Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes similar to human illusory percepts, allowing for demonstrable assessment of the applied mechanisms and principles. We tested 8 computational models on 13 typical displays used in lightness research (11 Illusions and 2 Mondrians), and compared them with results from human participants (N = 85). Results show that HighPass and MiR models predict empirical results for simultaneous lightness contrast (SLC) and its close variations. ODOG and its newer variants (ODOG-2 and L-ODOG) in addition to SLC displays were able to predict effect of White’s illusion. RETINEX was able to predict effects of both SLC displays and Dungeon illusion. Dynamic decorrelation model was able to predict obtained effects for all tested stimuli except two SLC variations. Finally, FL-ODOG model was best at simulating human data, as it was able to predict empirical results for all displays, bar the Reversed contrast illusion. Finally, most models underperform on the Mondrian displays that represent most natural stimuli for the human visual system.
Lightness contrast alters lightness of a target decreasing its similarity with neighbouring surfaces (inducers), while lightness assimilation has an opposite effect, similarity is increased. Previous studies emphasized some aspects of stimulation that favour occurrence of one or both of these two phenomena: spatial frequency of the inducers, magnitude and direction of the reflectance difference between the target and the inducers. More importantly, based on previous studies three precise hypotheses can be formulated that predict occurrence of the two phenomena: spatial frequency, differential stimulation and assimilation asymmetry. We manipulated target and inducers’ reflectance, and inducers’ spatial frequency. This enabled us not only to test the importance of these factors, but to predict lightness for each stimulus, according to all three hypotheses. Our results confirmed the importance of tested factors for both lightness contrast and assimilation. Unfortunately, the proposed hypotheses were poor in predicting the obtained data. Differential stimulation hypothesis correctly predicted obtained effect in less than half situations, since small reflectance differences produced contrast, and large differences produced assimilation. Spatial frequency hypothesis did not correctly predict the strength of obtained effects, and we obtained largest assimilation effects with low spatial frequency inducers. Finally, assimilation asymmetry hypothesis did not predict a single obtained effect. Contrary to this hypothesis predictions, we obtained contrast with decrement, and assimilation with increment inducers.
Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes similar to human illusory percepts, allowing for demonstrable assessment of the applied mechanisms and principles. We tested 8 computational models on 13 typical displays used in lightness research (11 Illusions and 2 Mondrians), and compared them with results from human participants (N = 85). Results show that HighPass and MIR models predict empirical results for simultaneous lightness contrast (SLC) and its close variations. ODOG and its newer variants (ODOG-2 and L-ODOG) in addition to SLC displays were able to predict effect of White’s illusion. RETINEX was able to predict effects of both SLC displays and Dungeon illusion. Dynamic decorrelation model was able to predict obtained effects for all tested stimuli except two SLC variations. Finally, FL-ODOG model was best at simulating human data, as it was able to predict empirical results for all displays, bar the Reversed contrast illusion. Finally, most models underperform on the Mondrian displays that represent most natural stimuli for the human visual system.
Lightness contrast alters lightness of a target decreasing its similarity with neighbouring surfaces (inducers), while lightness assimilation has an opposite effect, similarity is increased. Previous studies emphasized some aspects of stimulation that favour occurrence of one or both of these two phenomena: spatial frequency of the inducers, magnitude and direction of the reflectance difference between the target and the inducers. More importantly, based on previous studies three precise hypotheses can be formulated that predict occurrence of the two phenomena: spatial frequency, differential stimulation and assimilation asymmetry. We manipulated target and inducers’ reflectance, and inducers’ spatial frequency. This enabled us not only to test the importance of these factors, but to predict lightness for each stimulus, according to all three hypotheses. Our results confirmed the importance of tested factors for both lightness contrast and assimilation. Unfortunately, the proposed hypotheses were poor in predicting the obtained data. Differential stimulation hypothesis correctly predicted obtained effect in less than half situations, since small reflectance differences produced contrast, and large differences produced assimilation. Spatial frequency hypothesis did not correctly predict the strength of obtained effects, and we obtained largest assimilation effects with low spatial frequency inducers. Finally, assimilation asymmetry hypothesis did not predict a single obtained effect. Contrary to this hypothesis predictions, we obtained contrast with decrement, and assimilation with increment inducers.
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