The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform

Lance J Rips - One of the best experts on this subject based on the ideXlab platform.

  • predicting behavior from the world naive behaviorism in lay decision theory
    Cognitive Science, 2014
    Co-Authors: Samuel G B Johnson, Lance J Rips
    Abstract:

    Predicting Behavior from the World: Naive Behaviorism in Lay Decision Theory Samuel G. B. Johnson (samuel.johnson@yale.edu) Department of Psychology, Yale University 2 Hillhouse Ave., New Haven, CT 06520 USA Lance J. Rips (rips@northwestern.edu) Department of Psychology, Northwestern University 2029 Sheridan Road, Evanston, IL 60208 USA Abstract Situational Constraint), that the Mercury would leave sufficient space for his Honda (an end-state). Using this non-mentalistic, behaviorist system only requires seeking out and representing information about the world—and no inferences about the mental states of the Mercury’s driver. Infants can use world-based cues such as efficiency Constraints to reason about behavior before achieving a representational theory of mind (Gergely & Csibra, 2003), suggesting that a primitive, behaviorist system is present in infancy. The behaviorist system therefore seems to precede the mentalistic system in development (see also Povinelli & Vonk, 2004 on chimpanzee theory of mind). However, it is unclear whether the behaviorist system used by infants is replaced by the mentalistic system that we use as adults, or whether instead these systems coexist in adulthood. If these systems coexist, many of our everyday inferences about behavior may bypass mental- state inferences altogether, relying instead on directly observable information about the world, coupled with more general assumptions such as the efficiency of actions in achieving optimal end-states. Here, we test the possibility of a behaviorist system by studying judgments about agents making decisions under uncertainty, contrasting inferences about knowledgeable agents—those who know the efficacies of each option under consideration—and inferences about ignorant agents—those who do not know the efficacies of the options. For example, consider Jill, who wants her hair to smell like apples and is deciding which of three brands of shampoo to purchase: one with a high probability of leading to her goal (“Best”), one with a medium probability (“Middle”), and one with a low probability (“Worst”). Which option will Jill choose? Two principles could potentially be used for predicting Jill’s choice. First, people might use the Efficiency Principle (Dennett, 1987), which would lead Jill to choose Best—the optimal action relative to her goals. This principle alone would not lead Jill to be any more likely to choose Middle than to choose Worst, since both are inefficient relative to Best. Second, people might use a Preference Principle, which would lead Jill to form preferences for the options in proportion to their quality, and be more likely to choose more preferred options— that is, to be most likely to choose Best, less likely to Life in our social world depends on predicting and interpreting other people’s behavior. Do such inferences always require us to explicitly represent people’s mental states, or do we sometimes bypass such mentalistic inferences and rely instead on cues from the environment? We provide evidence for such behaviorist thinking by testing judgments about agents’ decision-making under uncertainty, comparing agents who were knowledgeable about the quality of each decision option to agents who were ignorant. Participants believed that even ignorant agents were most likely to choose optimally, both in explaining (Experiment 1) and in predicting behavior (Experiment 2), and assigned them greater responsibility when acting in an objectively optimal way (Experiment 3). Keywords: Theory of mind; lay decision theory; explanation; prediction; rationality. Introduction Sunny turned on his Honda’s right blinker as he drove down Dixwell Avenue. The Mercury to his right slowed down, and Sunny changed lanes. In changing lanes, Sunny wagered with his life—gambling that the driver of the Mercury would leave enough space for his Honda to enter the right lane—and he won. Indeed, his track record with such wagers is remarkable. How is Sunny able to make such successful predictions about others’ behavior? One strategy that Sunny may have followed in this case was to infer the driver’s behavior based on his or her inferred mental-states. That is, Sunny may have reasoned that the Mercury’s slowing down was a signal of the driver’s intention to let him change lanes, based on the driver’s assumed beliefs about road behavior and folk physics, and the driver’s assumed goals of being a good road citizen and avoiding a collision. Using this mentalistic system requires inferring and representing the agent’s mental states, then predicting and interpreting actions on the basis of those inferred mental states. This seems to accord with how we typically experience the process of making behavior inferences in day-to-day life. But Sunny could have reached the same conclusion using a different strategy, inferring the Mercury’s behavior based on observable states of the world. Sunny may have inferred from the Mercury’s change in speed (an action), combined with the geometry of driving (a

