It's about time! Time as a parameter for lexical and syntactic processing: an eye-tracking-while-listening investigation

Abstract

We examined the time-course of lexical activation, deactivation, and the syntactic operation of dependency linking during the online processing of object-relative sentence constructions using eye-tracking-while-listening. We explored how manipulating temporal aspects of the language input affects the tight lexical and syntactic temporal constraints found in sentence processing. The three temporal manipulations were (1) increasing the duration of the direct object noun, (2) adding the disfluency uh after the noun, and (3) replacing the disfluency with a silent pause. The findings from this experiment revealed that the disfluent and silence temporal manipulations enhanced the processing of subject and object noun phrases by modulating activation and deactivation. The manipulations also changed the time-course of dependency linking (increased reactivation of the direct object). The modulated activation dynamics of these lexical items are thought to play a role in mitigating interference and suggest that deactivation plays a beneficial role in complex sentence processing.

Keywords: Sentence processing; lexical activation; lexical deactivation; syntactic reactivation; temporal manipulation.

Figure 1.

Example waveforms of stimuli showing the control condition and temporal manipulations (disfluent, silence, and stretch).

Figure 2.

Procedure used in each trial of the experiment. The example display presents the direct object noun (e.g. clown), the second noun (e.g. elf), and two distractors (e.g. princess and stewardess). Auditory sentences began after a 1500 ms preview of the display.

Figure 3.

Mean gaze to N1 (the first noun), N2 (second noun), and two distractor images averaged across condition as a function of time beginning at the auditory onset of N1. Windows of interest (1 and 2) are outlined in black. Error bars (shaded areas) are within-subject 95% confidence intervals.

Figure 4.

Time window 1 (black box): Mean gaze trajectories and standard error (grey error ribbons) to N1 (red line) and N2 (blue line) in each condition: Control (top-left), Stretch (top-right), Disfluent (bottom-left), and Silence (bottom-right).

Figure 5.

Initial lexical processing of the displaced object (N1). Model fit (lines) of the gaze data (shapes are means for the 4 conditions; error bars are standard errors) from the onset of N1 (Time window 1).

Figure 6.

Syntactic reactivation of the displaced object (N1) and deactivation of N2. Model fit (dashed lines) of the observed gaze data (coloured lines are means; shaded ribbons are standard errors) from the onset of the main verb (Time window 2). Blue shaded regions indicate time span of significant clusters (p <.05).

Figure 7.

Results of cluster-based permutation analyses on initial N1 processing. This figure shows the mean probability of fixating on N1 and standard error (grey error ribbons) in each comparison: control vs stretch (A), control vs disfluent (B), and control vs silent (C). Blue shaded regions represent the time span for each significant cluster (p <.05).

Figure 8.

N2 processing. Model fit (lines) of the gaze data (shapes are means for the 4 conditions; error bars are standard errors) from the onset of N2.

Figure 9.

Results of cluster-based permutation analyses on N2 processing. This figure shows the mean probability of fixating on N2 and standard error (grey error ribbons) in each comparison: control vs stretch (A), control vs disfluent (B), and control vs silent (C). Blue shaded regions represent the time span for each significant cluster (p <.05).

Figure 10.

Overlays of the time course of activation of N1 (left) and N2 (right) for the control and disfluent conditions (solid line and dashed line, respectively) showing enhanced activation and deactivation for the disfluent condition as compared to the control condition. Grey ribbons indicate standard error.