Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning

Learning environmental biases is a rational behavior: by using prior odds, Bayesian networks rapidly became a benchmark in machine learning. Moreover, a growing body of evidence now suggests that humans are using base rate information. Unsupervised connectionist networks are used in computer science for machine learning and in psychology to model human cognition, but it is unclear whether they are sensitive to prior odds. In this paper, we show that hard competitive learners are unable to use environmental biases while recurrent associative memories use frequency of exemplars and categories independently. Hence, it is concluded that recurrent associative memories are more useful than hard competitive networks to model human cognition and have a higher potential in machine learning.     

审美的神经机制研究及其美学意义

本文主要介绍了审美神经机制研究的初步成果,特别有关Zeki,Solso等人开创性的研究工作,并着重讨论了这些研究工作的美学意义,指出审美神经机制的研究不但在美学理论的确证方面,具有更强的说服力和广泛的应用性,而且在21世纪美学理论的重建方面,也一定会起着不可替代的重要作用。     

Semantic boost on episodic associations: an empirically-based computational model

Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words more easily than unrelated words; this advantage increased linearly with repeated co-occurrence. Second, we developed a computational model, SEMANT, suggesting a possible mechanism for this semantic-episodic interaction. In SEMANT, episodic associations are implemented through lateral connections between nodes in a pre-existent self-organized map of word semantics. These connections are strengthened at each instance of concomitant activation, proportionally with the amount of the overlapping activity waves of activated nodes. In computer simulations SEMANT replicated the dynamics of associative learning in humans and led to testable predictions concerning normal associative learning as well as impaired learning in a diffuse semantic system like that characteristic of schizophrenia.     

Dynamic self-organization and early lexical development in children

In this study we present a self-organizing connectionist model of early lexical development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for lexical development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early lexical development.     

神经管缺陷的病因、预防与产前控制

神经管缺陷(NTDS)是一种常见的先天性中枢神经系统畸形,其发生率占据我国出生缺陷的前列。本文从其发病原因、产前诊断及预防措施进行系统分析,希望能找出一条降低其发生率的有效途径,从而进一步提高我国的人口素质。     

Autoshaping and automaintenance: a neural-network approach

This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency between inputs that simulated a conditioned stimulus (CS) and an input that simulated an unconditioned stimulus (US). Responding was simulated as an output activation that was neither elicited by nor required for the US. B was an omission-training procedure. Response directedness was defined as sensory feedback from responding, simulated as a dependence of other inputs on responding. In Simulation 1, the phenomena were simulated with a fully connected architecture and maximally intense response feedback. The other simulations used a partially connected architecture without competition between CS and response feedback. In Simulation 2, a maximally intense feedback resulted in substantial autoshaping and automaintenance. In Simulation 3, eliminating response feedback interfered substantially with autoshaping and automaintenance. In Simulation 4, intermediate autoshaping and automaintenance resulted from an intermediate response feedback. Implications for the operant-respondent distinction and the behavior-neuroscience relation are discussed.     

主观幸福感的脑机制

主观幸福感是社会认知领域一直关注的重要问题。Kahneman等人提出的评价系统模型为主观幸福感的研究提供了基本框架。主观幸福感形成过程中,评价系统的主要产物包括即时效用、回忆效用、抉择效用和预期效用,这些效用都有着各自特殊的神经机制。主观幸福感的影响因素分为内部和外部因素,它们也对应着各异的神经基础。未来主观幸福感领域的研究应遵循生理机制和心理机制相结合的整合研究思路。     

后悔的认知机制和神经基础

后悔是基于对不利或相对不利行为结果的反事实思维诱发的一种复杂的负性社会情绪,对我们日常生活的决策和身心健康具有重大的影响。与失望情绪相比,后悔在现象学、产生条件以及评价方式等方面具有明显的差异。后悔不仅会受到个体的行为方式、人格特征、归因等因素的影响,而且还会受到结果信息属性的影响。后悔的预期和体验涉及的功能性脑区主要包括：眶额皮层、扣带前回、海马、杏仁核。研究后悔的实验方法和技术手段有待于进一步拓展,后悔的认知机制和神经基础还有待于进一步探讨和完善     

A causal model theory of the meaning of cause, enable, and prevent

The verbs cause , enable , and prevent express beliefs about the way the world works. We offer a theory of their meaning in terms of the structure of those beliefs expressed using qualitative properties of causal models, a graphical framework for representing causal structure. We propose that these verbs refer to a causal model relevant to a discourse and that "A causes B" expresses the belief that the causal model includes a link from A to B. "A enables/allows B" entails that the model includes a link from A to B, that A represents a category of events necessary for B, and that an alternative cause of B exists. "A prevents B" entails that the model includes a link from A to B and that A reduces the likelihood of B. This theory is able to account for the results of four experiments as well as a variety of existing data on human reasoning.     

Artificial explanations: the epistemological interpretation of explanation in AI

In this paper I critically examine the notion of explanation used in artificial intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. At the end of the paper I outline a set of desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation.