Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results Correspondence should be sent to Himanshu Yadav is now affiliated with University of Potsdam, Germany. Vishakha Shukla is now affiliated with Shroff Charity Eye Hospital, Delhi, India. show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.

Quantum flatland i.e., the family of two dimensional (2D) quantum materials has become increscent and has already encompassed elemental atomic sheets (Xenes), 2D transition metal dichalcogenides (TMDCs), 2D metal nitrides/carbides/carbonitrides (MXenes), 2D metal oxides, 2D metal phosphides, 2D metal halides, 2D mixed oxides, etc. and still new members are being explored. Owing to the occurrence of various structural phases of each 2D material and each exhibiting a unique electronic structure; bestows distinct physical and chemical properties. In the early years, world record electronic mobility and fractional quantum Hall effect of graphene attracted attention. Thanks to excellent electronic mobility, and extreme sensitivity of their electronic structures towards the adjacent environment, 2D materials have been employed as various ultrafast precision sensors such as gas/fire/light/strain sensors and in trace-level molecular detectors and disease diagnosis. 2D materials, their doped versions, and their hetero layers and hybrids have been successfully employed in electronic/photonic/optoelectronic/spintronic and straintronic chips. In recent times, quantum behavior such as the existence of a superconducting phase in moiré hetero layers, the feasibility of hyperbolic photonic metamaterials, mechanical metamaterials with negative Poisson ratio, and potential usage in second/third harmonic generation and electromagnetic shields, etc. have raised the expectations further. High surface area, excellent young’s moduli, and anchoring/coupling capability bolster hopes for their usage as nanofillers in polymers, glass, and soft metals. Even though lab-scale demonstrations have been showcased, large-scale applications such as solar cells, LEDs, flat panel displays, hybrid energy storage, catalysis (including water splitting and CO2 reduction), etc. will catch up. While new members of the flatland family will be invented, new methods of large-scale synthesis of defect-free crystals will be explored and novel applications will emerge, it is expected. Achieving a high level of in-plane doping in 2D materials without adding defects is a challenge to work on. Development of understanding of inter-layer coupling and its effects on electron injection/excited state electron transfer at the 2D-2D interfaces will lead to future generation heterolayer devices and sensors.

We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (Computational Brain and Behavior, 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (Statistical Science 193–208, 2002) for a Bayes factor-based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments.

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In syntactic dependency trees, when arcs are drawn from syntactic heads to dependents, they rarely cross. Constraints on these crossing dependencies are critical for determining the syntactic properties of human language, because they define the position of natural language in formal language hierarchies. We study whether the apparent constraints on crossing syntactic dependencies in natural language might be explained by constraints on dependency lengths (the linear distance between heads and dependents). We compare real dependency trees from treebanks of 52 languages against baselines of random trees which are matched with the real trees in terms of their dependency lengths. We find that these baseline trees have many more crossing dependencies than real trees, indicating that a constraint on dependency lengths alone cannot explain the empirical rarity of crossing dependencies. However, we find evidence that a combined constraint on dependency length and the rate of crossing dependencies might be able to explain two of the most-studied formal restrictions on dependency trees: gap degree and well-nestedness.

Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for processing speed and cue weighting using 13 published datasets; hierarchical Approximate Bayesian Computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the higher weighting for structural cues, suggesting that reading proficiency might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing without compromising the complexity of the model.