Human Recognition Ability Research Articles

Antarctic skuas recognize individual humans.

Recent findings report that wild animals can recognize individual humans. To explain how the animals distinguish humans, two hypotheses are proposed. The high cognitive abilities hypothesis implies that pre-existing high intelligence enabled animals to acquire such abilities. The pre-exposure to stimuli hypothesis suggests that frequent encounters with humans promote the acquisition of discriminatory abilities in these species. Here, we examine individual human recognition abilities in a wild Antarctic species, the brown skua (Stercorarius antarcticus), which lives away from typical human settlements and was only recently exposed to humans due to activities at Antarctic stations. We found that, as nest visits were repeated, the skua parents responded at further distances and were more likely to attack the nest intruder. Also, we demonstrated that seven out of seven breeding pairs of skuas selectively responded to a human nest intruder with aggression and ignored a neutral human who had not previously approached the nest. The results indicate that Antarctic skuas, a species that typically inhabited in human-free areas, are able to recognize individual humans who disturbed their nests. Our findings generally support the high cognitive abilities hypothesis, but this ability can be acquired during a relatively short period in the life of an individual as a result of interactions between individual birds and humans.

Hierarchical Classification of Environmental Noise Sources Considering the Acoustic Signature of Vehicle Pass-Bys

Abstract This work is focused on the automatic recognition of environmental noise sources that affect humans’ health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens’ daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding). To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.

Four Common Conceptual Fallacies in Mapping the Time Course of Recognition

Determining the moment at which a visual recognition process is completed, or the order in which various processes come into play, are fundamental steps in any attempt to understand human recognition abilities, or to replicate the corresponding hierarchy of neuronal mechanisms within artificial systems. Common experimental paradigms for addressing these questions involve the measurement and/or comparison of backward-masking (or rapid serial visual presentation) psychometric functions and of physiological EEG/MEG/LFP signals (peak latencies, differential activities, single-trial decoding techniques). I review and illustrate four common mistakes that scientists tend to make when using these paradigms, and explain the conceptual fallacies that motivate their reasoning. First, contrary to collective intuition, presentation times, or stimulus-onset asynchrony masking thresholds cannot be taken to reflect, directly or indirectly, the timing of relevant brain processes. Second, psychophysical or electrophysiological measurements should not be compared without assessing potential physical differences between experimental stimulus sets. Third, such comparisons should not be performed in any manner contingent on subjective responses, so as to avoid response biases. Last, the filtering of electrophysiological signals alters their temporal structure, and thus precludes their interpretation in terms of time course. Practical solutions are proposed to overcome these common mistakes.

Onboard Evolutionary Risk Recognition System for Automobiles—Toward the Risk Map System

To achieve a system that improves the safety and comfort of the vehicle driving, a recognition system equivalent to the human recognition ability should be developed. However, the vehicle environment is complicated and involves situations so diverse that a uniform recognition processing approach cannot function sufficiently. For a solution to this problem, we have been studying a comprehensive risk recognition system, which we call the risk map system, with learning capability. As part of this paper, a system has been developed that autonomously obtains the image recognition processing. This paper presents a system as an example that automatically learns through genetic programming to obtain the image processing of pedestrians and vehicles taken by an onboard camera system

Model scenarios for the understanding of molecular recognition

Abstract