Measurement of Perceptual Effects of Afterimages

Introduction and Aims

The connection between decision-making and stimuli detection cannot be ignored as it determines human behaviours and any social relationship. Much depends on the way people perceive information and identify the factors that may have an impact on their behaviour. The process of receiving information begins with an interaction between a human eye and light. Luminance perception is determined by mid- and long-wavelength cones which are responsible for shape, motion, and distance recognition (King-Smith & Carden, 1976). Afterimages are created.

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As an important part of data perception, these optical illusions, known as afterimages, may have perceptual effects that have to be measured. The afterimage effect influences people’s responses (Arruga & Downey, 1965). This study aims at investigating the effects of afterimages on the detection of the subsequent stimulus through the prism of signal detection theory and three hypotheses to be approved or rejected.


A repeated measures ANOVA was used to analyse the information obtained during the experiment. Mauchly’s Test of Sphericity was the chosen assumption test (Field, 2013). It measured the differences between the conditions used for the within-subject study and proved the violation of the contrast of the image.

The first hypothesis (the presence of conditioning image creates an afterimage with a lower detection response) was rejected. Attention was paid to the conditioning stimulus when the afterimage effect was weakened (Wede & Francis, 2007). The presence of conditioning and moving stimuli did not influence the detection response.

The second hypothesis (moving stimulus creates a stronger afterimage) was rejected. Moving patterns had to create the strong motion aftereffect (Winawer, Huk, & Boroditsky, 2010). The presence of the motion aftereffect did not determine the accuracy of detection.

The third hypothesis (a high contrast image affects the accuracy of detection) was proved. It was necessary to localise the contrast of the image properly despite other stimuli (Anstis, Smith, & Mather, 2000). The higher the contrast of the image was, the more evident was the accuracy of detection.

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In this experimental study, the two independent variables when the original stimulus is present and when the original stimulus is absent and one dependent variable that is the contrast of the image were defined. Under the chosen within-subject study, all 10 participants (including five males and five females) receive the same eighteen conditions to observe the order effect. Purposive sampling is used to underline the significance of age (all participants aged between 18 and 25) and eye problems (no visual impairment) due to the fact that older or sick adults may need more time to detect an afterimage (Kaul, Budach, Graaf, Gollard, & Badakhshi, 2015). One participant should join the experiment at one time.

To present the visual stimulus prepared with the help of Matlab 2007b, a PC has to be combined with a Visage stimulus generator and a CO38216, 22 MultiSync FP2141 NEC CRT monitor that has a vertical refresh rate of 85Hz. Three experiments were conducted: (1) a control condition through a contrast vertical grating image with 6 contrasts, (2) a high-contrast vertical grating (fade) image, and (3) a high-contrast vertical grating image in a motion condition. Each contrast repeated 20 times. Two-minute rest was given to all participants after each experiment.


The current study helps to understand how it is necessary to measure perceptual effects of afterimages. It is not enough to take one picture and investigate how the human brain can work while perceiving information. There are many factors and variables that have to be taken into consideration. Despite the possibility to discuss each hypothesis chosen, this study is characterised by certain limitations due to the lack of the recorder and the inability to record movements free from blocking the screen. The sample was another limitation.

Regarding these limitations and possible contributions, this study promotes the necessity to develop new methods and approaches in order to understand and differentiate the motion aftereffect and afterimage effect. The activation of various brain regions leads to different outcomes. Therefore, the main implication of this study is the possibility to investigate the process of activation of the impulses that determine the work of the brain.

Future researchers can also use the results of this study to evaluate the role of the subsequent stimulus in terms of afterimages. Moving and conditioning stimuli cannot be ignored. Afterimages help to better evaluate the way people perceive information.


Anstis, S. M., Smith, D. R., & Mather, G. (2000). Luminance processing in apparent motion, Vernier offset and stereoscopic depth. Vision Research, 40(6), 657-675.

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Arruga, A., & Downey, R. (1965). Use of afterimages in the treatment of strabismus. American Orthoptic Journal, 14(1), 65-88.

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Thousand Oaks, CA: SAGE.

Kaul, D., Budach, V., Graaf, L., Gollard, J., & Badakhshi, H. (2015). Outcome of elderly patients with meningioma after image-guided stereotactic radiotherapy: A study of 100 cases. BioMed Research International, 2015. 

King-Smith, P. E., & Carden, D. (1976). Luminance and opponent-color contributions to visual detection and adaptation and to temporal and spatial integration. Journal of Optical Society of America, 66(7), 709-717.

Wede, J., & Francis, G. (2007). Attentional effects on afterimages: Theory and data. Vision Research, 47(17), 2249-2258.

Winawer, J., Huk, A. C., & Boroditsky, L. (2010). A motion aftereffect from visual imagery of motion. Cognition, 114(2), 276-284.

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NerdyRoo. (2022) 'Measurement of Perceptual Effects of Afterimages'. 16 July.

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