记叙文中因果连贯对空间情境模型回溯建构的促进

采用移动窗口阅读技术探讨记叙文理解中因果关系对于空间信息加工的作用。实验1和2的自变量是故事中空间信息与故事结果的因果关系,采用相关样本t检验对不同条件下探测词的反应时进行统计分析,结果表明因果关系不仅会影响文本明确提及的空间信息的加工而且能够促进隐含的空间关系建构情境模型。实验3的自变量是故事中物体与位置词的空间关系,采用相关样本t检验对不同条件下位置探测词的反应时进行统计分析,结果进一步表明空间情境模型是读者为保持故事因果连贯回溯建构的而不是在阅读进程中的实时建构。实验4中空间关系的复杂程度增大,结果发现当故事中空间信息的文本距离较远时即使有保持因果连贯的需要,读者仍无法回溯建构空间模型。     

Logical information and epistemic space

Gaining information can be modelled as a narrowing of epistemic space. Intuitively, becoming informed that such-and-such is the case rules out certain scenarios or would-be possibilities. Chalmers’s account of epistemic space treats it as a space of a priori possibility and so has trouble in dealing with the information which we intuitively feel can be gained from logical inference. I propose a more inclusive notion of epistemic space, based on Priest’s notion of open worlds yet which contains only those epistemic scenarios which are not obviously impossible. Whether something is obvious is not always a determinate matter and so the resulting picture is of an epistemic space with fuzzy boundaries.     

Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning

The objective of this study is to model the microscopic behaviour of mixed traffic (cyclist-pedestrian) interactions in non-motorized shared spaces. Video data were collected at two locations of Robson Square non-motorized shared space in downtown Vancouver, British Columbia. Trajectories of cyclists and pedestrians involved in interactions were extracted using computer vision algorithms. The extracted trajectories were used to obtain several variables that describe elements of road users’ behaviour including longitudinal and lateral distances, speed and speed differences, interaction angle, and cyclist acceleration and yaw rate. The road users behaviour was modeled as utility-based intelligent rational agents using the finite-state Markov Decision Process (MDP) framework with unknown reward functions. The study implemented Inverse Reinforcement Learning (IRL) using two algorithms: the Maximum Entropy (ME) algorithm, and the Feature Matching (FM) algorithm to recover/estimate the reward function weights of cyclists in two types of interactions with pedestrians: following and overtaking interactions. Reward function weights infer cyclist preferences during their interactions with pedestrians in non-motorized shared spaces, and can form the key component in developing agent based microsimulation model for road users. Furthermore, the estimated reward functions were used to estimate cyclists’ optimal policy for such interactions. A simulation platform was developed using the estimated reward functions and the cyclist optimal policies to simulate cyclist trajectories for the validation dataset. Results show that the Maximum Entropy (ME) IRL algorithm outperformed the Feature Matching (FM) IRL algorithm, and generally provided reasonable results for modeling such interactions in non-motorized shared spaces, considering the high degrees of freedom in movement and the more-complex road users interactions in such facilities. This research is considered an important step toward developing a full Agent-Based Model (ABM) for road users in shared space facilities to evaluate the safety and efficiency of such facilities.     

视觉注意选择性的认知心理学理论研究进展

对注意选择性及其作用的探讨又具有极大的方法论价值 ,它既涉及许多基于有注意参与的知觉、决策、学习和记忆等重大心理学理论问题 ,又涉及对这些理论的评价以及其他一系列的研究方法的理解 ,本文仅就目前认知心理学关于“基于空间”和“基于客体”两大理论学派的研究进行简要回顾。     

权力概念垂直空间隐喻表征激活的条件

本研究通过三个实验考察权力概念垂直空间隐喻表征激活的条件。实验1发现,在权力判断任务中,具有直接权力关系的相关词对和间接权力关系的无关词对都可以激活隐喻表征。实验2使用相关判断任务,只有相关词对激活了隐喻表征。这说明对词汇进行语义加工并理解权力关系是激活隐喻表征的重要条件。实验3引入水平方向干扰,并将被试的注意转移到干扰信息上,结果仍然出现了隐喻一致性效应,表明垂直空间线索不是隐喻表征激活的必要条件。     

语义空间的研究方法

对于语义空间的研究一直是认知心理学研究的一个热点。由于对词汇语义系统的不同观点,科学家们试图从不同的角度采用不同的方法来进行研究。目前,有代表性的语义空间研究方法主要有两种:潜在语义分析(LSA)和语言的多维空间类比(HAL)。潜在语义分析是指利用奇异值分解的方法来探索文章中潜在的语义关系的方法;语言的多维空间类比则是利用多维量表(MDS)的方法来提取语义信息。     

Implicit learning: A way to improve visual search in spatial neglect?

Studies have shown that neglect patients are able to use stimulus regularities to orient faster toward the neglected side, without necessarily being aware of that information, or at the very least without being able to verbalize their knowledge. In order to better control for the involvement of explicit processes, the present study sought to test neglect patients’ ability to detect more complex associations between stimuli using tasks similar to those used in implicit learning studies. Our results demonstrate that neglect patients had difficulties implicitly learning complex associations, contrary to what we found with controls. The possible influence of attentional and working memory impairments are discussed.     

Notes on attribution functions

Let be the knowledge space derived from an attribution function σ on Q. Under an assumption for σ, this paper gives some necessary and sufficient conditions such that is discriminative. It also discusses the resolubility of σ when Q is an infinite set. More precisely, this paper proves that σ is not resoluble if Q is uncountable, and gives a necessary and sufficient condition such that σ is resoluble when is -well-graded. By way of applications of these results, discriminativeness and resolubility are discussed around the merge of skill multimaps and the meshing of the delineated knowledge spaces.     

Approximately counting and sampling knowledge states

Approximately counting and sampling knowledge states from a knowledge space is a problem that is of interest for both applied and theoretical reasons. However, many knowledge spaces used in practice are far too large for standard statistical counting and estimation techniques to be useful. Thus, in this work we use an alternative technique for counting and sampling knowledge states from a knowledge space. This technique is based on a procedure variously known as subset simulation, the Holmes–Diaconis–Ross method, or multilevel splitting. We make extensive use of Markov chain Monte Carlo methods and, in particular, Gibbs sampling, and we analyse and test the accuracy of our results in numerical experiments.