  • CogSci - Predicting Behavior from the World: Naive Behaviorism in Lay Decision Theory
    Cognitive Science, 2014
    Co-Authors: Samuel G B Johnson, Lance J Rips
    Abstract:

    Predicting Behavior from the World: Naive Behaviorism in Lay Decision Theory Samuel G. B. Johnson (samuel.johnson@yale.edu) Department of Psychology, Yale University 2 Hillhouse Ave., New Haven, CT 06520 USA Lance J. Rips (rips@northwestern.edu) Department of Psychology, Northwestern University 2029 Sheridan Road, Evanston, IL 60208 USA Abstract Situational Constraint), that the Mercury would leave sufficient space for his Honda (an end-state). Using this non-mentalistic, behaviorist system only requires seeking out and representing information about the world—and no inferences about the mental states of the Mercury’s driver. Infants can use world-based cues such as efficiency Constraints to reason about behavior before achieving a representational theory of mind (Gergely & Csibra, 2003), suggesting that a primitive, behaviorist system is present in infancy. The behaviorist system therefore seems to precede the mentalistic system in development (see also Povinelli & Vonk, 2004 on chimpanzee theory of mind). However, it is unclear whether the behaviorist system used by infants is replaced by the mentalistic system that we use as adults, or whether instead these systems coexist in adulthood. If these systems coexist, many of our everyday inferences about behavior may bypass mental- state inferences altogether, relying instead on directly observable information about the world, coupled with more general assumptions such as the efficiency of actions in achieving optimal end-states. Here, we test the possibility of a behaviorist system by studying judgments about agents making decisions under uncertainty, contrasting inferences about knowledgeable agents—those who know the efficacies of each option under consideration—and inferences about ignorant agents—those who do not know the efficacies of the options. For example, consider Jill, who wants her hair to smell like apples and is deciding which of three brands of shampoo to purchase: one with a high probability of leading to her goal (“Best”), one with a medium probability (“Middle”), and one with a low probability (“Worst”). Which option will Jill choose? Two principles could potentially be used for predicting Jill’s choice. First, people might use the Efficiency Principle (Dennett, 1987), which would lead Jill to choose Best—the optimal action relative to her goals. This principle alone would not lead Jill to be any more likely to choose Middle than to choose Worst, since both are inefficient relative to Best. Second, people might use a Preference Principle, which would lead Jill to form preferences for the options in proportion to their quality, and be more likely to choose more preferred options— that is, to be most likely to choose Best, less likely to Life in our social world depends on predicting and interpreting other people’s behavior. Do such inferences always require us to explicitly represent people’s mental states, or do we sometimes bypass such mentalistic inferences and rely instead on cues from the environment? We provide evidence for such behaviorist thinking by testing judgments about agents’ decision-making under uncertainty, comparing agents who were knowledgeable about the quality of each decision option to agents who were ignorant. Participants believed that even ignorant agents were most likely to choose optimally, both in explaining (Experiment 1) and in predicting behavior (Experiment 2), and assigned them greater responsibility when acting in an objectively optimal way (Experiment 3). Keywords: Theory of mind; lay decision theory; explanation; prediction; rationality. Introduction Sunny turned on his Honda’s right blinker as he drove down Dixwell Avenue. The Mercury to his right slowed down, and Sunny changed lanes. In changing lanes, Sunny wagered with his life—gambling that the driver of the Mercury would leave enough space for his Honda to enter the right lane—and he won. Indeed, his track record with such wagers is remarkable. How is Sunny able to make such successful predictions about others’ behavior? One strategy that Sunny may have followed in this case was to infer the driver’s behavior based on his or her inferred mental-states. That is, Sunny may have reasoned that the Mercury’s slowing down was a signal of the driver’s intention to let him change lanes, based on the driver’s assumed beliefs about road behavior and folk physics, and the driver’s assumed goals of being a good road citizen and avoiding a collision. Using this mentalistic system requires inferring and representing the agent’s mental states, then predicting and interpreting actions on the basis of those inferred mental states. This seems to accord with how we typically experience the process of making behavior inferences in day-to-day life. But Sunny could have reached the same conclusion using a different strategy, inferring the Mercury’s behavior based on observable states of the world. Sunny may have inferred from the Mercury’s change in speed (an action), combined with the geometry of driving (a

Samuel G B Johnson - One of the best experts on this subject based on the ideXlab platform.

