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The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior
- Psychological Review
, 2006
"... Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sens ..."
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Cited by 68 (10 self)
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Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sense protected or conserved in that greater amounts of perceptual-motor effort will be expended to conserve lesser amounts of cognitive effort. One alternative, the minimum memory hypothesis (MMH), holds that people favor strategies that minimize the use of memory. SCH is compared with MMH across 3 experiments and with predictions of an Ideal Performer Model that uses ACT-R’s memory system in a reinforcement learning approach that maximizes expected utility by minimizing time. Model and data support the SCH view of resource allocation; at the under 1000-ms level of analysis, mixtures of cognitive and perceptual-motor resources are adjusted based on their cost-benefit tradeoffs for interactive behavior.
Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action
- Psychological Review
, 2009
"... The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition—cognitively bounded rational analysis—that sharpens th ..."
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Cited by 39 (13 self)
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The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition—cognitively bounded rational analysis—that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.
A cognitive constraint model of dual-task trade-offs in a highly dynamic driving task
- Human Factors in Computing Systems: CHI 2007 Conference Proceedings
, 2007
"... The paper describes an approach to modeling the strategic variations in performing secondary tasks while driving. In contrast to previous efforts that are based on simulation of a cognitive architecture interacting with a task environment, we take an approach that develops a cognitive constraint mod ..."
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Cited by 22 (15 self)
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The paper describes an approach to modeling the strategic variations in performing secondary tasks while driving. In contrast to previous efforts that are based on simulation of a cognitive architecture interacting with a task environment, we take an approach that develops a cognitive constraint model of the interaction between the driver and the task environment in order to make inferences about dual-task performance. Analyses of driving performance data reveal that a set of simple equations can be used to accurately model changes in the lateral position of the vehicle within the lane. The model quantifies how the vehicle’s deviation from lane center increases during periods of inattention, and how the vehicle returns to lane center during periods of active steering. We demonstrate the benefits of the approach by modeling the dialing of a cellular phone while driving, where drivers balance the speed in performing the dial task with accuracy (or safety) in keeping the vehicle centered in the roadway. In particular, we show how understanding, rather than simulating, the constraints imposed by the task environment can help to explain the costs and benefits of a range of strategies for interleaving dialing and steering. We show how particular strategies are sensitive to a combination of internal constraints (including switch costs) and the trade-off between the amount of time allocated to secondary task and the risk of extreme lane deviation. Author Keywords User modeling, multitasking, driver distraction.
Generating automated predictions of behavior strategically adapted to specific performance objectives
- Proceedings of ACM Conference on Human Factors in Computing Systems, CHI’06
, 2006
"... It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predic ..."
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Cited by 11 (5 self)
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It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predictions from a single task specification as a function of different performance objective functions. As a demonstration of this capability, the Cognitive Constraint Modeling approach was used to develop models for several tasks across two interfaces from the aviation domain. Performance objectives are explicitly declared as part of the model, and the CORE (Constraint-based Optimal Reasoning Engine) architecture itself formally derives the detailed strategies that are maximally adapted to these objectives. The models are analyzed for emergent strategic variation, comparing those optimized for task time with those optimized for working memory load. The approach has potential application in user interface and procedure design.
More than 8,192 Ways to Skin a Cat: Modeling Behavior in Multidimensional Strategy Spaces
"... How can we model behavior on complex, real-world tasks with a large range of possible strategies that may vary along multiple dimensions? In this paper, we show show how an emerging approach to cognitive modeling, cognitively bounded rational analysis, can be applied to efficiently specify large, mu ..."
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Cited by 2 (2 self)
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How can we model behavior on complex, real-world tasks with a large range of possible strategies that may vary along multiple dimensions? In this paper, we show show how an emerging approach to cognitive modeling, cognitively bounded rational analysis, can be applied to efficiently specify large, multidimensional strategy spaces, and to predict which strategies within the space are followed. The approach also supports a novel way of analyzing error control strategies, by directly modeling error recovery procedures and factoring these into strategy prediction. We apply this approach in a model of a typing task exemplifying three dimensions of strategic variability: decomposition of tasks into subtasks, parallel vs. serial processing of subtasks, and control of errors. We present empirical results showing the strategies people adopted on the task. The model successfully predicts the strategies used, by optimizing over the strategy space for a utility function defined as the performance-based payoff used in the experiment.
