Shukla et al ‘s results showed that these statistical segmentatio

Shukla et al.’s results showed that these statistical segmentation and word-mapping tasks can be accomplished at the same time and moreover in much younger infants (6-month-olds). This suggests that when designing single-cue laboratory experiments, we may be underestimating the learning capabilities of infants because they have already formed expectations about how multiple sources of information are correlated in natural language input. The counterintuitive Ibrutinib implication of this finding is that making an experimental design too simple may make the task for the infant more complex, thereby leading researchers to underestimate the infant’s actual learning capacity. To

summarize this section on the second problem facing the naïve learner—there must be constraints to enable learning to be Vemurafenib clinical trial tractable—the solution seems clear-cut. The computational complexity and interpretive ambiguity about which statistics are the “right” ones to keep track of is solved by a few innate constraints on what to attend to and a learning mechanism that feeds off of these innate constraints to become further constrained

by what has been learned so far during development. In the terminology of Bayes theorem, what a learner acquires (called the posterior probabilities) is a combination of what was given by the innate biases (called the priors) and what has already been observed from masses of data (called the likelihoods), filtered through the lens of the innate

biases. This is essentially an incremental bootstrapping model of learning, in which a hierarchy of information is built up from two mechanisms—a powerful and robust statistical-learning “engine” that is rendered tractable by a few innate biases, coupled with an enormous amount of raw data that once filtered by these innate biases is forever “blocked” from further computations that would divert the learner FER along an unfruitful path. But this view of the development of learning rests on an assumption of the infant as a rationale allocator of attention to those sources of information that are the most “fruitful”. How does the infant “know” that some information is worthy of their attention and other information is not? The next section tackles this question by reviewing recent work on the fundamental properties of how we interpret looking-time data from infants. The use of looking times as a measure of learning, and a whole host of other underlying perceptual and cognitive processes, has been exploited for the past 50 years of research on infants (Aslin, 2007). The canonical view of looking times is that they are reactions to stimulation, pulling the infant’s gaze hither and yon based on a combination of exogenous (i.e., stimulus salience) and endogenous (i.e., memory) factors.

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