Sins of omission: Children selectively explore when teachers are under-informative

Gweon, Hyowon; Pelton, Hannah; Konopka, Jaclyn A.; Schulz, Laura

doi:10.1016/j.cognition.2014.04.013

Cited by 149 publications

(138 citation statements)

References 21 publications

(18 reference statements)

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“…As such, in small and simple domains (e.g., a toy with just a few functions) where the cost of sharing information is negligible, agents should share all the information necessary for the learner to draw accurate inferences, and learners can use this expectation to make inferences accordingly. Consistent with this expectation, children assume that teachers share all relevant information in simple domains [86], and when a teacher demonstrates only one of many functions of a toy, children rate the teacher poorly and mistrust his subsequent teaching [87]. These inferences should be cost-sensitive, however: learners' expectation that informants will communicate all relevant information should be weaker when the costs are higher.…”

Section: Communication and Pedagogymentioning

confidence: 72%

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Jara‐Ettinger¹,

Gweon²,

Schulz³

et al. 2016

Trends in Cognitive Sciences

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We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: From a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This "naïve utility calculus" lets both children and adults observe others' behavior and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy. Commonsense PsychologyTheories of decision-making have been at the heart of psychology since the field's inception, but only recently has the field turned to the study of how humans -especially the youngest humansthink humans make decisions. When we watch someone make a choice, we explain it in terms of their goals, preferences, personalities, and moral beliefs. This capacity -our commonsense psychology -is the cognitive foundation of human society. It lets us share what we have and know, with those from whom we expect the same in return, and it guides how we evaluate those who deviate from our expectations.The representations and inferential power underlying commonsense psychology trace back to early childhood -before children begin kindergarten, and often even in infancy. Work on how children reason about other agents' goals [1][2][3][4][5][6][7][8], desires [9][10][11], beliefs [12][13][14][15][16][17][18], and pro-social behavior [19][20][21][22][23][24][25][26][27][28][29] has advanced our understanding of what in our commonsense psychology is at work in early infancy [30][31][32] and what develops [16][17][33][34][35]. Nonetheless, major theoretical questions remain unresolved. What computations underlie our commonsense psychology, and to what extent are they specific to the social domain? Are there a small number of general principles by which humans reason about and evaluate other agents, or do we instead learn a large number of special case rules and heuristics? To what extent is there continuity between the computations supporting commonsense psychology in infancy and later ages? Is children's social-cognitive development a progressive refinement of a computational system in place from birth, or are there fundamentally new computational principles coming into play?In this article we advance a hypothesis that offers answers to each of these questions, and provides a unifying framework in which to understand the diverse social-cognitive capacities we see even in young children. We propose that human beings, from early infancy, interpret others' intentional actions through the lens of a naïve utility calculus: that is, people assume that others choose actions to maximize utilities -the rewards they expect to obtain relative to the costs...

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Section: Communication and Pedagogymentioning

confidence: 72%

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Jara‐Ettinger¹,

Gweon²,

Schulz³

et al. 2016

Trends in Cognitive Sciences

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“…Children appear to structure their play in order to deconfound variables when causal mechanisms at play in the world are unclear (e.g., Denison et al, 2013; Gopnik, Meltzoff, & Kuhl, 1999; Gweon et al, 2014; Schulz, Gopnik, & Glymour, 2007; van Schijndel et al, 2015), and also make efficient use of information that they encounter in the world to learn correct causal structures (e.g., Gopnik & Schulz, 2007; Gopnik at al., 2001). These findings are important because they highlight the fact that children’s curiosity appears specifically well suited to teaching them about the causal structure of the world.…”

Section: The Development Of Curiositymentioning

confidence: 99%

The Psychology and Neuroscience of Curiosity

Kidd

Hayden

2015

Neuron

682

557

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SUMMARY Curiosity is a basic element of our cognition, yet its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development. One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity; another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab. Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking. We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.

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“…Human beings, along with other intelligent animals, are remarkable in their drive to actively explore their environment, whether in adult activities such as scientific research, or during the extended period of growth and development (Ryan and Deci, 2000; Deci et al, 2001; Gottlieb et al, 2013, Mirolli and Baldassarre, 2013; Gweon et al, 2014; Taffoni et al, 2014; Oudeyer and Smith, in press). Achieving efficient exploration in such open-ended conditions poses significant computational challenges, which stem from the fact that the agent explores in conditions of limited knowledge and time, the fact that it is faced with vast numbers of possible tasks, and the fact that many of these tasks are random or unlearnable and would optimally be avoided (e.g., one would ideally not spend much effort in trying to predict the stock market from the traffic pattern).…”

Section: Introductionmentioning

confidence: 99%

The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration

Baranès¹,

Oudeyer²,

Gottlieb³

2014

Front. Neurosci.

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Devising efficient strategies for exploration in large open-ended spaces is one of the most difficult computational problems of intelligent organisms. Because the available rewards are ambiguous or unknown during the exploratory phase, subjects must act in intrinsically motivated fashion. However, a vast majority of behavioral and neural studies to date have focused on decision making in reward-based tasks, and the rules guiding intrinsically motivated exploration remain largely unknown. To examine this question we developed a paradigm for systematically testing the choices of human observers in a free play context. Adult subjects played a series of short computer games of variable difficulty, and freely choose which game they wished to sample without external guidance or physical rewards. Subjects performed the task in three distinct conditions where they sampled from a small or a large choice set (7 vs. 64 possible levels of difficulty), and where they did or did not have the possibility to sample new games at a constant level of difficulty. We show that despite the absence of external constraints, the subjects spontaneously adopted a structured exploration strategy whereby they (1) started with easier games and progressed to more difficult games, (2) sampled the entire choice set including extremely difficult games that could not be learnt, (3) repeated moderately and high difficulty games much more frequently than was predicted by chance, and (4) had higher repetition rates and chose higher speeds if they could generate new sequences at a constant level of difficulty. The results suggest that intrinsically motivated exploration is shaped by several factors including task difficulty, novelty and the size of the choice set, and these come into play to serve two internal goals—maximize the subjects' knowledge of the available tasks (exploring the limits of the task set), and maximize their competence (performance and skills) across the task set.

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Sins of omission: Children selectively explore when teachers are under-informative

Cited by 149 publications

References 21 publications

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

The Psychology and Neuroscience of Curiosity

The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration

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