Statistical learning of action: The role of conditional probability

Statistical Learning of Unfamiliar Sounds as Trajectories Through a Perceptual Similarity Space

In typical statistical learning studies, researchers define sequences in terms of the probability of the next item in the sequence given the current item (or items), and they show that high probability sequences are treated as more familiar than low probability sequences. Existing accounts of these phenomena all assume that participants represent statistical regularities more or less as they are defined by the experimenters-as sequential probabilities of symbols in a string. Here we offer an alternative, or possibly supplementary, hypothesis. Specifically, rather than identifying or labeling individual stimuli discretely in order to predict the next item in a sequence, we need only assume that the participant is able to represent the stimuli as evincing particular similarity relations to one another, with sequences represented as trajectories through this similarity space. We present experiments in which this hypothesis makes sharply different predictions from hypotheses based on the assumption that sequences are learned over discrete, labeled stimuli. We also present a series of simulation models that encode stimuli as positions in a continuous two-dimensional space, and predict the next location from the current location. Although no model captures all of the data presented here, the results of three critical experiments are more consistent with the view that participants represent trajectories through similarity space rather than sequences of discrete labels under particular conditions.

Stacking the evidence: Parents' use of acoustic packaging with preschoolers

Segmenting continuous events into discrete actions is critical for understanding the world. As infants may lack top-down knowledge of event structure, caregivers provide audiovisual cues to guide the process, aligning action descriptions with event boundaries to increase their salience. This acoustic packaging may be specific to infant-directed speech, but little is known about when and why the use of this cue wanes. We explore whether acoustic packaging persists in parents' teaching of 2.5-5.5-year-old children about various toys. Parents produced a smaller percentage of action speech relative to studies with infants. However, action speech largely remained more aligned to action boundaries relative to non-action speech. Further, for the more challenging novel toys, parents modulated their use of acoustic packaging, providing it more for those children with lower vocabularies. Our findings suggest that acoustic packaging persists beyond interactions with infants, underscoring the utility of multimodal cues for learning, particularly for less knowledgeable learners in challenging learning environments.

Statistical learning of movement

The environment is dynamic, but objects move in predictable and characteristic ways, whether they are a dancer in motion, or a bee buzzing around in flight. Sequences of movement are comprised of simpler motion trajectory elements chained together. But how do we know where one trajectory element ends and another begins, much like we parse words from continuous streams of speech? As a novel test of statistical learning, we explored the ability to parse continuous movement sequences into simpler element trajectories. Across four experiments, we showed that people can robustly parse such sequences from a continuous stream of trajectories under increasingly stringent tests of segmentation ability and statistical learning. Observers viewed a single dot as it moved along simple sequences of paths, and were later able to discriminate these sequences from novel and partial ones shown at test. Observers demonstrated this ability when there were potentially helpful trajectory-segmentation cues such as a common origin for all movements (Experiment 1); when the dot's motions were entirely continuous and unconstrained (Experiment 2); when sequences were tested against partial sequences as a more stringent test of statistical learning (Experiment 3); and finally, even when the element trajectories were in fact pairs of trajectories, so that abrupt directional changes in the dot's motion could no longer signal inter-trajectory boundaries (Experiment 4). These results suggest that observers can automatically extract regularities in movement - an ability that may underpin our capacity to learn more complex biological motions, as in sport or dance.

Prediction suppression in monkey inferotemporal cortex depends on the conditional probability between images

When monkeys view two images in fixed sequence repeatedly over days and weeks, neurons in area TE of the inferotemporal cortex come to exhibit prediction suppression. The trailing image elicits only a weak response when presented following the leading image that preceded it during training. Induction of prediction suppression might depend either on the contiguity of the images, as determined by their co-occurrence and captured in the measure of joint probability P(A,B), or on their contingency, as determined by their correlation and as captured in the measures of conditional probability P(A|B) and P(B|A). To distinguish between these possibilities, we measured prediction suppression after imposing training regimens that held P(A,B) constant but varied P(A|B) and P(B|A). We found that reducing either P(A|B) or P(B|A) during training attenuated prediction suppression as measured during subsequent testing. We conclude that prediction suppression depends on contingency, as embodied in the predictive relations between the images, and not just on contiguity, as embodied in their co-occurrence.

Shining light on infants' discovery of structure

Learning and discovery seem often to begin with noting patterns. Human infants are skilled at pattern detection, even patterns only definable at an abstract level, which is key to their acquisition of complex knowledge systems such as language and music. However, research examining infants' abstract rule learning has generated inconsistent results. We propose that apparent domain differences in infants' abstract rule learning may be the result of extraneous stimulus variation and discrepancies in the methodologies employed across studies probing this skill. We discuss how a behavioral methodology indexing infants' online learning would be valuable in furthering understanding of infants' (as well as adults') abstract rule learning and its neurophysiological concomitants. We outline current research aimed at developing such an index, and we propose future research, pairing such techniques with neurophysiological methods, aimed at shining more light on human skill at discovering structure.