The Experts below are selected from a list of 22662 Experts worldwide ranked by ideXlab platform
Peter Pirolli - One of the best experts on this subject based on the ideXlab platform.
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ACT-R models of Information Foraging in geospatial intelligence tasks
Computational and Mathematical Organization Theory, 2015Co-Authors: Jaehyon Paik, Peter PirolliAbstract:We describe the development of computational cognitive models that predict Information selection behavior in simulated geospatial intelligence tasks. These map-based tasks require users to select layers that visualize different types of intelligence, and to revise probability estimates of attack by hypothetical insurgent groups. Our first model has vast amounts of task-specific declarative memory and selects Information layers that provide maximum expected Information gain. This first model exhibits layer selection sequences that are almost identical to a rational (Bayesian) model, but fails to predict the layer selection sequences of human participants’ performing the tasks. Our second model integrates instance-based learning with reinforcement learning and Information Foraging theory to predict the selection of Information layers. The second model replicates the distribution of participants’ layer selection sequences well. We conclude with some limitations that our current ACT-R model has and the role of cognitive models in the intelligence analysis tasks.
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informavores active Information Foraging and human cognition
Conference Cognitive Science, 2013Co-Authors: Douglas Markant, Todd M Gureckis, Bjorn Meder, Jonathan D Nelson, Peter PirolliAbstract:Informavores: Active Information Foraging and human cognition Doug Markant (Moderator) and Todd Gureckis Dept. of Psychology, New York University Bj¨orn Meder and Jonathan D. Nelson Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development Peter Pirolli Palo Alto Research Center Chen Yu Dept. of Psychological and Brain Sciences, Indiana University in humans. The symposium should appeal to a broad set of attendees including educators, developmental psychologists, cognitive modelers, and computer scientists. The influence of priors on sequential search decisions - Keywords: active learning, self-directed learning, Information search, sensemaking Just as the body survives by ingesting negative entropy, so the mind survives by ingesting Information. In a very general sense, all higher organisms are informavores. - Miller (1983) Doug Markant and Todd Gureckis Normative models of Information acquisition predict that people’s search decisions should be strongly influenced by their prior beliefs, which capture the set of alternative hy- potheses they are considering. In the present experiments we tested whether people adjusted their Information search be- havior in response to sequential changes in the prior. Par- ticipants played a search game in which they had to identify the shape and location of multiple hidden targets in a display (similar to the board game Battleship). During the task they were told that the set of possible shapes had changed, and the key question was whether they would adjust their search deci- sions according to the predictions of a normative model. Ma- nipulations of the prior included changes in the frequency of certain classes of targets as well as the introduction of higher- order constraints (e.g., that all targets would have the same shape). The results showed that an individual’s prior could be recovered from their sequences of search decisions, but that there were notable differences in their ability to adjust to certain changes in the hypothesis space, an effect that is not predicted by the normative model. We discuss the implica- tions of these findings for how people generate and represent hypotheses during the course of Information Foraging. Is people’s Information search behavior sensitive to differ- ¨ Meder and Jonathan Nelson ent reward structures? - Bj orn In situations where humans actively acquire Information for classification, Information search preferentially maxi- mizes accuracy (Nelson et al., 2010). However, the goal of obtaining Information to improve classification accuracy can strongly conflict with the goal of obtaining Information for improving utility when there are asymmetries in costs and benefits for classification decisions (e.g., in many medical diagnosis situations). Is people’s Information search behav- ior sensitive to such asymmetries? We addressed this ex- perimentally via multiple-cue probabilistic category-learning and Information-search experiments, where the payoffs cor- responded either to accuracy, with equal rewards associ- ated with the two categories, or to an asymmetric payoff function with different rewards associated with each cate- Unlike a passive sponge floating in a sea of Information, humans are active Information foragers – informavores – who gather and consume new knowledge. From controlling the movement of our eyes to determining which sources of news to consult, judging the quality of alternative sources of in- formation is a critical part of our behavior. The goal of this symposium is to bring together researchers who are working to understand the cognitive processes underlying active in- formation Foraging and how they interact with more general aspects of cognition. The study of active Information search is in the midst of a renaissance. Psychological research from diverse areas rang- ing from developmental psychology (Schulz & Bonawitz, 2007), to higher level cognition (Nelson, 2005) to visual per- ception (Najemnik & Geisler, 2005) have begun to under- stand Information gathering strategies in terms of a common set of computational principles. Simultaneous developments in machine learning on “active vision” and “active learn- ing” (Settles, 2009) have resulted in new algorithms that op- timize their own learning by focusing on useful training data. Similarly, models from optimal Foraging theory from biology are being brought to bear on cognitive search processes both within and outside the mind (Pirolli, 2007; Todd, Hills, & Robbins, 2012). This symposium aims to bring together leading experts in this area to discuss how active Information Foraging can be understood from a diverse set of perspectives within cognitive science. Key themes include how prior knowledge influences search (Markant & Gureckis), how Information and reward interact to determine choice (Meder & Nelson), developmen- tal patterns in Information seeking behavior (Nelson et al.), Information Foraging in complex sensemaking tasks (Pirolli), and the allocation of attention during statistical word learn- ing (Yu). While each represents a distinct area of research, all discussants in the symposium share a core approach of apply- ing computational models to understand Information search
