Prime Surprisal as a Tool for Assessing Error-Based Learning Theories: A Systematic Review

Abstract

Error-based learning theories of language acquisition are highly influential in language development research, yet the predictive learning mechanism they propose has proven difficult to test experimentally. Prime surprisal—the observation that structural priming is stronger following more surprising primes—has emerged as a promising methodology for resolving this issue as it tests a key prediction of error-based learning theories: surprising input leads to increased structure repetition as well as learning. However, as prime surprisal is a relatively new paradigm, it is worth evaluating how far this promise has been fulfilled. We have conducted a systemic review of PS studies to assess the strengths and limitations of existing approaches, with 13 contributions selected out of 66 search results. We found that alongside inconsistency in statistical power and how the level of surprisal is measured, the limited scope of current results cast doubt on whether PS can be used as a general tool to assess error-based learning. We suggest two key directions for future research: firstly, targeting the scope of the prime surprisal effect itself with reliable statistical power and appropriate surprisal measurements across a greater variety of languages and grammatical structures; and secondly, using the prime surprisal method as a tool to assess the scope of an error-based learning mechanism utilising conditions in which prime surprisal has been reliably established.