Spreading Activation Model Research Articles

A Novel User Profile Learning Approach with Fuzzy Constraint for News Retrieval

This study conducts a novel user profile learning approach based on fuzzy constraints. From the vantage of knowledge representation, a fuzzy constraint network is used not only to present the ambiguity of concepts and the diversity between concepts but also to express a single user profile with dependent multisubjects of interest. From the vantage of problem solving, the construction of a user profile is viewed as a problem of fuzzy constraint satisfaction. The subject of interest is extracted by a spreading activation model. To achieve the information filtering of the retrieved data, fuzzy information gain is employed to reduce unnecessary user feedback for matching the user's retrieval requirements.

Hemispheric asymmetry in the hierarchical perception of music and speech.

The perception of music and speech involves a higher level, cognitive mechanism that allows listeners to form expectations for future music and speech events. This article comprehensively reviews studies on hemispheric differences in the formation of melodic and harmonic expectations in music and selectively reviews studies on hemispheric differences in the formation of syntactic and semantic expectations in speech. On the basis of this review, it is concluded that the higher level mechanism flexibly lateralizes music processing to either hemisphere depending on the expectation generated by a given musical context. When a context generates in the listener an expectation whose elements are sequentially ordered over time, higher level processing is dominant in the left hemisphere. When a context generates in the listener an expectation whose elements are not sequentially ordered over time, higher level processing is dominant in the right hemisphere. This article concludes with a spreading activation model that describes expectations for music and speech in terms of shared temporal and nontemporal representations. (PsycINFO Database Record

启动效应中的语义相关

启动效应是在启动刺激作用下加工目标刺激时，在速度、正确率和反应倾向上发生变化的过程。在对启动效应机制的探讨上主要有基于联想关系的激活扩散模型和符合线索理论以及基于语义相关的分布记忆模型两大阵营。本研究采用眼动研究技术探讨了启动效应是基于语义相关还是基于联想关系的问题。实验采用单自变量三水平(语义距离：1、3和5)的被试内设计，测量了30名大学生的图形语义判断过程。结果表明，在排除其它因素的影响下，语义距离越大，眼动反应越强，启动效应越弱；语义距离越小，眼动反应越弱，启动效应越强。启动效应以语义相关为基础，而不以联想关系为基础。本研究支持了启动效应的分布记忆模型。 Priming effect is the changing of processing target stimulus in the speed, accuracy and reaction tendency under the action of priming stimulus. There are three kinds of model of priming effects: spreading-activation model, compound-cue theory and distributed memory model. The former two theories are based on the associative relationship priming effect theory, the last theory is based on the theory of semantic correlation of priming effect. The research discussed the problem that the priming effect is based on the semantic correlation or based on the associative relationship adopting the eye movement research techniques. The experiment is within-subjects design by a single independent variable of three levels (semantic distance: 1, 3, and 5). It measured the graphical semantic judgment process of 30 college students. Results show that the bigger the se-mantic distance is, the stronger the eye reaction is, the weaker the priming effect is; the smaller the semantic distance is, the weaker the eye response is, the stronger the priming effect under controlling for other factors is. Priming effect is not based on the associative relationship, but based on semantic correlation. This study supports the priming effect of distributed memory model.

Replicability and Models of Priming: What a Resource Computation Framework can Tell us About Expectations of Replicability

In this article, we argue that whether or not a replication attempt is informative is dependent on the accuracy of one’s underlying model to explain the effect, as it is the explanatory model that enumerates the contingencies necessary for producing the effect. If the model is incorrect, then a researcher may unknowingly change variables that the model says are irrelevant but which are really essential, rendering the replication results ambiguous. The expectation that effects of priming on social behavior should be widely invariant makes sense only under the assumptions of strict direct expression and spreading activation models, yet it has been shown that these models cannot adequately explain findings from the priming literature. We describe one model of priming that predicts variability across experimental contexts and populations: the resource computation model. We highlight variables that have been uncovered under the assumptions of this model that cannot be accounted for by direct expression models and which can explain replication failures. The model is also consistent with evolutionary understandings of the mind, in which information from multiple sources beyond just stimulus information is incorporated into behavioral decisions. To the degree that anything other than a strict, direct expression, spreading activation model is correct, the expectation that priming of social behaviors should be widely invariant is unreasonable.

