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Comparing and validating methods of reading instruction using behavioural and neural findings in an artificial orthography.

ABSTRACT: There is strong scientific consensus that emphasizing print-to-sound relationships is critical when learning to read alphabetic languages. Nevertheless, reading instruction varies across English-speaking countries, from intensive phonic training to multicuing environments that teach sound- and meaning-based strategies. We sought to understand the behavioral and neural consequences of these differences in relative emphasis. We taught 24 English-speaking adults to read 2 sets of 24 novel words (e.g., /buv/, /sig/), written in 2 different unfamiliar orthographies. Following pretraining on oral vocabulary, participants learned to read the novel words over 8 days. Training in 1 language was biased toward print-to-sound mappings while training in the other language was biased toward print-to-meaning mappings. Results showed striking benefits of print-sound training on reading aloud, generalization, and comprehension of single words. Univariate analyses of fMRI data collected at the end of training showed that print-meaning relative to print-sound relative training increased neural effort in dorsal pathway regions involved in reading aloud. Conversely, activity in ventral pathway brain regions involved in reading comprehension was no different following print-meaning versus print-sound training. Multivariate analyses validated our artificial language approach, showing high similarity between the spatial distribution of fMRI activity during artificial and English word reading. Our results suggest that early literacy education should focus on the systematicities present in print-to-sound relationships in alphabetic languages, rather than teaching meaning-based strategies, in order to enhance both reading aloud and comprehension of written words. (PsycINFO Database Record

SUBMITTER: Taylor JSH

PROVIDER: S-EPMC5458780 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Comparing and validating methods of reading instruction using behavioural and neural findings in an artificial orthography.

Taylor J S H JSH Davis Matthew H MH Rastle Kathleen K

Journal of experimental psychology. General 20170420 6

There is strong scientific consensus that emphasizing print-to-sound relationships is critical when learning to read alphabetic languages. Nevertheless, reading instruction varies across English-speaking countries, from intensive phonic training to multicuing environments that teach sound- and meaning-based strategies. We sought to understand the behavioral and neural consequences of these differences in relative emphasis. We taught 24 English-speaking adults to read 2 sets of 24 novel words (e. ...[more]

PMID: 28425742

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Project description:Reading is a unique human cognitive skill and its acquisition was proven to extensively affect both brain organization and neuroanatomy. Differently from western sighted individuals, literacy rates via tactile reading systems, such as Braille, are declining, thus imposing an alarming threat to literacy among non-visual readers. This decline is due to many reasons including the length of training needed to master Braille, which must also include extensive tactile sensitivity exercises, the lack of proper Braille instruction and the high costs of Braille devices. The far-reaching consequences of low literacy rates, raise the need to develop alternative, cheap and easy-to-master non-visual reading systems. To this aim, we developed OVAL, a new auditory orthography based on a visual-to-auditory sensory-substitution algorithm. Here we present its efficacy for successful words-reading, and investigation of the extent to which redundant features defining characters (i.e., adding specific colors to letters conveyed into audition via different musical instruments) facilitate or impede auditory reading outcomes. Thus, we tested two groups of blindfolded sighted participants who were either exposed to a monochromatic or to a color version of OVAL. First, we showed that even before training, all participants were able to discriminate between 11 OVAL characters significantly more than chance level. Following 6 hours of specific OVAL training, participants were able to identify all the learned characters, differentiate them from untrained letters, and read short words/pseudo-words of up to 5 characters. The Color group outperformed the Monochromatic group in all tasks, suggesting that redundant characters' features are beneficial for auditory reading. Overall, these results suggest that OVAL is a promising auditory-reading tool that can be used by blind individuals, by people with reading deficits as well as for the investigation of reading specific processing dissociated from the visual modality.

Project description:ObjectivesTo evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs.MethodsA retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution's radiologist for final review.ResultsIn total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution's radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35).ConclusionThe AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low.Clinical relevance statementThe AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography.Key points• A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist's reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

Dataset Information

Comparing and validating methods of reading instruction using behavioural and neural findings in an artificial orthography.

Publications

Comparing and validating methods of reading instruction using behavioural and neural findings in an artificial orthography.

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