Individual and developmental differences in semantic priming: empirical and computational support for a single-mechanism account of lexical processing

Existing accounts of single-word semantic priming phenomena incorporate multiple mechanisms, such as spreading activation, expectancy-based processes, and postlexical semantic matching. The authors provide empirical and computational support for a single-mechanism distributed network account. Previous studies have found greater semantic priming for low- than for high-frequency target words as well as inhibition following unrelated primes only at long stimulus-onset asynchronies (SOAs). A series of experiments examined the modulation of these effects by individual differences in age or perceptual ability. Third-grade, 6th-grade, and college students performed a lexical-decision task on high- and low-frequency target words preceded by related, unrelated, and nonword primes. Greater priming for low-frequency targets was exhibited only by participants with high perceptual ability. Moreover, unlike the college students, the children showed no inhibition even at the long SOA. The authors provide an account of these results in terms of the properties of distributed network models and support this account with an explicit computational simulation.     

A neurally plausible parallel distributed processing model of event-related potential word reading data

The Parallel Distributed Processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an attempt to advance the relationship between explicit, computational models and physiological data collected during the performance of cognitive tasks, we developed a PDP model of visual word recognition which simulates key results from the ERP reading literature, while simultaneously being able to successfully perform lexical decision—a benchmark task for reading models. Simulations reveal that the model’s success depends on the implementation of several neurally plausible features in its architecture which are sufficiently domain-general to be relevant to cognitive modeling more generally.     

The role of diversity practices and inclusion in promoting trust and employee engagement

This paper investigates the association of diversity practices with an important aspect of workplace well‐being, engagement. It was hypothesized that this association would be mediated by trust climate, and that this mediation relationship would be stronger when employees experienced feelings of inclusion in the workplace. Using a sample of 4,597 health sector employees, results indicated that diversity practices are associated with a trusting climate that, in turn, is positively related to employee engagement. Furthermore, the relationship between diversity practices and trust climate was moderated by inclusion. Theoretical and practical implications of the study are discussed along with recommendations for future research.     

A Connectionist Approach to Word Reading and Acquired Dyslexia: Extension to Sequential Processing

A connectionist approach to word reading, based on the principles of distributed representation, graded learning of statistical structure, and interactivity in processing, has led to the development of explicit computational models which account for a wide range of data on normal skilled reading and on patterns of reading impairment due to brain damage. There have, however, been recent empirical challenges to these models, and the approach in general, relating to the influence of orthographic length on the naming latencies of both normal and dyslexic readers. The current work presents a simulation which generates sequential phonological output in response to written input, and which can refixate the input when encountering difficulty. The normal model reads both words and nonwords accurately, and exhibits an effect of orthographic length and a frequency-by-consistency interaction in its naming latencies. When subject to peripheral damage, the model exhibits an increased length effect that interacts with word frequency, characteristic of letter-by-letter reading in pure alexia. Although the model is far from a fully adequate account of all the relevant phenomena, it suggests how connectionist models may be extended to provide deeper insight into sequential processes in reading.     

SD-squared: on the association between semantic dementia and surface dyslexia

Within the connectionist triangle model of reading aloud, interaction between semantic and phonological representations occurs for all words but is particularly important for correct pronunciation of lower frequency exception words. This framework therefore predicts that (a) semantic dementia, which compromises semantic knowledge, should be accompanied by surface dyslexia, a frequency-modulated deficit in exception word reading, and (b) there should be a significant relationship between the severity of semantic degradation and the severity of surface dyslexia. The authors evaluated these claims with reference to 100 observations of reading data from 51 cases of semantic dementia. Surface dyslexia was rampant, and a simple composite semantic measure accounted for half of the variance in low-frequency exception word reading. Although in 3 cases initial testing revealed a moderate semantic impairment but normal exception word reading, all of these became surface dyslexic as their semantic knowledge deteriorated further. The connectionist account attributes such cases to premorbid individual variation in semantic reliance for accurate exception word reading. These results provide a striking demonstration of the association between semantic dementia and surface dyslexia, a phenomenon that the authors have dubbed SD-squared.     

Psychogeriatric management and sectorized psychiatry

Together with a general assessment of gerontopsychiatric care in the Federal Republic of Germany, the paper presents a model in hospitalised gerontopsychiatric treatment that has been tested at Hanover College of Medical Studies (Medizinische Hochschule Hanover). Results presented cover a wide range of divergent disorders, and appear to justify the employment of the model elsewhere.     

Attractor dynamics in word recognition: converging evidence from errors by normal subjects, dyslexic patients and a connectionist model

People make both semantic and visual errors when trying to recognise the meaning of degraded words. This result mirrors the finding that deep dyslexic patients make both semantic and visual errors when reading aloud. We link the results with the demonstration that a recurrent connectionist network which produces the meaning of words in response to their spelling pattern produces this distinctive combination of errors both when its input is degraded and when it is lesioned. The reason why the network can simulate the errors of both normal subjects and patients lies in the nature of the attractors which it develops as it learns to map orthography to semantics. The key role of attractor structure in the successful simulation suggests that the normal adult semantic reading route may involve attractor dynamics.     

Short-term memory for serial order: a recurrent neural network model

Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.