  • predicting behavior from the world naive behaviorism in lay decision theory
    Cognitive Science, 2014
    Co-Authors: Samuel G B Johnson, Lance J Rips
    Abstract:

    Predicting Behavior from the World: Naive Behaviorism in Lay Decision Theory Samuel G. B. Johnson (samuel.johnson@yale.edu) Department of Psychology, Yale University 2 Hillhouse Ave., New Haven, CT 06520 USA Lance J. Rips (rips@northwestern.edu) Department of Psychology, Northwestern University 2029 Sheridan Road, Evanston, IL 60208 USA Abstract Situational Constraint), that the Mercury would leave sufficient space for his Honda (an end-state). Using this non-mentalistic, behaviorist system only requires seeking out and representing information about the world—and no inferences about the mental states of the Mercury’s driver. Infants can use world-based cues such as efficiency Constraints to reason about behavior before achieving a representational theory of mind (Gergely & Csibra, 2003), suggesting that a primitive, behaviorist system is present in infancy. The behaviorist system therefore seems to precede the mentalistic system in development (see also Povinelli & Vonk, 2004 on chimpanzee theory of mind). However, it is unclear whether the behaviorist system used by infants is replaced by the mentalistic system that we use as adults, or whether instead these systems coexist in adulthood. If these systems coexist, many of our everyday inferences about behavior may bypass mental- state inferences altogether, relying instead on directly observable information about the world, coupled with more general assumptions such as the efficiency of actions in achieving optimal end-states. Here, we test the possibility of a behaviorist system by studying judgments about agents making decisions under uncertainty, contrasting inferences about knowledgeable agents—those who know the efficacies of each option under consideration—and inferences about ignorant agents—those who do not know the efficacies of the options. For example, consider Jill, who wants her hair to smell like apples and is deciding which of three brands of shampoo to purchase: one with a high probability of leading to her goal (“Best”), one with a medium probability (“Middle”), and one with a low probability (“Worst”). Which option will Jill choose? Two principles could potentially be used for predicting Jill’s choice. First, people might use the Efficiency Principle (Dennett, 1987), which would lead Jill to choose Best—the optimal action relative to her goals. This principle alone would not lead Jill to be any more likely to choose Middle than to choose Worst, since both are inefficient relative to Best. Second, people might use a Preference Principle, which would lead Jill to form preferences for the options in proportion to their quality, and be more likely to choose more preferred options— that is, to be most likely to choose Best, less likely to Life in our social world depends on predicting and interpreting other people’s behavior. Do such inferences always require us to explicitly represent people’s mental states, or do we sometimes bypass such mentalistic inferences and rely instead on cues from the environment? We provide evidence for such behaviorist thinking by testing judgments about agents’ decision-making under uncertainty, comparing agents who were knowledgeable about the quality of each decision option to agents who were ignorant. Participants believed that even ignorant agents were most likely to choose optimally, both in explaining (Experiment 1) and in predicting behavior (Experiment 2), and assigned them greater responsibility when acting in an objectively optimal way (Experiment 3). Keywords: Theory of mind; lay decision theory; explanation; prediction; rationality. Introduction Sunny turned on his Honda’s right blinker as he drove down Dixwell Avenue. The Mercury to his right slowed down, and Sunny changed lanes. In changing lanes, Sunny wagered with his life—gambling that the driver of the Mercury would leave enough space for his Honda to enter the right lane—and he won. Indeed, his track record with such wagers is remarkable. How is Sunny able to make such successful predictions about others’ behavior? One strategy that Sunny may have followed in this case was to infer the driver’s behavior based on his or her inferred mental-states. That is, Sunny may have reasoned that the Mercury’s slowing down was a signal of the driver’s intention to let him change lanes, based on the driver’s assumed beliefs about road behavior and folk physics, and the driver’s assumed goals of being a good road citizen and avoiding a collision. Using this mentalistic system requires inferring and representing the agent’s mental states, then predicting and interpreting actions on the basis of those inferred mental states. This seems to accord with how we typically experience the process of making behavior inferences in day-to-day life. But Sunny could have reached the same conclusion using a different strategy, inferring the Mercury’s behavior based on observable states of the world. Sunny may have inferred from the Mercury’s change in speed (an action), combined with the geometry of driving (a

