The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation of causal learning in associative terms. A review of the development of the use of rational causal models in the psychology of learning is discussed concluding with the presentation of three areas of research related to cause-effect learning. We explain how rational context choices, a selective association effect (i.e., blocking of inhibition) as well as causal structure can all emerge from processes that can be modeled using elements of standard associative theory. We present the auto-associator (e.g., Baetu & Baker, 2009) as one such simple account of causal structure
A common distinction made by theorists examining the mental processes contributing to human learning...
Theories of associative learning have a long history in advancing the psychological account of behav...
Two key research issues in the field of causal learning are how people acquire causal knowledge when...
The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation ...
Theories of causal cognition describe how animals code cognitive primitives such as causal strength,...
The experiments reported here investigated the cognitive processes involved in causal reasoning. Par...
The past 50 years have seen an accumulation of evidence suggesting that associative learning depends...
Are humans unique in their ability to interpret exogenous events as causes? We addressed this questi...
Mitchell et al. contend that there is no need to posit a contribution based on the formation of asso...
Causal models are representations of causal structures and processes in the world. In this thesis tw...
Higher-level cognition depends on the ability to learn models of the world. We can characterize this...
The human ability to learn quickly about causal relationships requires abstract knowledge that provi...
I argue that psychologists interested in human causal judgment should understand and adopt a represe...
Conceiving of stimuli and responses as causes and effects, and assuming that rats acquire representa...
We aim to provide a new perspective on the old debate about whether evidence for higher order cognit...
A common distinction made by theorists examining the mental processes contributing to human learning...
Theories of associative learning have a long history in advancing the psychological account of behav...
Two key research issues in the field of causal learning are how people acquire causal knowledge when...
The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation ...
Theories of causal cognition describe how animals code cognitive primitives such as causal strength,...
The experiments reported here investigated the cognitive processes involved in causal reasoning. Par...
The past 50 years have seen an accumulation of evidence suggesting that associative learning depends...
Are humans unique in their ability to interpret exogenous events as causes? We addressed this questi...
Mitchell et al. contend that there is no need to posit a contribution based on the formation of asso...
Causal models are representations of causal structures and processes in the world. In this thesis tw...
Higher-level cognition depends on the ability to learn models of the world. We can characterize this...
The human ability to learn quickly about causal relationships requires abstract knowledge that provi...
I argue that psychologists interested in human causal judgment should understand and adopt a represe...
Conceiving of stimuli and responses as causes and effects, and assuming that rats acquire representa...
We aim to provide a new perspective on the old debate about whether evidence for higher order cognit...
A common distinction made by theorists examining the mental processes contributing to human learning...
Theories of associative learning have a long history in advancing the psychological account of behav...
Two key research issues in the field of causal learning are how people acquire causal knowledge when...