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CogSci - Informavores: Active Information Foraging and human cognition
2013Co-Authors: Douglas Markant, Todd M Gureckis, Bjorn Meder, Jonathan D Nelson, Peter PirolliAbstract:Informavores: Active Information Foraging and human cognition Doug Markant (Moderator) and Todd Gureckis Dept. of Psychology, New York University Bj¨orn Meder and Jonathan D. Nelson Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development Peter Pirolli Palo Alto Research Center Chen Yu Dept. of Psychological and Brain Sciences, Indiana University in humans. The symposium should appeal to a broad set of attendees including educators, developmental psychologists, cognitive modelers, and computer scientists. The influence of priors on sequential search decisions - Keywords: active learning, self-directed learning, Information search, sensemaking Just as the body survives by ingesting negative entropy, so the mind survives by ingesting Information. In a very general sense, all higher organisms are informavores. - Miller (1983) Doug Markant and Todd Gureckis Normative models of Information acquisition predict that people’s search decisions should be strongly influenced by their prior beliefs, which capture the set of alternative hy- potheses they are considering. In the present experiments we tested whether people adjusted their Information search be- havior in response to sequential changes in the prior. Par- ticipants played a search game in which they had to identify the shape and location of multiple hidden targets in a display (similar to the board game Battleship). During the task they were told that the set of possible shapes had changed, and the key question was whether they would adjust their search deci- sions according to the predictions of a normative model. Ma- nipulations of the prior included changes in the frequency of certain classes of targets as well as the introduction of higher- order constraints (e.g., that all targets would have the same shape). The results showed that an individual’s prior could be recovered from their sequences of search decisions, but that there were notable differences in their ability to adjust to certain changes in the hypothesis space, an effect that is not predicted by the normative model. We discuss the implica- tions of these findings for how people generate and represent hypotheses during the course of Information Foraging. Is people’s Information search behavior sensitive to differ- ¨ Meder and Jonathan Nelson ent reward structures? - Bj orn In situations where humans actively acquire Information for classification, Information search preferentially maxi- mizes accuracy (Nelson et al., 2010). However, the goal of obtaining Information to improve classification accuracy can strongly conflict with the goal of obtaining Information for improving utility when there are asymmetries in costs and benefits for classification decisions (e.g., in many medical diagnosis situations). Is people’s Information search behav- ior sensitive to such asymmetries? We addressed this ex- perimentally via multiple-cue probabilistic category-learning and Information-search experiments, where the payoffs cor- responded either to accuracy, with equal rewards associ- ated with the two categories, or to an asymmetric payoff function with different rewards associated with each cate- Unlike a passive sponge floating in a sea of Information, humans are active Information foragers – informavores – who gather and consume new knowledge. From controlling the movement of our eyes to determining which sources of news to consult, judging the quality of alternative sources of in- formation is a critical part of our behavior. The goal of this symposium is to bring together researchers who are working to understand the cognitive processes underlying active in- formation Foraging and how they interact with more general aspects of cognition. The study of active Information search is in the midst of a renaissance. Psychological research from diverse areas rang- ing from developmental psychology (Schulz & Bonawitz, 2007), to higher level cognition (Nelson, 2005) to visual per- ception (Najemnik & Geisler, 2005) have begun to under- stand Information gathering strategies in terms of a common set of computational principles. Simultaneous developments in machine learning on “active vision” and “active learn- ing” (Settles, 2009) have resulted in new algorithms that op- timize their own learning by focusing on useful training data. Similarly, models from optimal Foraging theory from biology are being brought to bear on cognitive search processes both within and outside the mind (Pirolli, 2007; Todd, Hills, & Robbins, 2012). This symposium aims to bring together leading experts in this area to discuss how active Information Foraging can be understood from a diverse set of perspectives within cognitive science. Key themes include how prior knowledge influences search (Markant & Gureckis), how Information and reward interact to determine choice (Meder & Nelson), developmen- tal patterns in Information seeking behavior (Nelson et al.), Information Foraging in complex sensemaking tasks (Pirolli), and the allocation of attention during statistical word learn- ing (Yu). While each represents a distinct area of research, all discussants in the symposium share a core approach of apply- ing computational models to understand Information search
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an elementary social Information Foraging model
Human Factors in Computing Systems, 2009Co-Authors: Peter PirolliAbstract:User interfaces and Information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a variety of observed phenomena in social Information Foraging and sketches a framework extending Information Foraging Theory towards making predictions about the effects of diversity, interference, and cost-of-effort on performance time, participation rates, and utility of discoveries.
