Using the classification image technique, the present experiments revealed several characteristics of human observers' spatiotemporal templates for the detection of orientation-defined targets. The stimulus consisted of a spatial 5 × 5 array of elements displayed in 5 ( Experiments 1 and 2) or 15 ( Experiment 3) temporal frames. A target was defined by the first- or the second-order characteristics of the textures. In Experiment 1, a target signal was presented across all five frames, and observers typically relied on the most reasonable cues in all five frames for detecting targets. In other words, they used the first-order cue for detecting the first-order target and used the second-order cue for detecting the second-order target. Moreover, the spatial profile for detecting the first-order sustained target was localized at the border of the target area, but that for the second-order sustained target showed broader spatial tuning. Presenting the target in just the third temporal frame, as was done in Experiment 2, changed the temporal profile of the observers' templates in the expected manner: Observers used the first-order cue for the first-order target detection and the second-order cue for the second-order target detection only in the third frame. However, changing the temporal characteristics also affected the kinds of spatial cues that were used to detect a target. For example, the classification images revealed that observers used second-order cues (as well as first-order cues) to detect a first-order target, and there was a trend toward increasing the extent of spatial information used when the temporal information was restricted. In Experiment 3, we found similar results for detecting the first-order flashed target with finer, 15-temporal-frame presentation. Lastly, we showed that the classification image is a useful way to reveal individual differences that are not shown with traditional psychophysical techniques.

*N*

_{AA},

*N*

_{AB},

*N*

_{BA}, and

*N*

_{BB}). Here,

*N*

_{AB}represents all samples of noise fields where Stimulus A was presented and the observer classified it as Stimulus B. The mean classification image (

*C*

_{mean}) is calculated as follows:

*C*

_{var}), which estimates one kind of nonlinear template, is calculated as follows:

^{2}). The spatial contrast profiles of the lines and blobs were defined as follows:

*x*′ = [(

*x*− 7)cos(

*θ*) + (

*y*− 7)sin(

*θ*)],

*y*′ = [−(

*x*− 7)sin(

*θ*) + (

*y*− 7)cos(

*θ*)],

*θ*is orientation (in radians), and

*x*and

*y*are integers ranging from 0 to 13. Observers attempted to discriminate textures that contained a target from textures that did not. In a nontarget texture, the orientations of all elements were drawn randomly from the same distribution. In a target texture, the orientations of all elements in the middle three rows were selected from one distribution, whereas the orientations of the remaining elements were drawn from a different distribution. In the first-order, or mean orientation, condition ( Figures 1B and 1C), the distributions of the target and nontarget orientations were uniform distributions (width = 40°, minimum step size = 0.5°) that differed only in mean orientation. Specifically, the mean target and nontarget orientations were 135° +

*d*and 135° −

*d*, respectively. The difference between means, 2

*d,*was adjusted for each observer to produce correct responses on approximately 75% of the trials. For the second-order, or orientation variance, condition ( Figures 1D and 1E), the distributions of the target and nontarget orientations were uniform distributions that had the same mean (135°) but different widths (i.e., variances). Specifically, the nontarget uniform distribution had a width of 40° and the target distribution had a width of 40° +

*w*. The difference in distribution width,

*w,*was adjusted for each observer to ensure correct responses on approximately 75% of the trials. In both conditions, the orientations of the texture elements were constrained to be within 135° ± 45° (i.e., 90–180°). In other words, the maximum values of

*d*and

*w*were 25° and 50°, respectively.

