A learning bias for word order harmony:Evidence from speakers of non-harmonic languages

Word order harmony describes the tendency, found across the world's languages, to consistently order syntactic heads relative to dependents. It is one of the most well-known and well-studied typological universals. Almost since it was first noted by Greenberg (1963), there has been disagreement about what role, if any, the cognitive system plays in driving harmony. Recently, a series of studies using artificial language learning experiments reported that harmonic noun phrase word orders were preferred over non-harmonic orders by English-speaking adults and children (Culbertson et al., 2012; Culbertson & Newport, 2015, 2017). However, this evidence is potentially confounded by the fact that English is itself a harmonic language (Goldberg, 2013). Here we sought to extend the results from these studies by exploring whether learners who have substantial experience with a non-harmonic language still showed a bias for harmonic patterns during learning. We found that monolingual French- and Hebrew-speaking children, whose language has a non-harmonic noun phrase order (N Adj, Num N) nevertheless preferred harmonic patterns when learning an artificial language. We also found evidence for a harmony bias across several populations of adult learners, although this interacted in complex ways with their L2 experience. Our results suggest that transfer from the L1 cannot explain the preference for harmony found in previous studies. Moreover, they provide the strongest evidence yet that a cognitive bias for harmony is a plausible candidate for shaping linguistic typology

Structural priming in artificial languages and the regularisation of unpredictable variation

We present a novel experimental technique using artificial language learning to investigate the relationship between structural priming during communicative interaction, and linguistic regularity. We use unpredictable variation as a test-case, because it is a well-established paradigm to study learners’ biases during acquisition, transmission and interaction. We trained participants on artificial languages exhibiting unpredictable variation in word order, and subsequently had them communicate using these artificial languages. We found evidence for structural priming in two different grammatical constructions and across human-human and human-computer interaction. Priming occurred regardless of behavioral convergence: communication led to shared word order use only in human-human interaction, but priming was observed in all conditions. Furthermore, interaction resulted in the reduction of unpredictable variation in all conditions, suggesting a role for communicative interaction in eliminating unpredictable variation. Regularisation was strongest in human-human interaction and in a condition where participants believed they were interacting with a human but were in fact interacting with a computer. We suggest that participants recognize the counter-functional nature of unpredictable variation and thus act to eliminate this variability during communication. Furthermore, reciprocal priming occurring in human-human interaction drove some pairs of participants to converge on maximally regular, highly predictable linguistic systems. Our method offers potential benefits to both the artificial language learning and the structural priming fields, and provides a useful tool to investigate communicative processes that lead to language change and ultimately language design

Music and language comprehension in the brain

Contains fulltext : 166652.pdf (publisher's version ) (Open Access)Radboud University, 10 februari 2017Promotor : Hagoort, P. Co-promotor : Willems, R.M.236 p

Associative Processes in Statistical Learning: Paradoxical Predictions of the Past

The ability to process sequences of input and extract regularity across the distribution of input is fundamental for making predictions from the observed past to the future. Prediction is rooted in the extraction of both frequency- and conditional statistics from the distribution of inputs. For example, an animal hunting for food may consistently return to a particular area to hunt if relative to all other areas visited, that area has the highest frequency of prey. In contrast, humans asked to predict the next word in a sentence must make a prediction based upon higher-order regularities rather than simple frequency statistics (the most frequent word in the English language is the). The Serial Reaction Time (SRT) task, a model for studying sequential behavior, is used to quantify sensitivity to sequential constraints present in structured environments (Nissen & Bullemer, 1987). The SRT task requires Ss to make a unique response to each individually presented element from a sequence of elements. The statistics of SRT sequences, such as the relative frequency of elements and simple pairwise associations between elements, can be controlled to create dependencies that can only be predicted by learning higher-order associations. Sensitivity to the sequential constraints present in the structured input is demonstrated through differences in reaction time to elements that are, and are not, predictable based upon the statistics of the input environment. Sensitivity to statistical regularity in the environment is also a critical dimension of various episodic learning methodologies. Graded associations have been demonstrated among elements extending in both forward and backward directions in episodic memory tasks, and are suggested to reflect a gradient of the underlying structural relationships among the study elements. Graded associations are beneficial to the extent that they increase the probability of recalling sequence elements.However, unlike free and serial recall tasks, backward associations, and remote associations in general, are anti-predictive in the SRT task. The formation of associations beyond the immediately predictive element in prediction tasks could be suggestive of a ubiquitous underlying associative mechanism, which universally gives rise to graded contiguity effects, regardless of the specifc application (Howard, Jing, Rao, Provyn, & Datey, 2009). The following experiment employed a probabilistic SRT task to quantify sensitivity to immediately backward, backward-remote, and forward-remote associations. Ss were presented sequences of elements probabilistically sampled from an underlying ring-structure, with the dependent measure Ss\u27 reaction time to elements that either followed, or deviated from, the structure. Results from the SRT task indicated that Ss demonstrated a robust backward association, as well as evidence for forward-graded associations. Moreover, in an explicit test of sequence knowledge, while Ss did not generate the probabilistic statistics from the structured learning environment, Ss did generate a statistically signifcant amount of backward-transitions, relative to other remote-backward transitions. The graded associations that were formed beyond the immediately predictive element in the prediction task provide evidence that a similar mechanism that mediates episodic learning may also mediate statistical learning. Backward and graded associations may be explained by a ubiquitous underlying associative mechanism, which universally gives rise to graded contiguity effects, regardless of the specific application

