It remains to be seen if similar quantitative metrics of information complexity
can be applied to static U0126 datasheet stimuli. Kidd et al. (2012, 2014) avoided special classes of stimuli such as faces or the mother’s voice precisely because such stimuli are thought to be treated differently, either by innate biases or by past experience, than arbitrarily novel stimuli. Clearly, the valence of certain classes of stimuli must be taken into account to extend the Goldilocks findings to events that are common in the natural environment. And finally, there are potential interactions between spontaneous allocation of attention and the “reward” that could follow—perhaps in the form of a “sense of mastery” or reduced “prediction error” if learning is achieved. In summary, the Goldilocks work is not merely a methodological sidebar to studies of attention, but also a catalyst for thinking more deeply about what factors control looking times and how these factors influence the interpretation of studies of infant learning. So far, we have focused on studies of statistical learning that were limited to asking whether infants can compute AZD2014 and remember items or events to which they were exposed in
an immediately preceding familiarization phase. We now turn to the more interesting case of how infants generalize from familiar to novel items or events. After all, knowledge based solely on what we have already experienced is overly restrictive and Leukocyte receptor tyrosine kinase inefficient—a “smart” learner must be able to make inferences about previously unexperienced items or events to attain the generative capacity of a mature learner. The preceding summary of the Goldilocks results highlighted the fact that learners discover structure in the input to which they are exposed by sampling that input with selective attentional mechanisms. Because any natural corpus of input,
whether language or vision, will contain variability, a “smart” learner should resist the temptation to gather small samples because they can be misleading—instead learners should integrate over a representative corpus. But this creates a dilemma and a tradeoff. The dilemma is that a learner cannot ignore variation within a corpus because the underlying structure to be learned may undergo a change or there may be more than one structure present in a large sample of the input. The tradeoff is between small samples that enable rapid learning but risk inferring multiple structures when a single structure (with variability) is present, and larger samples that enable more reliable estimates of the possible presence of multiple structures but slow down the rate of learning of these structures.