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Sensitivity index

The sensitivity index, commonly denoted as d' (d-prime), is a dimensionless statistic in signal detection theory that quantifies an observer's perceptual ability to distinguish a target signal from background noise, independent of any response bias. It is defined as the standardized distance between the means of the signal-plus-noise and noise-only distributions, typically assuming equal variance, and is calculated using the formula d' = z(HR) - z(FAR), where z(HR) is the z-score of the hit rate (correctly identifying the signal) and z(FAR) is the z-score of the false alarm rate (incorrectly identifying noise as signal).^[1]^[2] Signal detection theory, which introduced the sensitivity index, originated from World War II applications in radar signal processing and was adapted to psychophysics in the mid-20th century to address limitations in classical threshold models that conflated sensory sensitivity with decision-making criteria. The seminal work by David M. Green and John A. Swets formalized these concepts in their 1966 book Signal Detection Theory and Psychophysics, establishing d' as a core metric for evaluating detection performance across varying stimulus intensities and observer states. This separation of sensitivity from bias—where bias is measured by the criterion c = -[z(HR) + z(FAR)] / 2—enabled more rigorous experimental designs in sensory research.^[3]^[4] In practice, d' values range from 0 (indicating no discriminability, equivalent to chance performance) to higher magnitudes (e.g., d' ≈ 4 for near-perfect detection), and it is often derived from receiver operating characteristic (ROC) curves, where the area under the curve (A') approximates d' for certain conditions. The index has broad applications beyond basic psychophysics, including auditory and visual perception studies, medical diagnostics (such as imaging interpretation), recognition memory tasks, and even behavioral analysis in non-human animals under operant conditioning paradigms. Its robustness to bias makes it invaluable for assessing real-world detection scenarios, like hazard identification in safety-critical environments.^[1]^[5]^[2]

Introduction

Purpose and Overview

The sensitivity index, commonly denoted as d', serves as a dimensionless statistic in signal detection theory to assess the discriminability between two probability distributions: one for the signal-absent (noise-only) condition and the other for the signal-present condition. It quantifies the separation between the means of these distributions, normalized by the standard deviation, providing a standardized measure of how distinguishable the signal is from noise.^[1] This normalization enables direct comparisons across diverse experimental contexts, such as auditory thresholds or visual detection tasks, without dependence on absolute scale.^[6] Higher values of d' reflect greater separability between the distributions, indicating enhanced detectability of the signal while excluding considerations of response bias, which is addressed separately in the theory. For instance, a d' of 2 suggests the signal mean is two standard deviations away from the noise mean, resulting in minimal overlap and high performance potential.^[7] The index thus emphasizes pure sensitivity, allowing researchers to isolate perceptual acuity from decision-making tendencies.^[1] Although adapted from engineering to psychophysics to model human sensory discrimination, the sensitivity index primarily functions as a tool for statistical discriminability applicable beyond perception, including in machine learning classifiers and medical diagnostics.^[8]^[9] In the univariate case, this is illustrated by two overlapping normal distributions along a single axis, where increasing mean separation reduces overlap (e.g., noise centered at zero and signal shifted rightward). For the bivariate extension, consider two-dimensional normal distributions with means separated along a vector in the plane, highlighting how multidimensional evidence enhances overall discriminability when covariances are equal.^[10]

Relation to Signal Detection Theory

Signal detection theory (SDT) provides a framework for analyzing an observer's ability to detect a signal embedded in noise, where the task involves distinguishing trials with signal-plus-noise from those with noise alone.^[3] In this context, performance is quantified using hit rates, which represent the proportion of signal-present trials correctly identified, and false alarm rates, which indicate the proportion of noise-alone trials incorrectly classified as signal-present.^[3] Receiver operating characteristic (ROC) curves plot hit rates against false alarm rates across varying decision criteria, offering a visual summary of detection performance that remains invariant to changes in observer bias.^[3] SDT has roots in radar engineering applications during World War II and emerged more fully in the 1950s, with further development in the 1960s during the early Cold War era to improve signal detection amid electronic noise; it was subsequently adapted to psychophysics to model perceptual thresholds more robustly than classical methods.^[9] Key contributions came from researchers like David M. Green and John A. Swets, whose 1966 work formalized the theory's application to human sensory processes, emphasizing the role of internal noise and decision-making.^[3] Within SDT, discriminability refers to the observer's capacity to separate the underlying signal-plus-noise and noise-alone distributions, measured independently of response bias, which reflects tendencies to favor one response category over another.^[3] Under the assumption of equal variances for these distributions, discriminability can be estimated using the Z-transform, where the inverse cumulative normal distribution is applied to the hit rate and false alarm rate, and their difference yields a bias-free index.^[6] This approach, d', serves as a summary statistic for ROC curve performance in the equal-variance case.^[3]