*d,*yielding 75% correct responses, was 5.0° for observers I.A. and J.M., 12.0° for observer Y.Y., and 8.0° for observer V.A. Threshold in the orientation variance condition, defined as the increment in noise range,

*w,*was 8.0° for observer I.A., 12.0° for observer J.M., 14.0° for observer Y.Y., and 31.0° for observer V.A. The correct response rates in the mean orientation condition in the experimental sessions were close to 75% for all observers: 73.3% for observer I.A., 72.9% for observer J.M., 75.8% for observer Y.Y., and 75.6% for observer V.A. Percentage correct in the orientation variance condition was also close to 75%: 75.1% for observer I.A., 75.6% for observer J.M., 75.3% for observer Y.Y., and 73.3% for observer V.A. Hence, our estimates of threshold produced roughly equal performance across tasks and observers. Moreover,

*d*′ was not different between observers, and the response bias measure,

^{1}

*β,*did not show strong bias in this experiment (see Tables 1 and 2).

Observer | 75% Threshold (deg) | % Correct | d′ | β |
---|---|---|---|---|

I.A. | 5.0 | 73.3 | 1.250 | 0.875 |

J.M. | 5.0 | 72.9 | 1.225 | 1.127 |

Y.Y. | 12.0 | 75.8 | 1.399 | 1.035 |

V.A. | 8.0 | 75.7 | 1.388 | 0.993 |

Observer | 75% Threshold (deg) | % Correct | d′ | β |
---|---|---|---|---|

I.A. | 8.0 | 75.1 | 1.363 | 0.866 |

J.M. | 12.0 | 75.6 | 1.395 | 0.839 |

Y.Y. | 14.0 | 75.3 | 1.369 | 1.091 |

V.A. | 31.0 | 73.3 | 1.247 | 0.957 |

*deviations*(positive or negative) away from the mean orientation.

*o*, where

*o*is an element's orientation) and the probability of an observer responding “target present”. In variance classification images, red pixels indicate spatiotemporal locations where deviations (positive or negative) away from the mean orientation were significantly and positively correlated with the probability of an observer responding “target present”, whereas blue pixels indicate spatiotemporal locations where large deviations from the mean orientation were significantly and negatively correlated with “target present” responses. The significance levels were calculated by a permutation test: The responses of each subject were randomly shuffled, and the classification images (mean and variance) were then recalculated for this random permutation of responses. This process was repeated 1,000 times, and the resulting set of classification images was used to estimate the distribution of orientations at each pixel under the null hypothesis of no association between the observer's response and the element's orientation (Efron & Tibshirani, 1993). These distributions were then used to assess the statistical significance of each pixel in the original classification images. We used the Bonferroni method for controlling Type I error: The alpha level for each test was .002, which corresponds to a Type I error rate that is equal to or less than .05 for each 5 × 5 texture.

*w*= 31° vs.

*w*

_{avg}= 11.3°). Interestingly, the classification images measured in this condition do possess structure that may be related to this observer's poor performance. For example, V.A. used a second-order cue at only a single location and only during the first two movie frames ( Figure 3B). Moreover, observer V.A. seems to have relied on a first-order cue to detect the second-order target ( Figure 3A). In general, this observer relied on the first-order information and early temporal frames to detect both kinds of targets.

*d*) yielding 75% correct responses, was 13.0° for observer I.A., 21.0° for observer Y.Y., and 31.0° for observer V.A. Threshold in the orientation variance condition, defined as the increment added to the noise range (

*w*), was 20.0° for observer I.A. and 37.0° for observer Y.Y. Observer V.A. was unable to attain 75% correct performance, even with the maximum increment used in our experiment (i.e.,

*w*= 50°), and was, therefore, not tested further in that condition.

*d*′, and

*β*values are summarized in Tables 3 and 4. As in Experiment 1, the three observers were relatively unbiased and exhibited similar sensitivity.

