Abstract
When a degraded two-tone image such as a “Mooney” image is seen for the first time, it is unrecognizable in the initial seconds. The recognition of such an image is facilitated by giving prior information on the object, which is known as top-down facilitation and has been intensively studied. Even in the absence of any prior information, however, we experience sudden perception of the emergence of a salient object after continued observation of the image, whose processes remain poorly understood. This emergent recognition is characterized by a comparatively long reaction time ranging from seconds to tens of seconds. In this study, to explore this time-consuming process of emergent recognition, we investigated the properties of the reaction times for recognition of degraded images of various objects. The results show that the time-consuming component of the reaction times follows a specific exponential function related to levels of image degradation and subject's capability. Because generally an exponential time is required for multiple stochastic events to co-occur, we constructed a descriptive mathematical model inspired by the neurophysiological idea of combination coding of visual objects. Our model assumed that the coincidence of stochastic events complement the information loss of a degraded image leading to the recognition of its hidden object, which could successfully explain the experimental results. Furthermore, to see whether the present results are specific to the task of emergent recognition, we also conducted a comparison experiment with the task of perceptual decision making of degraded images, which is well known to be modeled by the stochastic diffusion process. The results indicate that the exponential dependence on the level of image degradation is specific to emergent recognition. The present study suggests that emergent recognition is caused by the underlying stochastic process which is based on the coincidence of multiple stochastic events.
Highlights
Visual object recognition requires a match to be established between an input image and an appropriate object representation stored in the high-level visual system [1, 2]
While the present results show that the properties of emergent recognition (ER) can be explained by the underlying stochastic process model, it is well known that stochastic processes play an essential role in some other cognitive tasks (e.g., [18])
The present study of ER shows that the time-consuming process of recognizing an object in a degraded image without any prior top-down information follows an exponential function related to two independent parameters: image difficulty and subject’s capability
Summary
Visual object recognition requires a match to be established between an input image and an appropriate object representation stored in the high-level visual system [1, 2]. Even in the absence of any such information for top-down processing, with continued observation, hidden objects in degraded images can be recognized in an emergent manner, being frequently accompanied by a feeling similar to the ‘‘Aha!’’ [13] or Eureka experience [17] This emergent recognition (ER) is characterized by a comparatively long reaction time (RT) ranging from seconds to tens of seconds, whereas detection or recognition of unambiguously depicted objects typically requires a RT of within a second or so as shown by many studies [18,19,20,21]. This long RT, which has seldom been investigated in quantitative studies, does not seem to be explained by simple combinations of bottom-up and top-down processes, suggesting that a separate neural mechanism is involved in the search for object representations that can match defective input patterns
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