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Connections and selections: Comparing multivariate predictions and parameter associations from latent variable models of picture naming.

ABSTRACT: Connectionist simulation models and processing tree mathematical models of picture naming have complementary advantages and disadvantages. These model types were compared in terms of their predictions of independent language measures and their associations between model components and measures that should be related according to their theoretical interpretations. The models were tasked with predicting independent picture naming data, neuropsychological test scores of semantic association and speech production, grammatical categories of formal errors, and lexical properties of target items. In all cases, the processing tree model parameters provided better predictions and stronger associations between parameters and independent language measures than the connectionist simulation model. Given the enhanced generalizability of latent variable measurements afforded by the processing tree model, evidence regarding mechanistic and representational features of the speech production system are re-evaluated. Several areas are indicated as being potentially viable targets for elaboration of the mechanistic descriptions of picture naming errors.

SUBMITTER: Walker GM

PROVIDER: S-EPMC7855206 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Connections and selections: Comparing multivariate predictions and parameter associations from latent variable models of picture naming.

Walker Grant M GM Fridriksson Julius J Hickok Gregory G

Cognitive neuropsychology 20201105 1

Connectionist simulation models and processing tree mathematical models of picture naming have complementary advantages and disadvantages. These model types were compared in terms of their predictions of independent language measures and their associations between model components and measures that should be related according to their theoretical interpretations. The models were tasked with predicting independent picture naming data, neuropsychological test scores of semantic association and spe ...[more]

PMID: 33150843

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Project description:PurposeMeaningful changes in picture naming responses may be obscured when measuring accuracy instead of quality. A statistic that incorporates information about the severity and nature of impairments may be more sensitive to the effects of treatment.MethodWe analyzed data from repeated administrations of a naming test to 72 participants with stroke aphasia in a clinical trial for anomia therapy. Participants were divided into two groups for analysis to demonstrate replicability. We assessed reliability among response type scores from five raters. We then derived four summary statistics of naming ability and their changes over time for each participant: (a) the standard accuracy measure, (b) an accuracy measure adjusted for item difficulty, (c) an accuracy measure adjusted for item difficulty for specific response types, and (d) a distance measure adjusted for item difficulty for specific response types. While accuracy measures address the likelihood of a correct response, the distance measure reflects that different response types range in their similarity to the target. Model fit was assessed. The frequency of significant improvements and the average magnitude of improvements for each summary statistic were compared between treatment groups and a control group. Effect sizes for each model-based statistic were compared with the effect size for the standard accuracy measure.ResultsInterrater and intrarater reliability were near perfect, on average, though compromised somewhat by phonological-level errors. The effects of treatment were more evident, in terms of both frequency and magnitude, when using the distance measure versus the other accuracy statistics.ConclusionsConsideration of item difficulty and response types revealed additional effects of treatment on naming scores beyond those observed for the standard accuracy measure. The results support theories that assume naming ability is decomposable into subabilities rather than being monolithic, suggesting new opportunities for measuring treatment outcomes.Supplemental materialhttps://doi.org/10.23641/asha.17019515.

Project description:BackgroundEvaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions - approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, L0L1 penalisation and L0L2 penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation.MethodsFive large (n4000) genomic datasets were extracted from Gene Expression Omnibus. 'Gold-standard' regression models were trained on subspaces of these datasets ( n4000 , p=500 ). Penalised regression models were trained on small samples from these subspaces ( n∈{25,75,150},p=500 ) and validated against the gold-standard models. Variable selection performance and out-of-sample prediction were assessed. Penalty 'preselection' according to test performance in the other 4 datasets was compared to selection internal cross-validation error minimisation.ResultsL1L2 -penalisation achieved the highest cosine similarity between estimated coefficients and those of gold-standard models. L0L2 -penalised models explained the greatest proportion of variance in test responses, though performance was unreliable in low signal:noise conditions. L0L2 also attained the highest overall median variable selection F1 score. Penalty preselection significantly outperformed selection by internal cross-validation in each of 3 examined metrics.ConclusionsThis analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use of L0L2 penalisation for structural selection and L1L2 penalisation for coefficient recovery in genomic data. Evaluation of learning algorithms according to observed test performance in external genomic datasets yields valuable insights into actual test performance, providing a data-driven complement to internal cross-validation in genomic regression tasks.

Dataset Information

Connections and selections: Comparing multivariate predictions and parameter associations from latent variable models of picture naming.

Publications

Connections and selections: Comparing multivariate predictions and parameter associations from latent variable models of picture naming.

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