CS295J/Proposal intros from class 9 - Revision history

Gideon Goldin: /* A Distributed Cognition Model of Human-Computer Interaction */

2009-04-03T14:46:29Z

A Distributed Cognition Model of Human-Computer Interaction

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	==Gideon & Jon==		==Gideon & Jon==

	===A ~~Distributed Cognition Model of~~ Human-Computer Interaction===		===A Theoretical Framework for Human-Computer Interaction===

	We propose a specific framework for the study of Human-Computer Interaction (HCI) from a cognitive science perspective. The architecture of our framework is designed to support the approach taken in Distributed Cognition (DC). In order to concretize our framework, model, and components, we review and incorporate 1000 studies from the field of cognitive science. Doing so will allow our project to dichotemise HCI into an applied side and a theoretical side, the latter of which has been mostly ignored in the history of the field. Rather than studying HCI as an applied subfield in computer science, or as an subfield of applied psychology, we show that the proposed theoretical study of HCI is a valid scientific program and would benefit from a systematic research agenda. Our framework ~~allows~~ this.		We propose a specific framework for the study of Human-Computer Interaction (HCI) from a cognitive science perspective. The architecture of our framework is designed to support the approach taken in Distributed Cognition (DC). In order to concretize our framework, model, and components, we review and incorporate 1000 studies from the field of cognitive science. Doing so will allow our project to dichotemise HCI into an applied side and a theoretical side, the latter of which has been mostly ignored in the history of the field. Rather than studying HCI as an applied subfield in computer science, or as a subfield of applied psychology, we show that the proposed theoretical study of HCI is a valid scientific program and would benefit from a systematic research agenda. There have been some attempts to study HCI from a DC perspective, but all studies have left the details and concrete facts too open, discouraging any follow-up research. Our framework will provide the first concrete basis from which to approach this type of research.

	Components in our framework are based on exhaustive understandings of the differences in processing capabilities in humans and computers. An extensive search of the literature dispels the "mind as a computer" metaphor. Now that a sufficient body of knowledge has shown that humans and computers most often process in very different ways, we can view theoretical HCI as the study of how to distribute tasks across man and machine.		Components in our framework are based on exhaustive understandings of the differences in processing capabilities in humans and computers. An extensive search of the literature dispels the "mind as a computer" metaphor. Now that a sufficient body of knowledge has shown that humans and computers most often process in very different ways, we can view theoretical HCI as the study of how to distribute tasks across man and machine.

Gideon Goldin: /* A Distributed Cognition Model of Human-Computer Interaction */

2009-04-03T14:42:59Z

A Distributed Cognition Model of Human-Computer Interaction

← Older revision		Revision as of 14:42, 3 April 2009
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	===A Distributed Cognition Model of Human-Computer Interaction===		===A Distributed Cognition Model of Human-Computer Interaction===

	We propose a specific framework for the study of Human-Computer Interaction (HCI) ~~based~~ from a cognitive science perspective. The architecture of our framework is designed to support the approach taken in Distributed Cognition (DC). In order to concretize our framework, model, and components, we review and incorporate 1000 studies from the field of cognitive science. Doing so will allow our project to dichotemise HCI into an applied side and a theoretical side, the latter of which has been mostly ignored in the history of the field. Rather than studying HCI as an applied subfield in computer science, or as an subfield of applied psychology, we show that the proposed theoretical study of HCI is a valid scientific program and would benefit from a systematic research agenda. Our framework allows this.		We propose a specific framework for the study of Human-Computer Interaction (HCI) from a cognitive science perspective. The architecture of our framework is designed to support the approach taken in Distributed Cognition (DC). In order to concretize our framework, model, and components, we review and incorporate 1000 studies from the field of cognitive science. Doing so will allow our project to dichotemise HCI into an applied side and a theoretical side, the latter of which has been mostly ignored in the history of the field. Rather than studying HCI as an applied subfield in computer science, or as an subfield of applied psychology, we show that the proposed theoretical study of HCI is a valid scientific program and would benefit from a systematic research agenda. Our framework allows this.