Observer | 75% Threshold (deg) | % Correct | d′ | β |
---|---|---|---|---|

I.A. | 13.0 | 74.5 | 1.132 | 0.985 |

Y.Y. | 21.0 | 75.5 | 1.390 | 1.180 |

V.A. | 31.0 | 74.1 | 1.314 | 1.272 |

Observer | 75% Threshold (deg) | % Correct | d′ | β |
---|---|---|---|---|

I.A. | 20.0 | 72.8 | 1.212 | 1.002 |

Y.Y. | 37.0 | 74.4 | 1.318 | 1.147 |

V.A. | N/A |

*both*the mean orientation and orientation variance conditions. This result differs from Experiment 1, where observers did not seem to use second-order cues to detect a first-order sustained target. How would a second-order cue aid the detection of a flashed target in the mean orientation condition? As noted in the Discussion section of Experiment 1, a second-order spatial cue exists for detecting the presence, but not the polarity, of the border between the target and the background. The flashed target presentation used in Experiment 2 also introduced a temporal cue that was not present in Experiment 1. More specifically, because the target was transient, the orientation variance measured across temporal frames at each target element's location was greater on target-present trials than on target-absent trials. Thus, there are two different second-order cues, that is, spatial and temporal, that might help observers detect the first-order flashed target. The influence of the latter second-order cue—temporal—is illustrated nicely by the collapsed variance classification image (far right columns, Figures 4B and 5B). These classification images are consistent with the idea that observers were influenced by the variation of orientation across stimulus frames.

*d,*yielding 75% correct responses, was 29.0° for observer A.K.; 16.0° for observer Y.Y.; 11.0° for observers K.H., I.K., and D.M.; and 15.0° for observer B.B. The percentage correct during the test sessions was 78.8% for observer A.K., 75.6% for observer Y.Y., 73.3% for observer K.H., 79.7% for observer B.B., 73.5% for observer I.K., and 76.8% for observer D.M. Estimates of threshold, percentage correct,

*d*′, and

*β*are summarized in Table 5.

Observer | 75% Threshold (deg) | % Correct | d′ | β |
---|---|---|---|---|

A.K. | 29.0 | 78.8 | 1.642 | 1.438 |

Y.Y. | 16.0 | 75.6 | 1.385 | 1.041 |

K.H. | 11.0 | 73.3 | 1.248 | 0.898 |

B.B. | 15.0 | 79.7 | 1.661 | 1.107 |

I.K. | 11.0 | 73.5 | 1.259 | 0.993 |

D.M. | 11.0 | 76.8 | 1.465 | 0.923 |

*p*< .0034; family-wise Type I error rate ≤ .05). The statistically significant frames are shown as circles in Figure 7A. Permutation tests were also performed to evaluate the significance of all pairwise comparisons among the values in the first-order image (family-wise Type I error rate = .05). These tests confirmed that the values on Frames 7, 8, and 9 were different from those on other frames. For observers A.K., Y.Y., and K.H., the values on Frames 7, 8, and 9 differed from values on all other frames. For observer B.B., the values on Frames 7, 8, and 9 differed from those on Frames 1, 5, and 12 and that on Frame 8 differed from that on Frame 4. For observer I.K., the values on Frames 7 and 8 differed from those on Frames 1, 2, 3, 5, 10, 11, 12, 13, 14, and 15 and that on Frame 9 differed from those on Frames 1, 2, 11, 12, 13, 14, and 15. For observer D.M., the values on Frames 7, 8, and 9 differed from those on Frames 1, 2, 3, 4, 13, 14, and 15; that on Frame 7 differed from those on Frames 5, 10, 11, and 12; and that on Frame 9 differed from those on Frames 5 and 11. None of the other comparisons were significant. Thus, all observers exhibited very clear temporal tuning for the use of first-order cues.

*p*< .0034; family-wise Type I error rate ≤ .05). However, pairwise comparisons among values in the variance classification images did not confirm these peaks prior to and during the target presentation, except that the values on Frame 2 differed from those on Frames 8 and 14 for observer Y.Y. (family-wise Type I error rate ≤ .05). Thus, we did not obtain clear evidence of strong temporal tuning in the use of second-order cues even for the representative element before the target timing for most of the observers.

^{1}Classification image calculation ( Equation 1) is based on unbiased observers (Murray et al., 2002).