Implications of Computational Cognitive Models for Information Retrieval (original) (raw)
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Cognitive modelling and Web search: Some heuristics and insights
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The role of Mental Models in Information Retrieval
Searchers in retrieval data systems, affected by various factors, used different approaches and methods. So, with proper knowledge and control of these factors can lead to identified and targeted paths Information seeking behavior. In this system, it is important to be appropriate bed for interaction between users and systems that one of them is mental model. So, aim of this study is to familiarity with mental models of users and interacts with information retrieval systems, such as web. This narrative review was conducted in 2015, searching the literature using the term of " mental model " and " information searching behavior " in databases, including PubMed, WOS, Science Direct, Emerald, Oxford, Magiran, and Scientific Information Database (SID) and Springer. After that, the relevant abstracts were reviewed and their full texts were extracted and classified using content analysis. Results showed that mental models have capability of being identified and extracted. Many users may have weak or incorrect mental models and mental models may affect the function of users and there are variety techniques to extract mental models and they should be used combined in order to reach better conclusion. Research done in the field of information retrieval showed the importance of this subject and since many users has defective mental models that cause to faint function, so the study of these models is essential in order to create and develop better information retrieval systems.
On models of information retrieval processes
Information Systems, 1979
A model called the binary independence model is presented as a generalization of a few models which have applied to the analyses of a clustered search process, some indexing strategies and a relevance feedback process. This model, together with the Swets model, the linked-2-Poisson model. the 2-Poisson model. the binary limited-dependence model, the tree dependence model. the binary dependence model and the non-binary independent model are compared and contrasted. Despite the fact that these models are intended for different applications. three aspects of modelling are identified, based on which, these models are compared. The three aspects are the class concept, the distribution of similarities and the relation between a matching function and its retrieval effectiveness. As results of the comparison between the models, more insight into the models is gained and a set of guidelines are suggested to help a system designer to choose a model. 1. I~TRODU~O~ The need for evaluation of information retrieval systems has long been recognized. Among the earliest attempts to evaluate information systems systematicaliy were the Cranfield project documented by Cleverdon et 01. fl J and Salton's SMART system(2j. Recall and precisionS are the measures of effectiveness in both projects. A different measure has been proposed by Swets[3,4] based on a probabilistic model. Although there have been doubts about the validity of some aspects of the hypotheses he made[5,33], he is the first one to propose a model which has directly related to retrieval performance, and the model has received more attention than any other models in information retrieval. After Swets model, there have been many other models proposed for evaluating various information retrieval processes. Since these models are intended for different applications, it is difficult to find common grounds to compare and contrast them. This paper is an attempt to examine the models and to find out their similarities and differences. The models to be presented in this paper are the Swets model[3,4], the 2-Poisson model [il. 201, the Linked-t-Poisson mode1[233, the binary independence model[6-8,18], the binary limiteddependence model f lo], the tree-dependence model B&37], the non-bin~y inde~nden~e model and the binary dependence model. The binary independence model is first presented in .some details so that its ter-*This research was supported in part by a grant from the National Research Council of Canada. SRecall is the proportion of the items relevant to the request which are retrieved. The proportion of retrieved items which are relevant to the request is precision.
A Computational Cognitive Model of Information Search in Textual Materials
Document foraging for information is a crucial and increasingly prevalent activity nowadays. We designed a computational cognitive model to simulate the oculomotor scanpath of an average web user searching for specific information from textual materials. In particular, the developed model dynamically combines visual, semantic, and memory processes to predict the user's focus of attention during information seeking from paragraphs of text. A series of psychological experiments was conducted using eye-tracking techniques in order to validate and refine the proposed model. Comparisons between model simulations and human data are reported and discussed taking into account the strengths and shortcomings of the model. The proposed model provides a unique contribution to the investigation of the cognitive processes involved during information search and bears significant implications for web page design and evaluation.
Towards a cognitive theory of information retrieval
Interacting with Computers, 1998
A framework for constructing a cognitive model of users' information searching behaviour is described. The motivation for the framework is to create explanatory and predictive theories of information searching to improve the design of information retrieval (IR) systems. The framework proposes a taxonomy of components for process models of the information seeking task, information need types and knowledge sources necessary to support the task. The framework is developed into a preliminary version of a cognitive theory of information searching by the addition of strategies and correspondence rules which predict user behaviour in different task stages according to information need types, facilities provided by the IR system and knowledge held by the user. The theory is evaluated by using claims analysis based on empirical observations of users information retrieval and by a walkthrough of an IR session to investigate how well the theory can account for empirical evidence. Results show that the theory can indicate the expert strategies which should be followed in different task contexts but predictions of actual user behaviour are less accurate. The future possibilities for employing the theoretical model as a tutorial advisor for information retrieval and as an evaluation method for IR systems are reviewed. The role and potential of cognitive theories of user task-action in Information Retrieval and Human Computer Interaction are discussed. 0 1997 Elsevier Science B.V.
