An amorphous model for morphological processing in visual comprehension based on naive discriminative learning.

Abstract

A two-layer symbolic network model based on the equilibrium equations of the Rescorla-Wagner model (Danks, 2003) is proposed. The study starts by presenting two experiments in Serbian, which reveal for sentential reading the inflectional paradigmatic effects previously observed by Milin, Filipović Durdević, and Moscoso del Prado Martín (2009) for unprimed lexical decision. The empirical results are successfully modeled without having to assume separate representations for inflections or data structures such as inflectional paradigms. In the next step, the same naive discriminative learning approach is pitted against a wide range of effects documented in the morphological processing literature. Frequency effects for complex words as well as for phrases (Arnon & Snider, 2010) emerge in the model without the presence of whole-word or whole-phrase representations. Family size effects Moscoso del Prado Martín, Bertram, Häikiö, Schreuder, & Baayen, 2004) emerge in the simulations across simple words, derived words, and compounds, without derived words or compounds being represented as such. It is shown that for pseudo-derived words no special morpho-orthographic segmentation mechanism as posited by Rastle, Davis, and New (2004) is required. The model also replicates the finding of Plag and Baayen (2009), that, on average, words with more productive affixes elicit longer response latencies, while at the same time predicting that productive affixes afford faster response latencies for new words. English phrasal paradigmatic effects modulating isolated word reading are reported and modelled, showing that the paradigmatic effects characterizing Serbian case inflection have cross-linguistic scope.

Morphological processing of printed nouns and verbs: Cross-class priming effects

“…Do these data challenge connectionist models of morphology (e.g., Gonnerman et al, 2007) or, more in general, models that dispense of explicit representations for morphemes (e.g., Baayen et al, 2011)? Surely, these models are not at ease with a time course whereby orthography is more important early and semantics is more important late during each instance of processing.…”

Towards cognitively plausible data science in language research

“…This reflects the competition between cues. The strengthening of weights reflects learning, and the weakening of links captures unlearning (for details see Baayen et al, 2011;Milin, Feldman, Ramscar, Hendrix, & Baayen, subm. ).…”

“…Such models have been used to model language data within traditions that are close in spirit to Cognitive Linguistics. Examples include Parallel-Distributed Processing or Connectionist Modelling (PDP: Plaut & Gonnerman, 2000;Rumelhart & McClelland, 1986;Seidenberg & Gonnerman, 2000), Analogical Modelling (AM: Skousen, 1989), Memory-based Learning (TiMBL: Daelemans & Van den Bosch, 2005), and more recently Naive Discriminative Learning (NDL: Baayen, Milin, Filipović Đurđević, Hendrix, & Marelli, 2011). The performance of several of these models has been compared (see Eddington, 2000 Baayen, Endresen, Janda, Makarova, & Nesset, 2013 compared the same set of techniques on four different morphological alternations in Russian).…”

A learning perspective on individual differences in skilled reading: Exploring and exploiting orthographic and semantic discrimination cues.

“…This activation represents the discriminability of the string given the cues in the input. Baayen, Milin, Filipović Đurđević, Hendrix, and Marelli (2011) refer to their approach as Ònaive discrimination learningÓ, because the support for a given outcome is estimated independently from all other outcomes, while both cues and outcomes are specified naively Ð without engaging rich but implicit knowledge in cues and outcomes representations.…”

“…BPs serve as indicators of high-level linguistic experiences that are encoded on or by lexical items and are informative about sentence semantics. The NDL has proven to be particularly useful in explaining how specific morphological effects, such as those typically ascribed to form frequencies, neighbourhood densities or family memberships (c.f., Baayen et al, 2011;Baayen, Milin, & Ramscar, 2016;Milin et al, 2017), emerge from simple input representations that drive implicit learning over many trials within natural utterances. Here we expand the range of cues with high-level semantic ones and show that they can reliably discriminate in which form nearsynonyms preferably occur within a discrimination learning framework.…”