Supporting resource-based analysis of task information needs

We investigate here an approach to modelling the dynamic information requirements of a user performing a number of tasks, addressing both the provision and representation of information, viewing the information as being distributed across a set of resources. From knowledge of available resources at the user interface, and task information needs we can identify whether the system provides the user with adequate support for task execution. We look at how we can use tools to help reason about these issues, and illustrate their use through an example.We also consider a full range of analyses suggested using this approach which could potentially be supported by automated reasoning systems.(undefined

Semantic diversity:A measure of contextual variation in word meaning based on latent semantic analysis

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials. © 2012 Psychonomic Society, Inc

Investigating Executive Working Memory and Phonological Short-Term Memory in Relation to Fluency and Self-Repair Behavior in L2 Speech

This paper reports the findings of a study investigating the relationship of executive working memory (WM) and phonological short-term memory (PSTM) to fluency and self-repair behavior during an unrehearsed oral task performed by second language (L2) speakers of English at two levels of proficiency, elementary and lower intermediate. Correlational analyses revealed a negative relationship between executive WM and number of pauses in the lower intermediate L2 speakers. However, no reliable association was found in our sample between executive WM or PSTM and self-repair behavior in terms of either frequency or type of self-repair. Taken together, our findings suggest that while executive WM may enhance performance at the conceptualization and formulation stages of the speech production process, self-repair behavior in L2 speakers may depend on factors other than working memory

Understanding multitasking through parallelized strategy exploration and individualized cognitive modeling

Human multitasking often involves complex task interactions and subtle tradeoffs which might be best understood through detailed computational cognitive modeling, yet traditional cognitive modeling approaches may not explore a sufficient range of task strategies to reveal the true complexity of multitasking behavior. This study proposes a systematic approach for exploring a large number of strategies using a computer-cluster-based parallelized modeling system. The paper demonstrates the efficacy of the approach for investigating and revealing the effects of different microstrategies on human performance, both within and across individuals, for a time-pressured multimodal dual task. The modeling results suggest that multitasking performance is not simply a matter of interleaving cognitive and sensorimotor processing but is instead heavily influenced by the selection of subtask microstrategies. Author Keywords Cognitive modeling; high performance computing; mode

Usercentric Operational Decision Making in Distributed Information Retrieval

Information specialists in enterprises regularly use distributed information retrieval (DIR) systems that query a large number of information retrieval (IR) systems, merge the retrieved results, and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, because different servers handle collections of different sizes and have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the DIR system has to decide which servers to query, how long to wait for responses, and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user’s cost associated with waiting for the servers to respond. We formulate the broker’s decision problem as a stochastic mixed-integer program and present analytical solutions for the problem. Using data gathered from FedStats—a system that queries IR engines of several U.S. federal agencies—we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, simulations suggest that the technique can be applied to solve the broker’s decision problem under more complex decision environments

Towards a framework for attention cueing in instructional animations: Guidelines for research and design

This paper examines the transferability of successful cueing approaches from text and static visualization research to animations. Theories of visual attention and learning as well as empirical evidence for the instructional effectiveness of attention cueing are reviewed and, based on Mayer’s theory of multimedia learning, a framework was developed for classifying three functions for cueing: (1) selection—cues guide attention to specific locations, (2) organization—cues emphasize structure, and (3) integration—cues explicate relations between and within elements. The framework was used to structure the discussion of studies on cueing in animations. It is concluded that attentional cues may facilitate the selection of information in animations and sometimes improve learning, whereas organizational and relational cueing requires more consideration on how to enhance understanding. Consequently, it is suggested to develop cues that work in animations rather than borrowing effective cues from static representations. Guidelines for future research on attention cueing in animations are presented

GDI as an alternative guiding interaction style for occasional users.

Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policiesIt is usually taken for granted that Direct Manipulation is the best interaction style for inexperienced or non-expert users; moreover, this style of interaction is generally considered the best for almost every situation and user. The recent shifts in technology that we all are currently experiencing have given rise to a great deal of new kinds of users performing specific tasks in a variety of scenarios. In this paper, we focus on users who access a system occasionally, infrequently, or in an unplanned way; i.e., users who do not want or cannot afford a learning curve. We show that for them, Direct Manipulation is not always the most suitable style of interaction. We assess the advantages of guiding this kind of users, in particular through the guided interaction frame- work known as Goal Driven Interaction. GDI can be viewed as a superset of wizards providing support far beyond a few steps through dialogs. Indeed, GDI is an interaction style with characteristics of its own. We report a complete user test that backs up previous hypotheses. The analysis of empirical data proves that GDI is more time-efficient than DM, requiring fewer moderator assistances for the users. Post-test questionnaires confirmed that participants had a strong preference for GDI

Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the min