Experience-Based Probabilities Modulate Expectations in a Gender-Coded Artificial Language

Abstract

The current study combines artificial language learning with visual world eyetracking to investigate acquisition of representations associating spoken words and visual referents using morphologically complex pseudowords. Pseudowords were constructed to consistently encode referential gender by means of suffixation for a set of imaginary figures that could be either male or female. During training, the frequency of exposure to pseudowords and their imaginary figure referents were manipulated such that a given word and its referent would be more likely to occur in either the masculine form or the feminine form, or both forms would be equally likely. Results show that these experience-based probabilities affect the formation of new representations to the extent that participants were faster at recognizing a referent whose gender was consistent with the induced expectation than a referent whose gender was inconsistent with this expectation. Disambiguating gender information available from the suffix did not mask the induced expectations. Eyetracking data provide additional evidence that such expectations surface during online lexical processing. Taken together, these findings indicate that experience-based information is accessible during the earliest stages of processing, and are consistent with the view that language comprehension depends on the activation of perceptual memory traces.

Keywords: artificial language; experience-based probabilities; frequencies of exposure; gender representations; mental representation; visual world eyetracking.

Figure 1

(A) Schematic representation of the structure underlying the artificial language and how stems and suffixes relate to different visual features (character and gender respectively) of the referents. Three out of twelve word stems and character pairs are exemplified here. (B) Example of how presentation frequencies for male vs. female versions of the same imaginary figures were used to induce experience-based expectations.

Figure 2

Different trial types used in the post-test.

Figure 3

Mean response times for correct responses, aggregated over subject, trial type and probability group.

Figure 4

Proportion of fixations toward the target image as a function of time for no competitor trials. Proportions were calculated as subject means over 100 ms bins. Note that Distractor1 is the distractor that has the same gender as the target. Thus, the increased fixation proportion toward this distractor in the late time window reflects rhyme competition (as gender information from the suffix becomes available).

Figure 5

Proportion of fixations toward the target image as a function of time for target competitor trials. Proportions were calculated as subject means over 100 ms bins. Here, competition effects can be observed until disambiguating information from the suffix becomes available.

Figure 6

Fixation proportions toward the target according to its probability (no competitor trials). Fixation proportions were aggregated in bins of 100 ms for each participant, and will not correspond directly to the model estimates (dotted lines), where random effects are taken into consideration. In the time window corresponding to the processing of the suffix, model estimates only contain an effect of time, and are therefore not color coded.

Figure 7

Fixation proportions toward the target according to its probability (target competitor trials). Fixation proportions were aggregated in bins of 100 ms for each participant, and will not correspond directly to model estimates (dotted lines), where random effects are taken into consideration.