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CHI - An elementary social Information Foraging model
Proceedings of the 27th international conference on Human factors in computing systems - CHI 09, 2009Co-Authors: Peter PirolliAbstract:User interfaces and Information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a variety of observed phenomena in social Information Foraging and sketches a framework extending Information Foraging Theory towards making predictions about the effects of diversity, interference, and cost-of-effort on performance time, participation rates, and utility of discoveries.
Jessie Chin - One of the best experts on this subject based on the ideXlab platform.
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Information Foraging Across the Life Span: Search and Switch in Unknown Patches
Topics in cognitive science, 2015Co-Authors: Jessie Chin, Brennan R Payne, Daniel G Morrow, Elizabeth A. L. Stine-morrowAbstract:In this study, we used a word search puzzle paradigm to investigate age differences in the rate of Information gain (RG; i.e., word gain as a function of time) and the cues used to make patch-departure decisions in Information Foraging. The likelihood of patch departure increased as the profitability of the patch decreased generally. Both younger and older adults persisted past the point of optimality as defined by the marginal value theorem (Charnov, 1976), which assumes perfect knowledge of the Foraging ecology. Nevertheless, there was evidence that adults were rational in terms of being sensitive to the change in RG for making the patch-departure decisions. However, given the limitations in cognitive resources and knowledge about the ecology, the estimation of RG may not be accurate. Younger adults were more likely to leave the puzzle as the long-term RG incrementally decreased, whereas older adults were more likely to leave the puzzle as the local RG decreased. However, older adults with better executive control were more likely to adjust their likelihood of patch-departure decisions to the long-term change in RG. Thus, age-dependent reliance on the long-term or local change in RG to make patch-departure decisions might be due to individual differences in executive control.
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Information Foraging in the unknown patches across the life span
Cognitive Science, 2012Co-Authors: Jessie Chin, Brennan R Payne, Andrew Battles, Daniel G Morrow, Elizabeth A L StinemorrowAbstract:Information Foraging in the Unknown Patches across the Life Span Jessie Chin 1 (chin5@illinois.edu), Brennan Payne 1 (payne12@illinois.edu), Andrew Battles 2 (battles2@illinois.edu), Wai-Tat Fu 3 (wfu@illinois.edu), Daniel Morrow 1 (dgm@illinois.edu), Elizabeth A. L. Stine-Morrow 1 (eals@illinois.edu) Department of Educational Psychology, 2 Department of Electrical & Computer Engineering, Department of Computer Science University of Illinois at Urbana Champaign 405 N Mathews Ave Urbana, IL 61801 USA Abstract This study used a word search puzzle paradigm to examine the effects of task environment and individual differences in ability on Information Foraging. Younger and older adults attempted to maximize the number of items found in a set of 4 puzzles in which they were at liberty to search within a puzzle or switch between them. Younger adults demonstrated faster uptake (i.e., number of words found as a function of time) from individual puzzles than older adults but experienced more deceleration of rates during the search. Additionally, older adults switched less often and their switching was less dependent on the uptake rate compared to younger adults. Both younger and older adults stayed longer than was optimal in a patch, older adults were especially likely to persevere suboptimally. Collectively, these results suggest that individuals may differentially optimize Information gain through self-regulation of exploration and exploitation. Keywords: Information Foraging; cognitive aging; adaptive behavior. Information uptake; Introduction Self-regulation of cognition in natural environments almost always involves alternating phases of exploration, which entails search in the service of deciding how effort will be allocated, and exploitation, or task engagement in which effort is allocated to meet task-specific goals. Information Foraging (IF) models are predicated on an analogy between these regulatory processes and the way in which animals forage for food in the wild. Information Foraging has been used to account for how people search for Information in external environments, such as the WWW (e.g., Fu & Pirolli, 2007; Payne et al., 2007; Pirolli & Card, 1999) and in memory (Hills et al., 2010, 2012). However, even though IF presents a compelling metaphor, there is actually very little empirical research investigating the alignment between IF principles and how people interact with the environment to search and make use of Information sources (Metcalfe & Jacobs, 2010). There is also little work that has examined how individual differences afford or constrain search in and uptake from Information sources. In this study, we used a simple word search puzzle to explore these issues. According to the IF theory (Fu & Pirolli, 2007; Pirolli & Card, 1999), certain basic properties of animal Foraging can be applied to the way human seek and consume Information. First, food is distributed in the wild in clusters, or “patches,” that vary in their profitability (i.e., potential yield) and in their tractability (i.e., how much of an investment of resources is needed for exploitation; e.g., apples on low branches or high branches). Resources in the patch are often finite and unknown to the foragers in advance, though “scent cues” may provide hints about profitability of the patch. Second, as patches become depleted, the rate of uptake decelerates. Third, the forager faces a