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Laura Schulz - One of the best experts on this subject based on the ideXlab platform.
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Exploratory play, Rational Action, and efficient search
2020Co-Authors: Junyi Chu, Laura SchulzAbstract:Play is a universal behavior widely held to be critical for learning and development. Recent studies suggest children’s exploratory play is consistent with formal accounts of learning. This “play as Rational exploration” view suggests that children’s play is sensitive to costs, rewards, and expected information gain. By contrast, here we suggest that a defining feature of human play is that children subvert normal utility functions in play, setting up problems where they incur needless costs to achieve arbitrary rewards. Across four studies, we show that 4-5-year-old children not only infer playful behavior from observed violations of Rational Action (Experiment 1), but themselves take on unnecessary costs and perform inefficient Actions during play, despite understanding and valuing efficiency in non-playful, instrumental contexts (Experiment 2-4). We end with a discussion of the value of apparently utility-violating behavior and why it might serve learning in the long run.
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Children's understanding of the costs and rewards underlying Rational Action.
Cognition, 2015Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:Humans explain and predict other agents' behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naïve utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. In four experiments, we show that, given an agent's choices, children (range: 5-6 year olds; N=96) can infer unobservable aspects of costs (differences in agents' competence) from information about subjective differences in rewards (differences in agents' preferences) and vice versa. Moreover, children can design informative experiments on both objects and agents to infer unobservable constraints on agents' Actions.
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CogSci - I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
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i d do anything for a cookie but i won t do that children s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jaraettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
Joshua B. Tenenbaum - One of the best experts on this subject based on the ideXlab platform.
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Children's understanding of the costs and rewards underlying Rational Action.
Cognition, 2015Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:Humans explain and predict other agents' behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naïve utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. In four experiments, we show that, given an agent's choices, children (range: 5-6 year olds; N=96) can infer unobservable aspects of costs (differences in agents' competence) from information about subjective differences in rewards (differences in agents' preferences) and vice versa. Moreover, children can design informative experiments on both objects and agents to infer unobservable constraints on agents' Actions.
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CogSci - I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
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i d do anything for a cookie but i won t do that children s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jaraettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
Hyowon Gweon - One of the best experts on this subject based on the ideXlab platform.
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Children's understanding of the costs and rewards underlying Rational Action.
Cognition, 2015Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:Humans explain and predict other agents' behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naïve utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. In four experiments, we show that, given an agent's choices, children (range: 5-6 year olds; N=96) can infer unobservable aspects of costs (differences in agents' competence) from information about subjective differences in rewards (differences in agents' preferences) and vice versa. Moreover, children can design informative experiments on both objects and agents to infer unobservable constraints on agents' Actions.
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CogSci - I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
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i d do anything for a cookie but i won t do that children s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jaraettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
Julian Jara-ettinger - One of the best experts on this subject based on the ideXlab platform.
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Children's understanding of the costs and rewards underlying Rational Action.
Cognition, 2015Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:Humans explain and predict other agents' behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naïve utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. In four experiments, we show that, given an agent's choices, children (range: 5-6 year olds; N=96) can infer unobservable aspects of costs (differences in agents' competence) from information about subjective differences in rewards (differences in agents' preferences) and vice versa. Moreover, children can design informative experiments on both objects and agents to infer unobservable constraints on agents' Actions.
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CogSci - I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying Rational Action
