Department Linguistik
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Human infants can segment action sequences into their constituent actions already during the first year of life. However, work to date has almost exclusively examined the role of infants' conceptual knowledge of actions and their outcomes in driving this segmentation. The present study examined electrophysiological correlates of infants' processing of lower-level perceptual cues that signal a boundary between two actions of an action sequence. Specifically, we tested the effect of kinematic boundary cues (pre-boundary lengthening and pause) on 12-month-old infants' (N = 27) processing of a sequence of three arbitrary actions, performed by an animated figure. Using the Event-Related Potential (ERP) approach, evidence of a positivity following the onset of the boundary cues was found, in line with previous work that has found an ERP positivity (Closure Positive Shift, CPS) related to boundary processing in auditory stimuli and action sequences in adults. Moreover, an ERP negativity (Negative Central, Nc) indicated that infants' encoding of the post-boundary action was modulated by the presence or absence of prior boundary cues. We therefore conclude that 12-month-old infants are sensitive to lower-level perceptual kinematic boundary cues, which can support segmentation of a continuous stream of movement into individual action units.
In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between- subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions.