A computational model of early argument structure acquisition

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions —associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.     

HIERARCHIES: THE CORE ARGUMENT FOR A NATURALISTIC CHRISTIAN FAITH

This article takes on a perhaps impossible task: not only to reconstruct the core argument of Arthur Peacocke's program in science and religion but also to evaluate it in two major areas where it would seem to be vulnerable, namely, more recent developments in systems biology and the philosophy of mind. If his theory of hierarchies is to be successful, it must stand up to developments in these two areas and then be able to apply the results in a productive way to Christian theological reflection. Peacocke recognized that one's model of the mind‐body relation is crucial for one's position on the God‐world relation and divine action. Of the three models that he constructed, it turns out that only the third can serve as a viable model for theology if it is to be more than purely deistic or metaphorical.     

Modeling the role of emotions in viewing oneself maturely

Adolescents develop a mature view on themselves: they view positive as well as negative self-attributes, but they hold a positive stance toward themselves. The literatures on identity, the self, parent-offspring relationships, and friendships provide hints on how such a view may develop. This article integrates this literature into an emotion-based dynamic systems mathematical model built to explain the development of maturity of such views. An innovative methodological design was used to collect empirical data: four students daily reported their experiences and emotions for a period of 5 months, while they temporarily studied abroad. At the beginning and end of this period, they were interviewed on their views on themselves. These mostly qualitative data were used to provide support to the suggested mathematical model. The usefulness, weaknesses, and strengths of such a methodological design and mathematical modeling are discussed.     

Expectation confirmation: An examination of three competing models

We discuss three theoretical models from met expectations research in the fields of organizational behavior and consumer psychology. Based on the fundamental arguments in the models, we term these models: disconfirmation, ideal point, and experiences only. We present three-dimensional graphical and analytical representations of the models, with satisfaction being a function of expectations and experiences. We tested the models in the context of a new information system implementation in an organization, with expectations, experiences, and system satisfaction measured for both ease of use and usefulness, the focal constructs of the Technology Acceptance Model (TAM). We found that an experiences only model in which expectations had no measurable effect best explained the data for ease of use. The results for usefulness indicated a modified version of the experiences only model in which the positive effect of experiences becomes slightly stronger—i.e., more positive—as expectations increase.     

Modeling and prediction of driver preparations for making a right turn based on vehicle velocity and traffic conditions while approaching an intersection

This paper focuses on modeling and predicting the influence of a vehicle’s velocity and the relative position between a driver’s vehicle and a vehicle to the front or rear on the onset of driver preparations for making a right turn at an intersection. Repeated experiments were carried out on a public road to measure driver preparations, including releasing the accelerator pedal, moving the right foot to cover the brake pedal, and activating the turn signal, as well as to record vehicle velocity and the relative distances to the leading and following vehicles. Structural equation modeling was applied to estimate these relationships quantitatively. Two separate latent variables accounting for the interaction between the driver’s vehicle and leading or following vehicles, and the onset point where driving behavior changes from straight mode to preparation mode before a right turn, were introduced in the specification of the structural equation model. The model estimates and testing indicate that the proposed structural equation model represents well the relationship hypothesized by observational analysis: that the vehicle velocity and the traffic conditions surrounding the driver’s vehicle strongly influence the driver’s deceleration and more weakly influenced turn signal activation. The proposed structural equation model contributes to the prediction of the onset locations of covering the brake pedal and activating the turn signal based on the vehicle velocity and the relative distances to the front and rear vehicles when the accelerator pedal is released. The prediction accuracy is high compared with predictions in which the vehicle velocity and the headway or rear distances are not taken into account or predictions using a single regression model with one independent variable, namely driving speed. Finally, a possible new addition to in-vehicle navigation systems that detects unusual driver behavior by predicting the driver’s preparatory maneuvers is discussed.     

