The traceback method in child language acquisition research: identifying patterns in early speech

abstract This paper discusses the traceback method, which has been the basis of some influential papers on first language acquisition. The method sets out to demonstrate that many or even all utterances in a test corpus (usually the last two sessions of recording) can be accounted for with the help of recurrent fixed strings (like What’s that?) or frame-and-slot patterns (like [What’s X?]) that can also be identified in the remaining dataset (i.e., the previous sessions of recording). This is taken as evidence that language learning is much more item-based than previously assumed. In the present paper we sketch the development of the method over the last two decades, and discuss its relation to usage-based theory, as well as the cognitive plausibility of its components, and we highlight both its potential and its limitations.

Of absent mothers, strong sisters and peculiar daughters: the constructional network of English NPN constructions

This paper analyzes symmetric NPN constructions (e.g., day to day, face to face, step by step) qualitatively and quantitatively by examining data from the Corpus of Contemporary American English (Davies, Mark. 2008–. The Corpus of Contemporary American English (COCA): 570 million words, 1990–present. http://corpus.byu.edu/coca/). The constructions’ frequency and productivity, as well as their semantics and extension potential (i.e., modification, complementation) is investigated (e.g., by conducting collostructional analysis). In terms of theoretical modeling, the paper takes a Usage-based, Cognitive Construction Grammar approach (UCCxG) and sketches the constructional network of this constructional family, postulating various constructional templates on different levels of specificity – among others – the existence of the following subtypes [CNsg,time i after CNsg,time i]Cx (e.g., day after day, night after night), [CNsg,measurement i by CNsg,measurement i]Cx (e.g., inch by inch, step by step) or [CNsg,bodypart i to CNsg,bodypart i]Cx (e.g., skin to skin, shoulder to shoulder). We show how these templates are vertically and horizontally connected to each other. Ultimately, we argue that in a usage-based model which strives for cognitive plausibility it is not always feasible to postulate the entrenchment of an abstract overarching schema (i.e., a ‘mothernode’) like [CNi P CNi]Cx or even [N P N]Cx high up in the network. It is unlikely that speakers abstract such a general schema in a bottom-up acquisition process for this family. Rather, the NPN group is a constructional family characterized by many sister ties and by the absence of mother nodes from which information can be inherited.

Contrasting RNN-based and simulation-based models of human physical parameter inference

A number of recent studies have used ideal observer models to capture human physical learning and reasoning as based on approximate mental simulation. While these approaches can match human competence in specific tasks, they are still relatively far from cognitive plausibility and are limited in their ability to capture patterns of human errors. In the current work, we train a recurrent neural network on the same physical reasoning task explored in Bramley, Gerstenberg, Tenenbaum, and Gureckis (2018) (passive condition), finding a closer match to human patterns than the ideal observer model previously used to make sense of the human judgement patterns.

Time, perspective and semantic representation

abstractI discuss the perspectival nature of temporality in discourse and argue that the human concept of time can no more be dissociated from the perspectival thought than the concept of the self can. The corollary of this observation is that perspectival temporality can no more be excluded from the semantic representation than the notion of the self can: neither can be reduced to the bare referent for the purpose of semantic representation if the latter is to retain cognitive plausibility. I present such a semantic qua conceptual approach to temporal reference developed within my theory of Default Semantics. I build upon my theory of time as epistemic modality according to which, on the level of conceptual qua semantic building blocks, temporality reduces to degrees of detachment from the certainty of the here and the now. I also address the questions of temporal asymmetry between the past and the future, and the relation between metaphysical time (timeM), psychological time (timeE, where ‘E’ marks the domain of epistemological enquiry), and time in natural language (timeL), concluding that the perspective-infused timeE and timeL are compatible with timeM of mathematical models of spacetime: all are definable through possibility and perspectivity.

The cognitive plausibility of statistical classification models: Comparing textual and behavioral evidence

Usage-based linguistics abounds with studies that use statistical classification models to analyze either textual corpus data or behavioral experimental data. Yet, before we can draw conclusions from statistical models of empirical data that we can feed back into cognitive linguistic theory, we need to assess whether the text-based models are cognitively plausible and whether the behavior-based models are linguistically accurate. In this paper, we review four case studies that evaluate statistical classification models of richly annotated linguistic data by explicitly comparing the performance of a corpus-based model to the behavior of native speakers. The data come from four different languages (Arabic, English, Estonian, and Russian) and pertain to both lexical as well as syntactic near-synonymy. We show that behavioral evidence is needed in order to fine tune and improve statistical models built on data from a corpus. We argue that methodological pluralism is the key for a cognitively realistic linguistic theory.