Visual Neglect Detector in Clinical & AI Systems

• Visual Neglect Detector is a system that identifies and quantifies overlooked visual cues in clinical assessments, deep vision modules, and multimodal language models.
• It employs techniques like Gaussian process regression, binary MLP classifiers, and logistic regression probes to detect and mitigate neglect-related failures.
• Its applications span tele-rehabilitation, autonomous safety, and hallucination reduction in AI, demonstrating improved diagnostic precision and reliability.

A Visual Neglect Detector (VND) is a system designed to detect, quantify, or compensate for the phenomenon of visual neglect or missed visual events. The term encompasses technical solutions in clinical neuroscience (diagnosis/treatment of visuospatial neglect in patients), deep vision systems (detection of missed or overlooked objects in autonomous perception pipelines), and multimodal LLMs (monitoring or repairing failures in the model’s attention to visual evidence). VNDs operationalize the concept of “neglect”—either as human- or model-internal failure to attend to pertinent visual information—through algorithmic probes, predictive modeling, or feature-mining across a range of domains.

1. Theoretical and Clinical Definition of Visual Neglect

Visual neglect, or hemispatial neglect, is a clinical syndrome predominantly associated with right-hemisphere stroke or cerebral injury, characterized by impaired awareness or inattention to stimuli presented in specific spatial regions, typically the contralesional side. It manifests as spatially biased search, failures to report targets in certain visual quadrants, and reduced engagement with affected peripersonal and extrapersonal spaces. Assessment historically relies on paper-based tests (e.g., line bisection, cancellation tasks) but more recent approaches integrate virtual environments and AI to capture deficits in complex, ecologically valid conditions. In computational modeling, “visual neglect” refers to a system’s failure to leverage evidence present in its visual input—a phenomenon observable in object detectors, vision-LLMs, or clinical diagnostic platforms (Boi et al., 2023, Sun et al., 3 Dec 2025, Rahman et al., 2019).

2. Visual Neglect Detector Architectures in Clinical Assessment

A clinical VND is exemplified by a system employing Gaussian process regression (GPR) and active learning in a VR-based assessment of visuospatial neglect (Boi et al., 2023). Here, the field of view is discretized into sampling locations $(x_i \in \mathbb{R}^2)$, at each of which reaction/search time $y_i$ serves as a surrogate for neglect:

• The response model is $y_i = f(x_i) + \epsilon_i$, with $f$ drawn from a GPR prior with squared-exponential kernel.
• After initial random or Sobol-sequence sampling, active learning proceeds by uncertainty sampling, i.e., selecting the next stimulus location $x_{next}$ maximizing the posterior variance $\text{Var}[f(x)|D]$.
• Each assessment session generates a heatmap of mean search times $\mu(x)$ and map uncertainty $\sigma(x)$, used to quantify spatially localized neglect.
• Trial structure involves participants donning an HMD with integrated eye tracking and searching for color-coded 3D targets. Metrics such as SAM for gaze-ray, head rotation, and eye rotation are derived and scored.
• Validation involves ROC analysis and test–retest reliability in a cross-sectional sample (healthy controls, stroke without neglect, stroke with neglect).

This platform demonstrated sensitivity/specificity at least as high as, and sometimes surpassing, conventional assessment tools, while supporting integration with tele-rehabilitation and personalized therapy protocols.

3. Visual Neglect Detection in Deep Vision Object Detectors

In the machine vision context, the VND concept is formalized as a False Negative Detector (FND) attached to a one-stage object detector such as SSD, YOLOv3, or RetinaNet (Rahman et al., 2019). The FND architecture is structured as follows:

• The object detector provides a set of intermediate activation volumes $F^{(j)}$ from multiple stages, which are spatially resized and stacked into a tensor $\mathcal{V} \in \mathbb{R}^{64 \times 64 \times K}$.
• During training, regions not covered by accepted detections ($d_k$ with score $\geq \lambda$) are identified by thresholding a max-pooled 2D excitation map $E$, then binarized and grouped into candidate regions $R_i$.
• Each candidate region $R_i$ is labeled based on its maximum IoU with ground-truth boxes: “failure” if $\mathrm{IoU}(R_i,g) \geq \gamma$ (typically $\gamma=0.5$), otherwise “imposter.”
• Region features $\mathbf{f}_i$ are formed by channel-wise max-pooling inside $R_i$: $f_i[c] = \max_{(x,y)\in R_i} \mathcal{V}(x,y,c)$.
• A binary classifier $\mathcal{B}$ (typically a 3-layer MLP) is trained to distinguish failures from imposters using cross-entropy loss.
• At inference, the FND processes the same feature stack and raises alarms for regions where $\mathcal{B}(\mathbf{f}_i) \geq \tau$.
• Performance is measured by precision and recall over alarmed regions with respect to missed detections in held-out test sets.