  • CogSci - Predicting Behavior from the World: Naive Behaviorism in Lay Decision Theory
    Cognitive Science, 2014
    Co-Authors: Samuel G B Johnson, Lance J Rips
    Abstract:

    Predicting Behavior from the World: Naive Behaviorism in Lay Decision Theory Samuel G. B. Johnson (samuel.johnson@yale.edu) Department of Psychology, Yale University 2 Hillhouse Ave., New Haven, CT 06520 USA Lance J. Rips (rips@northwestern.edu) Department of Psychology, Northwestern University 2029 Sheridan Road, Evanston, IL 60208 USA Abstract Situational Constraint), that the Mercury would leave sufficient space for his Honda (an end-state). Using this non-mentalistic, behaviorist system only requires seeking out and representing information about the world—and no inferences about the mental states of the Mercury’s driver. Infants can use world-based cues such as efficiency Constraints to reason about behavior before achieving a representational theory of mind (Gergely & Csibra, 2003), suggesting that a primitive, behaviorist system is present in infancy. The behaviorist system therefore seems to precede the mentalistic system in development (see also Povinelli & Vonk, 2004 on chimpanzee theory of mind). However, it is unclear whether the behaviorist system used by infants is replaced by the mentalistic system that we use as adults, or whether instead these systems coexist in adulthood. If these systems coexist, many of our everyday inferences about behavior may bypass mental- state inferences altogether, relying instead on directly observable information about the world, coupled with more general assumptions such as the efficiency of actions in achieving optimal end-states. Here, we test the possibility of a behaviorist system by studying judgments about agents making decisions under uncertainty, contrasting inferences about knowledgeable agents—those who know the efficacies of each option under consideration—and inferences about ignorant agents—those who do not know the efficacies of the options. For example, consider Jill, who wants her hair to smell like apples and is deciding which of three brands of shampoo to purchase: one with a high probability of leading to her goal (“Best”), one with a medium probability (“Middle”), and one with a low probability (“Worst”). Which option will Jill choose? Two principles could potentially be used for predicting Jill’s choice. First, people might use the Efficiency Principle (Dennett, 1987), which would lead Jill to choose Best—the optimal action relative to her goals. This principle alone would not lead Jill to be any more likely to choose Middle than to choose Worst, since both are inefficient relative to Best. Second, people might use a Preference Principle, which would lead Jill to form preferences for the options in proportion to their quality, and be more likely to choose more preferred options— that is, to be most likely to choose Best, less likely to Life in our social world depends on predicting and interpreting other people’s behavior. Do such inferences always require us to explicitly represent people’s mental states, or do we sometimes bypass such mentalistic inferences and rely instead on cues from the environment? We provide evidence for such behaviorist thinking by testing judgments about agents’ decision-making under uncertainty, comparing agents who were knowledgeable about the quality of each decision option to agents who were ignorant. Participants believed that even ignorant agents were most likely to choose optimally, both in explaining (Experiment 1) and in predicting behavior (Experiment 2), and assigned them greater responsibility when acting in an objectively optimal way (Experiment 3). Keywords: Theory of mind; lay decision theory; explanation; prediction; rationality. Introduction Sunny turned on his Honda’s right blinker as he drove down Dixwell Avenue. The Mercury to his right slowed down, and Sunny changed lanes. In changing lanes, Sunny wagered with his life—gambling that the driver of the Mercury would leave enough space for his Honda to enter the right lane—and he won. Indeed, his track record with such wagers is remarkable. How is Sunny able to make such successful predictions about others’ behavior? One strategy that Sunny may have followed in this case was to infer the driver’s behavior based on his or her inferred mental-states. That is, Sunny may have reasoned that the Mercury’s slowing down was a signal of the driver’s intention to let him change lanes, based on the driver’s assumed beliefs about road behavior and folk physics, and the driver’s assumed goals of being a good road citizen and avoiding a collision. Using this mentalistic system requires inferring and representing the agent’s mental states, then predicting and interpreting actions on the basis of those inferred mental states. This seems to accord with how we typically experience the process of making behavior inferences in day-to-day life. But Sunny could have reached the same conclusion using a different strategy, inferring the Mercury’s behavior based on observable states of the world. Sunny may have inferred from the Mercury’s change in speed (an action), combined with the geometry of driving (a

L. Seifert - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of elite swimmers' activity during an instrumented protocol
    Journal of Sports Sciences, 2009
    Co-Authors: D. Adé, Germain Poizat, Nathalie Gal-petitfaux, Huub Toussaint, L. Seifert
    Abstract:

