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Modeling Concept Activation in Working Memory during Online Sentence Processing

Modeling Concept Activation in Working Memory during Online Sentence Processing Patrick Plummer (pplummer@ucsd.edu) Hsueh-Cheng Wang (hchengwang@gmail.com) Yuhtsuen Tzeng (ttcytt@ccu.edu.tw) Marc Pomplun (marc@cs.umb.edu) Keith Rayner (krayner@ucsd.edu) Department of Psychology, University of California at San Diego, USA Department of Computer Science, University of Massachusetts at Boston, 100 Morrissey Boulevard, Boston, MA 02125 USA Center for Teacher Education & Institute of Curriculum, National Chung Cheng University, Taiwan Abstract identification of phonological/semantic form and completion of lexical access). The model also maintains that the predictability effect is stronger in L2 than in L1. Estimates of word predictability are typically derived from a modified cloze task procedure (Taylor, 1953) in which subjects are asked to guess the identity of a word when given the prior sentence context. Most reading studies utilize the cloze task to establish or confirm word predictability manipulations. These experiments use target words that differ substantially in cloze value (the probability with which subjects select the word), often with probabilities of .70 to .90 for highly predictable words and less than .10 for “low” predictability words. As an alternative to necessarily subjective cloze responses, several computational methods have been successfully utilized to approximate degrees of contextual constraint and predict the influence on eye movements during reading; including, transitional probabilities (McDonald and Shillcock, 2003; but see Frisson, Rayner, & Pickering, 2005), surprisal (Boston, Hale, Kliegl, Patil, and Vasishth, 2008; Levy, 2008), conditional co-occurrence probability (Ong and Kliegl, 2008). Additionally, Latent Semantic Analysis (LSA) (Landauer & Dumais, 1997) was used by Pynte, New, and Kennedy (2008) as well as Wang, Chen, Ko, Pomplun, and Rayner (2010), who reported that eye movement behavior during first-pass reading on content words could be predicted using LSA. McDonald and Shillcock (2003) and Wang et al. (2010) used the transitional probability (corpus-based statistical likelihood of encountering a word given the preceding or subsequent word) to categorize predictability conditions; both proposing that predictability effects could be accounted for using only the content word preceding a target. One limitation of these objective measures could be that prior context, before the immediately preceding lexical item, may affect processing of a word in many instances. Wang et al. (2010) also used all concepts in the preceding sentence context to compute contextual constraint for targets using the standard weighting from LSA. However, without a clearer understanding of working memory constraints There have been several computational alternatives to the cloze task (Taylor, 1953) intended to approximate word predictability effects on eye movements during reading. In this study, we implement a computational model that instantiates each content word in a sentence as an input that activates semantic concepts in working memory. The predictability of a word is then determined by the extent to which its corresponding semantic representation is associated with the network of concepts already active in working memory from the preceding context. The computation of concept activation is based on a connectionist model (Landscape model, see van den Broek, 2010). Latent semantic analysis (LSA) is used to establish connections between words and simulate the long-term semantic associations among concepts (Landauer & Dumais, 1997). This model provides a means of investigating how language comprehension and eye movement behavior are affected by the activation of concepts in working memory. Keywords: eye movements; reading; word predictability; latent semantic analysis; Landscape model. Introduction It has been well-established that eye movement behavior is affected by lexical variables such as frequency and predictability (Rayner, 1998; 2009). As such, the eye movement record provides an indication of language processing as it unfolds during normal reading. Rayner and Well (1996; see also Ehrlich & Rayner, 1981) found that the predictability of target words had a strong influence on eye movements during reading. In their experiment, subjects fixated unpredictable target words longer than either highly or moderately predictable target words; highly predictable words were also skipped more often than moderately predictable or unpredictable target words. Accordingly, in the E-Z Reader model (Pollatsek, Reichle, & Rayner, 2006; Reichle, Pollatsek, Fisher, & Rayner, 1998; Reichle, Rayner, & Pollatsek, 1999; 2003), word predictability within a given sentence context is considered in both first stage processing (i.e., L1, including identification of orthographic form and a familiarity check) and second stage processing (i.e., L2, including