8-month-old infants spontaneously learn and generalize hierarchical rules

Abstract

The ability to extract hierarchically organized rule structures from noisy environments is critical to human cognitive, social, and emotional intelligence. Adults spontaneously create hierarchical rule structures of this sort. In the present research, we conducted two experiments to examine the previously unknown developmental origins of this hallmark skill. In Experiment 1, we exploited a visual paradigm previously shown to elicit incidental hierarchical rule learning in adults. In Experiment 2, we used the same learning structure to examine whether these hierarchical-rule-learning mechanisms are domain general and can help infants learn spoken object-label mappings across different speaker contexts. In both experiments, we found that 8-month-olds created and generalized hierarchical rules during learning. Eyeblink rate, an exploratory indicator of striatal dopamine activity, mirrored behavioral-learning patterns. Our results provide direct evidence that the human brain is predisposed to extract knowledge from noisy environments, and they add a fundamental learning mechanism to what is currently known about the neurocognitive toolbox available to infants.

Keywords: cognition; cognitive development; cognitive neuroscience.

Fig. 1

Examples of hierarchical structures in (a) a real-word context and in the learning tasks from (b) Experiment 1 and (c) Experiment 2. During development, children may learn that specific higher-order contexts are associated with distinct rule sets that determine lower-order stimulus-response rules. For example, a child raised in a bilingual environment may come to expect that each parent will speak in a different language and, therefore, different words will be used to label the same objects. This mechanism was manipulated in two experiments. Experiment 1 used a visual hierarchical structure, in which two higher-order shapes each cued a separate rule set that dictated which quadrant (Q) of the screen the shape would appear in, given its color. Experiment 2 used a word-learning hierarchical structure, in which two higher-order face-voice combinations each cued a separate rule set that dictated which artificial words a pair of animated toys were associated with.

Fig. 2

Sample trial sequence and paradigm from Experiment 1. Each trial in the learning task (a) began with a centrally presented cue that varied in color (red or blue) and shape (square or triangle). Then an animated toy (the target) appeared in one of four quadrants of the computer screen (b). Eye movements were measured to determine how quickly infants looked toward the quadrant containing the target stimulus (highlighted here by the red box). Infants could use shape as a higher-order context to cluster the pairings into latent rule sets specifying lower-order color/target-location rules (c). The generalization task was similar to the learning task, except that the shapes were a diamond and a circle. The color pairings for one shape were the same as in the learning task, but the color pairings for the other shape required a new rule set.

Fig. 3

Results from Experiment 1: reaction time as a function of block and rule set during (a) the learning task and (b) the generalization task. Error bars indicate ±1 SEM.

Fig. 4

Hierarchies in Experiment 2. In the learning task, infants could use a face-voice combination as a higher-order context to assign rule sets to pairings of toys with pseudowords. In the generalization task, infants were shown a learned rule set now associated with a novel face-voice context; an additional toy-word pairing was also added to the set. During the inference test, infants were shown pairings that were consistent and inconsistent with the rule-set structure. All mappings between faces, voices, toys, and words were counterbalanced.

Fig. 5

Paradigm of (a) and results from (b) the inference test. During the inference test, infants saw pairings of faces and voices with toys; these pairings were either consistent or inconsistent with the hierarchical structure they had learned. The graph shows average looking time for consistent and inconsistent pairings. Error bars indicate ±1 SEM.