We investigated the difference in performance on an implicit learning task between humans and machines in the auditory domain. Implicit learning is the process of ingesting information, such as patterns of everyday life, without being actively aware of doing so and without formal instruction. In pattern and anomaly detection, it is desirable to learn the patterns of everyday life in order to detect irregularities. In addition, we also considered how affect or emotion-like aspects interacts with this process. In our experiments, we created a synthetic pattern for both positive and negative sounds using a Markov grammar, which we then asked a machine-learning algorithm or humans to process. Results indicated that the generated pattern is a trivial task for even a simple RNN. For a similar but more complex task, humans performed significantly better under the condition of positive affect inducing sounds than they performed with negative sounds. Possibilities for the outcomes are discussed, along with other potential methods to compare human and machine implicit learning performance.
Occupational noise frequently occurs in the work environment in military intelligence, surveillance, and reconnaissance operations. This impacts cognitive performance by acting as a stressor, potentially interfering with the analysts’ decision-making process. We investigated the effects of different noise stimuli on analysts’ performance and workload in anomaly detection by simulating a noisy work environment. We utilized functional near-infrared spectroscopy (fNIRS) to quantify oxy-hemoglobin (HbO) and deoxy-hemoglobin concentration changes in the prefrontal cortex (PFC), as well as behavioral measures, which include eye tracking, reaction time, and accuracy rate. We hypothesized that noisy environments would have a negative effect on the participant in terms of anomaly detection performance due to the increase in workload, which would be reflected by an increase in PFC activity. We found that HbO for some of the channels analyzed were significantly different across noise types (p<0.05). Our results also indicated that HbO activation for short-intermittent noise stimuli was greater in the PFC compared to long-intermittent noises. These approaches using fNIRS in conjunction with an understanding of the impact on human analysts in anomaly detection could potentially lead to better performance by optimizing work environments.
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