A review of the footsteps illusion

Kitaoka, Akiyoshi; Anstis, Stuart

doi:10.47691/joi.v2.5612

Cited by 7 publications

(5 citation statements)

References 25 publications

(72 reference statements)

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“…That is, there appears to be a noticeable "thinning" of the moving object on the lower-contrast side of the border, which may give rise to an apparent vertical shift. This observation suggests that the RLI may be a very close relative to the footstep/inchworm illusion (Anstis, 2001;Kitaoka & Anstis, 2021). We are also exploring possible connections to the slalom illusion (Cesàro & Agostini, 1998), although the very different stimulus configurations used in those studies, suggest the two effects may only share phenomenology.…”

Section: The Rocking Line Illusionmentioning

confidence: 94%

The Rocking Line Illusion

Thornton¹,

Todorović²

2023

Preprint

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A new visual illusion is described in which a smoothly translating object appears to rock around its own centre during motion. This “rocking line” illusion (RLI) occurs when the object passes through contrast boundaries formed by static background elements. However, for it to appear, the spatial scale of the display must be adjusted appropriately. We provide an online demo where the effect can be experienced and relevant parameters manipulated.

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Section: The Rocking Line Illusionmentioning

confidence: 94%

The Rocking Line Illusion

Thornton¹,

Todorović²

2023

Preprint

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“…1) IVEP: For IVEP stimulation, we applied four static motion illusions designed by A. Kitaoka (see Fig. 1(a)) [28]. All chosen illusory stimuli incorporated the elements to generate the illusory perception of rotational motion that an object is rotating when no physical rotation is actually occurring.…”

Section: B Stimulation Designmentioning

confidence: 99%

A Novel Hybrid Brain–Computer Interface Combining the Illusion-Induced VEP and SSVEP

Li,

Zhao,

Wang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated by two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.

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“…The motion perception tool explains an assortment of visual illusions: stepping feet (Anstis, 2001; Bach, 2004; Kitaoka & Anstis, 2021), Kinegram (Bach, 2014), structure from motion (Bach, 2002; Rogers & Graham, 1979), Pinna–Brelstaff (Bach, 2003; Pinna & Brelstaff, 2000), Translational Moirè Patterns (Bach, 2013; Spillmann, 1993), Spine drift (Bach, 2011; Kitaoka, 2010), grid masking (Bach, 2019), and global motion influenced by arrows (@jagarikin, 2022). …”

mentioning

confidence: 99%