Stochastic Process Underlying Emergent Recognition of Visual Objects Hidden in Degraded Images

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.

Static magnetic field stimulation of the supplementary motor area modulates resting-state activity and motor behavior

“…* p < 0.05, ** p < 0.01. d Schematic representation of the drift-diffusion model (modified from Fig. 16 by Murata et al 152 , used under CC BY 4.0). Reaction times are modeled as generated by a noisy process that integrates evidence with a positive drift rate v from a starting point z ( z = a /2 in the EZ-diffusion model) until reaching either a higher threshold a , leading to a correct decision, or a lower threshold 0 (i.e.…”

Accumulation System: Distributed Neural Substrates of Perceptual Decision Making Revealed by fMRI Deconvolution

“…The concept of the accumulation system may provide one step to solve a crucial puzzle of the wide applicability of the accumulation model over a variety of decision tasks. The accumulation model has been demonstrated to be effective for the tasks over modalities and contents of sensory stimuli; for example, visual tasks such as random-dot motion direction discrimination ( Palmer et al, 2005 ), symbol density discrimination ( Ratcliff et al, 1999 ), object distance discrimination ( Ratcliff et al, 2003 ), brightness discrimination ( Ratcliff et al, 2007 ), and object category discrimination ( Philiastides et al, 2006 ; Murata et al, 2014 ), somatosensory tasks such as vibrotactile frequency discrimination ( Mulder and van Maanen, 2013 ), and auditory tasks such as phonetic discrimination ( Binder et al, 2004 ). In these tasks, regions representing the alternatives vary in a wide range; for example, the alternatives of visual object categorization are represented by the high-level visual areas, while the alternative phonemes of the phonetic discrimination are represented by the auditory cortex ( Binder et al, 2004 ).…”

Single cell analysis reveals a biophysical aspect of collective cell-state transition in embryonic stem cell differentiation

“…The flashing ratchet behaviour has been observed at various biological levels and scales such as molecular motors, mitochondrial protein import, cell migration, and cell shaping 37 – 41 . Even object recognition in brain could be explained by the similar model 42 . Ratchets can be seen as controllers that act on stochastic systems with the aim of inducing directed motion through the rectification of fluctuations 34 , and flashing ratchets drive the on-and-off switching of the periodic potential of particles 37 , 38 .…”