摘要：
We present a model aimed at accounting for learning of predictive
and causal relationships involving stimulus compounds, by means
of a mechanism based on a normative-methodological analysis of causality
that goes beyond the traditional associative/rule-based controversy.
According to the model, causal learning is attained by computing
the validity of each stimulus in a given learning situation. The
situation is determined by the assumptions, objectives, and aims
held by the learner or demanded by the learning context. Hence,
validity computation depends on task demands: causal, predictive,
or diagnostic according to a general principle of normative contextualization
that allows learners to adapt a between-cues competition principle
in a flexible way. Validity is computed using the Relevance Relativization
mechanism, a linear model, based on the balance between the probability
of stimulus combinations and the probability of each cue. Thus,
cue interactions occur mainly when the combination of stimuli shows
predictive changes in relation to the same cues considered individually.
This model makes novel predictions concerning variations of the
competition principle as a function of the type of procedure, including
blocking, simultaneous blocking, and relative validity. In addition,
our model also integrates top-down and bottom-up processing levels,
including individuals’ assumptions or previous beliefs.