AI support for building cognitive models
- In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI), Nectar Track, Menlo Park
, 2006
"... Cognitive modeling techniques provide a way of evaluating user interface designs, based on what is known about human cognitive strengths and limitations. Cognitive modelers face a tradeoff, however: more detailed models require disproportionately more time and effort to develop than coarser models. ..."
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Cited by 1 (0 self)
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Cognitive modeling techniques provide a way of evaluating user interface designs, based on what is known about human cognitive strengths and limitations. Cognitive modelers face a tradeoff, however: more detailed models require disproportionately more time and effort to develop than coarser models. In this paper we describe a system, G2A, GOMS models into more detailed ACT-R models. G2A demonstrates how even simple AI techniques can facilitate the construction of cognitive models and suggests new directions for improving modeling tools.
Bounded Optimality in a Cognitive Utility Learning Paradigm
"... Abstract: In this paper we report two experiments that test a theory of adaptation to memory limits. A critical aspect of the theory is the assumption that people make bounded optimal decisions, where optimality is defined relative to a payoff function and relative to embodied cognitive constraints. ..."
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Cited by 1 (0 self)
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Abstract: In this paper we report two experiments that test a theory of adaptation to memory limits. A critical aspect of the theory is the assumption that people make bounded optimal decisions, where optimality is defined relative to a payoff function and relative to embodied cognitive constraints. The experiments use a no-choice/choice utility learning paradigm where the no-choice phase is used to elicit a profile of each participant's performance across the strategy space and the choice phase is used to test predicted choices within this space. The experiments provide evidence to support the hypothesis that, with practice, participants select strategies that are bounded optimal given limits on speed and accuracy. The experiments also provide evidence against the hypothesis that people routinely offload information processing to the task environment. The implications of the findings for bounded optimal theories are discussed. Cover Letter Dear Sir or Madam, Please find attached a manuscript entitled “Bounded Optimality in a Cognitive Utility Learning Paradigm ” for consideration for publication in the journal Cognitive Psychology. No portion of the Experiments or manuscript have been published or submitted for consideration elsewhere. The work was approved by the ethics panel from the University of Manchester.
2 Psychology 3 Carnegie Mellon & NASA
"... It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predic ..."
Abstract
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It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predictions from a single task specification as a function of different performance objective functions. As a demonstration of this capability, the Cognitive Constraint Modeling approach was used to develop models for several tasks across two interfaces from the aviation domain. Performance objectives are explicitly declared as part of the model, and the CORE (Constraint-based Optimal Reasoning Engine) architecture itself formally derives the detailed strategies that are maximally adapted to these objectives. The models are analyzed for emergent strategic variation, comparing those optimized for task time with those optimized for working memory load. The approach has potential application in user interface and procedure design.
High-level Behavior Representation Languages Revisited
, 2006
"... There has only been a short history of high level languages to model human cognition based on cognitive architectures. TAQL is an early example (Yost, 1993). TAQL showed a large (3x) speed increase over plain Soar, ..."
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There has only been a short history of high level languages to model human cognition based on cognitive architectures. TAQL is an early example (Yost, 1993). TAQL showed a large (3x) speed increase over plain Soar,
Towards a Constraint Analysis of Human Multitasking
"... When people conduct multiple tasks in tandem, they often interleave the various operators of each task. Just how these basic cognitive, perceptual and motor processes are ordered generally affords a range of possible multitasking strategies. ..."
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When people conduct multiple tasks in tandem, they often interleave the various operators of each task. Just how these basic cognitive, perceptual and motor processes are ordered generally affords a range of possible multitasking strategies.
