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di’ Toxin T 17 (Microcystis aeruginosa) custom synthesis values and single h’ worth deliver a superb empirical description with the data; as in, it appears that participants didn’t adapt their criterion placement as a function of the stimulus difficulty level, as expected when stimulus difficulty varies unpredictably from trial to trial, since it does in our experiment.Stimulus PubMed ID:http://jpet.aspetjournals.org/content/142/2/141 Sensitivity AlysisSensitivity values as a function of time are shown in 
Figure (symbols). Apparently stimulus sensitivity grows with stimulus duration initially and then levels off for all participants. To additional demonstrate that the sensitivity observed is consistent with all the shifted exponential function as in previous research, we then carried out a maximum likelihood match assuming sensitivity follows a delayed exponential function t{t d’ D’i {e{ t :Figure. Results of our perceptual decisionmaking task with unequal payoffs. For each combition of stimulus and delay conditions, the percentage of choices towards higher reward (ordite) is plotted against the mean response time, the time from the stimulus onset (time ) to a response (abscissa). Lines with filled symbols denote congruent conditions in which stimulus and reward favor the same direction, lines with open symbols denote incongruent conditions in which stimulus and reward favor opposite directions. Task difficulty is color coded: Red, green and blue for high, intermediate and low discrimibility levels respectively. Dashed vertical lines indicate the time of the “go” cue: msec after the stimulus onset.ponegwhere Di,i denotes the asymptotic sensitivity levels for the three stimulus conditions, t denotes the initial period of time ONE one.orgIntegration of Reward and Stimulus Informationcannot say, however, whether processing noise arising from microsaccades or neural sources, or some processing time FGFR4-IN-1 site constant somewhat independent of the noise level, ioverning the relatively long time constant seen in our experiment. An additiol finding that emerges from this alysis is that the asymptotic sensitivity Di scales approximately linearly with the stimulus level in this study. See Figure for the linear fitting results assuming: Di kSwhere S represents stimulus level taking values and k is a linear scalar.Reward BiasThe measured normalized decision criterion, h’, for each delay condition is depicted in Figure (open circles connected with dashed lines). As previously noted, this variable changes in the expected way for all participants except SL, whose behavior is uffected by the reward manipulation.For each of the remaining participants, we calculated the optimal decision criterion, h’opt, based on the sigl detection theoretic alysis presented in the introduction and the observed sensitivity data presented in the preceding section, and plotted these optimal values in Figure (solid curves) together with the normalized criterion value h’ estimated from the data as described above. Note that h’opt when d’ is equal to; for display purposes, such values are plotted at an ordite value of In the calculation of the stimulus sensitivity and the reward bias, di’,i and h’, we assumed the distributions of the evidence variables for the three stimulus levels have the same standard deviation: higher sensitivity, associated with higher stimulus levels, results from distributions that are farther apart. However, the increase in sensitivity could result from changes in the standard deviation, as well as the separation of the distributions. Does the finding that participants are un.E 3 di’ values and single h’ worth present a great empirical description with the data; as in, it appears that participants didn’t adapt their criterion placement as a function with the stimulus difficulty level, as expected when stimulus difficulty varies unpredictably from trial to trial, since it does in our experiment.Stimulus PubMed ID:http://jpet.aspetjournals.org/content/142/2/141 Sensitivity AlysisSensitivity values as a function of time are shown in Figure (symbols). Apparently stimulus sensitivity grows with stimulus duration initially then levels off for all participants. To further demonstrate that the sensitivity observed is consistent together with the shifted exponential function as in preceding research, we then carried out a maximum likelihood match assuming sensitivity follows a delayed exponential function t{t d’ D’i {e{ t :Figure. Results of our perceptual decisionmaking task with unequal payoffs. For each combition of stimulus and delay conditions, the percentage of choices towards higher reward (ordite) is plotted against the mean response time, the time from the stimulus onset (time ) to a response (abscissa). Lines with filled symbols denote congruent conditions in which stimulus and reward favor the same direction, lines with open symbols denote incongruent conditions in which stimulus and reward favor opposite directions. Task difficulty is color coded: Red, green and blue for high, intermediate and low discrimibility levels respectively. Dashed vertical lines indicate the time of the “go” cue: msec after the stimulus onset.ponegwhere Di,i denotes the asymptotic sensitivity levels for the three stimulus conditions, t denotes the initial period of time ONE one.orgIntegration of Reward and Stimulus Informationcannot say, however, whether processing noise arising from microsaccades or neural sources, or some processing time constant somewhat independent of the noise level, ioverning the relatively long time constant seen in our experiment. An additiol finding that emerges from this alysis is that the asymptotic sensitivity Di scales approximately linearly with the stimulus level in this study. See Figure for the linear fitting results assuming: Di kSwhere S represents stimulus level taking values and k is a linear scalar.Reward BiasThe measured normalized decision criterion, h’, for each delay condition is depicted in Figure (open circles connected with dashed lines). As previously noted, this variable changes in the expected way for all participants except SL, whose behavior is uffected by the reward manipulation.For each of the remaining participants, we calculated the optimal decision criterion, h’opt, based on the sigl detection theoretic alysis presented in the introduction and the observed sensitivity data presented in the preceding section, and plotted these optimal values in Figure (solid curves) together with the normalized criterion value h’ estimated from the data as described above. Note that h’opt when d’ is equal to; for display purposes, such values are plotted at an ordite value of In the calculation of the stimulus sensitivity and the reward bias, di’,i and h’, we assumed the distributions of the evidence variables for the three stimulus levels have the same standard deviation: higher sensitivity, associated with higher stimulus levels, results from distributions that are farther apart. However, the increase in sensitivity could result from changes in the standard deviation, as well as the separation of the distributions. Does the finding that participants are un.