瞳孔是心灵的窗口吗?——瞳孔在心理学研究中的应用及测量

瞳孔大小是眼动研究中一个重要的参数指标, 在一定程度上能够反映人的心理活动并影响他人的心理与行为。一方面, 瞳孔变化受到自上而下的知觉与注意、情绪与动机、心理努力、社会认知与发展等因素的影响, 另一方面, 大瞳孔能引起他人更多的积极评价和积极行为。瞳孔的神经机制(蓝斑-去甲肾上腺素系统)和自适应增益理论解释了瞳孔与心理之间存在紧密联系的原因。瞳孔测量(pupillometry)作为一种有效的眼动追踪方法有助于理解瞳孔与心理的关系, 研究者在测量瞳孔时需要关注无关变量(如亮度、注视位置), 原始数据处理(如基线校正、眨眼处理)及瞳孔指标选取(如瞳孔直径、震颤频率)等问题, 未来研究应继续探讨瞳孔与其他心理之间的关系, 并探索更有效地处理和使用瞳孔指标的方法。     

价值观的稳定性与可变性：基于认知神经科学的视角

价值观的稳定性和可变性一直是该领域争论的焦点所在。近些年来, 研究者开始从认知神经科学的角度来探讨导致价值观稳定性差异的机制问题。已有的神经反应证据表明, 当某种价值观是基于道义主义的绝对规则来进行认知建构的, 或与自我概念在表征上存在一致性时, 其更倾向于保持稳定的状态; 若非如此, 便可能会在外界影响(如他人劝说)下发生重要程度上的改变。未来需要进一步丰富和完善认识价值观稳定性与可变性关系的认知神经加工模型, 并探索价值观长效改变背后的认知神经机制, 以促进理论研究在价值观教育中的实践和应用。     

Predatory aggression in the mink (Mustela vison): Roles of serotonin and food satiation

5-Hydroxytrypotophan at a dose of 50 mg/kg intraperitoneally (i.p.) sharply increased neural serotonin (5-HT) levels in mink and considerably inhibited that animal's predatory attack on rats. Intraperitoneal injection of 5-HT (10 and 20 mg/kg) did not influence such rat-killing. Neural levels of 5-HT or 5-hydroxyindoleacetic acid (5-HIAA) and subsequent aggression by the predator did not change to any great degree after ingestion of a single meal. Abundance of natural mink food for 3 days was associated with an increased level of 5-HIAA in the lateral hypothalamus and the amygdala as well as with an increased latency to attack and to kill rats. 5-HT seems to represent an endogenous factor that inhibits predatory attack by the mink; this effect appears to function through increased metabolism of 5-HT in some brain regions, which is evident after abundant intake of tryptophan with the natural diet.     

Learning and development in neural networks--the importance of prior experience

Infants can discriminate between familiar and unfamiliar grammatical patterns expressed in a vocabulary that is distinct from that used earlier during familiarization (Cognition 70(2) (1999) 109; Science 283 (1999) 77). Various models have captured the data, although each required that discrimination be distinct, in terms of the computational process, from familiarization. This article describes a simple recurrent network (SRN), equipped only with the assumption that it should predict what comes next, which models the data without distinguishing between familiarization and discrimination. To accomplish this, the SRN requires pre-training on a range of sequences instantiating different structures and different vocabulary items to those used subsequently during familiarization and test. Pre-training enables the network to avoid replacing structure acquired during familiarization with structure experienced at test. An equivalent enabling condition may underpin infants' resistance to catastrophic interference between the different structures and vocabulary items to which they are exposed.     

Behavior analysis and revaluation

Revaluation refers to phenomena in which the strength of an operant is altered by reinforcer-related manipulations that take place outside the conditioning situation in which the operant was selected. As an example, if lever pressing is acquired using food as a reinforcer and food is later paired with an aversive stimulus, the frequency of lever pressing decreases when subsequently tested. Associationist psychology infers from such findings that conditioning produces a response-outcome (i.e., reinforcer) association and that the operant decreased in strength because pairing the reinforcer with the aversive stimulus changed the value of the outcome. Here, we present an approach to the interpretation of these and related findings that employs neural network simulations grounded in the experimental analysis of behavior and neuroscience. In so doing, we address some general issues regarding the relations among behavior analysis, neuroscience, and associationism.     

Toward a Comprehensive Strategy for Effective Practitioner–Scientist Partnerships and Larger-Scale Community Health and Well-Being

This article articulates joint priorities for the fields of prevention science and community psychology. These priorities are intended to address issues raised by the frequent observation of natural tensions between community practitioners and scientists. The first priority is to expand the knowledge base on practitioner–scientist partnerships, particularly on factors associated with positive outcomes within communities. To further articulate this priority, the paper first discusses the rapid growth in community-based partnerships and the emergent research on them. Next described is an illustrative research project on a partnership model that links state university extension and public school delivery systems. The article then turns to the second, related priority of future capacity-building for diffusion of effective partnership-based interventions to achieve larger-scale health and well-being across communities. It outlines two salient tasks: clarification of a conceptual framework and the formulation of a comprehensive capacity-building strategy for diffusion. The comprehensive strategy would require careful attention to the expansion of networks of effective partnerships, partnership-based research agendas, and requisite policy-making.     

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”。     

A formal model for structuring local perceptions of environmental space

The problem of constructing computational models of space that mimic those found in human spatial reasoning is addressed. This paper extends a formal model that addressed point objects in a 2D world to include the presence of line objects and barriers. The problem consists of determining an appropriate global model for the spatial configuration given a sequence of local views or observations. A formal model is proposed based on constraint network theory. The model leads to the identification of stable regions within which perceptual information about the environment changes slowly, and zones of transition within which the perceptual information changes more quickly. A special case of transition zones is a gateway, which may be viewed as a kind of discontinuity or singularity in the model. It is expected that the model obtained will be used to mimic human mental representations of navigable outdoor environments.     

Transitive choices by a simple, fully connected, backpropagation neural network: implications for the comparative study of transitive inference.

In search of the minimal requirements for transitive reasoning, a simple neural network was trained and tested on the non-verbal version of the conventional "five-term-series task" – a paradigm used with human adults, children and a variety of non-human species. The transitive performance of the network was analogous in several aspects to that reported for children and animals. The three effects usually associated with transitive choices i.e. "symbolic distance", "lexical marking" and "end-anchor", were also clearly shown by the neural network. In a second experiment, where the training conditions were manipulated, the network failed to match the behavioural pattern reported for human adults in the test following an ordered presentation of the premises. However, it mimicked young children's performance when tested with a novel comparison term. Although we do not intend to suggest a new model of transitive inference, we conclude, in line with other authors, that a simple error-correcting rule can generate transitive behaviour similar to the choice pattern of children and animals in the binary form of the five-term-series task without requiring high-order logical or paralogical abilities. The analysis of the training history and of the final internal structure of the network reveals the associative strategy employed. However, our results indicate that the scope of the associative strategy used by the network might be limited. The extent to which the conventional five-term-series task, in absence of appropriate manipulations of training and testing conditions, is suitable to detect cognitive differences across species is also discussed on the basis of our results. Accepted after revision: 29 May 2001 Electronic Publication     

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.