Neighborhood and Position Effects Interact in Naming Latency

Neighborhood and Position Effects Interact in Naming Latency Jeanne C. Milostan ( JMILOSTA @ CS . UCSD . EDU ) Victor Ferreira ( FERREIRA @ PSY . UCSD . EDU ) Garrison W. Cottrell ( GARY @ CS . UCSD . EDU ) Department of Psychology Computer Science and Engineering Department 0114 University of California San Diego La Jolla, CA 92093 USA University of California San Diego La Jolla, CA 92093 USA Filler Nonword Irregular Regular Difference Position of Irregular Exception Irregular Regular Difference Avg. Diff. Abstract Naming latency studies have recently shown a position-of- irregularity effect (words with early irregularities seem slowed compared to those with late irregularities), for which Dual- Route models of reading can account. Milostan & Cottrell (1998) showed that the initial studies contained a confound between irregularity position and friend/enemy ratio, and that the statistical confound could be captured by connectionist net- works which then show the supposed position effect. This pa- per presents work to disentangle the position/regularity con- found through a subject study and additional connectionist ex- plorations. The latency data show that, once friend/enemy ra- tios are controlled for, the supposed position effect is driven entirely by high-enemy words in the first position. Further, connectionist network simulations show that network error at the first phoneme position only is a better match for naming latency, while overall network error produces a better match to subject error counts. Introduction A major component of the task of learning to read is the de- velopment of a mapping from orthography to phonology. In a complete model of reading, message understanding must play a role, but many psycholinguistic phenomena can be ex- plained in the context of this simple mapping task. A dif- ficulty in learning this mapping is that in a language such as English, the mapping is quasiregular (Plaut et al., 1996); there are a wide range of exceptions to the general rules. As with nearly all psychological phenomena, more frequent stimuli are processed faster, leading to shorter naming laten- cies. The regularity of mapping interacts with this variable, a robust finding that is well-explained by connectionist ac- counts (Seidenberg and McClelland, 1989; Taraban and Mc- Clelland, 1987). In this paper we continue consideration of a recent effect that seems difficult to account for in terms of the standard par- allel network models. Coltheart & Rastle (1994) have shown that the amount of delay experienced in naming an exception word is related to the phonemic position of the irregularity in pronunciation. Specifically, the earlier the exception occurs in the word, the longer the latency to the onset of pronouncing the word. Table 1, adapted from (Coltheart and Rastle, 1994) shows the response latencies to two-syllable words by normal subjects. There is a clear left-to-right ranking of the latencies compared to controls in the last row of the Table. Coltheart et al. claim this delay ranking cannot be achieved by standard connectionist models. Earlier work (Milostan and Cottrell, 1998) showed that the origin of the effect seen in the Colt- heart study lies in a statistical regularity of English, related to Table 1: Naming Latency vs. Irregularity Position the number of “friends” and “enemies” of the pronunciation within the word’s neighborhood. 1 The human subject study and network simulations presented in this paper attempt to tease apart the effects of phoneme position and neighborhood ratio. Background Computational modeling of the reading task has been ap- proached from a number of different perspectives. Advocates of a dual-route model of oral reading claim that two separate routes, one lexical (a lexicon, often hypothesized to be an associative network) and one rule-based, are required to ac- count for certain phenomena in reaction times and nonword pronunciation seen in human subjects (Coltheart et al., 1993). Connectionist modelers claim that the same phenomena can be captured in a single-route model which learns simply by exposure to a representative dataset (Seidenberg and McClel- land, 1989). In the Dual-Route Cascade model (DRC) (Coltheart et al., 1993), the lexical route is implemented as an Interactive Ac- tivation (McClelland and Rumelhart, 1981) system, while the non-lexical route is implemented by a set of grapheme- phoneme correspondence (GPC) rules learned from a dataset. Input at the letter identification layer is activated in a left-to- right sequential fashion to simulate the reading direction of English, and fed simultaneously to the two pathways in the Friends are words with the same pronunciations for the ambigu- ous letter-to-sound correspondence; enemies are words with differ- ent pronunciations.