Syntax across domains: overlap in global and local structure processing

The research that has been presented in the current dissertation aims to address the recent debate concerning the extent to which structural processing across content domains (language, music, math, and action) might be supported by domain-general resources (Slevc & Okada, 2015). Following the development of novel pitch sequences and an off-line structural processing measure, we found interference during the joint structural processing of sentences and pitch sequences, which suggests that structural processing in both domains is supported by a domain-general pool of (working memory and cognitive control) resources. On the basis of this finding, we investigated to what extent such interactions between the structural processing of linguistic and non-linguistic materials could be found when studying ecologically valid materials. In an EEG study, we found that the event-related potentials (P2, P3, LAN, and P600) which were observed for dispreferred sentential disambiguations could be influenced by structural expectations on the basis of previously disambiguated pitch sequences. In two subsequent structural priming studies, we found that the completion of syntactically (Scheepers et al., 2011) and thematically (Allen et al., 2010) structured sentence beginnings (Scheepers et al., 2011) could be primed by the attachment structure of preceding linguistic, mathematical and pitch sequence materials. Furthermore, we found that similar cross-domain priming effects could be observed on the perception of implicitly structured pitch sequences. These findings thus strongly argue for broad, domain-general interactions in structural processing even when studying more naturalistic processing of ecologically valid materials. We tentatively interpret the current findings as evidence in favour of a domain-general pool of cognitive processing resources supporting structural processing across domains (Kljajevic, 2010; Slevc & Okada, 2014). With regards to our cross domain priming findings, we suggest that our results align with an ‘incremental-procedural’ account of attachment priming (see Scheepers & Sturt, 2014) according to which encountering a complexity in the structural processing of materials might (through a process of error-based implicit learning, Chang et al., 2006) influence the resource allocation during the structural processing of subsequent materials. In this way, our cross domain priming findings can be aligned with the idea of structural complexities processing being supported by domain-general cognitive resources (Slevc & Okada, 2015). At this point, it is important to remark that the results reported in the dissertation should of course be further replicated, and might be generalized to include harmonic processing and action perception as domains of structural processing. Furthermore, the interpretations of the current findings are not fully conclusive, as our studies were mainly guided by the goal of investigating whether there was evidence for interaction in structural processing across domains (showing several primary findings), rather than directly comparing alternative accounts in the interpretation of such interactions. Nevertheless, the research reported in the current dissertation clearly shows that, in relationship to the ongoing discussion on domain-generality of structural processing across domains (Slevc & Okada, 2015), interactions in structural processing across domains can be found when controlling for limitations of previous research (Perruchet & Poulin-Charronnat, 2013), and that those interactions can also be observed in situations that more closely approximate the processing of information from several domains in ‘daily life’. These primary findings suggest that domain-general cognitive processing resources support structural processing across domains, which provides several perspectives for theoretical approaches in psycholinguistics as well as other domains of cognition involving structural processing, such as math, music, and action

PIPS: A parallel planning model of sentence production

Subject–verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject–verb agreement from a sentence preamble (The key to the cabinets) and eliciting verb errors (… *were shiny). Through reanalysis of previous data (50 experiments; 102,369 observations), we show that this paradigm also results in many errors in preamble repetition, particularly of local noun number (The key to the *cabinet). We explore the mechanisms of both errors in parallelism in producing syntax (PIPS), a model in the Gradient Symbolic Computation framework. PIPS models sentence production using a continuous-state stochastic dynamical system that optimizes grammatical constraints (shaped by previous experience) over vector representations of symbolic structures. At intermediate stages in the computation, grammatical constraints allow multiple competing parses to be partially activated, resulting in stable but transient conjunctive blend states. In the context of the preamble completion task, memory constraints reduce the strength of the target structure, allowing for co-activation of non-target parses where the local noun controls the verb (notional agreement and locally agreeing relative clauses) and non-target parses that include structural constituents with contrasting number specifications (e.g., plural instead of singular local noun). Simulations of the preamble completion task reveal that these partially activated non-target parses, as well the need to balance accurate encoding of lexical and syntactic aspects of the prompt, result in errors. In other words: Because sentence processing is embedded in a processor with finite memory and prior experience with production, interference from non-target production plans causes errors

Relationships between musical and linguistic skills in early development: the role of informal musical experience in the home

Research on the relationship between formal musical training and cognitive abilities has been burgeoning over the last decade, with a specific focus on the relationship between language and music skills. However, a significant gap exists when looking at the start of the developmental path of the relationship between these abilities: whereas something is known about infants and a significant amount has been learned about school-aged children, very little is known about preschool children. Aiming to fill this gap, this research has moved along two interlocking paths: first, studying the early relationship between cognitive processing of both music and language, and second, evaluating a dimension so far unexplored: the influence of informal musical interaction and exposure in the home on musical and linguistic development. Using a correlational design and a set of novel age-appropriate musical abilities tasks, Study 1 examined the relationship between a range of musical skills and linguistic development in 3- and 4-year-old children. The second study investigated the contribution of informal musical experience in the home in the development of these skills. Based on the findings from Study 2, which suggested a significant association between informal musical experience in the home and the development of key language areas, Study 3 sought to develop a validated instrument with good psychometric properties for the assessment of informal musical experience. To this end, two online surveys were conducted, and factor analytical and confirmatory methods were used to explore and consolidate the factor structure of the new instrument (Music@Home Questionnaire). Reliability and validity of the new instrument were also investigated. Study 4 focused on a specific aspect of music and language processing namely, the processing of structure, and examined the hypothesis that these skills are related in 4- and 6-year-old children. Study 4 also investigated the impact of home experience with music, as assessed with the newly developed instrument, on language and music structural processing. The combined findings of the present thesis contribute towards a comprehensive account of the relationship between language and music from a developmental perspective. They also provide researchers with new tools to assess musical abilities in young children and with a novel parent-report instrument for the assessment of a largely unexplored area of environmental experience: i.e. informal musical experience in the home

A Connectionist Theory of Phenomenal Experience

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways