A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, (...) Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error-driven learning. (shrink)

The aim of this study was to test the claim that languages universally employ morphosyntactic marking to differentiate events of more‐ versus less‐direct causation, preferring to mark them with less‐ and more‐ overt marking, respectively (e.g., Somebody broke the window vs. Somebody MADE the window break; *Somebody cried the boy vs. Somebody MADE the boy cry). To this end, we investigated whether a recent computational model which learns to predict speakers’ by‐verb relative preference for the two causatives in English, Hebrew, (...) Hindi, Japanese, and K'iche’ Mayan is able to generalize to a sixth language on which it has never been trained: Balinese. Judgments of the relative acceptability of the less‐ and more‐transparent causative forms of 60 verbs were collected from 48 native‐speaking Balinese adults. The composite crosslinguistic computational model was able to predict these judgments, not only for verbs that it had seen, but also––in a split‐half validation test––to verbs that it had never seen in any language. A “random‐semantics” model showed only a relatively small decrement in performance with seen verbs, whose behavior can be learned on a verb‐by‐verb basis, but achieved zero correlation with human judgments when generalizing to unseen verbs. Together, these findings suggest that Balinese conceptualizes directness of causation in a similar way to these unrelated languages, and therefore constitute support for the view that the distinction between more‐ versus less‐distinct causation constitutes a morphosyntactic universal. (shrink)

Standard accounts of derivational morphology assume that it is incremental: some words are formed on the basis of others, and each derivational family has a base from which all of the other words are derived. The importance of the base has been questioned by paradigmatic approaches to morphology, which posit that word systems are about multidirectional relationships between words and paradigm cells, in which no word has a privileged status. This paper seeks to test which of these two views makes (...) more accurate predictions about speakers’ cognitive representations of derivational families. We perform an acceptability judgement experiment in which speakers are asked to evaluate the acceptability of a pseudoword conditional on another pseudoword in the same derivational family. We find that speakers are aware of implicative relationships between words in the same family, and that they opportunistically exploit probabilistic relationships between surface words, regardless of whether the base form is the predictor, the target of prediction, or not at all involved in the task. (shrink)

A central tenet of cognitive linguistics is that adults’ knowledge of language consists of a structured inventory of constructions, including various two-argument constructions such as the active, the passive and “fronting” constructions. But how do speakers choose which construction to use for a particular utterance, given constraints such as discourse/information structure and the semantic fit between verb and construction? The goal of the present study was to build a computational model of this phenomenon for two-argument constructions in Mandarin. First, we (...) conducted a grammaticality judgment study with 60 native speakers which demonstrated that, across 57 verbs, semantic affectedness – as determined by further 16 native speakers – predicted each verb’s relative acceptability in the bei-passive and ba-active constructions, but not the Notional Passive and SVO Active constructions. Second, in order to simulate acquisition of these competing constraints, we built a computational model that learns to map from corpus-derived input to an output representation corresponding to these four constructions. The model was able to predict judgments of the relative acceptability of the test verbs in the ba-active and bei-passive constructions obtained in Study 1, with model-human correlations in the region of r = 0.5 and r = 0.3, respectively. Surprisingly, these correlations increased when lexical verb identity was removed; perhaps because this information leads to over-fitting of the training set. These findings suggest the intriguing possibility that acquiring constructions involves forgetting as a mechanism for abstracting across certain fine-grained lexical details and idiosyncrasies. (shrink)

Cet article passe en revue les principaux points à retenir des travaux de Gilles Fauconnier, en particulier de son travail collaboratif avec Mark Turner sur la théorie du _blending_, pour les lecteurs qui ne sont pas familiers avec ce cadre théorique ou qui pourraient bénéficier d'un regard extérieur. Je souligne les leçons globales à tirer des notions d'espaces mentaux et de leurs connexions ou _mappings_, les avantages d'utiliser de petits packages au lieu de grands domaines pour analyser l’activité conceptuelle, comment (...) les réseaux d'intégration (_blending networks_) nous permettent d'aller au-delà de simples projections ou correspondances entre domaines de l'expérience, ainsi que la nécessité d'étudier des modèles qui fournissent un pont entre les principes généraux et les exemples riches et contextualisés qui les exposent. Je cherche avant tout à transmettre le message de Gilles Fauconnier sur la fascination, la curiosité, l'ambition et l'humilité requises par l'entreprise scientifiq... (shrink)

The uncertainty associated with paradigmatic families has been shown to correlate with their phonetic characteristics in speech, suggesting that representations of complex sublexical relations between words are part of speaker knowledge. To better understand this, recent studies have used two-layer neural network models to examine the way paradigmatic uncertainty emerges in learning. However, to date this work has largely ignored the way choices about the representation of inflectional and grammatical functions (IFS) in models strongly influence what they subsequently learn. To (...) explore the consequences of this, we investigate how representations of IFS in the input-output structures of learning models affect the capacity of uncertainty estimates derived from them to account for phonetic variability in speech. Specifically, we examine whether IFS are best represented as outputs to neural networks (as in previous studies) or as inputs by building models that embody both choices and examining their capacity to account for uncertainty effects in the formant trajectories of word final [ɐ], which in German discriminates around sixty different IFS. Overall, we find that formants are enhanced as the uncertainty associated with IFS decreases. This result dovetails with a growing number of studies of morphological and inflectional families that have shown that enhancement is associated with lower uncertainty in context. Importantly, we also find that in models where IFS serve as inputs—as our theoretical analysis suggests they ought to—its uncertainty measures provide better fits to the empirical variance observed in [ɐ] formants than models where IFS serve as outputs. This supports our suggestion that IFS serve as cognitive cues during speech production, and should be treated as such in modeling. It is also consistent with the idea that when IFS serve as inputs to a learning network. This maintains the distinction between those parts of the network that represent message and those that represent signal. We conclude by describing how maintaining a “signal-message-uncertainty distinction” can allow us to reconcile a range of apparently contradictory findings about the relationship between articulation and uncertainty in context. (shrink)