tradeoff between gaining nutrients from exploiting a patch and consuming energy from exploring for food (e.g., to move among patches). The optimal Foraging theory predicts that animals will stay in a patch until the expected rate of gain falls below the overall rate of gain, which takes into account the cost of moving to a new patch (Charnov, 1976; Stephens & Kreb, 1986). Finally, because food is crucial to survival, foragers work to maximize their food uptake and rarely revisit a depleted patch (Stephen, Brown & Ydenberg, 2007; Stephens & Krebs, 1986). There are similarities and differences between animal Foraging and human Information Foraging. For example, Information is often clustered into patches (e.g., particular forms of print resources, webpages), though units of Information are often hard to quantify in everyday life. Although Information seekers may sometimes find it difficult to estimate profitability and tractability before visiting a patch, they may judge the richness or relevance of Information based on their knowledge or expertise. Learners often selectively allocate their attention to materials as long as they perceive themselves to be learning, and disengage if they perceive their rate of learning to decrease below a threshold (e.g., Metcalfe, 2002; Metcalfe & Kornell, 2005). While Information seekers have been found to adjust their search behavior to the statistical structures of the task environments (e.g., Fu & Pirolli, 2007), given the limited computational capacity and imperfect knowledge of human beings, the decision to explore a new task or exploit the current one is often suboptimal due to the biased representation of the local environment (e.g., Simon, 1956). For example, Payne, Duggan and Neth (2007) found, in a series of cognitive Foraging experiments, that switch decisions could not be entirely predicted by the rate of gain from a patch. Rather, people tended to switch more than optimal without monitoring the real-time change of expected gain. Finally, empirical studies show that Information seekers often revisit Information patches (e.g. Payne et al., 2007). In fact, unlike food, Information will not be exhausted after consumption. Therefore, the benefit of
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CogSci - Information Foraging in the Unknown Patches across the Life Span
Cognitive Science, 2012Co-Authors: Jessie Chin, Brennan R Payne, Andrew Battles, Daniel G Morrow, Elizabeth A. L. Stine-morrowAbstract:Information Foraging in the Unknown Patches across the Life Span Jessie Chin 1 (chin5@illinois.edu), Brennan Payne 1 (payne12@illinois.edu), Andrew Battles 2 (battles2@illinois.edu), Wai-Tat Fu 3 (wfu@illinois.edu), Daniel Morrow 1 (dgm@illinois.edu), Elizabeth A. L. Stine-Morrow 1 (eals@illinois.edu) Department of Educational Psychology, 2 Department of Electrical & Computer Engineering, Department of Computer Science University of Illinois at Urbana Champaign 405 N Mathews Ave Urbana, IL 61801 USA Abstract This study used a word search puzzle paradigm to examine the effects of task environment and individual differences in ability on Information Foraging. Younger and older adults attempted to maximize the number of items found in a set of 4 puzzles in which they were at liberty to search within a puzzle or switch between them. Younger adults demonstrated faster uptake (i.e., number of words found as a function of time) from individual puzzles than older adults but experienced more deceleration of rates during the search. Additionally, older adults switched less often and their switching was less dependent on the uptake rate compared to younger adults. Both younger and older adults stayed longer than was optimal in a patch, older adults were especially likely to persevere suboptimally. Collectively, these results suggest that individuals may differentially optimize Information gain through self-regulation of exploration and exploitation. Keywords: Information Foraging; cognitive aging; adaptive behavior. Information uptake; Introduction Self-regulation of cognition in natural environments almost always involves alternating phases of exploration, which entails search in the service of deciding how effort will be allocated, and exploitation, or task engagement in which effort is allocated to meet task-specific goals. Information Foraging (IF) models are predicated on an analogy between these regulatory processes and the way in which animals forage for food in the wild. Information Foraging has been used to account for how people search for Information in external environments, such as the WWW (e.g., Fu & Pirolli, 2007; Payne et al., 2007; Pirolli & Card, 1999) and in memory (Hills et al., 2010, 2012). However, even though IF presents a compelling metaphor, there is actually very little empirical research investigating the alignment between IF principles and how people interact with the environment to search and make use of Information sources (Metcalfe & Jacobs, 2010). There is also little work that has examined how individual differences afford or constrain search in and uptake from Information sources. In this study, we used a simple word search puzzle to explore these issues. According to the IF theory (Fu & Pirolli, 2007; Pirolli & Card, 1999), certain basic properties of animal Foraging can be applied to the way human seek and consume Information. First, food is distributed in the wild in clusters, or “patches,” that vary in their profitability (i.e., potential yield) and in their tractability (i.e., how much of an investment of resources is needed for exploitation; e.g., apples on low branches or high branches). Resources in the patch are often finite and unknown to the foragers in advance, though “scent cues” may provide hints about profitability of the patch. Second, as patches become depleted, the rate of uptake decelerates. Third, the forager faces a tradeoff between gaining