“Imagine that there is some real structure in the world – a three-dimensional object, a grammar, or, especially relevant to theories, a network of causal relationships. That structure gives rise to some patterns of observable evidence rather than others – a particular set of retinal images, or spoken sentences, or statistical contingencies between events. That spatial or grammatical or causal structure can be represented mathematically, by a 3-d map or tree structure or a causal graph. You could think of such a representation as a hypothesis about what the actual structure is like. This representation will also allow you to mathematically generate patterns of evidence from that structure. So you can predict the patterns of evidence that follow from the hypothesis, and make new inferences accordingly. For example, a map or a tree or a causal graph will let you predict how an object will look from a different angle, whether a new sentence will be acceptable, or that a new event will be followed by other events. If the hypothesis is correct, then these inferences will turn out to be right.” “Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory” Alison Gopnik, Henry M. Wellman, 2012

“Imagine that there is some real structure in the world – a three-dimensional object, a grammar, or, especially relevant to theories, a network of causal relationships. That structure gives rise to some patterns of observable evidence rather than others – a particular set of retinal images, or spoken sentences, or statistical contingencies between events. That spatial or grammatical or causal structure can be represented mathematically, by a 3-d map or tree structure or a causal graph. You could think of such a representation as a hypothesis about what the actual structure is like. This representation will also allow you to mathematically generate patterns of evidence from that structure. So you can predict the patterns of evidence that follow from the hypothesis, and make new inferences accordingly. For example, a map or a tree or a causal graph will let you predict how an object will look from a different angle, whether a new sentence will be acceptable, or that a new event will be followed by other events. If the hypothesis is correct, then these inferences will turn out to be right.”

“Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory”
Alison Gopnik, Henry M. Wellman, 2012

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