Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.     

A Modular Neural Network Model of Concept Acquisition

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom-up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top-down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others.     

内隐学习的人工神经网络模型

近年来,人工神经网络模型常被用来模拟各种心理活动,从而为心理学的一些相关理论提供丰富的证据,内隐学习也不例外。基于权重调整来学习正确反应的人工神经网络模型和内隐学习的两大本质特征间有着极为相应的匹配,因此,人工神经网络模型特别适用于内隐学习研究。到目前为止,针对两种较为普遍的内隐学习任务,已经相应地出现了两种使用较为广泛的神经网络模型——自动联系者和简单循环网络     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.     

拖延的决策模型

拖延是指尽管预见到该行为会带来不利后果,人们仍自愿推迟开始或完成某一计划好的行为。先前的研究对拖延的类型、影响因素、成因以及干预等方面进行了广泛的探讨,然而拖延的决策过程——"现在做还是以后做?"认知机制还非常不清楚。因此,本文提出拖延决策模型试图从三方面阐明"现在做还是以后做?"的决策机制:首先,拖延动机和不拖延动机的斗争是决定是否拖延的根本;其次,拖延动机的斗争可以进一步简化为任务负性过程和任务正性结果的权衡;最后,主动推迟任务使负性过程发生延迟折扣是拖延的核心目的。拖延决策模型不仅有助于探明拖延的核心认知机制,也能够帮助预测拖延行为的发生及解释各种影响因素的作用机制,因此未来的研究可以借此整合一个从拖延的核心发生机制到各种影响因素的理论系统。     

A Market Response Model for Sales Management Decision Making

Abstract

Sales managers are faced with a variety of decisions that require the evaluation of the probable response of the market to feasible decision alternatives. A conceptual model of market response is presented to offer management a framework for understanding the market response relationships in their selling situation and to provide an operational approach for evaluating the potential effect of decision alternatives. An empirical test illustrates the usefulness of the model and suggests several improvements in market response modeling. Future research directions are identified and important management implications are discussed.     

A Transcultural Integrative Model for Ethical Decision Making in Counseling

The Transcultural Integrative Ethical Decision‐Making Model in counseling addresses the need for including cultural factors in the process of ethical dilemma resolution. This transcultural model incorporates state‐of‐the‐art concepts from multicultural theory into an ethical decision‐making model that is adapted primarily from the Integrative Model developed by V. M. Tarvydas (1998). When appropriate, this transcultural model includes aspects of other ethical resolution models, such as R. R. Cottone's (2001) Social Constructivist Model and A. H. Davis's (1997) Collaborative Model. The proposed model is presented in a step‐by‐step, linear format that can be used by counselors facing ethical dilemmas in a variety of settings and with different cultural groups.     

Neural Network Simulations Support Heuristic Processing Model of Cultivation Effects

Many studies have shown that heavy TV viewers make social reality judgments more in line with televised reality. Shrum's (2001) heuristic model of cultivation effects predicted and found that biases in first-order cultivation judgments resemble heuristic processing. Systematic processing eliminated the effect. This study presents a series of computational simulations to examine whether a simple feed-forward neural network model exhibits learning behavior in accordance with Shrum's model. The data from the model are tested in contrast to data from human participants. Results closely fit human data. Simulations show that increased television exposure increases construct accessibility; television exemplars are not discounted when exhibiting the cultivation effect; and systematic processing reduces or eliminates the cultivation effect.     

Mapping across Domains Without Feedback: A Neural Network Model of Transfer of Implicit Knowledge

This paper shows how a neural network can model the way people who have acquired knowledge of an artificial grammar in one perceptual domain (e.g., sequences of tones differing in pitch) can apply the knowledge to a quite different perceptual domain (e.g., sequences of letters). It is shown that a version of the Simple Recurrent Network (SRN) can transfer its knowledge of artificial grammars across domains without feedback. The performance of the model is sensitive to at least some of the same variables that affect subjects' performance—for example, the model is responsive to both the grammaticality of test sequences and their similarity to training sequences, to the cover task used during training, and to whether training is on bigrams or larger sequences.     

A Network Perspective on the Processes of Empowered Organizations

Organizational empowerment is a multi-faceted concept that involves processes occurring both within and between organizations that facilitate achievement of their goals. This paper takes a closer look at three interorganizational processes that lead to empowered organizations: building alliances, getting the word out, and capturing others’ attention. These processes are located within the broader nomological network of empowerment and organizational empowerment, and are linked to particular patterns of interorganizational relationships that facilitate organizations’ ability to engage in them. A new network-based measure, γ-centrality, is introduced to capture the particular network structure associated with each process to be assessed. It is demonstrated first in a hypothetical organizational network, then applied to take a closer look at organizational empowerment in the context of a coordinating council composed of human service agencies. The paper concludes with a discussion of the implications of relationships between these processes, and the potential for unintended consequences in the empowerment of organizations.     

