Studies of agreement attraction in language production have shown that speakers systematically produce verb agreement errors in the presence of a local noun whose features differ from that of the agreement controller. However, in attraction experiments, these errors only ever occur in a subset of trials. In the present study, we applied a naturalistic scene-description paradigm to investigate how attraction affects the distribution of errors and the time-course of correctly inflected verbs. We conducted our experiment both in the lab and in an unsupervised web-based setting. The results were strikingly similar across the experimental settings for both the error and timing analyses, demonstrating that it is possible to conduct production experiments via the internet with a high level of similarity to those done in the lab. The experiments replicated the basic number attraction effect, though they elicited comparable interference from both singular and plural local nouns, challenging common assumptions about a strong plural markedness effect in attraction. We observed slowdowns before correct verbs that paralleled the distribution of agreement errors, suggesting that the process resulting in attraction can be active even when no error is produced. Our results are easily captured by a model of agreement attraction in which errors arise at the point of computing agreement, rather than reflecting earlier errors made during initial encoding of the subject number.

Although reflexive–antecedent agreement shows little susceptibility to number attraction in comprehension, prior production research using the preamble-completion paradigm has demonstrated attraction for both verbs and anaphora. In four production experiments, we compared number attraction effects on subject–verb and reflexive–antecedent agreement using a novel scene-description task in addition to a more traditional preamble elicitation paradigm. While the results from the preamble task align with prior findings, the more naturalistic scene description task produced the same contrast observed in comprehension, with robust verb attraction but minimal anaphor attraction. In addition to analyzing agreement error distributions, we also analyzed the production time-course of participant responses, finding timing effects that pattern with error distributions, even when no error is present. The results suggest that production agreement processes show similar profiles to comprehension processes. We discuss potential sources of variable susceptibility to agreement attraction, suggesting that differences may arise from the time-course of information processing across tasks and linguistic dependencies.

Until recently, most web-based psycholinguistic experiments have focused on simple responses such as button presses or typed text. In this paper, we compare two sentence production experiments carried out in the lab and in an unsupervised web-based setting in order to test the robustness of error patterns and speech timing effects in web-based experimental settings. The open-ended spoken responses generated in this task could elicit more variable and noisier recordings when conducted outside a controlled lab environment. The experiments investigate patterns of well-documented attraction effects in the production of subject–verb agreement, examining error rates as well as speech timing effects in correctly produced sentences. In both the error and timing analyses, the results are strikingly similar across the experimental settings. Furthermore, the two experiments challenge common assumptions about a strong plural markedness effect in agreement attraction. Through this replication and comparison, we have found that we can do production experiments via the internet with a high level of similarity to those done in the lab. These results will allow for future production research to be conducted online, which will provide more flexibility and efficiency to this type of experimentation.

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We assess the feasibility of conducting web-based eye-tracking experiments with children using two methods of webcam-based eye-tracking: automatic gaze estimation with the WebGazer.js algorithm and hand annotation of gaze direction from recorded webcam videos. Experiment 1 directly compares the two methods in a visual-world language task with five to six year-old children. Experiment 2 more precisely investigates WebGazer.js’ spatiotemporal resolution with four to twelve year-old children in a visual-fixation task. We find that it is possible to conduct web-based eye-tracking experiments with children in both supervised (Experiment 1) and unsupervised (Experiment 2) settings, however the webcam eye-tracking methods differ in their sensitivity and accuracy. Webcam video annotation is well-suited to detecting fine-grained looking effects relevant to child language researchers. In contrast, WebGazer.js gaze estimates appear noisier and less temporally precise. We discuss the advantages and disadvantages of each method and provide recommendations for researchers conducting child eye-tracking studies online.