nutrients from exploiting a patch and consuming energy from exploring for food (e.g., to move among patches). The optimal Foraging theory predicts that animals will stay in a patch until the expected rate of gain falls below the overall rate of gain, which takes into account the cost of moving to a new patch (Charnov, 1976; Stephens & Kreb, 1986). Finally, because food is crucial to survival, foragers work to maximize their food uptake and rarely revisit a depleted patch (Stephen, Brown & Ydenberg, 2007; Stephens & Krebs, 1986). There are similarities and differences between animal Foraging and human Information Foraging. For example, Information is often clustered into patches (e.g., particular forms of print resources, webpages), though units of Information are often hard to quantify in everyday life. Although Information seekers may sometimes find it difficult to estimate profitability and tractability before visiting a patch, they may judge the richness or relevance of Information based on their knowledge or expertise. Learners often selectively allocate their attention to materials as long as they perceive themselves to be learning, and disengage if they perceive their rate of learning to decrease below a threshold (e.g., Metcalfe, 2002; Metcalfe & Kornell, 2005). While Information seekers have been found to adjust their search behavior to the statistical structures of the task environments (e.g., Fu & Pirolli, 2007), given the limited computational capacity and imperfect knowledge of human beings, the decision to explore a new task or exploit the current one is often suboptimal due to the biased representation of the local environment (e.g., Simon, 1956). For example, Payne, Duggan and Neth (2007) found, in a series of cognitive Foraging experiments, that switch decisions could not be entirely predicted by the rate of gain from a patch. Rather, people tended to switch more than optimal without monitoring the real-time change of expected gain. Finally, empirical studies show that Information seekers often revisit Information patches (e.g. Payne et al., 2007). In fact, unlike food, Information will not be exhausted after consumption. Therefore, the benefit of
Margaret Burnett - One of the best experts on this subject based on the ideXlab platform.
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how end user programmers debug visual web based programs an Information Foraging theory perspective
Journal of Computer Languages, 2019Co-Authors: Sandeep Kaur Kuttal, Margaret Burnett, Anita Sarma, Gregg Rothermel, Ian Koeppe, Brooke ShepherdAbstract:Abstract Web-active end-user programmers squander much of their time Foraging for bugs and related Information in mashup programming environments as well as on the web. To analyze this Foraging behavior while debugging, we utilize an Information Foraging Theory perspective. Information Foraging Theory models the human (predator) behavior to forage for specific Information (prey) in the webpages or programming IDEs (patches) by following the Information features (cues) in the environment. We qualitatively studied the debugging behavior of 16 web-active end users. Our results show that end-user programmers spend substantial amounts (73%) of their time just Foraging. Further, our study reveals new cue types and Foraging strategies framed in terms of Information Foraging Theory, and it uncovers which of these helped end-user programmers succeed in their debugging efforts.
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VL/HCC - Putting Information Foraging theory to work: Community-based design patterns for programming tools
2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL HCC), 2016Co-Authors: Tahmid Nabi, Margaret Burnett, Christopher Scaffidi, David Piorkowski, Kyle M. D. Sweeney, Sam Lichlyter, Scott D FlemingAbstract:The design of programming tools is slow and costly. To ease this process, we developed a design pattern catalog aimed at providing guidance for tool designers. This catalog is grounded in Information Foraging Theory (IFT), which empirical studies have shown to be useful for understanding how developers look for Information during development tasks. New design patterns, authored by members of the research community for the catalog, concretely explain how to apply IFT in tool design. In our evaluation, qualitative analyses revealed the community-written design patterns compared well in quality to patterns that we had ourselves published in a smaller, peer-reviewed catalog.
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to fix or to learn how production bias affects developers Information Foraging during debugging
International Conference on Software Maintenance, 2015Co-Authors: David Piorkowski, Margaret Burnett, Christopher Scaffidi, Scott D Fleming, Irwin Kwan, Austin Z Henley, Jamie Macbeth, Charles Hill, Amber HorvathAbstract:Developers performing maintenance activities must balance their efforts to learn the code vs. their efforts to actually change it. This balancing act is consistent with the “production bias” that, according to Carroll's minimalist learning theory, generally affects software users during everyday tasks. This suggests that developers' focus on efficiency should have marked effects on how they forage for the Information they think they need to fix bugs. To investigate how developers balance fixing versus learning during debugging, we conducted the first empirical investigation of the interplay between production bias and Information Foraging. Our theory-based study involved 11 participants: half tasked with fixing a bug, and half tasked with learning enough to help someone else fix it. Despite the subtlety of difference between their tasks, participants foraged remarkably differently-making Foraging decisions from different types of “patches,” with different types of Information, and succeeding with different Foraging tactics.
