Biology subjects

Callahan-Flintoft, C.

Publications and source records attributed to Callahan-Flintoft, C..

7 recordsLinked to original sources

bioRxiv2026

Delaying the onset of aided target recognition highlights allows for a more dispersed allocation of overt attention

Visual search is a critical component of many professions such as military operations, baggage screening, and radiology. Aided Target Recognition (AiTR) systems are designed to highlight potential threats across the operators visual field in real-time, directing attention and improving accuracy. However, these systems may impact search and, consequently, situational awareness by diverting attentional resources from non-highlighted, yet relevant, locations. Previous work suggests that scene gist is extracted within the first 250 ms of scene onset (Vo & Henderson, 2010). As such, this study examined whether a 250 ms AiTR onset delay could encourage a more even distribution of attention. Participants searched synthetically generated scenes and classified each person in the scene as armed or unarmed. Depending on their condition, participants either saw the scenes unaugmented (No AiTR condition), with AiTR highlights consisting of red bounding boxes around armed people and yellow boxes around unarmed (AiTR condition), or with AiTR highlights presented 250 ms post scene onset (Delayed AiTR condition). A surprise memory test of background objects presented in the search scenes was administered to all participants upon completion of the search task. As predicted and preregistered, results showed less overt attentional deployment to background information (anything other than the people themselves) in the AiTR condition compared to No AiTR, however, decreased overt attentional deployment was not seen in the Delayed AiTR group. A similar pattern was observed in the memory data (with the AiTR condition having a lower score than the No AiTR condition and the Delayed AiTR condition), this difference was not significant. Significance statementAided target recognition (AiTR) systems are designed to augment a users field of view with salient highlights at locations containing task-relevant or threatening information in the environment. As computer vision models advance, such technologies will become an even more critical tool for people that perform visual search professionally (e.g. Soldiers, baggage screeners, radiologists). In a military context especially, having a system that potentially improves threat detection accuracy and reduces response times could be lifesaving. However, such benefits can only be realized if AiTR systems are designed to be integrated with the cognitive mechanisms of the human user. Without this, displays run the risk of being distracting, creating attentional blind spots, and generally producing a poorer user experience and performance. The current work, motivated by findings in the literature, tested the introduction of a short delay between scene onset and the onset of AiTR highlights. It was found that this very brief delay (250 ms) was sufficient to allow participants to direct overt attention to areas of the scene not highlighted by the AiTR system. This finding is critical because, while the purpose of AiTR highlights is to encourage the placement of attentional resources across the visual field, it is imperative that the human operator ultimately be able to attend to non-highlighted locations in the event of system failures (e.g. threats missed by the AiTR). Moreover, this work demonstrates how cognitive research can be used to systematically explore complex application spaces for improve human-machine integration.

animal behavior and cognition↗

bioRxiv2025

Neural Computational Model Predicts Attentional Dynamics during Immersive Search in Virtual Reality

Much of the scientific understanding of visual attention has come from desktop paradigms where body, head, and eye movements are restricted. This stands in contrast to the ability to search and navigate with few constraints in the real world. To bridge this gap, a computational model parametrized on a wide range of well-controlled desktop search tasks was evaluated on its ability to predict search behaviors in a less constrained, immersive environment in virtual reality. A set of validation metrics showed that the models attention map could predict empirical behaviors such as eye gaze and manual responses in VR despite the field-of-view varying from one moment to another from the participants movements. The present work quantifies the real-world applicability of laboratory-based theory and highlights ways to address outstanding limitations in explaining naturalistic visual search behaviors.

neuroscience↗

bioRxiv2022

What the flip? What the P-N flip can tell us about proactive suppression

It has been debated whether salient distractors in visual search can be proactively suppressed to completely prevent attentional capture, as the occurrence of proactive suppression implies that the initial shift of attention is not entirely driven by physical salience. While the presence of a Pd component in the EEG (associated with suppression) without a preceding N2pc component (associated with selection) has been used as evidence for proactive suppression, the link between these ERPs and the underlying mechanisms is not always clear. This is exemplified in two recent papers that observed the same waveform pattern, where an early Pd-like component flipped to a N2pc-like component, but provided very different interpretations (Drisdelle & Eimer, Psychophysiology, 58(9), 1-12, 2021; Kerzel & Burra, Journal of Cognitive Neuroscience, 32(6), 1170-1183, 2020). Using RAGNAROC (Wyble et al., Psychological Review, 127(6), 1163-1198, 2020), a computational model of reflexive attention, we successfully simulated this ERP pattern with minimal changes to its existing architecture, providing a parsimonious and mechanistic explanation for this flip in the EEG that is unique from both of the previous interpretations. Our account supports the occurrence of proactive suppression and demonstrates the benefits of incorporating computational modelling into theory building.

neuroscience↗

bioRxiv2019

An 11-bit High Dynamic Range (HDR) Luminance Display and Its Use to Discover Contextual Mechanisms of Real-World Luminance Normalization for Visual Acuity and Target Discrimination