	Components in our framework are based on exhaustive understandings of the differences in processing capabilities in humans and computers. An extensive search of the literature dispels the "mind as a computer" metaphor. Now that a sufficient body of knowledge has shown that humans and computers most often process in very different ways, we can view theoretical HCI as the study of how to distribute tasks across man and machine.		Components in our framework are based on exhaustive understandings of the differences in processing capabilities in humans and computers. An extensive search of the literature dispels the "mind as a computer" metaphor. Now that a sufficient body of knowledge has shown that humans and computers most often process in very different ways, we can view theoretical HCI as the study of how to distribute tasks across man and machine.

Gideon Goldin: /* Collaborators so far */

2009-04-03T14:42:40Z

Collaborators so far

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	* Adam		* Adam
	* Trevor		* Trevor
	* Gideon
	* Eric		* Eric

Gideon Goldin: /* A Distributed Cognition Model of Human-Computer Interaction */

2009-04-03T14:42:20Z

A Distributed Cognition Model of Human-Computer Interaction

← Older revision		Revision as of 14:42, 3 April 2009
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	===A Distributed Cognition Model of Human-Computer Interaction===		===A Distributed Cognition Model of Human-Computer Interaction===

	We propose a ~~model which describes~~ HCI ~~by framing interactions in terms of distributed cognition~~. The ~~development~~ of ~~this~~ model ~~takes into account a vast body of research in cognitive science~~, ~~psychology~~, and ~~computer~~ science. ~~Though much of~~ HCI ~~research is~~ applied and ~~does not subscribe to the scientific method in~~ a ~~strict sense~~, ~~our approach considers~~ the ~~study~~ of of HCI as ~~theoretical~~, as an ~~interdisciplinary field based on established~~ and ~~accepted~~ research.		We propose a specific framework for the study of Human-Computer Interaction (HCI) based from a cognitive science perspective. The architecture of our framework is designed to support the approach taken in Distributed Cognition (DC). In order to concretize our framework, model, and components, we review and incorporate 1000 studies from the field of cognitive science. Doing so will allow our project to dichotemise HCI into an applied side and a theoretical side, the latter of which has been mostly ignored in the history of the field. Rather than studying HCI as an applied subfield in computer science, or as an subfield of applied psychology, we show that the proposed theoretical study of HCI is a valid scientific program and would benefit from a systematic research agenda. Our framework allows this.

	~~The basis of~~ our ~~model is an~~ exhaustive ~~understanding~~ of the differences in processing capabilities in humans and computers. An extensive search of the literature ~~in both cognitive science and computer science~~ dispels the "mind as a computer" metaphor. Now that a sufficient body of ~~experiments~~ has shown that humans and computers most often process in very different ways, we can ~~begin to solve a~~ theoretical ~~optimization problem, and still make strong implications for applied research: What role does~~ the ~~distribution~~ of tasks across ~~humans and computers play in HCI?~~		Components in our framework are based on exhaustive understandings of the differences in processing capabilities in humans and computers. An extensive search of the literature dispels the "mind as a computer" metaphor. Now that a sufficient body of knowledge has shown that humans and computers most often process in very different ways, we can view theoretical HCI as the study of how to distribute tasks across man and machine.

	~~This project will require five years and cost 2.5 million dollars. Where there are gaps in the literature, we are able~~ and ~~willing to conduct experiments using our group of researchers across various departments~~.

			This project will require five years and cost 2.5 million dollars.

	==Eric==		==Eric==

Eric Sodomka: /* Eric */

2009-04-03T14:03:08Z

Eric

← Older revision		Revision as of 14:03, 3 April 2009
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	Different users have different abilities and interface preferences. For example, a user at NASA probably cares more about interface accuracy than speed. By passing this information to our committee of experts, we can create interfaces that are tuned to maximize the utility of a particular user type.		Different users have different abilities and interface preferences. For example, a user at NASA probably cares more about interface accuracy than speed. By passing this information to our committee of experts, we can create interfaces that are tuned to maximize the utility of a particular user type.

