PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 7566899

Vision thresholds from psychometric analyses: alternatives to probit analysis.

T L Simpson
View original source ↗Export metadata ↓

Abstract

Vision scientists and clinicians regularly obtain measures of vision and determine a person's vision threshold by choosing one of numerous methods of analysis. Analytical methods differ in their presumptions about the measures, in their complexity and in the ease of obtaining the threshold estimate. In light of these differences among so-called methods of psychometric analysis, the question is, "Do various analytical methods provide essentially equivalent vision threshold estimates or are there consequential differences that require consideration?" Measures of visual acuity, motion processing, and texture processing were obtained from 20 subjects. Each set of measures was analyzed by five psychometric methods: Logit Analysis, Normit Analysis, and linear regression of z-score transformed, logit transformed and untransformed probabilities vs. stimulus strength. The resulting thresholds were compared to the threshold obtained from Probit Analysis, which was used as a reference or "gold standard." Thresholds from the procedures were remarkably similar to those from Probit Analysis. In addition, examination of the speed of the procedures revealed that Probit Analysis was up to 10 times slower than some of the others. Considering the similarity of thresholds, the speed of computation, and the ease of implementation, Logit and Normit Analyses especially provide effective alternatives to the current gold standard, Probit Analysis, for the estimation of psychometric thresholds. In addition, z-score, logit, and linear regressions also produced unbiased threshold estimates under many circumstances, but the latter method should be applied with some caution.

Explore related subjects

AlgorithmsForm PerceptionHumansMotion PerceptionPsychometricsRegression AnalysisSensory ThresholdsSoftwareVision, OcularVisual AcuityVisual PerceptionJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T L Simpson. 1995. Vision thresholds from psychometric analyses: alternatives to probit analysis.. https://doi.org/10.1097/00006324-199506000-00004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