    The aim of this study was to examine swimmers' activity-technical device coupling during an experimental protocol (MADsystem). The study was conducted within a course-of-action theoretical and methodological framework. Two types of data were collected: (a) video recordings and (b) verbalizations during post-protocol interviews. The data were processed in two steps: (a) reconstruction of each swimmer's course of action and (b) comparison of the courses of action. Analysis from the actors' point of view allowed a description of swimmer-technical device coupling. The results showed that the technical device modified the athletes' range of perceptions and repertoire of actions. They also indicated that changes in coupling between the swimmers and the MAD-system were linked to utilization Constraints: the swimmers' experiences were transformed in the same speed intervals, suggesting that this was an essential Situational Constraint to swimmer-technical device coupling. This study highlights how a technical device and the conditions of its use changed athletes' activity and suggests that it is important to develop activity-centred design in sport.

D. Adé - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of elite swimmers' activity during an instrumented protocol
    Journal of Sports Sciences, 2009
    Co-Authors: D. Adé, Germain Poizat, Nathalie Gal-petitfaux, Huub Toussaint, L. Seifert
    Abstract:

    The aim of this study was to examine swimmers' activity-technical device coupling during an experimental protocol (MADsystem). The study was conducted within a course-of-action theoretical and methodological framework. Two types of data were collected: (a) video recordings and (b) verbalizations during post-protocol interviews. The data were processed in two steps: (a) reconstruction of each swimmer's course of action and (b) comparison of the courses of action. Analysis from the actors' point of view allowed a description of swimmer-technical device coupling. The results showed that the technical device modified the athletes' range of perceptions and repertoire of actions. They also indicated that changes in coupling between the swimmers and the MAD-system were linked to utilization Constraints: the swimmers' experiences were transformed in the same speed intervals, suggesting that this was an essential Situational Constraint to swimmer-technical device coupling. This study highlights how a technical device and the conditions of its use changed athletes' activity and suggests that it is important to develop activity-centred design in sport.

Huub Toussaint - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of elite swimmers ’ activity during an instrumented protocol
    2015
    Co-Authors: Ade David, Germain Poizat, Nathalie Gal-petitfaux, Huub Toussaint, Ludovic M. Seifert
    Abstract:

    The aim of this study was to examine swimmers ’ activity–technical device coupling during an experimental protocol (MADsystem).The study was conducted within a course-of-action theoretical and methodological framework. Two types of datawere collected: (a) video recordings and (b) verbalizations during post-protocol interviews. The data were processed in twosteps: (a) reconstruction of each swimmer’s course of action and (b) comparison of the courses of action. Analysis from theactors ’ point of view allowed a description of swimmer–technical device coupling. The results showed that the technicaldevice modified the athletes ’ range of perceptions and repertoire of actions. They also indicated that changes in couplingbetween the swimmers and the MAD-system were linked to utilization Constraints: the swimmers ’ experiences weretransformed in the same speed intervals, suggesting that this was an essential Situational Constraint to swimmer–technicaldevice coupling. This study highlights how a technical device and the conditions of its use changed athletes ’ activity andsuggests that it is important to develop activity-centred design in sport

  • Analysis of elite swimmers' activity during an instrumented protocol
    Journal of Sports Sciences, 2009
    Co-Authors: D. Adé, Germain Poizat, Nathalie Gal-petitfaux, Huub Toussaint, L. Seifert
    Abstract:

    The aim of this study was to examine swimmers' activity-technical device coupling during an experimental protocol (MADsystem). The study was conducted within a course-of-action theoretical and methodological framework. Two types of data were collected: (a) video recordings and (b) verbalizations during post-protocol interviews. The data were processed in two steps: (a) reconstruction of each swimmer's course of action and (b) comparison of the courses of action. Analysis from the actors' point of view allowed a description of swimmer-technical device coupling. The results showed that the technical device modified the athletes' range of perceptions and repertoire of actions. They also indicated that changes in coupling between the swimmers and the MAD-system were linked to utilization Constraints: the swimmers' experiences were transformed in the same speed intervals, suggesting that this was an essential Situational Constraint to swimmer-technical device coupling. This study highlights how a technical device and the conditions of its use changed athletes' activity and suggests that it is important to develop activity-centred design in sport.