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39
Strategic adaptation to performance objectives in a dual-task setting
- Cognitive science
, 2010
"... driving; performance trade‐offs, performance operating characteristicDual‐Task Strategy Adaptation 2 How do people interleave attention when multitasking? One dominant account is that the completion of a subtask serves as a cue to switch tasks. But what happens if switching solely at subtask boundar ..."
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Cited by 10 (4 self)
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driving; performance trade‐offs, performance operating characteristicDual‐Task Strategy Adaptation 2 How do people interleave attention when multitasking? One dominant account is that the completion of a subtask serves as a cue to switch tasks. But what happens if switching solely at subtask boundaries led to poor performance? We report a study in which participants manually dialed a UK‐style telephone number while driving a simulated vehicle. If the driver were to exclusively return their attention to driving after completing a subtask (i.e., using the single break in the xxxxx‐xxxxxx representational structure of the number), then we would expect to see relatively poor driving performance. In contrast, our results show that drivers choose to return attention to steering control before the natural subtask boundary. A computational modeling analysis shows that drivers had to adopt this strategy to meet the required performance objective of maintaining an acceptable lateral position in the road while dialing. Taken together these results support the idea that people can strategically control the allocation of attention in multitask settings to meet specific performance criteria. Dual‐Task Strategy Adaptation 3 1.
Identifying optimum performance trade-offs using a cognitively bounded rational analysis model of discretionary task interleaving
- Topics in Cognitive Science
"... We report the results of a dual-task study in which participants performed a tracking and typing task under various experimental conditions. An objective payoff function was used to provide explicit feedback on how participants should trade-off performance between the tasks. Results show that partic ..."
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We report the results of a dual-task study in which participants performed a tracking and typing task under various experimental conditions. An objective payoff function was used to provide explicit feedback on how participants should trade-off performance between the tasks. Results show that participants ’ dual-task interleaving strategy was sensitive to changes in the difficulty of the tracking task, and resulted in differences in overall task performance. To test the hypothesis that people select strategies that maximize payoff, a Cognitively Bounded Rational Analysis model was developed. This analysis evaluated a variety of dual-task interleaving strategies to identify the optimal strategy for maximizing payoff in each condition. The model predicts that the region of optimum performance is different between experimental conditions. The correspondence between human data and the prediction of the optimal strategy is found to be remarkably high across a number of performance measures. This suggests that participants were honing their behavior to maximize payoff. Limitations are discussed.
The nature and transfer of cognitive skills
- Psychological Review
, 2013
"... This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. This article presents the primitive elements theory of cognitive skills. The cent ..."
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Cited by 8 (2 self)
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This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. This article presents the primitive elements theory of cognitive skills. The central idea is that skills are broken down into primitive information processing elements that move and compare single pieces of information regardless of the specific content of this information. Several of these primitive elements are necessary for even a single step in a task. A learning process therefore combines the elements in increasingly larger, but still context-independent, units. If there is overlap between tasks, this means the larger units learned for 1 task can be reused for the other task, producing transfer. The theory makes it possible to construct detailed process models of 2 classic transfer studies in the literature: a study of transfer in text editors and 1 in arithmetic. I show that the approach produces better fits of the amount of transfer than Singley and Anderson’s (1985) identical productions model. The theory also offers explanations for far transfer, in which the 2 tasks have no surface characteristics in common, which I demonstrate with 2 models in the domain of cognitive control, where training on either task-switching or working memory control led to an improvement of performance on other control tasks. The theory can therefore help evaluate the effectiveness of cognitive training that has the goal to improve general cognitive abilities.
Task-constrained interleaving of perceptual and motor processes in a time-critical dual task as revealed through eye tracking
- Drexel University
, 2010
"... A multimodal dual task experiment that contributed to the original development and tuning of the EPIC cognitive architecture is revised and revisited with the collection of new high fidelity human performance data, most notably detailed eye movement data, that reveal the complex overlapping of perce ..."
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Cited by 5 (5 self)
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A multimodal dual task experiment that contributed to the original development and tuning of the EPIC cognitive architecture is revised and revisited with the collection of new high fidelity human performance data, most notably detailed eye movement data, that reveal the complex overlapping of perceptual and motor processes within and between the two competing tasks. The data permit a new detailed evaluation of assumptions made in previous models of the task, and contribute to the development of new models that explore opportunities for overlapping visual-perceptual, auditoryperceptual, ocular-motor, and manual-motor activities. Three models are presented: (a) A hierarchical task-switching model in which each task locks out the other; the model explains reaction time but does not account for eye movement data. (b) A maximum-perceptual-overlap model that maximizes parallel processing and predicts the trends in the eye movement data, but performs too quickly. (c) A moderately-overlapped model that introduces task-motivated constraints and predicts both reaction time and eye movement data. The best-fitting model demonstrates the complex taskconstrained interleaving of perceptual and motor processes in a time-pressured dual task.
Computational rationality: linking mechanism and behavior through bounded utility maximization.
- Topics in Cognitive Science
, 2014
"... Abstract We propose a framework for including information-processing bounds in rational analyses. It is an application of bounded optimality ..."
