Suboptimal tradeoffs in information seeking

Fu, Wai Tat; Gray, Wayne D.

doi:10.1016/j.cogpsych.2005.08.002

Cited by 122 publications

(82 citation statements)

References 34 publications

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“…To preview our results, we found that selection of links seem to be sensitive to their position on the web page. The results led us to refine our model to SNIF-ACT 2.0, in which we incorporated mechanisms from the Bayesian satisficing model (Fu & Gray, 2006;Fu, in press) that combine the measure of information scent and the position of links on the web page into a satisficing process that determines which link to select.…”

Section: Snif-act 10mentioning

confidence: 99%

“…SNIF-ACT 1.0 establishes how information scent is used in navigation, but makes the strong assumption that all links from a Web page are attended and assessed prior to a decision about the next navigation action. SNIF-ACT 2.0 extends the first version of the model by incorporating the Bayesian satisficing model (Fu & Gray, 2006;Fu, in press) in the evaluation of Web links. The process of satisficing assumes that, instead of searching for the optimal choice, choices are often made once they are good enough based on some estimation of the characteristics of the environment.…”

Section: Introductionmentioning

confidence: 99%

“…SNIF-ACT provides an account of how people use information scent cues, such as the text associated with Web links, in order to make navigation decisions such as judging where to go next on the Web, or when to give up on a particular path of knowledge search. SNIF-ACT is shaped by rational analyses of the Web developed by combining the Bayesian satisficing model (Fu & Gray, 2006;Fu, in press) with the information foraging theory (Pirolli, 2005;Pirolli & Card, 1999), and is implemented in a modified version of the ACT-R cognitive architecture (Anderson et. al, 2004) 2 .…”

Section: Introductionmentioning

confidence: 99%

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SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web

Pirolli

2007

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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Section: Snif-act 10mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web

Pirolli

2007

150

250

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“…However, these aids can act as shortcuts and prevents the trainees from active exploration which is necessary to perform the task independently in non-supervised environment [10][11][12]. Therefore, a mechanism is needed to use these aids while minimizing their negative effects.…”

Section: Introductionmentioning

confidence: 99%

Exploring 3D-Virtual Learning Environments with Adaptive Repetitions

Alam¹,

Ullah²,

Ali³

2017

IOSR JM

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I. Background3D-Virtual Learning Environments (3D-VLEs) are the representation of 3D space inside the computer memory which enables students to navigate freely inside the virtual world and process different objects in real time [1,2]. Adaptive 3D-Virtual Learning Environments (3D-VLEs) change its contents at run time according to the learning needs of individual student [3,4]. These environments have many advantages. It display personalized learning contents to each student, reduces the probability of astray navigation inside the virtual environment and makes the distinction between education and entertainment which improve the learning process of students [5][6][7] . In literature a lot of work has been done which support the use of cognitive aids (Audio, Visual and textual etc) in virtual learning environments. For example, Nguyen et al. [8] used lighting source , arrows, and a compass as navigational guiding aids which help trainees to find the target. Gavish et al. [9] presented a 3D virtual puzzle in which verbal instructions plus mouse pointing are used to find the target location on learner screen. Similarly, Ali et al. [3] developed Multimodal Virtual laboratory (MMVL in which students were provided both audio and visual support during experiments which improved their efficiency.The use of these aids reduce mental load on learner as it provide a specific and easy to follow instructions mechanism to perform the task [9]. However, these aids can act as shortcuts and prevents the trainees from active exploration which is necessary to perform the task independently in non-supervised environment [10][11][12]. Therefore, a mechanism is needed to use these aids while minimizing their negative effects. Jorge, R. et al.[13] recommended the control use of cognitive aids and proved that it will not affect the performance of the trainee in the real environment. Similarly, Rehman et al. [14], suggested the use of semantic aids with direct aids to maximize active exploration in virtual environments. In this paper we used adaptive repetition as a strategy to explore the 3D-Virtual Learning environments. The proposed approach enables the trainee to get the benefits of cognitive support while at the same time he is actively involved to explore the environment. We also tried to find the number of adaptive repetition required by the weak, average and good students to actively explore the virtual environment and perform the task successfully in non-supervised environment". The remaining paper is organized as below.In section II we discussed the proposed approach followed by implementation in section III. In section IV we presented experimentations. Discussion is given in section V followed by conclusion in section VII. II. The Proposed Approach The Learning Decision Function (LDF)With the help of Learning Decision Function (LDF), we can measure the learning skill of students in 3D-VLEs and declare it as weak, average and good learners [15]. The LDF takes time, no of errors and test scores of a student as inputs, calculate ...

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“…In decision making theory and more generally in cognitive sciences, the famous "effort-accuracy " and "speed-accuracy" trade off (see Payne et al [1] and Rinkenauer [2]) examine how animals and agents balance between the quality of a decision, how "good enough" it must be, and the time, speed and effort spend to find such a "good enough" decision, some solution to a problem. More generally, biology and behavioral sciences have emphasized the proeminent role of the famous "exploration-exploitation" trade off, Holland [3] in biology, March [4] in management sciences, and their followers in organizational learning ( Levitt-March, [5], Cohen-Levinthal [6], Levinthal-March [7], and an impressive list of others), Fu-Gray [8] and Fu [9], in Psychology...…”

Section: Introductionmentioning

confidence: 99%

Proximal Point Method on Finslerian Manifolds and the “Effort–Accuracy” Trade-off

Neto

Oliveira

Soares³

et al. 2013

J Optim Theory Appl

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In this paper we consider minimization problems with constraints. We will show that if the set of constraints is a Finslerian manifold of non positive flag curvature, and the objective function is differentiable and satisfies the property Kurdyka-Lojasiewicz, then the proximal point method is naturally extended to solve that class of problems. We will prove that the sequence generated by our method is well defined and converges to a minimizer point. We show how tools of Finslerian geometry, more specifically non symmetrical metrics, can be used to solve nonconvex constrained problems in Euclidean spaces. As an application, we give one result on the speed of decision and making and costs to change.

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Suboptimal tradeoffs in information seeking

Cited by 122 publications

References 34 publications

SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web

SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web

Exploring 3D-Virtual Learning Environments with Adaptive Repetitions

Proximal Point Method on Finslerian Manifolds and the “Effort–Accuracy” Trade-off

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