Luminance can vary widely when scanning across a scene, by up to 10^9 to 1, requiring multiple normalizing mechanisms spanning from the retina to cortex to support visual acuity and recognition. Vision models based on standard dynamic range luminance contrast ratios below 100 to 1 have limited ability to generalize to real-world scenes with contrast ratios over 10,000 to 1 (high dynamic range [HDR]). Understanding and modeling brain mechanisms of HDR luminance normalization is thus important for military applications, including automatic target recognition, display tone mapping, and camouflage. Yet, computer display of HDR stimuli was until recently unavailable or impractical for research. Here we describe procedures for setup, calibration, and precision check of an HDR display system with over 100,000 to 1 luminance dynamic range (650-0.0065 cd/m^2), pseudo 11-bit grayscale precision, and 3-ms temporal precision in the MATLAB/Psychtoolbox software environment. The setup is synchronized with electroencephalography and IR eye-tracking measurements. We report measures of HDR visual acuity and the discovery of a novel phenomenon--that abrupt darkening (from 400 to 4 cd/m^2) engages contextual facilitation, distorting the perceived orientation of a high-contrast central target. Surprisingly, the facilitation effect depended on luminance similarity, contradicting both classic divisive and subtractive models of contextual normalization.

neuroscience↗

bioRxiv2019

A delay in sampling information from temporally autocorrelated visual stimuli

Much of our world changes smoothly in time, yet the allocation of attention is typically studied with sudden changes - transients. When stimuli change gradually there is a sizeable lag between when a cue is presented and when an object is sampled (Carlson, Hogendoorn, & Verstraten, 2006; Sheth, Nijhawan & Shimojo, 2000). Yet this lag is not seen with rapid serial visual presentation (RSVP) stimuli where temporally uncorrelated stimuli are presented (Vul, Kanwisher & Nieuwenstein 2008; Goodbourn & Holcombe, 2015). These findings collectively suggest that temporal autocorrelation of a feature paradoxically increases the latency at which information is sampled. This hypothesis was tested by comparing stimuli changing smoothly in time (autocorrelated) to stimuli that change randomly. Participants attempted to report the color coincident with a visual cue. The result was a smaller selection lag for the randomly varying condition relative to the condition with a smooth color trajectory. Our third experiment finds that the increase in selection latency is due to the smoothness of the color change after the cue rather than extrapolated predictions based on the color changes presented before the cue. Together, these results support a theory of attentional drag, whereby attention remains engaged at a location longer when features are changing smoothly. A computational model provides insights into neural mechanisms that might underlie the effect.

neuroscience↗

bioRxiv2018

Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

A quintessential challenge for any perceptual system is the need to focus on task-relevant information without being blindsided by unexpected, yet important information. The human visual system incorporates several solutions to this challenge, one of which is a reflexive covert attention system that is rapidly responsive to both the physical salience and the task-relevance of new information. This paper presents a model that simulates behavioral and neural correlates of reflexive attention as the product of brief neural attractor states that are formed across the visual hierarchy when attention is engaged. Such attractors emerge from an attentional gradient distributed over a population of topographically organized neurons and serve to focus processing at one or more locations in the visual field, while inhibiting the processing of lower priority information. The model moves towards a resolution of key debates about the nature of reflexive attention, such as whether it is parallel or serial, and whether suppression effects are distributed in a spatial surround, or selectively at the location of distractors. Most importantly, the model develops a framework for understanding the neural mechanisms of visual attention as a spatiotopic decision process within a hierarchy and links them to observable correlates such as accuracy, reaction time, and the N2pc and PD components of the EEG. This last contribution is the most crucial for repairing the disconnect that exists between our understanding of behavioral and neural correlates of attention.

neuroscience↗

bioRxiv2018

Conceptual content in images triggers rapid shifts of covert attention

The visual system can use conceptual information to search for targets even in the absence of clear featural signifiers1, and visual saccades are often directed at target objects defined by conceptual content2. These abilities are a core component of our facility with the visual world. Here, we evaluate whether contingent mechanisms of visual attention, known to trigger in response to target features such as motion, color or luminance3, are also triggered by visual patterns that match conceptually specified categories. These pre-registered experiments provide convergent behavioral and electrophysiological support that covert spatial attention is rapidly triggered by natural image exemplars from superordinate conceptually described target sets such as dinner food or four-legged animal, even when each target was viewed only once. In the behavioral experiment when two targets were presented with onsets separated by only 167ms, subjects reported the second target more often when it was in the same spatial location as the first. In the EEG experiment, images elicited clear N2pc and P3 components only when they matched the conceptually specified target set. The latency of the N2pc peaked at roughly 250ms, which is comparable to that commonly found in other N2pc studies for simpler stimulus types. These results suggest that vision quickly decodes conceptual information from natural images and selectively deploys spatial attention to locations containing information that matches current search goals.

neuroscience↗