Written word production and lexical self-organisation: evidence from English (pseudo)compounds
Authors: Marcello Ferro, Franco Alberto Cardillo, Vito Pirrelli, Christina L. Gagné, Thomas L. Spalding (CNR-ILC, University of Alberta, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Elevation in typing latency for the initial letter of the second constituent of an English compound, relative to the latency for the final letter of the first constituent of the same compound, provides evidence that implementation of a motor plan for written compound production involves smaller constituents, in both semantically transparent and semantically opaque compounds. We investigate here the implications of this evidence for algorithmic models of lexical organisation, to show that effects of differential perception of the internal structure of compounds and pseudo-compounds can also be simulated as peripheral stages of lexical access by a self-organising connectionist architecture, even in the absence of morpho-semantic information. This complementary evidence supports a maximization-of-opportunity approach to lexical modelling, accounting for the integration of effects of pre-lexical and lexical access.

Introduction
A key question concerning the representation and processing of compound words has focused on whether (and, if so, how) morphological structure plays a role. The bulk of the research on this issue has come from recognition or comprehension tasks such as lexical decision or reading. However, written production provides a useful counterpart and allows researchers to examine whether morphemes are used even after a word has been accessed. One advantage of a typing task (in which the time to type each letter of a word is recorded) is that researchers can examine differences in processing difficulty at various points in the word. Previous research found an elevation in typing latency for the initial letter of the second constituent relative to the latency for the final letter of the first constituent for English AUTHOR and German compounds AUTHOR. This elevation in typing latency at the morpheme boundary suggests that the system plans the output morpheme by morpheme, rather than as a whole unit, and that morphological programming is not complete when the motor system begins the output of the word AUTHOR. AUTHOR examined the role of morphemic structure and semantic transparency on typing latency. The stimuli consisted in 200 compounds, 50 pseudo-compounds, and 250 monomorphemic words matched pairwise with the compounds and pseudo-compounds in the number of syllables and letters. The pseudo-compounds contain two words that do not function as morphemes (e.g., carpet contains car and pet). The compounds varied in whether the first and second constituent were semantically transparent. The items were displayed individually using a progressive demasking procedure and participants typed the word as the computer recorded the time required to type each letter. The time to initiate the first letter was equivalent for monomorphemic and compound words. Typing times got faster across the word for both word types, but the rate of change was faster for monomorphemic words than for compound words. This difference was not observed when comparing monomorphemic words and pseudo-compounds. For compounds, the rate of speed-up was slower when the first constituent was transparent than when it was opaque, but was unaffected by the transparency of the second constituent. The elevation in typing latency at the morpheme boundary was larger when the first constituent was transparent than when it was opaque, but was unaffected by the transparency of the second constituent. This difference is due to the final letter of the first constituent when the first constituent requiring less time to type when it was transparent than when it was opaque. The data for the pseudo-compounds indicated that embedded morphemes influence production, even when they do not function as morphemes. Typing latency increased one letter prior to the end of the first constituent of a pseudo-compound and remained elevated through the boundary (e.g., both r and c in scarcity were elevated relative to the a).

Conclusion
Trained on both compounds and pseudo-compounds, TSOMs develop a growing sensitivity to surface distributional properties of input data, turning chains of randomly connected, general-purpose nodes into specialised sub-chains of BMUs that respond to specific letter strings at specific positions. Compounds not only tend to occur, on average, less frequently than their C1/C2 constituents do as independent words (Ji et al. 2011), but they tend to present lower-frequency bigrams at the C1-C2 boundary than do pseudo-compounds. Principles of Hebbian learning allow TSOMs to capitalise on both effects. Entrenchment makes expectations for high-frequency bigrams stronger and expectations for low-frequency bigrams weaker. At the same time, the competition between C1 as an independent word and C1 as the first constituent in a C1-C2 compound biases the map’s expectation towards the most frequent event (C1 in isolation). Compound families, i.e. sets of compounds sharing C1 (windmill, windshield etc.) or C2 (snowball, basketball etc.), magnify these effects, making the map more sensitive to formal discontinuity at morpheme boundaries. When more C2s can follow the same C1 in complementary distribution, the left-to-right expectation for a particular C2 to occur, given C1, decreases. Likewise, when more C1s competitively select the same C2, the individual contribution of each C1 to the prediction of C2 decreases. We conjecture that more global effects of lexical organisation like these may eventually blur local memory effects based on position-independent bigram frequencies. Our simulations with TSOMs can model the correlation between continuously varying distributional regularities in the input data and peripheral levels of routinized recognition and production patterns. These patterns are in line with Gagné & Spalding’s evidence of (a) the influence of embedded pseudo-morphemes on cascaded models of written word production, and (b) faster anticipation rates for monomorphemic vs. compound words. Further experimental results (not reported here), obtained by including compound families in the training data, confirm slower anticipation rates for true compound constituents, due to the combined effect of word frequency distributions and word compositionality in compound families. The size of a compound family can arguably be a function of the degree of productivity and semantic transparency of its members (Baroni et al. 2007). The influence of the compound family size on anticipation rates can shed light on the influence of levels of semantic transparency on compound processing. Simulation evidence suggests that the bigger the family, the stronger its influence will be. Finally, we also monitored the influence of increasing token frequencies of monomorphemic words in the training data on the map perception of constituent boundaries within compounds. As expected, for constant frequency values of compounds in the training set, the higher the token frequency of monomorphemic words, the higher the pointwise entropy of connections at the C1-C2 boundary. A full account of Gagné & Spalding’s evidence of a graded influence of semantic transparency on compound processing is beyond the reach of the computational architecture presented here. Surface effects of discontinuity in the internal structure of compounds (as opposed to pseudo-compounds) appear to provide a purely formal, pre-lexical scaffolding for truly morpho-semantic effects to emerge at later processing stages. To model these effects, we appear to be in need of a parallel processing architecture able to effectively integrate several representational levels (orthographic, phonological, morphological, and conceptual) and different processing steps within a single distributed system (Smolka et al. 2009). Nonetheless, our simulations show that by letting compounds, pseudo-compounds and (pseudo)constituents compete for the same level of memory resources on a topological map, it is possible to account for apparently contradictory effects of a) graded perception of constituent boundary in both compounds and pseudo-compounds, apparently requiring prelexical decomposition, and b) higher anticipation rates for pseudo-compounds than compounds, supporting full form representations for lexical access.