Development of infants' use of continuity cues in their perception of causality.

Oakes, Lisa M.

doi:10.1037/0012-1649.30.6.869

Cited by 115 publications

(135 citation statements)

References 24 publications

(74 reference statements)

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“…The inferences infants make about hidden objects (e.g., permanence, contact causality) have the same informational content as the output of spatiotemporal analysis of the perceptual array. For example, infants, like adults, perceive mechanical causality directly when the spatiotemporal conditions for Michotte's launching events are met (Michotte 1963, Leslie 1988, Oakes 1994, and it is exactly this sort of contact causality that is inferred in the hidden events of the Ball studies cited above (Ball 1973. For another example, infants and adults analyze self-generated motion and patterns of contingent reaction between entities as evidence for intentional, goal directed action (Gergely et al 1995, Watson 1979).…”

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confidence: 99%

Science and Core Knowledge

1996

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While endorsing Gopnik's proposal that studies of the emergence and modification of scientific theories and studies of cognitive development in children are mutually illuminating, we offer a different picture of the beginning points of cognitive development from Gopnik's picture of “theories all the way down.” Human infants are endowed with several distinct core systems of knowledge which are theory-like in some, but not all, important ways. The existence of these core systems of knowledge has implications for the joint research program between philosophers and psychologists that Gopnik advocates and we endorse. A few lessons already gained from this program of research are sketched.

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confidence: 99%

Science and Core Knowledge

1996

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“…During the first year of life, infants acquire a rich array of physical knowledge in a consistent order; 3-to 4-mo-old infants understand that the world is composed of bounded, unitary objects (10) that are continuous in time and space (11). By 5 mo of age, most infants are able to differentiate a liquid from a solid using motion cues and have expectations about how nonsolids behave and interact (12, 13); by 6 or 7 mo, they are sensitive to the causal roles of one object striking and launching another (14,15), and by 8 mo, they can determine which objects must be attached for a configuration to be stable under gravity (16). By 12 mo, they are sensitive to the rough location of an object's center of mass, relative to the edge of a supporting surface, needed for that support relationship to be stable (17).…”

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confidence: 99%

Functional neuroanatomy of intuitive physical inference

Fischer

Mikhael

Tenenbaum

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

142

159

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To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.physical scene understanding | mental simulation | fMRI | premotor cortex | action planning U nderstanding, predicting, and acting on the world requires an intuitive grasp of physics ( Fig. 1). We see not just a table and a coffee cup, but a table supporting a coffee cup. We see not just a ping pong ball moving after contact with a paddle, but the paddle causing the ball to move by exerting a force through that contact. We use physical intuitions to not just understand the world but predict what will happen next-that a stack of dishes is unstable and likely to fall or that a squash ball is on a trajectory to ricochet off the wall and head in our direction. We also need rich physical knowledge to plan our own actions. Before we pick up an object, we must assess its material and weight and prepare our muscles accordingly. To navigate our environment, we need to determine which surfaces will support us (e.g., a linoleum floor but maybe not the surface of a frozen stream; this tree branch but probably not that one) and what barriers are penetrable (e.g., a beaded curtain but not a glass wall). How do we compute these everyday physical inferences with such apparent ease and speed?Battaglia et al.(1) recently proposed a computational mechanism for how humans can make a wide range of physical inferences in natural scenes via a mental simulation engine akin to the "physics engines" used in many video games. Physics engines are software systems that support efficient but approximate simulations of rigid body, soft body, or fluid mechanics for the purpose of generating realistic interactive gameplay in a virtual physical world. Rather than striving for fine-grained physical accuracy, game physics engines make shortcu...

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“…During the first year, infants can distinguish biological motion from nonbiological motion for both people and other mammals (Arterberry & Bornstein, 2002;Bertenthal, 1993), identify both rational and intentional actions (Csibra, Gergely, Biro, Koos, & Brockbank, 1999;Woodward, 1999), and reason about the physical interaction between objects such as causality (e.g., Leslie, 1982;Oakes, 1994). Infants also parse actions in events (e.g., Baldwin, Baird, Saylor, & Clark, 2001;Sharon & Wynn, 1998;Spelke, Born, & Chu, 1983;Wynn, 1996).…”

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confidence: 99%

Trading Spaces: Carving up Events for Learning Language

Göksun

Hirsh‐Pasek

Golinkoff

2010

Perspect Psychol Sci

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Relational terms (e.g., verbs and prepositions) are the cornerstone of language development, bringing together two distinct fields: linguistic theory and infants’ event processing. To acquire relational terms such as run, walk, in, and on, infants must first perceive and conceptualize components of dynamic events such as containment—support, path—manner, source—goal, and figure—ground. Infants must then uncover how the particular language they are learning encodes these constructs. This review addresses the interaction of language learning with infants’ conceptualization of these nonlinguistic spatial event components. We present the thesis that infants start with language-general nonlinguistic constructs that are gradually refined and tuned to the requirements of their native language. In effect, infants are trading spaces, maintaining their sensitivity to some relational distinctions while dampening other distinctions, depending on how their native language expresses these constructs.

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Development of infants' use of continuity cues in their perception of causality.

Cited by 115 publications

References 24 publications

Science and Core Knowledge

Science and Core Knowledge

Functional neuroanatomy of intuitive physical inference

Trading Spaces: Carving up Events for Learning Language

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