ICCM V

A central question in the psycholinguistic study of multilingualism is how syntax is shared across languages. We implement a model to investigate whether error-based implicit learning can provide an account of cross-language structural priming. The model is based on the Dual-path model of sentence-production (Chang, 2002). We implement our model using the Bilingual version of Dual-path (Tsoukala, Frank, & Broersma, 2017). We answer two main questions: (1) Can structural priming of active and passive constructions occur between English and Spanish in a bilingual version of the Dual-path model? (2) Does cross-language priming differ quantitatively from within-language priming in this model? Our results show that cross-language priming does occur in the model. This finding adds to the viability of implicit learning as an account structural priming in general and cross-language structural priming specifically. Furthermore, we find that the within-language priming effect is somewhat stronger than the cross-language effect. In the context of mixed results from behavioral studies, we interpret the latter finding as an indication that the difference between cross-language and within-language priming is small and difficult to detect statistically.

Successfully performing everyday activities such as loading the dishwasher or setting the table relies on the involvement of many cognitive abilities. As such, everyday activities provide a unique window for investigating the involved cognitive abilities as well as their interaction, promising high ecological validity of the obtained findings. Against this background we investigated two cognitive abilities and their combination, which are crucial for virtually all everyday activities. Specifically, we investigated the nature of mental spatial representation and planning depth in rational planning by analyzing table setting behavior across many environments and actors. As recent modeling work indicates that rational planning is influenced by spatial properties of the environment, we investigate how representation of and reasoning about the spatial environment impact sequential action planning. Using a modeling approach, we compare models implementing different plannings depths and differently complex spatial representations. Our findings indicate that people plan opportunistically (one step ahead) and rely on a two-dimensional representation of their environment. These findings lend credit to the idea that humans minimize their cognitive effort (simpler representations, shallow planning) to efficiently perform everyday tasks.

In inquiry-based learning tasks students are actively involved in learning knowledge and skills through experimentation. The success of these activities largely depends on student’s inquiry practices. While traditional assessment infers student competency from their responses and problem-solving steps, the pauses between these actions provide a valuable source of information. Pauses during inquiry tasks capture a wide range of productive and unproductive activities such as planning, reasoning and mind-wandering. We present efforts to characterize the pauses behaviors during a science inquiry task using hidden Markov modeling. We explore how theory can inform data driven modeling approaches, describe initial evidence of meaningful pause states, and consider the limitations of this approach for supporting inferences about students’ science inquiry practices.

Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder often characterized by the unwanted re-experiencing of a traumatic event through nightmares, flashbacks, and/or intrusive memories. This paper presents a neurocomputational model using the ACT-R cognitive architecture that simulates intrusive memory retrieval following a potentially traumatic event (PTE) and derives predictions about hippocampus volume observed in PTSD. Memory intrusions were captured in the ACT-R Bayesian framework by weighting the posterior probability with an emotional intensity term I to capture the degree to which an event was perceived as dangerous or traumatic. It is hypothesized that (1) Increasing the intensity I of a PTE will increase the odds of memory intrusions; and (2) Increased intrusions will result in a concurrent decrease in hippocampal size. A series of simulations were run and it was found that I had a significant effect on the probability of experiencing traumatic memory intrusions following a PTE. The model also found that I was a significant predictor of hippocampal volume reduction, where the mean and range of simulated volume loss match results of existing meta-analysis. The authors believe that this is the first model to both describe traumatic memory retrieval and provide a mechanistic account of changes in hippocampal volume, capturing one plausible link between PTSD and hippocampus size.

This paper presents a novel approach to the cognitive modelling of human sentence processing in ACT-R. The model assumes a cognitive distinction between cross-linguistic knowledge of the overall possibilities for combining elements of language structure, represented in procedural memory, and language-specific knowledge of the combinatorial constraints on structure-building, which are stored as part of the lexicon in declarative memory. Sentence structure is built incremen- tally using an extension of an established, computationally robust grammar theory, Lexical Functional Grammar (Bresnan, 1982). Using a single set of productions, together with a dual lexicon representing grammar fragments of English and Korean, the model is able to parse complex sentences in both lan- guages, constructing syntactic representations that match human judgements. The model reproduces garden path phenomena reported by English and Korean native speakers, and introduces a cross-linguistic treatment of prosodic breaks to avoid garden-paths during processing. Limitations to the model are discussed, as well as questions that are currently under investigation.