Cognitive Science, 2014Co-Authors: Julian Jara-ettinger, Hyowon Gweon, Joshua B. Tenenbaum, Laura SchulzAbstract:I’d do anything for a cookie (but I won’t do that): * Children’s understanding of the costs and rewards underlying Rational Action Julian Jara-Ettinger (jjara@mit.edu), Hyowon Gweon (hyora@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), & Laura E. Schulz (lschulz@mit.edu) Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, MA 02139 USA Abstract Tenenbaum, 2010; Jara-Ettinger, Baker, & Tenenbaum, 2012). MDPs are a framework widely used in artificial intelligence and other engineering fields for determining sequences of Actions, or plans, an agent can take to achieve the highest-utility states in the most efficient manner, given a specification of the possible world states, the agent’s possible Actions and their likely outcomes, and the agent’s utility function 2 (positively and negatively valued rewards) associated with different combinations of Actions and world states. Bayesian inference over these probabilistic generative models can implement a form of Rational inverse planning, working backwards from observations of an agent’s Actions to infer aspects of the agent’s world model or utility function. Bayesian inverse planning accounts have been used to make fine-grained quantitative predictions of adults’ judgments about an agent’s desires, beliefs, and states of the world (Baker, et al., 2009, Baker, Saxe, & Tenenbaum 2011; Jara-Ettinger et al., 2012). The details of this computational approach are not critical here, but it is helpful to consider the qualitative intuitions behind these models, as well as what they leave out, because they motivate our present work. Intuitively we can think of an agent’s utility function as the difference between two terms: a (positive) reward term associated with goals to be achieved, measuring the value of a goal to the agent, and a (negative) cost term associated with Actions that can be taken to achieve these goals, measuring the difficulty of an Action. Formally, we can decompose the utility function (normally a joint function of the agent’s state and Actions) into a reward associated with each state, and a cost associated with each Action: U(a,s)=R(s)-C(a). Note that observing an agent taking an Action, a, to achieve state, s, implies only that the relative reward for s is significantly higher than the cost of a; it does not determine either of these values in absolute terms: positing that the Action has high cost but the goal state generates very high rewards, or that the Action is relatively lower cost and the goal state is comparably lower in reward, maybe equally viable explanations of the same behavior. Psychologically however, high cost/high reward plans are very different from low cost/low reward ones. If Sally jumps over a wall to get a cookie is it because she likes the cookies so much Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of Rational Action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of Rational Action is instantiated by a naive utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with Actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ Actions. 1 Keywords: Naive Utility Calculus; Social Cognition; Theory of Mind Introduction One of the assumptions underlying our ability to draw rich inferences from sparse data is that agents act Rationally. In its simplest form, this amounts to the expectation that agents will take the shortest path to a goal subject to physical constraints imposed by the world (Gergely & Csibra, 2003). Even this simple formulation is inferentially powerful, supporting predictions about future events and inferences about unobserved aspects of events. For instance, if Sally hops over a wall to get a cookie, we assume that she would not hop, but walk straight to the cookie, if the wall weren’t there. Studies suggest that even infants expect agents to act Rationally. Infants can use information about an agent’s goal and situational constraints (e.g., gaps, occluders, walls, etc.) to predict her Actions (Gergely, Nadasdy, Csibra, & Biro, 1995); an agent’s Actions and situational constraints to infer her goals (Csibra, Biro, Koos, & Gergeley, 2003), and an agent’s Actions and goals to infer unobserved situational constraints (see Csibra et al., 2003 for review; see also Brandone & Wellman, 2009; Gergeley, Bekkering, & Kiraly, 2002; Phillips & Wellman, 2005; Schwier, Van Maanen, Carpenter, & Tomasello, 2006; Scott & Baillargeon, 2013). Computationally, this approach to Action understanding can be formalized as Bayesian inference over a model of Rational Action planning, such as a Markov Decision Process (MDP) (Baker, Saxe, & Tenenbaum, 2009, 2011; Ullman, Baker, Macindoe, Evans, Goodman, & In the artificial intelligence literature this is sometimes referred to as the reward function. However, since this function is derived from rewards minus costs, we refer to it as the utility function for clarity. * Or That’s the way the utility crumbles.
Anthony Dickinson - One of the best experts on this subject based on the ideXlab platform.
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Rational Action selection in 1 to 3 year olds following an extended training experience
Journal of Experimental Child Psychology, 2012Co-Authors: Ulrike M. H. Klossek, Anthony DickinsonAbstract:Abstract Previous studies failed to find evidence for Rational Action selection in children under 2 years of age. The current study investigated whether younger children required more training to encode the relevant causal relationships. Children between 1½ and 3 years of age were trained over two sessions to perform Actions on a touch-sensitive screen to obtain video clips as outcomes. Subsequently, a visual habituation procedure was employed to devalue one of the training outcomes. As in previous studies, 2- and 3-year-olds chose Actions associated with an expected valued outcome significantly more often during a subsequent choice test. Moreover, analysis of children’s first responses in the post-devaluation test revealed evidence of Rational Action selection even in the youngest age group (18–23 months). Consistent with dual-process accounts of Action control, the findings support the view that the ability to make Rational Action choices develops gradually.
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Rational Action selection in 1½- to 3-year-olds following an extended training experience.
Journal of experimental child psychology, 2011Co-Authors: Ulrike M. H. Klossek, Anthony DickinsonAbstract:Previous studies failed to find evidence for Rational Action selection in children under 2 years of age. The current study investigated whether younger children required more training to encode the relevant causal relationships. Children between 1½ and 3 years of age were trained over two sessions to perform Actions on a touch-sensitive screen to obtain video clips as outcomes. Subsequently, a visual habituation procedure was employed to devalue one of the training outcomes. As in previous studies, 2- and 3-year-olds chose Actions associated with an expected valued outcome significantly more often during a subsequent choice test. Moreover, analysis of children's first responses in the post-devaluation test revealed evidence of Rational Action selection even in the youngest age group (18-23 months). Consistent with dual-process accounts of Action control, the findings support the view that the ability to make Rational Action choices develops gradually.