Autonomous construction of ecologically and socially relevant semantics

This article presents a synthetic modeling approach to the problem of grounded construction of concepts. In many computational models of grounded language acquisition and evolution, meanings are created in the process of discrimination between a chosen object and other objects present on the scene of communication. We argue that categories constructed for the purpose of identification rather than discrimination are more suitable for the detached language use (talking about things not present here and now). We describe a semantics based on so-called identification criteria constructed by extracting cross-situational similarities among instances of a category, and present several computational models. In the model of individual category construction, the instances are grouped to categories by common motor programs (affordances), while in the model of social learning, focused on the influence of naming on category formation, entities are considered members of the same category, if they are labeled with the same word by an external teacher. By these two mechanisms, the learner can construct interactionally grounded representation of objects, properties, relations, changes, complex situations and events. We also report and analyze simulation results of an experiment focused on the dynamics of meanings in iterated intergenerational transmission.     

Human and machine perception of biological motion

More than 30 years ago, Johansson was the first to show that humans are capable of recovering information about the identity and activity of animate creatures rapidly and reliably from very sparse visual inputs – the phenomenon of biological motion. He filmed human actors in a dark setting with just a few strategic points on the body marked by lights – so-called moving light displays (MLDs). Subjects viewing the MLDs reported a vivid impression of moving human forms, and were even able to tell the activity in which the perceived humans were engaged. Subsequently, the phenomenon has been widely studied and many attempts have been made to model and to understand it. Typical questions that arise are: How precisely is the sparse low-level information integrated over space and time to produce the global percept, and how important is world knowledge (e.g., about animal form, locomotion, gravity, etc.)? In an attempt to answer such questions, we have implemented a machine-perception model of biological motion. If the computational model can replicate human data then it might offer clues as to how humans achieve the task. In particular, if it can do so with no or minimal world knowledge then this knowledge cannot be essential to the perception of biological motion. To provide human data for training and against which to assess the model, an extensive psychophysical experiment was undertaken in which 93 subjects were shown 12 categories of MLDs (e.g., normal, walking backwards, inverted, random dots, etc.) and were asked to indicate the presence or absence of natural human motion. Machine perception models were then trained on normal sequences as positive data and random sequences as negative data. Two models were used: a k-nearest neighbour (k-NN) classifier as an exemplar of ‘lazy’ learning and a back-propagation neural network as an exemplar of ‘eager’ learning. We find that the k-NN classifier is better able to model the human data but it still fails to represent aspects of knowledge about body shape (especially how relative joint positions change under rotation) that appear to be important to human judgements.     

Supporting the formal verification of mathematical texts

The formal verification of mathematical texts is one of the most interesting applications for computer systems. In fact, we argue that the expert language of mathematics is the natural choice for achieving efficient mathematician–machine interaction. Our empirical approach, the analysis of carefully authored textbook proofs, forces us to focus on the language and the reasoning pattern that mathematician use when presenting proofs to colleagues and students. Enabling a machine to understand and follow such language and argumentation is seen to be the key to usable and acceptable math assistant systems. In this paper, we first perform an analysis of three textbook proofs by hand; we then describe a computational framework that aims at mechanising such an analysis. The resulting proof-of-concept implementation is capable of processing simple textbook proofs and constitutes promising steps towards a natural mathematician–machine interface for proof development and verification.     

小学生数学应用题解题水平影响因素的研究——视空间能力、认知方式及表征方式的影响

研究采用3(年级)×2(场认知方式)×2(视空间能力水平)×3(问题表征方式)四因素混合实验设计,探讨了这四种因素对小学生数学应用题解题水平的影响。结果表明:年级、认知方式、空间能力以及问题表征方式均是影响小学生应用题解决水平的关键因素;问题表征方式、认知方式、空间能力以及年级四个因素对小学生解题水平存在交互作用,其中图形提示有利于场依存性学生解题水平的提高,低空间-场依存型的学生更适合图形提示;高空间-场依存型的学生随年级升高解题能力发展较快,而低空间-场依存型的学生解题水平提高较慢;随着年级的升高,图式表征对小学生问题解决的促进作用增强。