A web-based interface to calculate phonological neighborhood density for words and nonwords in Modern Standard Arabic

The availability of online databases (e.g., Balota et al., 2007) and calculators (e.g., Storkel & Hoover, 2010) has contributed to an increase in psycholinguistic-related research, to the development of evidence-based treatments in clinical settings, and to scientifically supported training programs in the language classroom. The benefit of online language resources is limited by the fact that the majority of such resources provide information only for the English language (Vitevitch, Chan & Goldstein, 2014). To address the lack of diversity in these resources for languages that differ phonologically and morphologically from English, the present article describes an online database to compute phonological neighborhood density (i.e., the number of words that sound similar to a given word) for words and nonwords in Modern Standard Arabic (MSA). A full description of how the calculator can be used is provided. It can be freely accessed at https://calculator.ku.edu/density/about.

Lexical access in Portuguese stress

Categorical approaches to lexical stress typically assume that words have either regular or irregular stress, and imply that only the latter needs to be stored in the lexicon, while the former can be derived by rule. In this paper, we compare these two groups of words in a lexical decision task in Portuguese to examine whether the dichotomy in question affects lexical retrieval latencies in native speakers, which could indirectly reveal different processing patterns. Our results show no statistically credible effect of stress regularity on reaction times, even when lexical frequency, neighborhood density, and phonotactic probability are taken into consideration. The lack of an effect is consistent with a probabilistic approach to stress, not with a categorical (traditional) approach where syllables are either light or heavy and stress is either regular or irregular. We show that the posterior distribution of credible effect sizes of regularity is almost entirely (96.28%) within the region of practical equivalence, which provides strong evidence that no effect of regularity exists in the lexical decision data modelled. Frequency and phonotactic probability, in contrast, showed statistically credible effects given the experimental data modelled, which is consistent with the literature.

Spoken word recognition in listeners with mild dementia symptoms

BackgroundDifficulty understanding speech is a common complaint of older adults. In quiet, speech perception is often assumed to be relatively automatic. In background noise, however, higher-level cognitive processes play a more substantial role in successful communication. Cognitive resources are often limited in adults with dementia, which may therefore hamper word recognition. ObjectiveThe goal of this study was to determine the impact of mild dementia on spoken word recognition in quiet and noise.MethodsParticipants were adults aged 53–86 years with (n=16) or without (n=32) dementia symptoms as classified by a clinical dementia rating scale. Participants performed a word identification task with two levels of neighborhood density in quiet and in speech shaped noise at two signal-to-noise ratios (SNRs), +6 dB and +3 dB. Our hypothesis was that listeners with mild dementia would have more difficulty with speech perception in noise under conditions that tax cognitive resources. ResultsListeners with mild dementia had poorer speech perception accuracy in both quiet and noise, which held after accounting for differences in age and hearing level. Notably, even in quiet, adults with dementia symptoms correctly identified words only about 80% of the time. However, phonological neighborhood density was not a factor in the identification task performance for either group.ConclusionThese results affirm the difficulty that listeners with mild dementia have with spoken word recognition, both in quiet and in background noise, consistent with a key role of cognitive resources in spoken word identification. However, the impact of neighborhood density in these listeners is less clear.

Going carless in different urban fabrics: socio-demographics of household car ownership

Diverse physical features of urban areas alongside socio-demographic characteristics affect car ownership, and hence the daily mobility choices. As a case of sustainable mobility, we explore how various urban environments and socio-demographics associate with the spatial and social distribution of household car ownership and carlessness in the Helsinki Metropolitan Area, Finland. Three urban fabrics characterizing the study area are established based on the transportation mode (walking, public transportation, or automobile) the physical urban environment primarily supports. The national level Monitoring System of Spatial Structure and Urban Form database, and the National Travel Survey (2016) are utilized to further include spatial and socio-demographic variables into our analysis across these fabrics. Our results show that households with and without cars differ in terms of residential distance to the city center, neighborhood density, house type, and socio-demographic profiles. Single pensioners and students are most likely to be carless, whereas families represent the opposite. Within the carless households the differences are also evident between different groups. For the more affluent households residing in dense and well-connected areas, and mostly possessing driver’s licenses, carlessness is presumably a choice. Contrarily, many other carless households represent the less affluent often located in the more distant, low-density, and less accessible areas, while also possessing less driver’s licenses, making carlessness more of a constraint, as the local urban fabric does not support such lifestyle. Consequently, carless households should be increasingly recognized as a focus group in sustainable urban planning in terms of identifiable best practices and potential vulnerability.

Eye Movement Measures of Within-Language and Cross-Language Activation During Reading in Monolingual and Bilingual Children and Adults: A Focus on Neighborhood Density Effects

We used eye movement measures of first-language (L1) and second-language (L2) paragraph reading to investigate how the activation of multiple lexical candidates, both within and across languages, influences visual word recognition in four different age and language groups: (1) monolingual children; (2) monolingual young adults; (3) bilingual children; and (4) bilingual young adults. More specifically, we focused on within-language and cross-language orthographic neighborhood density effects, while controlling for the potentially confounding effects of orthographic neighborhood frequency. We found facilitatory within-language orthographic neighborhood density effects (i.e., words were easier to process when they had many vs. few orthographic neighbors, evidenced by shorter fixation durations) across the L1 and L2, with larger effects in children vs. adults (especially the bilingual ones) during L1 reading. Similarly, we found facilitatory cross-language neighborhood density effects across the L1 and L2, with no modulatory influence of age or language group. Taken together, our findings suggest that word recognition benefits from the simultaneous activation of visually similar word forms during naturalistic reading, with some evidence of larger effects in children and particularly those whose words may have differentially lower baseline activation levels and/or weaker links between word-related information due to divided language exposure: bilinguals.

Determinants of early lexical acquisition: Effects of word- and child-level factors on Dutch children's acquisition of words

Previous research on the effects of word-level factors on lexical acquisition has shown that frequency and concreteness are most important. Here, we investigate CDI data from 1,030 Dutch children, collected with the short form of the Dutch CDI, to address (i) how word-level factors predict lexical acquisition, once child-level factors are controlled, (ii) whether effects of these word-level factors vary with word class and age, and (iii) whether any interactions with age are due to differences in receptive vocabulary. Mixed-effects regressions yielded effects of frequency and concreteness, but not of word class and phonological factors (e.g., word length, neighborhood density). The effect of frequency was stronger for nouns than predicates. The effects of frequency and concreteness decreased with age, and were not explained by differences in vocabulary knowledge. These findings extend earlier results to Dutch, and indicate that effects of age are not due to increases in vocabulary knowledge.

Wood–leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information

Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we present an automated wood–leaf classification with a three-step classification and wood point verification. The tree point cloud was classified into wood points and leaf points using intensity threshold, neighborhood density and voxelization successively, and was then verified. Twenty-four willow trees were scanned using the RIEGL VZ-400 scanner. Our results were compared with the manual classification results. To evaluate the classification accuracy, three indicators were introduced into the experiment: overall accuracy (OA), Kappa coefficient (Kappa), and Matthews correlation coefficient (MCC). The ranges of OA, Kappa, and MCC of our results were from 0.9167 to 0.9872, 0.7276 to 0.9191, and 0.7544 to 0.9211, respectively. The average values of OA, Kappa, and MCC were 0.9550, 0.8547, and 0.8627, respectively. The time costs of our method and another were also recorded to evaluate the efficiency. The average processing time was 1.4 seconds per million points for our method. The results show that our method represents a potential wood–leaf classification technique with the characteristics of automation, high speed, and good accuracy.