Unifying Prospective and Retrospective Interval-time Estimation: A Fading-gaussian Activation-based Model of Interval-timing

Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried and cognitive load on participants’ judgments of interval duration. For retrospective time judgments, estimates under high cognitive load are longer than under low cognitive load. For prospective judgments, the reverse pattern holds, with increased cognitive load leading to shorter estimates. We describe GAMIT, a Gaussian spreading-activation model, in which the sampling rate of an activation trace is differentially affected by cognitive load. The model unifies prospective and retrospective time estimation, normally considered separately, by relating them to the same underlying process. The scalar property of time estimation arises naturally from the model dynamics and the model shows the appropriate interaction between mode of query and cognitive load.

Theoretical analysis of word production deficits in adult aphasia

The cognitive analysis of adult language disorders continues to draw heavily on linguistic theory, but increasingly it reflects the influence of connectionist, spreading activation models of cognition. In the area of spoken word production, 'localist' connectionist models represent a natural evolution from the psycholingistic theories of earlier decades. By contrast, the parallel distributed processing framework forces more radical rethinking of aphasic impairments. This paper exemplifies these multiple influences in contemporary cognitive aphasiology. Topics include (i) what aphasia reveals about semantic-phonological interaction in lexical access; (ii) controversies surrounding the interpretation of semantic errors and (iii) a computational account of the relationship between naming and word repetition in aphasia. Several of these topics have been addressed using case series methods, including computational simulation of the individual, quantitative error patterns of diverse groups of patients and analysis of brain lesions that correlate with error rates and patterns. Efforts to map the lesion correlates of nonword errors in naming and repetition highlight the involvement of sensorimotor areas in the brain and suggest the need to better integrate models of word production with models of speech and action.

GAMIT – A Fading-Gaussian Activation Model of Interval-Timing: Unifying Prospective and Retrospective Time Estimation

Two recent findings constitute a serious challenge for all existing models of interval timing. First, Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies of participants’ judgments of interval duration (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried (i.e., retrospectively or prospectively) and cognitive load. For retrospective time judgments, estimates under high cognitive load are longer than under low cognitive load. For prospective judgments, the reverse pattern holds, with increased cognitive load leading to shorter estimates. We describe GAMIT, a Gaussian spreading activation model of interval timing, in which the decay and sampling rate of an activation trace are differentially affected by cognitive load. The model unifies prospective and retrospective time estimation, normally considered separately, by relating them to the same underlying process. The scalar property of time estimation arises naturally from the model dynamics and the model shows the appropriate interaction between mode of query and cognitive load.

Personalized search in digital libraries via spreading activation model

With the tremendous development of information technology, the volume of data in digital libraries is increasing enormously, and the magnitude is putting users at risk of information overload. Personalized search aims at solving the problem by tailoring search results for individual demands. We propose a novel approach to provide personalized search by learning users' interests from historic behaviors and re-ranking search results by a Spreading Activation SA model. In our approach, users' interests are categorized by the level of recency to form user profiles, which in turn serve as the input of the SA model. Then, SA runs on the domain ontology incorporated with a newly defined relationship borrowIntent derived from the assumption of collaborative filtering. We further demonstrate the effectiveness of the proposed methodology through experiments on real data from a university library. The presented approach can also be applied in other contexts such as electronic commerce.

The Expression of Active Structural Network in Intellectual Environment Design

This essay proposes a combination of commonsense knowledge and the spreading activation theory to strengthen the breadth of spreading activation of user's behavioral information in the field of intelligent environment design. The ontology cited in traditional artificial intelligence is roughly used to express and share knowledge. It is described with concepts, the properties of concepts, and the relationships among concepts, and is mostly presented with the tree-structured classification. However, there are often only vertical subordinate relationships between concepts with no horizontal and cross-layer interactions. Hence, we employ the constrained spreading activation model to perform a series of algorithms, further constructing the knowledge rules which can be commonly utilized to reinforce the breadth of activation process of user's behavioral information in an intelligent environment. These rules can provide a reference to the network service of digitalized cities in the future, and can also support the simulation assessment of applicability of intelligent environment design by employing embedded interactive technology.