Basic Assumptions and Equal Variance Case

Univariate Equal Variance Definition

In signal detection theory, the univariate equal variance case assumes that the internal responses to signal-present (A) and signal-absent (B) stimuli follow univariate normal distributions with equal standard deviations, providing the foundational model for measuring sensitivity. The sensitivity index, denoted as d', quantifies the observer's ability to discriminate between these two distributions and is defined as the absolute difference in their means divided by the common standard deviation:

d' = \frac{|\mu_A - \mu_B|}{\sigma}

where \mu_A and \mu_B are the means of the signal-plus-noise and noise-alone distributions, respectively, and \sigma is the shared standard deviation.^[11]^[12] This formulation arises from the optimal decision process in which the observer compares the likelihood of a response arising from either distribution, assuming Gaussian noise.^[11] The value of d' inversely reflects the overlap between the two distributions: d' = 0 indicates complete overlap and no discriminable difference, while higher values signify greater separation and better sensitivity, with d' > 3 typically denoting excellent discriminability in psychophysical tasks.^[11]^[6] For instance, consider two normal distributions where \mu_A = 1, \mu_B = 0, and \sigma = 0.5; here, d' = |1 - 0| / 0.5 = 2, representing substantial but not maximal separation suitable for many detection scenarios.^[12] Empirically, d' is often estimated from observed response rates without direct knowledge of the underlying parameters, using the approximation d' ≈ Z(H) - Z(F), where H is the hit rate (correctly identifying signals), F is the false alarm rate (incorrectly identifying noise as signals), and Z denotes the inverse cumulative distribution function of the standard normal distribution.^[13] This estimation assumes the equal variance Gaussian model and allows sensitivity to be separated from response bias in experimental data.

Multivariate Equal Variance Extension

The multivariate equal variance extension of the sensitivity index d' generalizes the univariate case to scenarios where perceptual representations are modeled as multivariate normal distributions with equal covariance matrices across stimulus classes. In this framework, known as General Recognition Theory (GRT), the sensitivity index measures the discriminability between two stimuli, such as signal-plus-noise (A_2) and noise-only (A_1), by accounting for the full covariance structure of the perceptual space. The formula for the multivariate d' is given by the square root of the quadratic form involving the difference in mean vectors and the inverse of the common covariance matrix S:

d' = \sqrt{(\boldsymbol{\mu}_a - \boldsymbol{\mu}_b)^T S^{-1} (\boldsymbol{\mu}_a - \boldsymbol{\mu}_b)},

where \boldsymbol{\mu}_a and \boldsymbol{\mu}_b are the mean vectors for the two distributions, and this expression corresponds to the Mahalanobis distance between the means, standardized to reflect perceptual separability under equal covariances.^[14] This measure reduces to the univariate d' when the dimensionality is 1, as the covariance matrix simplifies to a scalar variance. In the bivariate case, assuming unit variances along each dimension, the squared sensitivity index expands to

{d'}^2 = \frac{{d'_x}^2 + {d'_y}^2 - 2\rho d'_x d'_y}{1 - \rho^2},

where \rho is the correlation coefficient between the dimensions, and d'_x and d'_y are the univariate sensitivity indices along each dimension. This formula illustrates how correlations influence overall discriminability: positive \rho introduces redundancy, reducing d' compared to the uncorrelated case (\rho = 0), while negative \rho can enhance it by providing complementary information across dimensions.^[14] Computationally, evaluating multivariate d' requires inverting the covariance matrix S, which is straightforward and efficient for low-dimensional spaces (e.g., 2–5 dimensions) using standard linear algebra routines, though it becomes more demanding as dimensionality increases due to the O(n^3) cost of inversion.