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ICSME - To fix or to learn? How production bias affects developers' Information Foraging during debugging
2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2015Co-Authors: David Piorkowski, Margaret Burnett, Christopher Scaffidi, Scott D Fleming, Irwin Kwan, Austin Z Henley, Jamie Macbeth, Charles Hill, Amber HorvathAbstract:Developers performing maintenance activities must balance their efforts to learn the code vs. their efforts to actually change it. This balancing act is consistent with the “production bias” that, according to Carroll's minimalist learning theory, generally affects software users during everyday tasks. This suggests that developers' focus on efficiency should have marked effects on how they forage for the Information they think they need to fix bugs. To investigate how developers balance fixing versus learning during debugging, we conducted the first empirical investigation of the interplay between production bias and Information Foraging. Our theory-based study involved 11 participants: half tasked with fixing a bug, and half tasked with learning enough to help someone else fix it. Despite the subtlety of difference between their tasks, participants foraged remarkably differently-making Foraging decisions from different types of “patches,” with different types of Information, and succeeding with different Foraging tactics.
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an Information Foraging theory perspective on tools for debugging refactoring and reuse tasks
ACM Transactions on Software Engineering and Methodology, 2013Co-Authors: Scott D Fleming, Margaret Burnett, Joseph Lawrance, Rachel K E Bellamy, Christopher Scaffidi, David Piorkowski, Irwin KwanAbstract:Theories of human behavior are an important but largely untapped resource for software engineering research. They facilitate understanding of human developers’ needs and activities, and thus can serve as a valuable resource to researchers designing software engineering tools. Furthermore, theories abstract beyond specific methods and tools to fundamental principles that can be applied to new situations. Toward filling this gap, we investigate the applicability and utility of Information Foraging Theory (IFT) for understanding Information-intensive software engineering tasks, drawing upon literature in three areas: debugging, refactoring, and reuse. In particular, we focus on software engineering tools that aim to support Information-intensive activities, that is, activities in which developers spend time seeking Information. Regarding applicability, we consider whether and how the mathematical equations within IFT can be used to explain why certain existing tools have proven empirically successful at helping software engineers. Regarding utility, we applied an IFT perspective to identify recurring design patterns in these successful tools, and consider what opportunities for future research are revealed by our IFT perspective.
Elizabeth A. L. Stine-morrow - One of the best experts on this subject based on the ideXlab platform.
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Information Foraging Across the Life Span: Search and Switch in Unknown Patches
Topics in cognitive science, 2015Co-Authors: Jessie Chin, Brennan R Payne, Daniel G Morrow, Elizabeth A. L. Stine-morrowAbstract:In this study, we used a word search puzzle paradigm to investigate age differences in the rate of Information gain (RG; i.e., word gain as a function of time) and the cues used to make patch-departure decisions in Information Foraging. The likelihood of patch departure increased as the profitability of the patch decreased generally. Both younger and older adults persisted past the point of optimality as defined by the marginal value theorem (Charnov, 1976), which assumes perfect knowledge of the Foraging ecology. Nevertheless, there was evidence that adults were rational in terms of being sensitive to the change in RG for making the patch-departure decisions. However, given the limitations in cognitive resources and knowledge about the ecology, the estimation of RG may not be accurate. Younger adults were more likely to leave the puzzle as the long-term RG incrementally decreased, whereas older adults were more likely to leave the puzzle as the local RG decreased. However, older adults with better executive control were more likely to adjust their likelihood of patch-departure decisions to the long-term change in RG. Thus, age-dependent reliance on the long-term or local change in RG to make patch-departure decisions might be due to individual differences in executive control.