Quantitative benchmarks demonstrate FND outperforms baselines under both nominal and degraded visual conditions, achieving up to 89.9% precision at 80% recall on the Belgium Traffic Sign Detection dataset (Rahman et al., 2019).

4. VNDs in Multimodal LLMs

Recent advances in multimodal LLMs have surfaced a new manifestation of visual neglect, defined as systematic underweighting or disregard of visual tokens, leading to hallucinated or image-inconsistent outputs (Sun et al., 3 Dec 2025). The Visual Neglect Detector in this context is formulated as follows:

• For each input $X$, per-head activations $o_l^h$ are computed; these are mean-pooled to yield a feature vector $f_l^h \in \mathbb{R}^D$.
• Each head $(l,h)$ is attached to a logistic regression probe parameterized by $\theta_l^h$, outputting $p_l^h(X) = \sigma(\theta_l^{h\, T} f_l^h)$ (probability of neglect).
• The probe is trained on pairs $(X,c)$ with $c=1$ for perturbed (e.g., visually noised) samples and $c=0$ for clean samples, using binary cross-entropy loss.
• Probe thresholds (typically $\tau = 0.5$) demarcate neglect versus non-neglect. Heads with top validation accuracy (e.g., top 10%) are selected for intervention.
• Integration with a Visual Recall Intervenor allows gating the head output $o_l^h$ with a normalized activation $\mu_l^h$ precomputed over true vision tokens, but only when $p_l^h$ signals neglect, thereby preventing spurious interventions.

This module is shown to mitigate hallucination in MLLMs across multiple benchmarks by conditionally enforcing visual grounding only when neglect is detected, providing a principled architectural point for “when-to-intervene” decisions (Sun et al., 3 Dec 2025).

5. Comparative Summary of VND Methodologies and Metrics

Context	Core Model/Probe	Detection Signal
Clinical Neuroscience (Boi et al., 2023)	Gaussian process regression, active learning	Search times mapped over $2$D field, heatmap of neglect
Deep Vision (Rahman et al., 2019)	Feature mining + binary MLP classifier	Excited intermediate feature-map regions not detected
Multimodal LLMs (Sun et al., 3 Dec 2025)	Per-head pooled activation probe, logistic regression	Head activation probe on vision token representations

All approaches share a model-internal monitoring mechanism—either of neural activations, prediction errors, or behaviorally relevant responses. Evaluation employs application-specific metrics (ROC/AUC, precision/recall, intra-rater reliability, specificity) but converges on the core principle of associating “neglect” with model failures to exploit available evidence.

6. Applications and Integration in Deployment Pipelines

VNDs address safety, reliability, and diagnostic needs across domains:

• In autonomous systems, FNDs serve as fail-safe modules capable of flagging potentially catastrophic detector oversights prior to downstream decision-making (e.g., vehicular navigation).
• Clinical VNDs integrate with tele-rehabilitation platforms, supporting both real-time assessment and adaptive therapy through stimulus cueing at estimated neglect borders. Data is uploaded for remote monitoring and adjustment.
• In multimodal LLMs, VND modules prevent vision-based hallucinations by regulating cross-modal flows only when evidence of neglect is algorithmically established, thereby minimizing overcorrection and computational cost.

A plausible implication is that as deployment environments and models grow in complexity, VNDs will constitute a critical component of trustworthy and interpretable AI systems, especially in safety-critical and high-stakes human–machine interaction contexts.

7. Quantitative Performance and Reliability

• Clinical VND: Sensitivity and specificity in VR-based assessment (.e.g., SAM_GR at P2: sensitivity = 80%, specificity ≈ 33%; AUC > 0.80 for multiple metrics). High intra-rater reliability, minimal cybersickness, and fine-grained behavioral logging (Boi et al., 2023).
• Deep vision FND: At 80% recall, achieved up to 89.9% (BTSD normal) and 90.8% (GTSDB normal) precision; precision degrades under rain/fog but outperforms proposal-based and uncertainty-based baselines (Rahman et al., 2019).
• MLLM VND: Probe accuracy peaked at 87.6% in selected heads; systematic reduction in hallucination frequency and preservation of downstream task accuracy across diverse benchmarks (Sun et al., 3 Dec 2025).

This suggests that the VND principle, instantiated through distinct modality- and architecture-specific designs, yields robust improvements to both diagnostic accuracy and system safety in a broad range of visual and multimodal applications.