	We evaluate our framework through a series of user studies. Interfaces passed to our committee of experts receive evaluation scores on a number of different dimensions, such as time, accuracy, and ease of use for novices versus experts. We can compare these predicted scores to the actual scores observed in user studies to evaluate performance. We can also retroactively weight the experts' opinions to determine which weighting would have given the best predictions ~~for~~ user behavior for the given interface, and observe whether that weighting generalizes to other interface evaluations.		We evaluate our framework through a series of user studies. Interfaces passed to our committee of experts receive evaluation scores on a number of different dimensions, such as time, accuracy, and ease of use for novices versus experts. We can compare these predicted scores to the actual scores observed in user studies to evaluate performance. We can also retroactively weight the experts' opinions to determine which weighting would have given the best predictions of user behavior for the given interface, and observe whether that weighting generalizes to other interface evaluations.


	==EJ==		==EJ==

Eric Sodomka at 14:00, 3 April 2009

2009-04-03T14:00:03Z

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	This project will require five years and cost 2.5 million dollars. Where there are gaps in the literature, we are able and willing to conduct experiments using our group of researchers across various departments.		This project will require five years and cost 2.5 million dollars. Where there are gaps in the literature, we are able and willing to conduct experiments using our group of researchers across various departments.


			==Eric==

			We propose a framework for interface evaluation and recommendation that integrates behavioral models and design guidelines from both cognitive science and HCI. Our framework behaves like a committee of specialized experts, where each expert provides its own assessment of the interface, given its particular knowledge of HCI or cognitive science. For example, an expert may provide an evaluation based on the GOMS method, Fitts's law, Maeda's design principles, or cognitive models of learning and memory. An aggregator collects all of these assessments and weights the opinion of each expert based on its past accuracy, and outputs to the developer a merged evaluation score and a weighted set of recommendations.

			Different users have different abilities and interface preferences. For example, a user at NASA probably cares more about interface accuracy than speed. By passing this information to our committee of experts, we can create interfaces that are tuned to maximize the utility of a particular user type.

			We evaluate our framework through a series of user studies. Interfaces passed to our committee of experts receive evaluation scores on a number of different dimensions, such as time, accuracy, and ease of use for novices versus experts. We can compare these predicted scores to the actual scores observed in user studies to evaluate performance. We can also retroactively weight the experts' opinions to determine which weighting would have given the best predictions for user behavior for the given interface, and observe whether that weighting generalizes to other interface evaluations.


	==EJ==		==EJ==

Eric Sodomka: /* Alternative Collaborative (In progress...) */

2009-04-03T12:08:29Z

Alternative Collaborative (In progress...)

← Older revision		Revision as of 12:08, 3 April 2009
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	==Alternative Collaborative (In progress...)==		==Alternative Collaborative (In progress...)==
	Established guidelines for designing human computer interfaces are based on experience, intuition and introspection. Because there is no integrated theoretical foundation, many rule-sets have emerged despite the absence of comparative evaluations. We propose to develop a theoretical foundation for interface design, drawing on recent advances in cognitive science -- the study of how people think, perceive and interact with the world. We will distill a broad range of principles and computational models of cognition that are relevant to interface design and use them to compare and unify existing guidelines. To validate our theoretical foundation, we will use our findings to develop a quantitative mechanism for assessing interface designs, identifying interface elements that are detrimental to user performance, and suggesting effective alternatives. Results from this system will be explored over a set of case studies.		Established guidelines for designing human computer interfaces are based on experience, intuition and introspection. Because there is no integrated theoretical foundation, many rule-sets have emerged despite the absence of comparative evaluations. We propose to develop a theoretical foundation for interface design, drawing on recent advances in cognitive science -- the study of how people think, perceive and interact with the world. We will distill a broad range of principles and computational models of cognition that are relevant to interface design and use them to compare and unify existing guidelines. To validate our theoretical foundation, we will use our findings to develop a quantitative mechanism for assessing interface designs, identifying interface elements that are detrimental to user performance, and suggesting effective alternatives. Results from this system will be explored over a set of case studies, and the quantitative assessments output by this system will be compared to actual user performance.

