the most interesting applications for computer systems. In fact, we argue that the expert language of mathematics is the natural choice for achieving efficient mathematician–machine interaction. Our empirical approach, the analysis of carefully authored textbook proofs, forces us to focus on the language and the reasoning pattern that mathematician use when presenting proofs to colleagues and students. Enabling a machine to understand and follow such language and argumentation is seen to be the key to usable and acceptable math assistant systems. In this paper, we first perform an analysis of three textbook proofs by hand; we then describe a computational framework that aims at mechanising such an analysis. The resulting proof-of-concept implementation is capable of processing simple textbook proofs and constitutes promising steps towards a natural mathematician–machine interface for proof development and verification.     

Mother knows best: effects of maternal modelling on the acquisition of fear and avoidance behaviour in toddlers

The present study was conducted to investigate the influence of parental modelling on the acquisition of fear and avoidance towards novel, fear-relevant stimuli in a sample of 30 toddlers. The toddlers were shown a rubber snake and spider, which were alternately paired with either negative or positive facial expressions by their mothers. Both stimuli were presented again after a 1- and a 10-min delay, while mothers maintained a neutral expression. The children showed greater fear expressions and avoidance of the stimuli following negative reactions from their mothers. This was true for both genders although the degree of modelled avoidance was greater in girls than in boys. The strong observational learning results are consistent with views that modelling constitutes a mechanism by which fear may be acquired early in life.     

Primate errors in transitive ‘inference’: a two-tier learning model

Transitive performance (TP) is a learning-based behaviour exhibited by a wide range of species, where if a subject has been taught to prefer A when presented with the pair AB but to prefer B when presented with the pair BC, then the subject will also prefer A when presented with the novel pair AC. Most explanations of TP assume that subjects recognize and learn an underlying sequence from observing the training pairs. However, data from squirrel monkeys (Saimiri sciureus) and young children contradict this, showing that when three different items (a triad) are drawn from the sequence, subjects' performance degrades systematically (McGonigle and Chalmers, Nature 267:694-696, 1977; Chalmers and McGonigle, Journal of Experimental Child Psychology 37:355-377, 1984; Harris and McGonigle, The Quarterly Journal of Experimental Psychology 47B:319-348, 1994). We present here the two-tier model, the first learning model of TP which accounts for this systematic performance degradation. Our model assumes primate TP is based on a general-purpose task learning system rather than a special-purpose sequence-learning system. It supports the hypothesis of Heckers et al. (Hippocampus 14:153-162, 2004) that TP is an expression of two separate general learning elements: one for associating actions and contexts, another for prioritising associations when more than one context is present. The two-tier model also provides explanations for why phased training is important for helping subjects learn the initial training pairs and why some subjects fail to do so. It also supports the Harris and McGonigle (The Quarterly Journal of Experimental Psychology 47B:319-348, 1994) explanation of why, once the training pairs have been acquired, subjects perform transitive choice automatically on two-item diads, but not when exposed to triads from the same sequence.     

Culture and facial expressions of emotion

With over a century of theoretical developments and empirical investigation in broad fields (e.g., anthropology, psychology, evolutionary biology), the universality of facial expressions of emotion remains a central debate in psychology. How near or far, then, is this debate from being resolved? Here, I will address this question by highlighting and synthesizing the significant advances in the field that have elevated knowledge of facial expression recognition across cultures. Specifically, I will discuss the impact of early major theoretical and empirical contributions in parallel fields and their later integration in modern research. With illustrative examples, I will show that the debate on the universality of facial expressions has arrived at a new juncture and faces a new generation of exciting questions.     

模型构建方法与结构方程建模──与张建平同志商讨

本文就心理所张建平同志的“一种新的统计方法和研究思路一结构方程建模述评”一文讨论了结构方程建模一般概念与ＬＩＳＲＥＬ模型的差异。并简要介绍了另一种结构方程建模方法一偏最小平方模型（ＰａｒｔｉａｌＬｅａｓｔＳｑｕａｒｅＭｏｄｅｌ,简称ＰＬＳ）。     

The extended ramp model: A biomimetic model of behaviour arbitration for lightweight cognitive architectures

In this article, we present an idea for a more intuitive, low-cost, adjustable mechanism for behaviour control and management. One focus of current development in virtual agents, robotics and digital games is on increasingly complex and realistic systems that more accurately simulate intelligence found in nature. This development introduces a multitude of control parameters creating high computational costs. The resulting complexity limits the applicability of AI systems. One solution to this problem it to focus on smaller, more manageable, and flexible systems which can be simultaneously created, instantiated, and controlled. Here we introduce a biologically inspired systems-engineering approach for enriching behaviour arbitration with a low computational overhead. We focus on an easy way to control the maintenance, inhibition and alternation of high-level behaviours (goals) in cases where static priorities are undesirable. The models we consider here are biomimetic, based on neuro-cognitive research findings from dopaminic cells responsible for controlling goal switching and maintenance in the mammalian brain. The most promising model we find is applicable to selection problems with multiple conflicting goals. It utilizes a ramp function to control the execution and inhibition of behaviours more accurately than previous mechanisms, allowing an additional layer of control on existing behaviour prioritization systems.