Slower Perception Followed by Faster Lexical Decision in Longer Words: A Diffusion Model Analysis

Linear and nonlinear profiles of weak behavioral and neural differentiation between numerical operations in children with math learning difficulties

Mathematical knowledge is constructed hierarchically during development from a basic understanding of addition and subtraction, two foundational and inter-related, but semantically distinct, numerical operations. Early in development, children show remarkable variability in their numerical problem-solving skills and difficulties in solving even simple addition and subtraction problems are a hallmark of math learning difficulties. Here, we use novel quantitative analyses to investigate whether less distinct representations are associated with poor problem-solving abilities in children during the early stages of math-skill acquisition. Crucially, we leverage dimensional and categorical analyses to identify linear and nonlinear neurobehavioral profiles of individual differences in math skills. Behaviorally, performance on the two different numerical operations was less differentiated in children with low math abilities, and lower problem-solving efficiency stemmed from weak evidence-accumulation during problem-solving. Children with low numerical abilities also showed less differentiated neural representations between addition and subtraction operations in multiple cortical areas, including the fusiform gyrus, intraparietal sulcus, anterior temporal cortex and insula. Furthermore, analysis of multi-regional neural representation patterns revealed significantly higher network similarity and aberrant integration of representations within a fusiform gyrus-intraparietal sulcus pathway important for manipulation of numerical quantity. These findings identify the lack of distinct neural representations as a novel neurobiological feature of individual differences in children's numerical problem-solving abilities, and an early developmental biomarker of low math skills. More generally, our approach combining dimensional and categorical analyses overcomes pitfalls associated with the use of arbitrary cutoffs for probing neurobehavioral profiles of individual differences in math abilities.

Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics

Heterogeneity in cognitive and academic abilities is a prominent feature of autism spectrum disorder (ASD), yet little is known about its underlying causes. Here we combine functional brain imaging during numerical problem-solving with hierarchical drift-diffusion models of behavior and standardized measures of numerical abilities to investigate neural mechanisms underlying cognitive variability in children with ASD, and their IQ-matched Typically Developing (TD) peers. Although the two groups showed similar levels of brain activation, the relation to individual abilities differed markedly in ventral temporal-occipital, parietal and prefrontal regions important for numerical cognition: children with ASD showed a positive correlation between functional brain activation and numerical abilities, whereas TD children showed the opposite pattern. Despite similar accuracy and response times, decision thresholds were significantly higher in the ASD group, suggesting greater evidence required for problem-solving. Critically, the relationship between individual abilities and engagement of prefrontal control systems anchored in the anterior insula was differentially moderated by decision threshold in subgroups of children with ASD. Our findings uncover novel cognitive and neural sources of variability in academically-relevant cognitive skills in ASD and suggest that multilevel measures and latent decision-making dynamics can aid in characterization of cognitive variability and heterogeneity in neurodevelopmental disorders.

On the limits of familiarity accounts in lexical decision: The case of repetition effects

Recent modelling accounts of the lexical decision task have suggested that the reading system performs evidence accumulation to carry out some functions. Evidence accumulation models have been very successful in accounting for effects in the lexical decision task, including the dissociation of repetition effects for words and nonwords (facilitative for words but inhibitory for nonwords). The familiarity of a repeated item triggers its recognition, which facilitates 'word' responses but hampers nonword rejection. However, reports of facilitative repetition effects for nonwords with several repetitions in short blocks challenge this hypothesis and favour models based on episodic retrieval. To shed light on the nature of the repetition effects for nonwords in lexical decision, we conducted four experiments to examine the impact of extra-lexical source of information-we induced the use of episodic retrieval traces via instructions and list composition. When the initial block was long, the repetition effect for nonwords was inhibitory, regardless of the instructions and list composition. However, the inhibitory effect was dramatically reduced when the initial block included two presentations of the stimuli and it was even facilitatory when the initial block was short. This composite pattern suggests that evidence accumulation models of lexical decision should take into account all sources of evidence-including episodic retrieval-during the process of lexical decision.

The SLS-Berlin: Validation of a German Computer-Based Screening Test to Measure Reading Proficiency in Early and Late Adulthood

Reading proficiency, i.e., successfully integrating early word-based information and utilizing this information in later processes of sentence and text comprehension, and its assessment is subject to extensive research. However, screening tests for German adults across the life span are basically non-existent. Therefore, the present article introduces a standardized computerized sentence-based screening measure for German adult readers to assess reading proficiency including norm data from 2,148 participants covering an age range from 16 to 88 years. The test was developed in accordance with the children's version of the Salzburger LeseScreening (SLS, Wimmer and Mayringer, 2014). The SLS-Berlin has a high reliability and can easily be implemented in any research setting using German language. We present a detailed description of the test and report the distribution of SLS-Berlin scores for the norm sample as well as for two subsamples of younger (below 60 years) and older adults (60 and older). For all three samples, we conducted regression analyses to investigate the relationship between sentence characteristics and SLS-Berlin scores. In a second validation study, SLS-Berlin scores were compared with two (pseudo)word reading tests, a test measuring attention and processing speed and eye-movements recorded during expository text reading. Our results confirm the SLS-Berlin's sensitivity to capture early word decoding and later text related comprehension processes. The test distinguished very well between skilled and less skilled readers and also within less skilled readers and is therefore a powerful and efficient screening test for German adults to assess interindividual levels of reading proficiency.

Multi-factor analysis in language production: Sequential sampling models mimic and extend regression results

For multi-factor analyses of response times, descriptive models (e.g., linear regression) arguably constitute the dominant approach in psycholinguistics. In contrast empirical cognitive models (e.g., sequential sampling models, SSMs) may fit fewer factors simultaneously, but decompose the data into several dependent variables (a multivariate result), offering more information to analyze. While SSMs are notably popular in the behavioural sciences, they are not significantly developed in language production research. To contribute to the development of this modelling in language, we (i) examine SSMs as a measurement modelling approach for spoken word activation dynamics, and (ii) formally compare SSMs to the default method, regression. SSMs model response activation or selection mechanisms in time, and calculate how they are affected by conditions, persons, and items. While regression procedures also model condition effects, it is only in respect to the mean RT, and little work has been previously done to compare these approaches. Through analyses of two language production experiments, we show that SSMs reproduce regression predictors, and further extend these effects through a multivariate decomposition (cognitive parameters). We also examine a combined regression-SSM approach that is hierarchical Bayesian, which can jointly model more conditions than classic SSMs, and importantly, achieve by-item modelling with other conditions. In this analysis, we found that spoken words principally differed from one another by their activation rates and production times, but not their thresholds to be activated.

Drifting through Basic Subprocesses of Reading: A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition

Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423) performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modeling approach, which provides more information than standard response time/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modeling allowed us to differentiate between age-related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing, whereas a general age-disadvantage was observed at the sublexical and phonological levels. Low-performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose age-related disadvantages for older readers to be caused by inefficiencies in temporal sampling and activation and/or inhibition processes.