arXiv subjects

Harrison J. Goldwyn

Publications and source records attributed to Harrison J. Goldwyn.

4 recordsLinked to original sources

arXiv2026

Beyond the Post Hoc User Study: Modeling Visual Decision-Making with Active Inference

Empirical user studies are essential for evaluating visual encodings and can reveal perceptual and cognitive mechanisms, but they do not by themselves provide causal, predictive accounts of interpretation errors. Evaluations are therefore often post hoc: they measure performance after a design has been specified rather than predicting how attention, uncertainty, memory, and bias may produce accurate or erroneous judgments. To address this mechanistic gap, we translate a cognitive theory of visualization interpretation into executable simulation using Active Inference, a probabilistic framework for perception, learning, and action. We model chart reading as dynamic visual search in which agents update beliefs and choose actions that balance uncertainty reduction against cognitive effort. As a proof of concept, we implement Fast, heuristic (Type 1) and Slow, analytic (Type 2) agents for a bar-chart average-estimation task. The Fast agent is vulnerable to tick-salience bias, whereas the Slow agent is more vulnerable to working-memory decay. Both produce inspectable cognitive traces, including evolving belief uncertainty and fixation sequences. By expressing these hypothesized failure mechanisms as interpretable parameters, the architecture provides a framework for formalizing and testing mechanistic hypotheses about visualization interpretation. Empirical studies can then parameterize, refine, or falsify these simulations, supporting earlier and more predictive in silico evaluation of visualization efficacy.

cs.HC↗

arXiv2025

Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating closed-form predictive distributions over outputs with non-identically distributed and heteroscedastic structure. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy -- referred to as information sharing -- that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

cs.LG↗

arXiv2021

Resolving resonance effects in the theory of single particle photothermal imaging

Photothermal spectroscopy and microscopy provides a route to measure the spectral and spatial properties of individual nanoscopic absorbers, independent from scattering, extinction, and emission. The approach relies upon use of two light sources, one that resonantly excites and heats the target and its surrounding environment and a second off-resonant probe that scatters from the resulting volume of thermally modified refractive index. Over the past twenty years, considerable effort has been extended to apply photothermal methods to detect, spatially resolve, and perform absorption spectroscopy on single non-emissive molecules and other absorbers like plasmonic nanoparticles at room temperature conditions. Companion theoretical models have been developed to interpret these experimental advances, yet it is not clear how they are related to each other nor how the effects of lock-in detection modify the theory. For larger target systems that host their own intrinsic scattering resonances as well as for background media that do not instantaneously thermalize with the absorbing target, additional dependencies arise that are yet to be explored theoretically. The aim of this Perspective is to overview the theory of photothermal spectroscopy and microscopy and present a unifying theoretical approach that recovers past models in certain limits while explicitly including the effects of target scattering resonances, thermal and optical retardation, and lock-in detection. Focus is made on plasmonic particles to interpret the photothermal signal, yet all results are applicable equally to individual molecules or nanoparticle absorbers. Consequently, we expect this review to provide a useful foundation for the understanding of photothermal measurements independent of target identity.

physics.optics↗

arXiv2021

Wavelength-dependent photothermal imaging probes nanoscale temperature differences among sub-diffraction coupled plasmonic nanorods

While the thermal and electromagnetic properties of plasmonic nanostructures are well understood, nanoscale thermometry still presents an experimental and theoretical challenge. Plasmonic structures can confine electromagnetic energy at the nanoscale, resulting in local, inhomogeneous, controllable heating. But reading out the temperature with nanoscale precision using optical techniques poses a difficult challenge. Here we report on the optical thermometry of individual gold nanorod trimers that exhibit multiple wavelength-dependent plasmon modes resulting in measurably different local temperature distributions. Specifically, we demonstrate how photothermal microscopy encodes different wavelength-dependent temperature profiles in the asymmetry of the photothermal image point spread function. These point spread function asymmetries are interpreted through companion numerical simulations of the photothermal images to reveal how differing thermal gradients within the nanorod trimer can be controlled by exciting its hybridized plasmonic modes. We also find that hybrid plasmon modes that are optically dark can be excited by our focused laser beam illumination geometry at certain beam positions, thereby providing an additional route to modify thermal profiles at the nanoscale beyond wide-field illumination. Taken together these findings demonstrate an all-optical thermometry technique to actively create and measure thermal gradients at the nanoscale below the diffraction limit.

physics.optics↗