	A central focus of our work will be to broaden the range of cognitive theories that are used in HCI design. Few low-level theories of perception and action, such as Fitts's law, have garnered general acceptance in the HCI community due to their simple, quantitative nature, and wide-spread applicability. Our aim is to produce similar predictive models that apply to lower levels of perception as well as higher levels of cognition, including higher-level vision, learning, memory, attention, reasoning and task management.		A central focus of our work will be to broaden the range of cognitive theories that are used in HCI design. Few low-level theories of perception and action, such as Fitts's law, have garnered general acceptance in the HCI community due to their simple, quantitative nature, and wide-spread applicability. Our aim is to produce similar predictive models that apply to lower levels of perception as well as higher levels of cognition, including higher-level vision, learning, memory, attention, reasoning and task management.

Eric Sodomka: /* Alternative Collaborative (In progress...) */

2009-04-03T12:04:42Z

Alternative Collaborative (In progress...)

← Older revision		Revision as of 12:04, 3 April 2009
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	A central focus of our work will be to broaden the range of cognitive theories that are used in HCI design. Few low-level theories of perception and action, such as Fitts's law, have garnered general acceptance in the HCI community due to their simple, quantitative nature, and wide-spread applicability. Our aim is to produce similar predictive models that apply to lower levels of perception as well as higher levels of cognition, including higher-level vision, learning, memory, attention, reasoning and task management.		A central focus of our work will be to broaden the range of cognitive theories that are used in HCI design. Few low-level theories of perception and action, such as Fitts's law, have garnered general acceptance in the HCI community due to their simple, quantitative nature, and wide-spread applicability. Our aim is to produce similar predictive models that apply to lower levels of perception as well as higher levels of cognition, including higher-level vision, learning, memory, attention, reasoning and task management.

	We will focus on generating extensible, generalizable models of cognition that can be applied to a broad range of interface design challenges. Much research has accumulated regarding how people manage multiple tasks, and we will apply it to principles of how an interface should be designed ~~with not only its own purpose in mind but~~ such that it both helps maintain focus in a multi-tasking environment and minimizes the cost of switching to other tasks or applications in the same working sphere. The newer approach of distributed cognition also provides a useful perspective by examining the human-computer system as a unified cognitive entity.		We will focus on generating extensible, generalizable models of cognition that can be applied to a broad range of interface design challenges. Much research has accumulated regarding how people manage multiple tasks, and we will apply it to principles of how an interface should be designed such that it both helps maintain focus in a multi-tasking environment and minimizes the cost of switching to other tasks or applications in the same working sphere. The newer approach of distributed cognition also provides a useful perspective by examining the human-computer system as a unified cognitive entity.

	===Collaborators so far===		===Collaborators so far===
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	* Trevor		* Trevor
	* Gideon		* Gideon
			* Eric

	==Gideon & Jon==		==Gideon & Jon==

Andrew Bragdon: /* Meta-work and Task-Interruption Support Interfaces */

2009-04-03T06:50:45Z

Meta-work and Task-Interruption Support Interfaces

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	Owner: Andrew Bragdon		Owner: Andrew Bragdon

	Our proposal centers on two closely related yet distinct phases and corresponding goals. In the first phase, we will develop a qualitative theory for predicting user performance with and without automatic meta-work tools for saving and resuming context~~, creating rich task annotations, and organizing TODOs~~. In the second phase, once this theory has been developed and tested, we will then design and evaluate tools designed to support meta-work and task interruptioin based on the model developed in the first phase.		Our proposal centers on two closely related yet distinct phases and corresponding goals. In the first phase, we will develop a qualitative theory for predicting user performance with and without automatic meta-work tools for saving and resuming context. In the second phase, once this theory has been developed and tested, we will then design and evaluate tools designed to support meta-work and task interruptioin based on the model developed in the first phase.