Absolute and Relative Decision Processes in Eyewitness Identification

A prominent and long-standing theory of eyewitness identification decision making distinguishes between absolute judgments, based on the lineup members' match to the witness's memory of the perpetrator, versus relative judgments, based on match values relative to other lineup members. This distinction was implemented in a computational model and simulations showed that the model predicts an accuracy advantage for absolute judgments over relative judgments under some conditions. The present experiment tested this prediction by evaluating the accuracy of witnesses instructed to use relative or absolute rules. Contrary to predictions, the overall analysis did not show an absolute advantage. Additional exploratory analyses showed a relative advantage when the suspect was surrounded by high-similarity foils. These results are consistent with a model that assumes that side-by-side comparisons of lineup members increase diagnostic accuracy by allowing witnesses to give greater weight to more diagnostic features and less weight to less diagnostic features.     

Effects of Interruptions on Consumer Online Decision Processes

The growth of e‐commerce and its attendant new technology features has increased interactivity in consumer information processing and decision‐making processes. The pull and push of information can be both more personalized and more commonly used. The ease of pushing information may lead to interruptions in consumer information processing that are more pronounced than those experienced in non‐Internet environments. Our study investigated the impact of interruption frequency, timing, and content and the moderating effects of consumer knowledge, control, and goal type on time spent on the decision task as well as satisfaction with the decision process and the choice. Our results show that the right configuration of interruptions may lead to increased online viewing time, whereas ill‐designed interruptions may be detrimental.     

The Role of Decision Processes in Conscious Recollection

Dual-process models of recognition memory posit a rapid retrieval process that produces a general sense of familiarity and a slower retrieval process that produces conscious recollections of prior experience. The remember / know paradigm has been used to study the subjective correlates of these two processes, with remember judgments assumed to index conscious recollection and know judgments assumed to index familiarity. Recently, a two-criterion signal detection model has been proposed as an alternative account of this paradigm. This model assumes only a single memory process with a criterion remember from know responses. This report presents an empirical test of the model's critical prediction that manipulations that influence criterion placement should influence both remember and know judgments. An experiment confirmed this prediction, demonstrating that subjects who were told that 70% of the test items were study items produced more remember and know responses than subjects who were told that 30% of the test items were study items.     

The Cascade Neural Network Model and a Speed-Accuracy Trade-Off of Arm Movement

We propose a hybrid neural network model of aimed arm movements that consists of a feedforward controller and a postural controller. The cascade neural network of Kawato, Maeda, Uno, and Suzuki (1990) was employed as a computational implementation of the feedforward controller. This network computes feedforward motor commands based on a minimum torque-change criterion. If the weighting parameter of the smoothness criterion is fixed and the number of relaxation iterations is rather small, the cascade model cannot calculate the exact torque, and the hand does not reach the desired target by using the feedforward control alone. Thus, one observes an error between the final position and the desired target location. By using a fixed weighting parameter value and a limited iteration number to simulate target-directed arm movements, we found that the cascade model generated a planning time-accuracy trade-off, and a quasi-power-law type of speed-accuracy trade-off. The model provides a candidate neural mechanism to explain the stochastic variability of the time course of the feedforward motor command. Our approach also accounts for several invariant features of multijoint arm trajectories, such as roughly straight hand paths and bell-shaped speed profiles.     

The Cascade Neural Network Model and a Speed-Accuracy Trade-Off of Arm Movement

We propose a hybrid neural network model of aimed arm movements that consists of a feedforward controller and a postural controller. The cascade neural network of Kawato, Maeda, Uno, and Suzuki (1990) was employed as a computational implementation of the feedforward controller. This network computes feedforward motor commands based on a minimum torque-change criterion. If the weighting parameter of the smoothness criterion is fixed and the number of relaxation iterations is rather small, the cascade model cannot calculate the exact torque, and the hand does not reach the desired target by using the feedforward control alone. Thus, one observes an error between the final position and the desired target location. By using a fixed weighting parameter value and a limited iteration number to simulate target-directed arm movements, we found that the cascade model generated a planning time–accuracy trade-off, and a quasi–power-law type of speed–accuracy trade-off. The model provides a candidate neural mechanism to explain the stochastic variability of the time course of the feedforward motor command. Our approach also accounts for several invariant features of multijoint arm trajectories, such as roughly straight hand paths and bell-shaped speed profiles.     

基于GRM模型的BP神经网络参数估计

目前参数估计多采用统计方法,存在耗时长、要求被试样本容量大和项目数多等缺点。本文将BP神经网络和降维法相结合,对GRM的项目参数和考生能力参数进行估计。蒙特卡洛模拟结果显示：（1）不管是人多题少还是题多人少,该网络设计下的参数估计精度都较高;（2）可以应用到多个不同等级评分的参数估计中,甚至是超过15个等级的项目参数,估计精度也较高,这是其他参数估计方法所不可比拟的;（3）运行的时长和统计估计方法相比大大缩减。