Unequal Variance Cases

Overview of Challenges

In real-world applications of signal detection theory (SDT), the assumption of equal variances between signal and noise distributions often fails to hold, as empirical data frequently exhibit heteroscedasticity—unequal variances—or differing covariances across conditions.^[15] This discrepancy arises in diverse domains, such as psychophysics and recognition memory, where factors like stimulus variability, encoding processes, or environmental noise lead to greater spread in signal responses compared to noise.^[16] Forcing the equal-variance formula for the sensitivity index d' onto such data results in biased estimates, as the metric no longer accurately reflects the true separation between distributions.^[15] The consequences of this assumption are particularly pronounced in classification tasks, where it can lead to systematic underestimation of discriminability. For instance, in psychophysical experiments involving detection of faint visual or auditory signals amid background noise, the signal distribution's variance often exceeds that of the noise, causing the equal-variance d' to underestimate performance relative to more accurate measures.^[15] Similarly, in memory tasks, unequal variances between "old" and "new" item strengths distort sensitivity estimates, skewing interpretations of perceptual or cognitive efficiency and potentially misleading conclusions about response criteria.^[16] To address these limitations, alternative sensitivity indices have been developed that accommodate unequal variances through distribution-specific or pooled statistics, such as the Bayes discriminability index, RMS standard deviation discriminability index, and average standard deviation discriminability index.^[15] These approaches extend beyond the equal-variance baseline by directly incorporating variance differences, providing more robust metrics for scenarios where the standard d' proves inadequate. At its core, optimal discriminability in SDT is defined by the Bayes error rate—the minimal achievable classification error under ideal decision rules—offering a theoretical benchmark for any pair of distributions.^[15] However, computing this exactly requires numerical approximations for tractability, especially in multivariate or unequal-variance cases, motivating the use of specialized indices as practical proxies.^[15]

Key Indices for Unequal Variances

In cases of unequal variances between signal and noise distributions, traditional equal-variance sensitivity indices like d' become inadequate, prompting the development of alternative discriminability measures that account for differing spreads.^[15] The three primary indices for such scenarios are the Bayes discriminability index, the RMS standard deviation discriminability index, and the average standard deviation discriminability index, each offering distinct conceptual approaches to quantifying separability.^[15] The Bayes discriminability index represents an optimal measure, derived from the minimum error rate achievable using the Bayes decision rule, which minimizes classification errors by integrating over the posterior probabilities of the distributions.^[15] In contrast, the RMS standard deviation discriminability index employs a root-mean-square pooling of the variances to approximate the effective spread, providing a symmetric gauge of separation that normalizes the mean difference by this pooled metric.^[15] The average standard deviation discriminability index, meanwhile, uses the arithmetic mean of the individual standard deviations as the normalizing factor, offering a simpler aggregation that balances the variances without squaring.^[15] These indices extend naturally to multivariate settings by incorporating pooled or averaged covariance matrices in place of scalar variances, allowing assessment of discriminability across multiple dimensions such as in visual or sensory signal detection tasks.^[15] A key shared property is their symmetry: swapping the signal and noise distributions yields the same index value, ensuring consistent measurement regardless of labeling.^[15] Selection among them depends on computational feasibility and context; the Bayes index is favored for its precision when error rates can be accurately computed, while the RMS and average approximations suit high-dimensional cases where exact integration is prohibitive.^[15]

Detailed Definitions of Unequal Indices

Bayes Discriminability Index

The Bayes discriminability index, denoted d'_b, serves as the optimal measure of separability between two probability distributions in signal detection theory, particularly when variances are unequal, by quantifying the minimum achievable classification error using the Bayes decision rule. This index corresponds to the value of the standard equal-variance discriminability d' that would produce the same overlap (Bayes error) if the distributions had equal unit variance.^[15] The index is formally defined as d'_b = 2 Z(A_b), where A_b is the maximum accuracy rate (1 minus the Bayes error rate under equal priors), and Z denotes the inverse cumulative distribution function of the standard normal distribution; equivalently, d'_b = -2 Z(E_b), with E_b as the Bayes error rate. For two univariate normal distributions with means \mu_a, \mu_b and standard deviations \sigma_a, \sigma_b, the Bayes error is computed as the average of the misclassification probabilities across the quadratic decision boundary. Assuming \mu_b > \mu_a, the boundary is found by solving the quadratic equation from setting the likelihoods equal: x = \frac{1}{2(\sigma_b^2 - \sigma_a^2)} \left[ (\mu_b^2 - \mu_a^2) + (\sigma_a^2 - \sigma_b^2)(\log(\sigma_b^2 / \sigma_a^2)) \pm \sqrt{ \text{discriminant} } \right], selecting the relevant root, and then

