使用似然比D2统计量的题目属性定义方法

题目属性的定义是实施认知诊断评价的关键步骤, 通过有丰富经验的领域专家对题目的属性进行定义是当前的主要方法, 然而该方法受到许多主观经验因素的影响。寻找客观的题目属性定义或验证方法可以为主观定义过程提供策略支持或对结果进行改进, 因此已经引起研究者们的关注。本研究构建了一种简单高效的题目属性定义方法, 研究使用似然比D2统计量从作答数据中估计题目属性的方法, 实现属性掌握模式、题目参数和题目属性向量的联合估计。模拟研究结果表明, 使用似然比D2统计量可以有效地识别题目的属性向量, 该方法一方面可以实现新编制题目属性向量的在线估计, 另一方面可以验证已经定义的题目属性向量的准确性。     

Empirical underidentification in estimating random utility models: The role of choice sets and standardizations

A standard approach to distinguishing people’s risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often-used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback–Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose-built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely.     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

《心理统计学》教学内容的新探索

本文在对当前国内外主要心理统计学教材进行比较的基础上,指出与上个世纪八十年代的心理统计学教材内容相比较,在内容上的新探索主要体现在（1）由“假设检验”的内容中发展出“统计检验力”和“效果大小”的统计指标和估计方法;（2）引进一般线性模型来统合方差分析和回归分析这两种统计方法;（3）适度增加一些“多元统计分析”的内容等三个方面.本文对前两个方面的新内容作了简要评述,并对教材内容的编排方面提出了新的思路.     

How do we convert a number into a finger trajectory?

How do we understand two-digit numbers such as 42? Models of multi-digit number comprehension differ widely. Some postulate that the decades and units digits are processed separately and possibly serially. Others hypothesize a holistic process which maps the entire 2-digit string onto a magnitude, represented as a position on a number line. In educated adults, the number line is thought to be linear, but the “number sense” hypothesis proposes that a logarithmic scale underlies our intuitions of number size, and that this compressive representation may still be dormant in the adult brain. We investigated these issues by asking adults to point to the location of two-digit numbers on a number line while their finger location was continuously monitored. Finger trajectories revealed a linear scale, yet with a transient logarithmic effect suggesting the activation of a compressive and holistic quantity representation. Units and decades digits were processed in parallel, without any difference in left-to-right vs. right-to-left readers. The late part of the trajectory was influenced by spatial reference points placed at the left end, middle, and right end of the line. Altogether, finger trajectory analysis provides a precise cognitive decomposition of the sequence of stages used in converting a number to a quantity and then a position.     

Finding Creativity in an Artificial Artist

Creativity is an important component of human intelligence, and imbuing artificially intelligent systems with creativity is an interesting challenge. In particular, it is difficult to quantify (or even qualify) creativity. Recently, it has been suggested that conditions for attributing creativity to a system include: appreciation, imagination, and skill. We demonstrate and describe an original computer system (called DARCI) that is designed to produce images through creative means. We present methods for evaluating DARCI and other artificially creative systems with respect to appreciation, imagination, and skill, and use these methods to show that DARCI is arguably a creative system.     

Monaural Loudness Adaptation for Middle-Intensity Middle-Frequency Signals: The Importance of Measurement Technique

Using the Simple Adaptation technique (SA) and the Ipsilateral Comparison Paradigm (ICP), the authors studied monaural loudness adaptation to a middle-intensity [60 dB(A)] tone at signal frequencies of 250, 1000, and 4000 Hz in the left and right ears. Adaptation effects were absent when the SA procedure was used. However, they were observed uniformly across all frequency values with the ICP, a result that challenges the assertion in the literature, on the basis of SA measures, that loudness adaptation for middle-intensity signals occurs only at frequencies above 4000 Hz. The ICP features periodic intensity modulations (±10 dB relative to the base signal) to accommodate listeners' needs for referents by which they can gauge subtle changes in the loudness of the adapting tone, a key component that is missing in the SA method. Adaptation effects in this investigation were similar in both ears, supporting the equal susceptibility assumption common in loudness adaptation studies.     

Do Children Perceive Postural Constraints When Estimating Reach or Action Planning?

Estimation of whether an object is reachable from a specific body position constitutes an important aspect in effective motor planning. Researchers who estimate reachability by way of motor imagery with adults consistently report the tendency to overestimate, with some evidence of a postural effect (postural stability hypothesis). This idea suggests that perceived reaching limits depend on an individual's perceived postural constraints. Based on previous work with adults, the authors expected a significant postural effect with the Reach 2 condition, as evidenced by reduced overestimation. Furthermore, the authors hypothesized that the postural effect would be greater in younger children. They then tested these propositions among children aged 7, 9, and 11 years by asking them to estimate reach while seated (Reach 1) and in the more demanding posture of standing on 1 foot and leaning forward (Reach 2). Results indicated no age or condition difference, therefore providing no support for a postural effect. When the authors compared these data to a published report of adults, a developmental difference emerged. That is, adults recognize the perceived postural constraint of the standing position resulting in under- rather than overestimation, as displayed in the seated condition. Although preliminary, these observations suggest that estimates of reach (action planning) continue to be refined between late childhood and young adulthood.     

A Dual Process Account of Coarticulation in Motor Skill Acquisition

Many tasks, such as typing a password, are decomposed into a sequence of subtasks that can be accomplished in many ways. Behavior that accomplishes subtasks in ways that are influenced by the overall task is often described as “skilled” and exhibits coarticulation. Many accounts of coarticulation use search methods that are informed by representations of objectives that define skilled. While they aid in describing the strategies the nervous system may follow, they are computationally complex and may be difficult to attribute to brain structures. Here, the authors present a biologically- inspired account whereby skilled behavior is developed through 2 simple processes: (a) a corrective process that ensures that each subtask is accomplished, but does not do so skillfully and (b) a reinforcement learning process that finds better movements using trial and error search that is not informed by representations of any objectives. We implement our account as a computational model controlling a simulated two-armed kinematic “robot” that must hit a sequence of goals with its hands. Behavior displays coarticulation in terms of which hand was chosen, how the corresponding arm was used, and how the other arm was used, suggesting that the account can participate in the development of skilled behavior.