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CogSci - Information Foraging in the Unknown Patches across the Life Span
Cognitive Science, 2012Co-Authors: Jessie Chin, Brennan R Payne, Andrew Battles, Daniel G Morrow, Elizabeth A. L. Stine-morrowAbstract:Information Foraging in the Unknown Patches across the Life Span Jessie Chin 1 (chin5@illinois.edu), Brennan Payne 1 (payne12@illinois.edu), Andrew Battles 2 (battles2@illinois.edu), Wai-Tat Fu 3 (wfu@illinois.edu), Daniel Morrow 1 (dgm@illinois.edu), Elizabeth A. L. Stine-Morrow 1 (eals@illinois.edu) Department of Educational Psychology, 2 Department of Electrical & Computer Engineering, Department of Computer Science University of Illinois at Urbana Champaign 405 N Mathews Ave Urbana, IL 61801 USA Abstract This study used a word search puzzle paradigm to examine the effects of task environment and individual differences in ability on Information Foraging. Younger and older adults attempted to maximize the number of items found in a set of 4 puzzles in which they were at liberty to search within a puzzle or switch between them. Younger adults demonstrated faster uptake (i.e., number of words found as a function of time) from individual puzzles than older adults but experienced more deceleration of rates during the search. Additionally, older adults switched less often and their switching was less dependent on the uptake rate compared to younger adults. Both younger and older adults stayed longer than was optimal in a patch, older adults were especially likely to persevere suboptimally. Collectively, these results suggest that individuals may differentially optimize Information gain through self-regulation of exploration and exploitation. Keywords: Information Foraging; cognitive aging; adaptive behavior. Information uptake; Introduction Self-regulation of cognition in natural environments almost always involves alternating phases of exploration, which entails search in the service of deciding how effort will be allocated, and exploitation, or task engagement in which effort is allocated to meet task-specific goals. Information Foraging (IF) models are predicated on an analogy between these regulatory processes and the way in which animals forage for food in the wild. Information Foraging has been used to account for how people search for Information in external environments, such as the WWW (e.g., Fu & Pirolli, 2007; Payne et al., 2007; Pirolli & Card, 1999) and in memory (Hills et al., 2010, 2012). However, even though IF presents a compelling metaphor, there is actually very little empirical research investigating the alignment between IF principles and how people interact with the environment to search and make use of Information sources (Metcalfe & Jacobs, 2010). There is also little work that has examined how individual differences afford or constrain search in and uptake from Information sources. In this study, we used a simple word search puzzle to explore these issues. According to the IF theory (Fu & Pirolli, 2007; Pirolli & Card, 1999), certain basic properties of animal Foraging can be applied to the way human seek and consume Information. First, food is distributed in the wild in clusters, or “patches,” that vary in their profitability (i.e., potential yield) and in their tractability (i.e., how much of an investment of resources is needed for exploitation; e.g., apples on low branches or high branches). Resources in the patch are often finite and unknown to the foragers in advance, though “scent cues” may provide hints about profitability of the patch. Second, as patches become depleted, the rate of uptake decelerates. Third, the forager faces a tradeoff between gaining nutrients from exploiting a patch and consuming energy from exploring for food (e.g., to move among patches). The optimal Foraging theory predicts that animals will stay in a patch until the expected rate of gain falls below the overall rate of gain, which takes into account the cost of moving to a new patch (Charnov, 1976; Stephens & Kreb, 1986). Finally, because food is crucial to survival, foragers work to maximize their food uptake and rarely revisit a depleted patch (Stephen, Brown & Ydenberg, 2007; Stephens & Krebs, 1986). There are similarities and differences between animal Foraging and human Information Foraging. For example, Information is often clustered into patches (e.g., particular forms of print resources, webpages), though units of Information are often hard to quantify in everyday life. Although Information seekers may sometimes find it difficult to estimate profitability and tractability before visiting a patch, they may judge the richness or relevance of Information based on their knowledge or expertise. Learners often selectively allocate their attention to materials as long as they perceive themselves to be learning, and disengage if they perceive their rate of learning to decrease below a threshold (e.g., Metcalfe, 2002; Metcalfe & Kornell, 2005). While Information seekers have been found to adjust their search behavior to the statistical structures of the task environments (e.g., Fu & Pirolli, 2007), given the limited computational capacity and imperfect knowledge of human beings, the decision to explore a new task or exploit the current one is often suboptimal due to the biased representation of the local environment (e.g., Simon, 1956). For example, Payne, Duggan and Neth (2007) found, in a series of cognitive Foraging experiments, that switch decisions could not be entirely predicted by the rate of gain from a patch. Rather, people tended to switch more than optimal without monitoring the real-time change of expected gain. Finally, empirical studies show that Information seekers often revisit Information patches (e.g. Payne et al., 2007). In fact, unlike food, Information will not be exhausted after consumption. Therefore, the benefit of
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VL/HCC - Putting Information Foraging theory to work: Community-based design patterns for programming tools
2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL HCC), 2016Co-Authors: Tahmid Nabi, Margaret Burnett, Christopher Scaffidi, David Piorkowski, Kyle M. D. Sweeney, Sam Lichlyter, Scott D FlemingAbstract:The design of programming tools is slow and costly. To ease this process, we developed a design pattern catalog aimed at providing guidance for tool designers. This catalog is grounded in Information Foraging Theory (IFT), which empirical studies have shown to be useful for understanding how developers look for Information during development tasks. New design patterns, authored by members of the research community for the catalog, concretely explain how to apply IFT in tool design. In our evaluation, qualitative analyses revealed the community-written design patterns compared well in quality to patterns that we had ourselves published in a smaller, peer-reviewed catalog.