E_b = \frac{1}{2} \left[ \Phi\left( \frac{\text{boundary} - \mu_a}{\sigma_a} \right) + \Phi\left( \frac{\mu_b - \text{boundary}}{\sigma_b} \right) \right],

where \Phi is the standard normal CDF; numerical methods are generally required to obtain d'_b.^[15] In multivariate settings, d'_b extends naturally to distributions with arbitrary means \boldsymbol{\mu}_a, \boldsymbol{\mu}_b and full covariance matrices \mathbf{\Sigma}_a, \mathbf{\Sigma}_b without any pooling of variances, relying on the likelihood ratio to define a quadratic decision boundary \beta(\mathbf{x}) = \mathbf{x}^T \mathbf{Q}_2 \mathbf{x} + \mathbf{q}_1^T \mathbf{x} + q_0 = 0. The Bayes error is then evaluated through numerical integration of the multivariate normals over the regions defined by this boundary, using efficient techniques such as ray-tracing along rays from the origin or decomposition into generalized chi-square distributions to handle the non-central quadratic forms. This computation preserves the index's symmetry—d'_b remains unchanged when swapping the two distributions—and its invariance to invertible linear transformations of the feature space, ensuring it captures intrinsic separability regardless of scaling or rotation.^[15] For illustration in univariate unequal-variance scenarios where \sigma_a \neq \sigma_b, d'_b outperforms the conventional equal-variance d' = (\mu_b - \mu_a) / \sqrt{(\sigma_a^2 + \sigma_b^2)/2} by accounting for the true optimal boundary, exceeding it by up to 30% in cases of moderate distributional overlap.^[15] The relation to the receiver operating characteristic (ROC) curve underscores d'_b's interpretability: the area under the optimal ROC (likelihood ratio-based) approximates \Phi(d'_b / 2), mirroring the equal-variance case, yet d'_b offers a direct, error-grounded metric without relying on curve fitting.^[15]

RMS Standard Deviation Discriminability Index

The RMS standard deviation discriminability index, denoted as d'_a, serves as a conservative heuristic for estimating discriminability under unequal variance assumptions in signal detection theory. In the univariate case, it is computed as d'_a = \frac{|\mu_a - \mu_b|}{\sigma_{\text{rms}}}, where \sigma_{\text{rms}} = \sqrt{\frac{\sigma_a^2 + \sigma_b^2}{2}} represents the root mean square of the standard deviations from the two distributions, effectively pooling their variances in a quadratic manner. For multivariate extensions, the index employs the Mahalanobis distance with a pooled covariance matrix \mathbf{\Sigma}_{\text{rms}} = \frac{\mathbf{\Sigma}_a + \mathbf{\Sigma}_b}{2}, defined as d'_a = \sqrt{ (\mu_a - \mu_b)^\top \mathbf{\Sigma}_{\text{rms}}^{-1} (\mu_a - \mu_b) }. This averaging of covariance matrices preserves the RMS pooling principle, providing a single effective metric for multidimensional discriminability while assuming Gaussian distributions. This index tends to underestimate the true discriminability d'_b (the optimal Bayes index minimizing classification error), with the degree of underestimation increasing as variances diverge. For instance, when \sigma_a \gg \sigma_b, d'_a \approx \frac{|\mu_a - \mu_b|}{\sigma_a / \sqrt{2}}, effectively penalizing the larger variance and yielding a lower bound on performance. Theoretical comparisons indicate biases of 10-30% in scenarios with substantial variance differences.^[17] Key advantages include its computational efficiency—no iterative integration or optimization is required—and applicability for rapid evaluations in experimental designs where exact Bayes computation is impractical. As a heuristic approximation to the Bayes index, it balances simplicity with reasonable accuracy for moderately unequal variances. However, it is suboptimal for extreme imbalances, potentially leading to overly conservative interpretations of sensitivity.

