Abstract
Visual systems evolve to process the stimuli that arise in the organism's natural environment, and hence, to fully understand the neural computations in the visual system, it is important to measure behavioral and neural responses to natural visual stimuli. Here, we measured psychometric and neurometric functions in the macaque monkey for detection of a windowed sine-wave target in uniform backgrounds and in natural backgrounds of various contrasts. The neurometric functions were obtained by near-optimal decoding of voltage-sensitive-dye-imaging (VSDI) responses at the retinotopic scale in primary visual cortex (V1). The results were compared with previous human psychophysical measurements made under the same conditions. We found that human and macaque behavioral thresholds followed the generalized Weber's law as function of contrast, and that both the slopes and the intercepts of the threshold as a function of background contrast match each other up to a single scale factor. We also found that the neurometric thresholds followed the generalized Weber's law with slopes and intercepts matching the behavioral slopes and intercepts up to a single scale factor. We conclude that human and macaque ability to detect targets in natural backgrounds are affected in the same way by background contrast, that these effects are consistent with population decoding at the retinotopic scale by down-stream circuits, and that the macaque monkey is an appropriate animal model for gaining an understanding of the neural mechanisms in humans for detecting targets in natural backgrounds. Finally, we discuss limitations of the current study and potential next steps.NEW & NOTEWORTHY We measured macaque detection performance in natural images and compared their performance to the detection sensitivity of neurophysiological responses recorded in the primary visual cortex (V1), and to the performance of human subjects. We found that 1) human and macaque behavioral performances are in quantitative agreement and 2) are consistent with near-optimal decoding of V1 population responses.