@misc{oai:ir.soken.ac.jp:00001195,
 author = {松茂良, 岳広 and マツモラ, タケヒロ and MATSUMORA, Takehiro},
 month = {2016-02-17},
 note = {On the basis of lesion or electrophysiological studies, it is suggested that area TE (anterior part of IT) of the monkey, which is the highest stage in the ventral stream of cortical visual information processing pathway, plays an important role for information processing of color.<br />　　The relationships between the activity of a single cortical neuron and the discrimination behavior of the monkey have been previously studied for stereoscopic depth and motion perception. However, no study has been conducted to study the relationship between the neural activity and discrimination behavior for color vision.<br />　　To study whether there exists close link between color selective responses of TE neurons and color perception, he examined the correlation between monkey's color judgment and the response of single color-selective TE neuron. He trained a fine color judgment task in two monkeys and simultaneously recorded the color discrimination behavior and single neural activity from the color-sensitive sub-region in area TE. In this task, a sample color stimulus was presented at the center of the display, and the monkey had to make either the rightward or leftward saccade depending on the similarity of the sample color to the two target colors. Two target colors were determined based on the color selectivity of each neuron. One target color (preferred target color) evoked stronger response than the other color (anti-preferred target color). A sample color set consisted of seven isoluminant colors that include the target colors and are linearly aligned on the CIE-xy chromaticity diagram with equal intervals. The range of the sample colors was set slightly larger than the discrimination threshold of the monkey.<br />　　In each recording session, he quantified both the color discrimination threshold of the monkey and that of the single neuron and compared these two thresholds. Psychometric threshold was calculated from the psychometric function that indicates the proportion that the monkey made saccade toward the direction associated with the preferred target color (pref-choice) as a function of sample colors, and it was defined as the color distance on the CIE-xy chromaticy diagram where the monkey can discriminate colors at the accuracy level of 80% correct. To determine the neurometric threshold, he constructed a neurometric function from the neural responses to sample colors based on a ‘neuron/anti-neuron’ model.<br />　　This model assumes that the ideal observer choose the preferred-target color if the spike count of the recorded neuron ('neuron') is larger than that of a hypothetical neuron with the opposite tuning ('anti-neuron') that is selective for the non-preferred target color. He applied ROC analysis to compute the probability that an ideal observer choose the preferred target color for each sample color using the frequency distributions of the spike counts of the 'neuron' and 'anti-neuron' to that color. Then he constructed 'neurometric function' that indicates the performance of a given 'neuron'/'anti-neuron' pair to the entire set of sample colors. He computed neural threshold based on the neurometric function as the color separation that yielded 80% correct color judgments. When he compared psychometric threshold and neurometric threshold obtained simultaneously, he found that neurometric threshold was on average slightly higher than the psychometric threshold although some neurons had the thresholds comparable to that of the monkey.<br />　　When he studied the color discrimination ability of the monkey and neuron at various positions across the chromaticity diagram, he found that both psychometric and neurometric thresholds systematically depended on the color used in each experiment. So, in the next analysis, he divided the CIE-xy chromaticy diagram into ten color areas and examined the relationships between the variations of psychometric threshold and neurometric threshold across the color areas. He found that there was strong positive correlation between these two thresholds. This result indicates that, if the discrimination of a given pair of colors is easy for neurons, it is easy for the monkey as well. These results strongly support the idea that there is association between the activity of TE color selective neurons and color perception of the monkey.<br />　　If the activity of TE color selective neuron affects the color perception of the monkey, he can expect that the monkey tends to make pref-choice in a trial when the recorded neuron responded more strongly because signals coding the preferred target should be stronger in such a trial. To examine whether this happens, he calculated the 'Choice Probability (CP)' that is a quantitative measure of correlation between trial-to-trial fluctuation of neural responses and the monkey's color judgment. He found that average of CP is significantly larger than 0.5 and this is consistent with the expectation that the activities of TE color-selective neurons positively correlate with the monkey's color judgment. This result suggests that the TE color selective neurons contribute to the color judgment of the monkey.<br />　　Finally, he examined the relationship between the neural sensitivity for color and contribution of the same neuron to color judgment of the monkey. The neural sensitivity is defined as the slope of the neurometric function and the contribution corresponds to CP. He found that there is no significant correlation between the neural sensitivity for color discrimination and CP. This rejects the idea that a specific subset of neurons with particularly high color sensitivity make large contribution to the color discrimination performance. Rather, it suggests that a large population of color selective TE neurons having various color sensitivity contribute to the color judgment., 総研大甲第1174号},
 title = {Relationships between the activity of color selective neurons in the inferior temporal cortex of the monkey and color discrimination behavior},
 year = {}
}