Average Standard Deviation Discriminability Index

The average standard deviation discriminability index, denoted as d'_e, provides a measure of discriminability between two distributions with unequal variances by normalizing the mean difference by the arithmetic average of the standard deviations. In the univariate case, it is defined as

d'_e = \frac{|\mu_a - \mu_b|}{\sigma_{\text{avg}}},

where \sigma_{\text{avg}} = (\sigma_a + \sigma_b)/2. This index reduces to the standard equal-variance d' when \sigma_a = \sigma_b. For multivariate distributions, the index extends via the Mahalanobis distance using an averaged covariance structure, where the covariance matrix is \mathbf{\Sigma}_{\text{avg}} = (\mathbf{\Sigma}_a + \mathbf{\Sigma}_b)/2, defined as d'_e = \sqrt{ (\boldsymbol{\mu}_a - \boldsymbol{\mu}_b)^\top \mathbf{\Sigma}_{\text{avg}}^{-1} (\boldsymbol{\mu}_a - \boldsymbol{\mu}_b) }. This approach pools the variability in a linear manner, facilitating computation in higher-dimensional settings such as visual search tasks. The index d'_e serves as a balanced approximation to the optimal Bayes discriminability index d'_b, outperforming the root mean square (RMS) variant in scenarios of moderate discriminability by providing a less conservative estimate of separation. As the discriminability d' increases, d'_e converges toward d'_b, reflecting improved alignment with ideal observer performance under growing signal strength. For illustration, consider univariate normal distributions with \sigma_a = 1, \sigma_b = 2, and mean difference |\mu_a - \mu_b| = 2: then \sigma_{\text{avg}} = 1.5, yielding d'_e \approx 1.33, compared to the RMS index value of approximately 1.26. This index is particularly suitable for applications where variances differ moderately, as it avoids the greater underestimation of the RMS approach in such cases, and its linear averaging simplifies extensions to non-normal or approximated distributions in perceptual modeling. Early comparisons of detectability indices highlighted the utility of such averaged measures for robust sensitivity estimation across variance conditions.^[18]

Comparing the Indices

Performance in Univariate Scenarios

In univariate scenarios involving two normal distributions with unequal variances, the discriminability indices satisfy the inequality d'_a \leq d'_e \leq d'_b, where d'_a is the average standard deviation discriminability index (Simpson & Fitter, 1973), d'_e is the unequal-variance extension based on average or harmonic mean standard deviations (Egan & Clarke, 1963), and d'_b is the Bayes discriminability index; equality holds when the standard deviations are identical (\sigma_a = \sigma_b).^[19] This ordering reflects the relative conservatism of each index in capturing the true separability, with d'_a providing a lower bound based on pooled variance assumptions and d'_b offering the upper bound derived from optimal Bayesian classification error minimization.^[19]^[20] Quantitatively, d'_a underestimates d'_b by up to 30% in cases of highly unequal variances, particularly at high discriminability, while d'_e provides a close approximation to d'_b at high discriminability values.^[19] Simulations of univariate normal distributions confirm these bounds; for instance, when d'_b = 1, typical values are d'_a \approx 0.8 and d'_e \approx 0.9, illustrating the progressive tightening of estimates from d'_a to d'_b.^[19] These empirical results, generated through Monte Carlo methods varying means and variances, highlight how variance inequality amplifies discrepancies at lower separability levels but diminishes them at higher ones.^[19] Given these performance characteristics, d'_b is recommended for precise analysis in univariate data where computational resources allow exact integration of the distributions, as it maximizes the expected gain in classification accuracy.^[19] In contrast, d'_e serves as a reliable approximation for routine univariate applications, particularly when quick estimates are needed without full Bayesian computation.^[19]

Applicability in Multivariate Settings

In multivariate settings, the inequalities relating the discriminability indices—specifically d'_a \leq d'_e \leq d'_b—generally hold, mirroring univariate bounds, though correlations among dimensions can widen or narrow the gaps between them depending on the alignment of mean differences and covariance structures. For instance, high inter-dimensional correlation \rho tends to reduce distributional overlap when means differ along correlated axes, bringing d'_e (the average standard deviation index) closer to d'_b (the Bayes discriminability index) compared to uncorrelated cases, where d'_e may underestimate d'_b by up to 20-30% in moderate separations. This effect arises because correlations effectively compress or expand the Mahalanobis-like separation in the pooled or averaged covariance spaces used by d'_a and d'_e.^[21] Computationally, evaluating d'_b requires integrating the Bayes error rate over complex quadratic decision boundaries, achievable up to around 4-5 dimensions using efficient ray-tracing algorithms or generalized chi-squared distributions, with runtimes on the order of seconds to ~40 seconds on standard hardware for 3D cases; higher dimensions necessitate Monte Carlo sampling. In contrast, d'_a and d'_e rely on simpler matrix operations involving averaged or root-mean-square covariance estimates, scaling linearly with dimensionality and remaining feasible in hundreds of dimensions without approximation. These trade-offs favor d'_b for accuracy in low-to-moderate dimensions but necessitate Monte Carlo sampling or dimensionality reduction for high-dimensional applications to maintain precision.^[21] Selection of an index in multivariate data with correlations depends on the analysis goals: d'_a, using the root-mean-square of pooled covariances, offers conservative discriminability estimates suitable for correlated psychophysical data where overestimation must be avoided, while d'_b enables precise receiver operating characteristic (ROC) curve modeling in low-dimensional settings like visual search tasks. Building briefly on univariate foundations, multivariate correlations introduce additional variance in index comparisons, emphasizing the need for covariance-aware choices to avoid biasing sensitivity assessments.^[21] Literature on these indices as of 2021 predominantly assumes multivariate normality, leaving gaps in handling non-normal distributions; ongoing work explores numerical methods for non-normal extensions.^[21]

