Tracking multiple inputs: An investigation of the primacy effect in monolinguals and bilinguals

Open Access
Author:
Bulgarelli, Federica
Graduate Program:
Psychology
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
June 03, 2015
Committee Members:
  • Daniel J Weiss, Thesis Advisor
Keywords:
  • statistical learning
  • bilingualism
  • primacy effect
Abstract:
A fundamental challenge of statistical learning is to determine whether variance observed in the input signals a change in the underlying structure. When asked to segment two consecutive artificial languages, learners show a reliable tendency to only learn the first of two presented languages unless the change is correlated with a contextual cue or exposure to the second structure is lengthened (Gebhart, Aslin, & Newport, 2009). In this thesis, I explored whether the primacy effect can be attenuated by manipulating the amount of exposure to the first language. I presented participants with five one-minute blocks of two artificial languages, each followed by a test. In one condition, learners received fixed input, whereas in the other they advanced to the second language immediately after learning the first. When participants advanced to the second language as soon as learning occurred, the primacy effect was attenuated. Notably, contextual cues did not boost performance using this latter paradigm, suggesting that without becoming entrenched in the first language there is no additive effect for such cues. In a second set of experiments, I compared monolinguals to advanced second language learners, to determine whether experience learning and managing two linguistic systems impacts learner’s ability to learn two artificial languages presented in succession. Results thus far reveal a greater percentage of second language learners acquiring the second presented language relative to the monolingual group. Overall, the findings suggest that anchoring effects may be due to additional exposure after a single structure has been learned, possibly as a function of learners reducing their sampling rate once mastering a set of statistics.