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents ( HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief–desire–intention ( BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods—such as Hebbian strengthening of links between co-active elements, and belief–intention learning of task-specific relationships—modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.

EPG patterns in a patient with phonemic paraphasic errors

Auditorily-detected phonemic paraphasias such as substitutions are said to characterize aphasias such as Wernicke’s and conduction aphasia. However previous research has shown that the articulatory details underlying such substitutions recorded by techniques such as Electropalatography (EPG) may reveal a level of complexity not detected by the auditory analysis alone. Articulatory anomalies such as so-called Misdirected Articulatory Gestures (MAGs) may accompany the production of the substitutions and are variously perceived by listeners. A perceptual study is carried out in which 16 adult listeners rate sounds identified on the basis of EPG analysis as potential substitutions, on a scale from alveolar to velar. Some of the target sounds looked like ‘pure’ substitutions from the EPG patterns and some exhibited MAGs. Listener judgements were not always entirely predictable on the basis of EPG patterns. The occurrence of the anomalous EPG patterns (and the auditory results) is explored further in terms of a modified spreading activation model and implications for clinical management are discussed.

Dynamics of activation of semantically similar concepts during spoken word recognition.

Semantic similarity effects provide critical insight into the organization of semantic knowledge and the nature of semantic processing. In the present study, we examined the dynamics of semantic similarity effects by using the visual world eyetracking paradigm. Four objects were shown on a computer monitor, and participants were instructed to click on a named object, during which time their gaze position was recorded. The likelihood of fixating competitor objects was predicted by the degree of semantic similarity to the target concept. We found reliable, graded competition that depended on degree of target-competitor similarity, even for distantly related items for which priming has not been found in previous priming studies. Time course measures revealed a consistently earlier fixation peak for near semantic neighbors relative to targets. Computational investigations with an attractor dynamical model, a spreading activation model, and a decision model revealed that a combination of excitatory and inhibitory mechanisms is required to obtain such peak timing, providing new constraints on models of semantic processing.

Contrast effects in priming paradigms: Implications for theory and research on implicit attitudes.

Contrast effects have been studied in dozens of experimental paradigms, including the measurement of attitudes in the social psychological literature. However, nearly all of this work has been conducted using explicit reports. In the present research the authors employed a variety of different types of priming tasks in order to gain insight into the nature of contrast effects and the role that automatic processes might play in their emergence. They report 6 experiments. In Experiments 1 and 2 the replicability and robustness of automatized contrast effects across 2 types of implicit tasks are established. Experiments 3-6 were conducted in order to further understand the nature of these effects and whether they are best understood in terms of spreading activation vs. response-based models of priming. In the course of accounting for their findings, the authors propose and validate a response-mapping framework, which provides insight into some longstanding ambiguities in the priming literature. Implications for theories of contrast and models of evaluative priming are discussed.

Clustering algorithm for Ad Hoc network based on spreading activation model

Security factors and their impacts on network performance are not considered simultaneously in traditional clustering methods. As for this problem, a novel trust relation based clustering algorithm for Ad Hoc network was proposed, which combined the spreading activation model, an organization structure for human long-term memory, with trust relation graph, and can partition the whole network into many clusters according to limited local knowledge automatically. Simulation results show that the newly proposed distributed algorithm has good clustering accuracy similar to the centralized clustering approach, that is to say, it can improve the performance of Ad Hoc networks, and at the same time can improve its security.

Category Activation Model: A Spreading Activation Network Model of Subcategory Positioning When Categorization Uncertainty Is High

We develop a spreading activation model, which we call the category activation model, to predict where within a category structure consumers are likely to position a subcategory that they have created to accommodate a new hybrid product. Based on this model, we hypothesize that the probability that an individual will position a new category subordinate to a particular category i is proportional to the relative number of categories that are already subordinate to i. We report the results of two studies that support this hypothesis and provide evidence that accessibility is an underlying mechanism.

Priming effects of television news bumpers and teasers on attention and memory

This study uses the spreading activation model of information processing to examine the effects of television news previews called bumpers and teasers. Bumpers occur at the beginning of a newscast and teasers occur before commercial breaks. News stories that were preceded by teasers or by both bumpers and teasers elicited more attention than stories that were not primed by a bumper or a teaser. In addition, the verbal information in news stories primed by bumpers and teasers was remembered better. Viewers did not allocate more attention to news stories that had been introduced with bumpers, and priming seemed to have little effect on visual memory.

