Biology subjects

Kalantari, S.

Publications and source records attributed to Kalantari, S..

3 recordsLinked to original sources

bioRxiv2022

An Evidence-based Cognitive Model of Human Wayfinding under Uncertainty

Existing computational models lack adequate representation of the uncertainty experienced in human wayfinding tasks. They overly rely on optimized pathing algorithms, which reduces realism and limits insights on human responses to architectural designs. To address this, we developed an empirically grounded model that predicts human wayfinding uncertainty experience. Using data from 28 participants navigating an educational building with varying signage, we constructed the model (Study 1), and validated it with data from 11 other participants (Study 2). We found the wayfinding uncertainty correlated with the time elapsed since seeing the last helpful sign. The cognitive agent based on this model closely replicated human-reported uncertainty levels during wayfinding tasks under different signage conditions. Although the model more closely resembled human behavior compared to a shortest-route algorithm, additional environmental variables and heuristics are needed for better human outcome alignment. Our study showcases that evidence-based cognitive agent modeling can provide nuanced, human-like wayfinding behavior, enhancing the potential for effective computational design evaluation.

animal behavior and cognition↗

bioRxiv2021

Identifying Uncertainty States during Wayfinding in Indoor Environments: An EEG Classification Study

The researchers used a machine-learning classification approach to better understand neurological features associated with periods of wayfinding uncertainty. The participants (n=30) were asked to complete wayfinding tasks of varying difficulty in a virtual reality (VR) hospital environment. Time segments when participants experienced navigational uncertainty were first identified using a combination of objective measurements (frequency of inputs into the VR controller) and behavioral annotations from two independent observers. Uncertainty time-segments during navigation were ranked on a scale from 1 (low) to 5 (high). The machine-learning model, a random forest classifier implemented using scikit-learn in Python, was used to evaluate common spatial patterns of EEG spectral power across the theta, alpha, and beta bands associated with the researcher-identified uncertainty states. The overall predictive power of the resulting model was 0.70 in terms of the area under the Receiver Operating Characteristics curve (ROC-AUC). These findings indicate that EEG data can potentially be used as a metric for identifying navigational uncertainty states, which may provide greater rigor and efficiency in studies of human responses to architectural design variables and wayfinding cues.

neuroscience↗

bioRxiv2021

Evaluating Wayfinding Designs in Healthcare Settings through EEG Data and Virtual Response Testing

Wayfinding difficulties in healthcare facilities have been shown to increase anxiety among patients and visitors and reduce staff operational efficiency. Wayfinding-oriented interior design features have proven beneficial, but the evaluation of their performance is hindered by the unique nature healthcare facilities and the expense of testing different navigational aids. This study implemented a virtual-reality testing platform to evaluate the effects of different signage and interior hospital design conditions during navigational tasks; evaluated through behavioral responses and mobile EEG. The results indicated that using color to highlight destinations and increase the contrast of wayfinding information yielded significant benefits when combined with wayfinding-oriented environmental affordances. Neural dynamics from the occipital cortex showed beta-band desynchronization with enhanced color condition and additional theta-band desynchronization with enhanced environmental affordance. This multimodal testing platform has the potential to establish a robust body of evidence for future wayfinding design strategies.

neuroscience↗