What a difference a syllable makes—Rhythmic reading of poetry - Beck, Judith; Konieczny, Lars (Frontiers in Psychology, 2023)
In oral reading conventional poems, the rhythmic experience is coupled with the projection of meter, enabling the prediction of subsequent input. However, it is unclear how top-down- and bottom-up-processes interact. To investigate this, poems were manipulated by substituting random syllables with the syllable “tack”. Readers were instructed to read the poems aloud and the auditory signal was recorded. If the rhythmicity in reading aloud is governed by the top-down prediction of metric patterns of weak and strong stress, these should be projected also onto the tack-syllables. If bottom-up information such as the phonetic quality of consecutive syllables plays a functional role in establishing a structured rhythm, the occurrence of tacks should affect reading and the number of tacks in a metrical line should modulate this effect. We calculated the syllable onset interval (SOI) as a measure of articulation duration, as well as the mean syllable intensity. Both variables captured stress. Results show that the average articulation duration of strongly stressed regular-syllables was longer than for weak syllables, but not for tacks. Syllable intensities however, captured stress variation, but only for musically active participants. In a second step, we calculated the normalized pairwise variability index (nPVI) for each line, as an indicator for rhythmic contrast, i.e. the alternation between long and short, as well as louder and quieter syllables, to estimate the effect of tacks on the reading rhythm. For SOI the nPVI revealed a clear negative effect: When tacks occurred, lines appeared to be read less alternating, and this effect was proportional to the number of tacks per line. Results suggests that top down prediction does not always suffice to maintain a rhythmic gestalt across a series of syllables that carry little bottom up prosodic information. Instead, the constant integration of sufficiently varying bottom-up information appears necessary to maintain a stable metrical pattern prediction.