“Inquiring Love of This World”: An Implicit Love Theory of Chinese University Students

This paper reports a preliminary descriptive approach to the representation of the concept of love. Based on Spreading Activation Model, the word of love was presented as a stimulus to which 278 college students were asked to respond with at least 15 words/phrases that came to mind. Then top 100 love-related words/phrases with frequency above 4.3% were collected as units of analysis. Based on the interrelations among those words/phrases, a complete-linkage cluster analysis reached 5 high-order clusters, i.e. the five facets of the representation of love concept: 1) ethics and responsibilities; 2) romantic experience; 3) conflict and pain; 4) rationality; 5) friendly care. The results show that word associations can fundamentally reflect some differences in cognition and culture of love. Compared with Sternberg’s triangular theory of love, the findings show differences in two clusters: “conflict and pain” and “rationality”, which are not included in the triangle of love. More importantly, this research contributes to literatures on Chinese people’s love and will also inspire future research with some meaningful results.

Modeling individual cognitive structure in contextual information retrieval

In contextual information retrieval (CIR), the retrieval of information depends on the time and place of the submitting query, history of interaction, task in hand, and many other factors that are not given explicitly, but lie implicitly in the interaction and surroundings of searching, namely the context [P. Ingwersen, N. Belkin, Information retrieval in context, ACM SIGIR Forum 2 (2004)]. A user’s individual cognition is one of important contextual factors to help understand his or her personal needs. In this paper, we give a formal definition for a user’s individual cognitive structure (ICS) in CIR, and propose an approach called DOSAM to model it. DOSAM is inspired by the spreading activation model of psychology, and built on the domain ontology, while its goal is to get a user’s cognitive structure. Cost analysis of construction algorithm shows that it is feasible to get ICS by DOSAM, and personalized search experimental results on a digital library indicate that ICS based search can improve the search effectiveness and a user’s satisfaction.

활성화 확산 모형을 이용한 아이콘 평가

Icon conveys symbolic information which they have become ubiquitous as main element of GUI. However, users often cannot intuitively understand its functional meanings. Icon has to be designed for users to easily understand functional meanings. In order to evaluate icon, the spreading activation model can be used to effectively understand the process of information retrieval. In asymmetric spreading activation model is that in the two nodes the degree of spreading activation is different according to direction. Thus, asymmetric spreading activation theory was performed evaluating the strength of association when users see visual image to associate their verbal meaning (visual image - verbal meaning pair) and users see verbal meaning to associate their visual image (verbal meaning - visual image pair). According to the direction, this study hypothesizes that the well-designed icons have symmetric relationship rather than asymmetric relationship between the two pairs. The strength of association is measured through the reaction time and the accuracy rate. In performing SAT (spreading activation test), the ten icons were selected as word processing software icons. After first SAT, newly designed icons were developed based on Korean mental model, and second SAT was conducted using them. The results showed that the accuracy rate of newly designed icon has been improved. Also there is significant difference of reaction time between current icons and newly designed icons. Well-designed icon is confirmed that the strength of association relationship arises symmetrically rather than asymmetrically between the two pairs. User centered icons could be designed by improving the strength of association between the two pairs. Asymmetric SAT evaluates the strength of the association between the visual image and the verbal meanings to contribute to the development of icon which it related to human's association structure.

CAM: A Spreading Activation Network Model of Subcategory Construction when Categorization Uncertainty is High

Many new products (e.g., PDA phones) share features with multiple categories, but are also substantially different from each of these categories. When consumers encounter such a product, they may create a new subcategory (e.g., smart phones) to accommodate it. In such situations, consumers must decide where within the category structure to position the new subcategory (e.g., under the PDA category or under the phone category). We develop a spreading activation model that we call the Category Activation Model (CAM) to predict where within a category structure consumers are likely to position a subcategory that they have created to accommodate a new, hybrid product. Based on this model, we hypothesize that the probability that an individual will position a new category subordinate to a particular category i is proportional to the relative number of categories that are already subordinate to i. We report the results of three studies that support this hypothesis, and provide evidence that accessibility is an underlying mechanism.