Talker-specific predictions during language processing

ABSTRACT

Language comprehension is shaped by world knowledge. After hearing about “a farm animal,” meanings of typical (“cow”) versus atypical exemplars (“ox”) are more accessible, as evidenced by N400 responses. Moreover, atypical exemplars elicit a larger post-N400 frontal positivity than typical and incongruous (“ivy”) exemplars, indexing the integration of unexpected information. Do listeners adapt this category knowledge to specific talkers? We first replicated typicality effects in the auditory modality. Then, we extended the design to a two-talker context: talkers alternated cueing (Bob: “Susan, name a farm animal”) and answering (Susan: “cow”). Critically, participants first heard interviews in which one talker revealed strong associations with atypical exemplars (Susan works on an ox farm). We observed increased frontal positivity to a typical exemplar (“cow”) said by Susan compared to Bob, indicating participants appreciated that the typical exemplar was atypical for Susan. These results suggest that comprehenders can tailor their expectations to the talker.

KEYWORDS:

• Comprehension
• semantics
• category typicality
• prediction
• ERP

Acknowledgments

We would like to thank Nathaniel Anderson, Ariel James, and Geoffrey McKinley for help with recording audio stimuli.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Data loss due to artefacts and incorrect responses was uniformly distributed across conditions (M = 11%, min = 0%, max = 30%)

2 See Supplemental Material 3 for analyses with trial-level data. Note that we observe the same patterns in both analyses.

3 The effect size was estimated to be approximately 2 μV in previous work (Federmeier et al., 2010)

4 See Supplemental Material 3 for analyses with trial-level data. We found evidence for the same contrasts in these analyses, unless noted otherwise in the main text.

5 There was not strong evidence of a LP vs. HP effect (40% posterior probability of an effect > 0) in the visual modality in the trial-level analysis (see S3.1).

6 Data loss due to artefacts and incorrect responses was uniform across conditions (M = 7%, min = 0%, max = 29%)

7 See Supplemental Material 3 for analyses with trial-level data. We found evidence for the same contrasts in these analyses, unless noted otherwise in the main text.

8 See Supplemental Material 3 for analyses with trial-level data. We found evidence for the same contrasts in these analyses, unless noted otherwise in the main text.

9 As requested by a reviewer, in a post-hoc analysis, we compared the HP and LP conditions directly (Contrast 1: IN = −0.66 vs. HP = 0.33 & LP = 0.33; Contrast 2: HP = −0.5 vs. LP = 0.5). HP and LP words elicited a more positive response than IN words (b = 2.954, SE = 0.615, t = 4.804, p < 0.001). LP words elicited a more positive response on average than HP words (b = 1.142, SE = 0.525, t = 2.175, p = 0.0397). There was no main effect of talker condition (b = −0.269, SE = 0.391, t = −0.687, p = 0.499). Neither interaction between talker condition and predictability condition was significant (Contrast 1: b = −1.577, SE = 0.909, t = −1.734, p = 0.096; Contrast 2: b = 1.234, SE = 0.740, t = 1.667, p = 0.109).

10 BF₀₁ = 11.31 in favor of the null hypothesis (using model in Supplemental Material 3). Note the Bayes Factor calculation is highly dependent on the prior distribution: in this case, a normal distribution centered on 0 with a SD of 10