On Human Information Processing in Information Retrieval (Position Paper)
Experimental psychology, cognitive science or, more recently, cognitive neuroscience, is the main framework to place hu- man information processing under extensive empirical scru- tiny. The last decade has seen a surge of interest in the appli- cation of psychological measurements for evaluating increas- ingly complex human-technology interactions. While most welcome from the psychological perspective, we propose that the use of these methodologies should not rely only on the application of sophisticated measurement tools, but also on the application of contemporary knowledge on psychological phenomena and dynamics of human information processing. In addition, we argue that the latest developments in mul- timodal signals and data mining techniques offer a unique opportunity to extend psychological methodologies to large scale testing grounds. Thus, the application of psychological knowledge to information retrieval research will not only be beneficial for the latter, but for the former as well, inasmuch as information retrieval provides a real field of application for its hypotheses about human information processing.
Perceptions of document relevance
Frontiers in Psychology, 2014
This article presents a study of how humans perceive and judge the relevance of documents. Humans are adept at making reasonably robust and quick decisions about what information is relevant to them, despite the ever increasing complexity and volume of their surrounding information environment. The literature on document relevance has identified various dimensions of relevance (e.g., topicality, novelty, etc.), however little is understood about how these dimensions may interact. We performed a crowdsourced study of how human subjects judge two relevance dimensions in relation to document snippets retrieved from an internet search engine. The order of the judgment was controlled. For those judgments exhibiting an order effect, a q-test was performed to determine whether the order effects can be explained by a quantum decision model based on incompatible decision perspectives. Some evidence of incompatibility was found which suggests incompatible decision perspectives is appropriate for explaining interacting dimensions of relevance in such instances.
A Review of the Cognitive Information Retrieval Concept, Process and Techniques
Journal of Global Research in Computer Sciences, 2013
The word "cognitive" refers to the thought process toward awareness or knowledge. In terms of Cognitive Science, it provides bridge between information processing, conceptualization of the resources, perceptual skills and topics related to the cognitive psychology. By retrieving information that based on cognitive concepts, process and techniques one can represent the current user’s information need, their problem state and domain work or area of interest in the outline of structure and casualties. This poly-representational approach leads to cognitive process which is multitasking in the way of perception, attention, interpretation, understanding and remembrance of human behaviour interaction. With the help of implementation techniques of relevance feedback which validate and provide reliability metrics to calculate user behaviour using knowledge domain visualization, Training frameworks provide users how to proceed in searching and retrieving information
Relevance theory and distributions of judgments in document retrieval
Information Processing & Management
This article extends relevance theory (RT) from linguistic pragmatics into information retrieval. Using more than 50 retrieval experiments from the literature as examples, it applies RT to explain the frequency distributions of documents on relevance scales with three or more points. The scale points, which judges in experiments must consider in addition to queries and documents, are communications from researchers. In RT, the relevance of a communication varies directly with its cognitive effects and inversely with the effort of processing it. Researchers define and/or label the scale points to measure the cognitive effects of documents on judges. However, they apparently assume that all scale points as presented are equally easy for judges to process. Yet the notion that points cost variable effort explains fairly well the frequency distributions of judgments across them. By hypothesis, points that cost more effort are chosen by judges less frequently. Effort varies with the vagueness or strictness of scale-point labels and definitions. It is shown that vague scales tend to produce U-or V-shaped distributions, while strict scales tend to produce right-skewed distributions. These results reinforce the paper's more general argument that RT clarifies the concept of relevance in the dialogues of retrieval evaluation.
2017
Disposing or not, of the necessary information at the right time, can mean the success or failure of any operation.. The field of information retrieval since its inception in the year 1950, has provided tools that allow users to find answers to their needs and questions. Information retrieval systems are the most used internationally, since they have interfaces and functionalities easy to understand. The main function of these systems is track the web, store the information found and then respond to user queries. Due to the large amount of information that have search engines, are a rich source of knowledge and support decision-making on information published on the web. Companies like Google do not provide concrete information of which models they use to develop the components of their search engines. In addition the calculation of the relevance of their documents responds to commercial and governmental policies, reason why it is difficult to develop systems as complex as the search engines without owning a computational model that supports the process of development of the same. The present article gives the design of a computational model for document processing and support decision-making in information retrieval systems used to design, development and deployment of searchers at national and international level.