自我面孔识别的独特性

与识别他人的面孔相比,自我面孔识别在行为反应、种系发生、个体发展以及脑机制等方面都具有很大的独特性。行为指标方面,自我面孔识别在速度上快于识别他人的面孔;种系发生方面,只有人类和大猩猩等高级灵长类动物才具有识别自己面孔的能力;个体发展方面,儿童出生后不久即能识别他人面孔,但要在18个月左右才能表现出自我面孔识别能力;脑机制方面,神经心理学和脑成像研究结果表明自我面孔识别可能主要是右脑的功能。该领域的研究进展将为深入了解自我的形成机理提供一个新的探视“窗口”。     

Selective influence through conditional independence

Let each of several (generally interdependent) random vectors, taken separately, be influenced by a particular set of external factors. Under what kind of the joint dependence of these vectors on the union of these factor sets can one say that each vector is selectively influenced by “its own” factor set? The answer proposed and elaborated in this paper is: One can say this if and only if one can find a factor-independent random vector given whose value the vectors in question are conditionally independent, with their conditional distributions selectively influenced by the corresponding factor sets. Equivalently, the random vectors should be representable as deterministic functions of “their” factor sets and of some mutually independent and factor-independent random variables, some of which may be shared by several of the functions.     

视觉刺激探测的警觉性注意特征初探

采用"预警提示-目标探测"范式,对视觉刺激探测的警觉性注意特征进行初步探索。实验一表明,随着预备到目标刺激的时间间隔(interstimulus interval,ISI)变化,固定位置上探测目标刺激的速度也随之变化,并且对大概率刺激的探测更快,表现出了警觉性注意的稳定性和选择性特征。实验二进一步考察了目标刺激在不同ISI和不同空间位置的情况,除发现与实验一相似的结果以外,被试探测目标刺激的速度随注意视角增大而下降,表现出了警觉性注意的集中性特征。     

Finding MAPs using strongly equivalent high order recurrent symmetric connectionist networks

Belief revision is the problem of finding the most plausible explanation for an observed set of evidences. It has many applications in various scientific domains like natural language understanding, medical diagnosis and computational biology. Bayesian Networks (BN) is an important probabilistic graphical formalism widely used for belief revision tasks. In BN, belief revision can be achieved by finding the maximum a posteriori (MAP) assignment. Finding MAP is an NP-Hard problem. In previous work, we showed how to find the MAP assignment in BN using High Order Recurrent Neural Networks (HORN) through an intermediate representation of Cost-Based Abduction. This method eliminates the need to explicitly construct the energy function in two steps, objective and constraints. This paper builds on that previous work by providing the theoretical foundation and proving that the resultant HORN used to find MAP is strongly equivalent to the original BN it tries to solve.     

Using high-probability foods to increase the acceptance of low-probability foods

Studies have evaluated a range of interventions to treat food selectivity in children with autism and related developmental disabilities. The high-probability instructional sequence is one intervention with variable results in this area. We evaluated the effectiveness of a high-probability sequence using 3 presentations of a preferred food on increasing acceptance in a child with autism who refused a few specific foods. The high-probability sequence increased acceptance of 3 foods. We then systematically faded the intervention for 2 foods.     

远距离规则的内隐学习使用了何种记忆存储器：来自神经网络模拟的证据

关于远距离规则的知识是如何被内隐学习的,研究尚未得出结论。该研究通过采用和人类被试相同的实验材料和程序,考察了简单循环网络模型（SRN）对两种汉语声调远距离规则——倒映和逆行规则的内隐学习。结果发现：1.在广泛的参数范围上,SRN能够学会倒映和逆行规则,表明模型的记忆缓冲器可以模拟人类远距离规则的内隐学习;2.SRN对倒映规则的学习比对逆行规则的学习更好,表明在功能上远距离规则的内隐学习可能优先使用了先进先出的记忆存储器及信息加工模式。该研究为探究远距离规则内隐学习的机制提供了新的证据和视角。