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Abstract We propose a framework for including information-processing bounds in rational analyses. It is an application of bounded optimality
A cognitively bounded rational analysis model of dual-task performance trade-offs
- In Proceedings of the 10th International Conference on Cognitive Modeling
, 2010
"... The process of interleaving two tasks can be described as making trade-offs between performance on each of the tasks. This can be captured in performance operating characteristic curves. However, these curves do not describe what, given the specific task circumstances, the optimal strategy is. In th ..."
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Cited by 4 (3 self)
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The process of interleaving two tasks can be described as making trade-offs between performance on each of the tasks. This can be captured in performance operating characteristic curves. However, these curves do not describe what, given the specific task circumstances, the optimal strategy is. In this paper we describe the results of a dual-task study in which participants performed a tracking and typing task under various experimental conditions. An objective payoff function was used to describe how participants should trade-off performance between the tasks. Results show that participants ’ dual-task interleaving strategy was sensitive to changes in the difficulty of the tracking task, and resulted in differences in overall task performance. To explain the observed behavior, a cognitively bounded rational analysis model was developed to understand participants ’ strategy selection. This analysis evaluated a variety of dual-task interleaving strategies against the same payoff function that participants were exposed to. The model demonstrated that in three out of four conditions human performance was optimal; that is, participants adopted dual-task strategies that maximized the payoff that was achieved.
Integration and reuse in cognitive skill acquisition
, 2013
"... Previous accounts of cognitive skill acquisition have demonstrated how procedural knowl-edge can be obtained and transformed over time into skilled task performance. This article focuses on a complementary aspect of skill acquisition, namely the integration and reuse of previously known component sk ..."
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Cited by 3 (1 self)
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Previous accounts of cognitive skill acquisition have demonstrated how procedural knowl-edge can be obtained and transformed over time into skilled task performance. This article focuses on a complementary aspect of skill acquisition, namely the integration and reuse of previously known component skills. The article posits that, in addition to mechanisms that pro-ceduralize knowledge into more efficient forms, skill acquisition requires tight integration of newly acquired knowledge and previously learned knowledge. Skill acquisition also benefits from reuse of existing knowledge across disparate task domains, relying on indexicals to refer-ence and share necessary information across knowledge components. To demonstrate these ideas, the article proposes a computational model of skill acquisition from instructions focused on integration and reuse, and applies this model to account for behavior across seven task domains.
Amortized Inference in Probabilistic Reasoning
"... Recent studies of probabilistic reasoning have postulated general-purpose inference algorithms that can be used to an-swer arbitrary queries. These algorithms are memoryless, in the sense that each query is processed independently, without reuse of earlier computation. We argue that the brain oper-a ..."
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Recent studies of probabilistic reasoning have postulated general-purpose inference algorithms that can be used to an-swer arbitrary queries. These algorithms are memoryless, in the sense that each query is processed independently, without reuse of earlier computation. We argue that the brain oper-ates in the setting of amortized inference, where numerous related queries must be answered (e.g., recognizing a scene from multiple viewpoints); in this setting, memoryless algo-rithms can be computationally wasteful. We propose a simple form of flexible reuse, according to which shared inferences are cached and composed together to answer new queries. We present experimental evidence that humans exploit this form of reuse: the answer to a complex query can be systematically predicted from a person’s response to a simpler query if the simpler query was presented first and entails a sub-inference (i.e., a sub-component of the more complex query). People are also faster at answering a complex query when it is preceded by a sub-inference. Our results suggest that the astonishing ef-ficiency of human probabilistic reasoning may be supported by interactions between inference and memory.
When, What, and How Much to Reward in Reinforcement Learning‐Based Models of Cognition
- Cognitive science
, 2012
"... Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when ..."
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Cited by 2 (0 self)
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Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magni-tude: with binary, categorical, or continuous values). In this article, we explore the problem space of these three parameters in the context of a task whose completion entails some combination of 36 state–action pairs, where all intermediate states (i.e., after the initial state and prior to the end state) represent progressive but partial completion of the task. Different choices produce profoundly different learning paths and outcomes, with the strongest effect for moment. Unfortunately, there is little discussion in the literature of the effect of such choices. This absence is disappointing, as the choice of when, what, and how much needs to be made by a modeler for every learning model.
How Long Have I Got? Making Optimal Visit Durations in a Dual-Task Setting
"... Can people multitask optimally? We use a dual-task paradigm in which participants had to enter digits while monitoring a randomly moving cursor. Participants earned points for entering digits correctly and were docked points if they let the cursor drift outside of a target area. The severity of the ..."
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Can people multitask optimally? We use a dual-task paradigm in which participants had to enter digits while monitoring a randomly moving cursor. Participants earned points for entering digits correctly and were docked points if they let the cursor drift outside of a target area. The severity of the tracking penalty was varied between conditions. Participants therefore had to decide how long to leave the tracking task unattended. As expected, participants left the tracking task for longer when the penalty was less severe and also when the cursor moved less erratically. To test whether participants were adjusting their behavior in an optimal manner, observed behavior was compared to a prediction of the optimal visit duration for each condition. Overall, the degree of correspondence between the observed behavior and the predicted optimum was very good, suggesting that people can multitask in a near optimal fashion given explicit feedback on their performance.
