Origins of knowledge.

By Elizabeth S. Spelke, Karen Breinlinger, J. Macomber, Kristen C. Jacobson. 1992

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Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, background): “ational inductive biases, in the form of specic architectural assumptions, which guide these approaches towards learning about entities and relations (Mitchell, 1980), which we, joining many others (Spelke et al., 1992; Spelke and Kinzler, 2007; Marcus, 2001; Tenenbaum et al., 2011; Lake et al., 201”
Flexible Neural Representation for Physics Prediction (2018, background): “Humans efﬁciently decompose their environment into objects, and reason effectively about the dynamic interactions between these objects [43, 45].”
Discovering objects and their relations from entangled scene representations (2017, background): “The ability to reason about objects and relations is important for solving a wide variety of tasks (Spelke et al., 1992; Lake et al., 2016).”
A theory of causal learning in children: causal maps and Bayes nets. (2004, background): “…from other hypotheses about causal learning in children. is possible that young children do not learn genuinely new causal relations but initially rely on a few innate domain-specific causal schemas that are later enriched (see, e.g., Atran, 1990; Leslie & Roth, 1993; Spelke et al., 1992).”
Inductive biases for deep learning of higher-level cognition (2020, background): “Most humans understand physics first of all at an intuitive level and in solving practical problems we typically combine such implicit knowledge with explicit verbalizable knowledge (McCloskey, 1983; Baillargeon et al., 1985; Spelke et al., 1992; Battaglia et al., 2013).”
Interaction Networks for Learning about Objects, Relations and Physics (2016, background): “Representing and reasoning about objects, relations and physics is a “core” domain of human common 17 sense knowledge [25], and among the most basic and important aspects of intelligence [27, 15].”
Infant Manual Exploration of Objects, Surfaces, and Their Interrelations (2005, background): “When do infants evidence sensitivity to object–surface relations in their manual behavior? At the level of visual processing, infants in the second half-year already possess a basic understanding of the kinds of physical relations that objects can enter into, including ones involving objects and sur”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “A prominent view in developmental psychology (e.g., Spelke et al., 1992; Spelke and Kinzler, 2007; Marcus, 2018b) argues that learning relies on innate knowledge about the structure of the world (e.g., knowledge about grammar, object permanence, numerosity, etc.).”
The misunderstood limits of folk science: an illusion of explanatory depth (2002, background): “1969 ). Theories seem to tell us what features to emphasize in learning new concepts as well as highlighting the relevant dimensions of similarity ( Murphy, 2002 ). Intuitive theories have also been heavily emphasized in accounts of the cognitive development of children ( Gelman & Koenig, 2002 ) and”
The endogenous origins of experience, routines, and organizational capabilities: the poverty of stimulus (2010, background): “…external versus internal models of cognition and learning, the authors conclude that ‘cognition develops from its own foundations, rather than from a foundation of perception and action’ lending support for an internal, a priori approach or a ‘core knowledge thesis’ (Spelke et al., 1992: 605, 628).”
Developmental origin of the animate-inanimate distinction. (2001, background): “Implications We see no reason why the same perceptual processing mechanism discussed here cannot also be applied to infants’ acquisition of certain physical principles such as solidity, support, or gravity (e.g., Baillargeon, 1993, 1995, 1999; Spelke et al., 1992).”
Psychological and Sociomoral Reasoning in Infancy (2014, background): “…systems (e.g., Chomsky, 1965), is that infants are born with a physicalreasoning system, an abstract computational system that provides them with a skeletal causal framework for reasoning and learning about physical events (e.g., Baillargeon, 2008; Gelman, 1990; Leslie, 1994b; Spelke, 1994).”