Visual one-shot learning as an ‘anti-camouflage device’: a novel morphing paradigm

Abstract

Once people perceive what is in the hidden figure such as Dallenbach's cow and Dalmatian, they seldom seem to come back to the previous state when they were ignorant of the answer. This special type of learning process can be accomplished in a short time, with the effect of learning lasting for a long time (visual one-shot learning). Although it is an intriguing cognitive phenomenon, the lack of the control of difficulty of stimuli presented has been a problem in research. Here we propose a novel paradigm to create new hidden figures systematically by using a morphing technique. Through gradual changes from a blurred and binarized two-tone image to a blurred grayscale image of the original photograph including objects in a natural scene, spontaneous one-shot learning can occur at a certain stage of morphing when a sufficient amount of information is restored to the degraded image. A negative correlation between confidence levels and reaction times is observed, giving support to the fluency theory of one-shot learning. The correlation between confidence ratings and correct recognition rates indicates that participants had an accurate introspective ability (metacognition).The learning effect could be tested later by verifying whether or not the target object was recognized quicker in the second exposure. The present method opens a way for a systematic production of ''good'' hidden figures, which can be used to demystify the nature of visual one-shot learning.

Psychophysical Measures of Sensitivity to Facial Expression of Emotion

“…The Norrkross software is a freeware, open-source program that allows morphing of two photographic images creating a prototypical facial image from exemplars using a sophisticated morphing algorithm that implements the principles described by Benson and Perrett, 1993, as cited by Pearson and Adamson, 2004). The software is widely used in research (Pearson and Adamson, 2004; Liu and Jagadeesh, 2008; Akrami et al, 2009; Vida and Mondloch, 2009; Ishikawa and Mogi, 2011). Similar to the work of Pearson and Adamson (2004), an average of 75 key points were allocated to identify points of similarity between the faces, with more points assigned around areas of greater change with increasing emotional intensity, such as around the pupils, eyelids, eyebrows, lips, and nose.…”

How and When? Metacognition and Solution Timing Characterize an “Aha” Experience of Object Recognition in Hidden Figures

“…After an insightful realization, the learned knowledge sometimes prevents subjects from going back to the previous naïve state. This unique type of learning is a long-term memory encoding of one-shot experience (Ludmer et al, 2011), or called “one-shot learning” (Giovannelli et al, 2010; Ishikawa and Mogi, 2011; Dudai and Morris, 2013).…”

“…In this “no change paradigm (NCP),” the task difficulty is not easy to adjust properly, as the process of utilizing the combination of blurring and thresholding to create a hidden figure often makes the image too hard or too easy to recognize. If the problem is too difficult to solve, the answer rate within a certain period of time decreases, while the responded data available to analyze also decreases (Ishikawa and Mogi, 2011). On the other hand, if the problem is too easy, there is no stagnation (“impasse”) in the first place, compromising the suitability as a problem-solving task (Salvi et al, 2016).…”

Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

“…To tackle the problem of having a limited number of labelled instances, some methods based on the concepts of learning by knowledge transfer have been proposed and this approach has been termed as one-shot learning. The aim is to use a large number of available instances of a related domain (the source domain) to the target domain, and only one or a few labelled instances per class of the target domain, to build a model (Fei-Fei et al, 2006;Ishikawa and Mogi, 2011;Salakhutdinov et al, 2012). Fei-Fei et al (2006) proposed a Bayesian-based model for the problem of object recognition.…”