Knowledge that Transforms

According to a two-step account of the mere-exposure effect, repeated exposure leads to the subjective feeling of perceptual fluency, which in turn influences liking. If so, perceptual fluency manipulated by means other than repetition should influence liking. In three experiments, effects of perceptual fluency on affective judgments were examined. In Experiment 1, higher perceptual fluency was achieved by presenting a matching rather than nonmatching prime before showing a target picture. Participants judged targets as prettier if preceded by a matching rather than nonmatching prime. In Experiment 2, perceptual fluency was manipulated by figure-ground contrast. Stimuli were judged as more pretty, and less ugly, the higher the contrast. In Experiment 3, perceptual fluency was manipulated by presentation duration. Stimuli shown for a longer duration were liked more, and disliked less. We conclude (a) that perceptual fluency increases liking and (b) that the experience of fluency is affectively positive, and hence attributed to positive but not to negative features, as reflected in a differential impact on positive and negative judgments.

Spieler and Balota (1997) showed that connectionist models of reading account for relatively little item-specific variance. In assessing this finding, it is important to recognize two factors that limit how much variance such models can possibly explain. First, item means are affected by several factors that are not addressed in existing models, including processes involved in recognizing letters and producing articulatory output. These limitations point to important areas for future research but have little bearing on existing theoretical claims. Second, the item data include a substantial amount of error variance that would be inappropriate to model. Issues concerning comparisons between simulation data and human performance are discussed with an emphasis on the importance of evaluating models at a level of specificity (“grain”) appropriate to the theoretical issues being addressed.

A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants' responses to words versus part-words (trisyllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable discrimination of words from part-words, thereby demonstrating rapid segmentation of continuous speech into words on the basis of transitional probabilities of syllable pairs.