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to fix or to learn how production bias affects developers Information Foraging during debugging
International Conference on Software Maintenance, 2015Co-Authors: David Piorkowski, Margaret Burnett, Christopher Scaffidi, Scott D Fleming, Irwin Kwan, Austin Z Henley, Jamie Macbeth, Charles Hill, Amber HorvathAbstract:Developers performing maintenance activities must balance their efforts to learn the code vs. their efforts to actually change it. This balancing act is consistent with the “production bias” that, according to Carroll's minimalist learning theory, generally affects software users during everyday tasks. This suggests that developers' focus on efficiency should have marked effects on how they forage for the Information they think they need to fix bugs. To investigate how developers balance fixing versus learning during debugging, we conducted the first empirical investigation of the interplay between production bias and Information Foraging. Our theory-based study involved 11 participants: half tasked with fixing a bug, and half tasked with learning enough to help someone else fix it. Despite the subtlety of difference between their tasks, participants foraged remarkably differently-making Foraging decisions from different types of “patches,” with different types of Information, and succeeding with different Foraging tactics.
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ICSME - To fix or to learn? How production bias affects developers' Information Foraging during debugging
2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2015Co-Authors: David Piorkowski, Margaret Burnett, Christopher Scaffidi, Scott D Fleming, Irwin Kwan, Austin Z Henley, Jamie Macbeth, Charles Hill, Amber HorvathAbstract:Developers performing maintenance activities must balance their efforts to learn the code vs. their efforts to actually change it. This balancing act is consistent with the “production bias” that, according to Carroll's minimalist learning theory, generally affects software users during everyday tasks. This suggests that developers' focus on efficiency should have marked effects on how they forage for the Information they think they need to fix bugs. To investigate how developers balance fixing versus learning during debugging, we conducted the first empirical investigation of the interplay between production bias and Information Foraging. Our theory-based study involved 11 participants: half tasked with fixing a bug, and half tasked with learning enough to help someone else fix it. Despite the subtlety of difference between their tasks, participants foraged remarkably differently-making Foraging decisions from different types of “patches,” with different types of Information, and succeeding with different Foraging tactics.
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VL/HCC - Towards aiding within-patch Information Foraging by end-user programmers
2014 IEEE Symposium on Visual Languages and Human-Centric Computing (VL HCC), 2014Co-Authors: Balaji Athreya, Christopher ScaffidiAbstract:Many tools help professional programmers with the difficult problem of finding Information during code maintenance. The empirical success of these tools can be explained by Information Foraging Theory (IFT), which predicts how a person seeks Information by navigating through an Information system based on the visual weight of Information features presented to the person. Motivated by the success of these tools, we investigated the reasonable expectation that end-user programmers would likewise benefit from tools that increased the relative visual weight of important Information features. We prototyped and evaluated two tools, each of which uses an existing algorithm to identify the most important lines of code. One prototype highlights important lines of code; the other prototype hides unimportant lines of code. An empirical study revealed that increasing the relative weight of important Information features by highlighting did positively impact the amount of Information foraged and the rate of Information gained; on the other hand, decreasing the relative weight of unimportant Information features by hiding had a modest negative impact. These results reveal opportunities for enhancing existing IFT-based Foraging models and applying them to design more effective end-user programming tools for coding, debugging, and code reuse. Keywords—end-user programming; code maintenance; Information Foraging
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an Information Foraging theory perspective on tools for debugging refactoring and reuse tasks
ACM Transactions on Software Engineering and Methodology, 2013Co-Authors: Scott D Fleming, Margaret Burnett, Joseph Lawrance, Rachel K E Bellamy, Christopher Scaffidi, David Piorkowski, Irwin KwanAbstract:Theories of human behavior are an important but largely untapped resource for software engineering research. They facilitate understanding of human developers’ needs and activities, and thus can serve as a valuable resource to researchers designing software engineering tools. Furthermore, theories abstract beyond specific methods and tools to fundamental principles that can be applied to new situations. Toward filling this gap, we investigate the applicability and utility of Information Foraging Theory (IFT) for understanding Information-intensive software engineering tasks, drawing upon literature in three areas: debugging, refactoring, and reuse. In particular, we focus on software engineering tools that aim to support Information-intensive activities, that is, activities in which developers spend time seeking Information. Regarding applicability, we consider whether and how the mathematical equations within IFT can be used to explain why certain existing tools have proven empirically successful at helping software engineers. Regarding utility, we applied an IFT perspective to identify recurring design patterns in these successful tools, and consider what opportunities for future research are revealed by our IFT perspective.