Advanced Applications

Dimensional Contributions to Discriminability

In multivariate settings, the discriminability of two distributions, such as multivariate normal distributions with unequal covariances, can be decomposed to assess the unique contribution of each dimension to the overall separability. This decomposition is particularly useful for understanding how individual features add to the total Bayes discriminability index d', which represents the optimal classification performance under Bayesian decision theory (Hou & Liu, 2020).^[22] The contribution of dimension i is quantified as

\sqrt{d'^2 - d'_{-i}^2}

, where d' is the full discriminability across all dimensions, and d'_{-i} is the discriminability computed after marginalizing out (i.e., removing) dimension i from the distributions.^[22] This approach accounts for the full covariance structure, including correlations between dimensions, by recalculating the Bayes error rate or equivalent metric without the specified dimension.^[22] The formula measures the unique added value of a dimension beyond what is provided by the others, revealing redundancies when features are correlated. For instance, if dimensions are independent, the contribution approximates the univariate d'_i; however, with positive correlations, the contribution is typically less than d'_i because shared information reduces the marginal gain.^[22] In applications to feature selection for classification tasks, this metric helps identify dimensions that most enhance separability, aiding in dimensionality reduction while preserving discriminative power. By leveraging the inverse covariance matrix in the computation of d', it directly incorporates inter-dimensional dependencies, making it suitable for perceptual modeling or machine learning scenarios involving high-dimensional data. In asymmetric cases, such as where one dimension has higher univariate d', the contributions can vary further, emphasizing partial redundancy under high correlations.^[22]

Scaling Methods for Distributions

One common technique for adjusting discriminability measures to match observed task performance involves interpolating between two known Gaussian distributions representing signal-present (b) and signal-absent (a) conditions (Thurstonian scaling; Lahne et al., 2025).^[23] The mean vector is linearly interpolated as \mu(\theta) = (1 - \theta) \mu_a + \theta \mu_b, and the covariance matrix via convex combination S(\theta) = (1 - \theta) S_a + \theta S_b, where \theta \in [0, 1]. The parameter \theta is then numerically solved such that the resulting discriminability d', defined as the Mahalanobis distance between the interpolated distributions, equals the target value derived from empirical data. This method facilitates the generation of intermediate stimulus conditions with controlled difficulty levels, preserving the multivariate structure of the distributions while enabling precise calibration.^[23] Another approach focuses on scaling the decision variable in signal detection models to align with observed behavioral outcomes. The log likelihood ratio boundaries are adjusted by shifting the decision criterion c = -\frac{z(H) + z(F)}{2}, where z(H) and z(F) are the z-scores of hit and false alarm rates, to fit empirical hit and false alarm rates from experimental data (Maniscalco et al., 2024).^[24] This scaling ensures the model predicts the observed response bias without altering the underlying sensitivity d'. Such adjustments are essential for accounting for variations in task demands or observer strategies.^[24] These scaling methods find applications in calibrating computational models for detection tasks, particularly in neuroscience where they bridge theoretical predictions with empirical accuracy. For instance, in visual perception experiments, interpolated distributions are used to scale d' to match neuronal response patterns or behavioral thresholds observed in fMRI studies, allowing researchers to validate models of sensory processing (e.g., in ensemble-based visual change detection; Shen & Ma, 2024).^[25] In multivariate settings, the convex combination preserves covariance structure, making the techniques suitable for high-dimensional data analysis, such as neural population responses.^[25]

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