Storage and retrieval of addition facts: The role of number comparison

It is proposed that arithmetical facts are organized in memory in terms of a principle that is unique to numbers—the cardinal magnitudes of the addends. This implies that sums such as 4 + 2 and 2 + 4 are represented, and searched for, in terms of the maximum and minimum addends. This in turn implies that a critical stage in solving an addition problem is deciding which addend is the larger. The COMP model of addition fact retrieval incorporates a comparison stage, as well as a retrieval stage and a pronunciation stage. Three tasks, using the same subjects, were designed to assess the contribution of these three stages to retrieving the answers to single-digit addition problems. Task 3 was the addition task, which examined whether reaction times (RTs) were explained by the model; Task 1 was a number naming task to assess the contribution of the pronunciation stage; Task 2 was a magnitude comparison task to assess the contribution, if any, of the comparison stage. A regression equation that included just expressions of these three stages was found to account for 71% of the variance. It is argued that the COMP model fits not only the adult RT data better than do alternatives, but also the evidence from development of additional skills.     

The importance of being convex: an advantage for convexity when judging position.

Perception of contour polarity was investigated in five experiments in which observers had to judge the vertical position of a vertex. When the vertex was perceived as convex, the level of performance as measured by reaction time and errors was higher than when the same vertex was perceived as concave. I conclude that contour polarity affects how observers perceive shape, and in particular part structure, and that the position of a part is more readily available than the position of a boundary between parts.     

Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition (AoA) while controlling for frequency, contextual diversity, concreteness, length, dominant part of speech, orthographic neighborhood density, and diachronic frequency variation. We analyze measures of language change tackling both the change in lexical representations and the change in the relation between lexical representations and the words with the most similar usage patterns, showing that they capture different aspects of language change. Our results show a unique relation between language change and AoA, which is stronger when considering neighborhood-level measures of language change: Words with more coherent diachronic usage patterns tend to be acquired earlier. The results support theories positing a link between ontogenetic and ethnogenetic processes in language.     

Longitudinal Relationships Between Positive Affect,Loneliness, and Suicide Ideation: Age‐Specific Factors in a General Population

The aims of this study were to analyze whether positive and negative affect, social support, and loneliness are factors longitudinally related to suicide ideation in the general population in different age groups. A total of 2,392 individuals from a nationally representative sample of the Spanish general population were evaluated in 2011–2012 and in 2014–2015. After including relevant control variables in the analyses, lower positive affect was prospectively related to ideation in 18‐ to 59‐year‐old individuals, whereas feelings of loneliness were related to ideation in 60‐year‐and‐older individuals. Social support was not associated with suicide ideation in any age group. These results are in line with the need for age‐tailored suicide prevention programs. The present findings might also suggest that health care professionals should consider feelings of loneliness rather than social support to assess the presence of suicide ideation in older people.     

A Computational and Empirical Investigation of Graphemes in Reading

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full‐blown model of multi‐syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.