	Traditionally, software design and usability testing is focused on low-level task performance. However, prior work (Gonzales, et al.) provides strong empirical evidence that users also work at a higher, working sphere level. Su, et al., developed a predictive model of task switching based on communication chains. Our model will specifically identify and predict key aspects of higher level information work behaviors, such as task switching. To truly design computing systems which are designed around the way users work, we must understand how users work. To do this, we must establish a predictive model of user workflow that encompasses multiple levels of workflow: individual task items, larger goal-oriented working spheres, multi-tasking behavior, and communication chains. Current information work systems are almost always designed around the lowest level of workflow, the individual task, and do not take into account the larger workflow context. Fundamentally, a predictive model would allow us to design computing systems which significantly increase worker productivity in the United States and around the world, by designing these systems around the way people work.		Traditionally, software design and usability testing is focused on low-level task performance. However, prior work (Gonzales, et al.) provides strong empirical evidence that users also work at a higher, working sphere level. Su, et al., developed a predictive model of task switching based on communication chains. Our model will specifically identify and predict key aspects of higher level information work behaviors, such as task switching. To truly design computing systems which are designed around the way users work, we must understand how users work. To do this, we must establish a predictive model of user workflow that encompasses multiple levels of workflow: individual task items, larger goal-oriented working spheres, multi-tasking behavior, and communication chains. Current information work systems are almost always designed around the lowest level of workflow, the individual task, and do not take into account the larger workflow context. Fundamentally, a predictive model would allow us to design computing systems which significantly increase worker productivity in the United States and around the world, by designing these systems around the way people work.

Andrew Bragdon: /* Meta-work and Task-Interruption Support Interfaces */

2009-04-03T06:50:13Z

Meta-work and Task-Interruption Support Interfaces

← Older revision		Revision as of 06:50, 3 April 2009
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	Owner: Andrew Bragdon		Owner: Andrew Bragdon

	Our proposal centers on ~~twin~~ closely related yet distinct phases and corresponding goals. In the first phase, develop a qualitative theory for predicting user performance with and without automatic meta-work tools for saving and resuming context. In the second phase, once this theory has been developed and tested, we will then design and evaluate tools designed to support meta-work and task interruptioin based on the model developed in the first phase.		Our proposal centers on two closely related yet distinct phases and corresponding goals. In the first phase, we will develop a qualitative theory for predicting user performance with and without automatic meta-work tools for saving and resuming context, creating rich task annotations, and organizing TODOs. In the second phase, once this theory has been developed and tested, we will then design and evaluate tools designed to support meta-work and task interruptioin based on the model developed in the first phase.

	Traditionally, software design and usability testing is focused on low-level task performance. However, prior work (Gonzales, et al.) provides strong empirical evidence that users also work at a higher, working sphere level. Su, et al., developed a predictive model of task switching based on communication chains. Our model will specifically identify and predict key aspects of higher level information work behaviors, such as task switching. To truly design computing systems which are designed around the way users work, we must understand how users work. To do this, we must establish a predictive model of user workflow that encompasses multiple levels of workflow: individual task items, larger goal-oriented working spheres, multi-tasking behavior, and communication chains. Current information work systems are almost always designed around the lowest level of workflow, the individual task, and do not take into account the larger workflow context. Fundamentally, a predictive model would allow us to design computing systems which significantly increase worker productivity in the United States and around the world, by designing these systems around the way people work.		Traditionally, software design and usability testing is focused on low-level task performance. However, prior work (Gonzales, et al.) provides strong empirical evidence that users also work at a higher, working sphere level. Su, et al., developed a predictive model of task switching based on communication chains. Our model will specifically identify and predict key aspects of higher level information work behaviors, such as task switching. To truly design computing systems which are designed around the way users work, we must understand how users work. To do this, we must establish a predictive model of user workflow that encompasses multiple levels of workflow: individual task items, larger goal-oriented working spheres, multi-tasking behavior, and communication chains. Current information work systems are almost always designed around the lowest level of workflow, the individual task, and do not take into account the larger workflow context. Fundamentally, a predictive model would allow us to design computing systems which significantly increase worker productivity in the United States and around the world, by designing these systems around the way people work.