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Daniel H. Baker; A psychophysically derived model of signal combination predicts neural responses in two stimulus domains. Journal of Vision 2014;14(10):1180. https://doi.org/10.1167/14.10.1180.
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© ARVO (1962-2015); The Authors (2016-present)
How closely do computational models derived to understand psychophysical data relate to neural activity? Steady-state EEG provides a measure of the response of neural populations to visual inputs. Here, this technique is used to test the predictions of a general model of signal combination and suppression, recently proposed by Meese & Baker (2013, iPerception, 4: 1-16). Stimuli were 1c/deg gratings presented either to the left and right eyes (to test binocular combination) or interdigitated across space in micropatch checks of four cycles (to test combination across space). The two components (left and right eyes, or adjacent spatial locations) flickered at either the same frequency (5Hz), or different frequencies (5 & ~7Hz) for a range of contrast combinations. With no free parameters, the model predicted several key findings in the complex pattern of contrast response functions that were observed empirically in both stimulus domains: (i) there is little increase in the response when a second component is added, especially at high contrasts, (ii) under specific conditions, increasing the contrast of one stimulus can reduce the overall response (analogous to Fechners paradox), (iii) when components flicker at different frequencies, a high contrast mask component shifts the contrast response function to the right, (iv) when components increase in contrast together, the contrast response function is twice as steep for same frequency flicker as for different frequency flicker. The accuracy of these predictions is surprising, as the model was derived to explain data from psychophysical (contrast discrimination) experiments, with no expectation that it should generalise to other experimental paradigms. That it does so suggests that psychophysical methods are informative regarding the activity of large populations of neurons, and that the general combination model provides a good account of signal interactions across multiple dimensions.
Meeting abstract presented at VSS 2014
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