Fig 1.
V1 saliency hypothesis states that the bottom-up saliency of a location is represented by the maximum V1 response to this location.
In this schematic, V1 is simplified to contain only two kinds of neurons, one tuned to color (their responses are visualized by the purple dots) and the other tuned to orientation (black dots). Each input bar evokes responses in a cell tuned to its color and another cell tuned to its orientation (indicated for two input bars by linking each bar to its two evoked responses by dotted lines), and the receptive fields of these two cells cover the same bar location even though (for better visualization) the dots representing these cells are not overlapping in the cortical map. Iso-feature suppression makes nearby V1 neurons tuned to similar features (e.g., similar color or similar orientation) suppress each other. The orientation singleton in this image evokes the highest V1 response to this image because the orientation-tuned neuron responding to it escapes iso-orientation suppression. The color tuned neuron tuned and responding to the singleton’s color is under iso-color suppression. The saliency map is likely read out by the superior colliculus to execute gaze shifts to salient locations [9].
Fig 2.
Schematics of visual stimuli for singleton searches.
Due to iso-feature suppression, the highest response to each image is from a neuron responding to the singleton bar. This most activated neuron is tuned to orientation for image A, tuned to color for image B, and to color, orientation, or both features of the singleton for image C. The maximum V1 response to the singleton signals the saliency of its location.
Fig 3.
Behavioral refutation of a spurious prediction based on the incorrect assumption that V1 lacks neurons tuned simultaneously to both orientation and color.
The graphs show distributions (in discrete time bins) of RTO, RTC, and RTCO (and the average and the standard deviation of RTCO) of a particular observer SA in searches of the singletons. Experimental data are shown in red, the prediction is in blue. The predicted and actual distributions of RTCO are significantly different from each other, indicated by a p < 0.002 in the bottom plot.
Fig 4.
Schematics of the seven kinds of feature singleton scenes.
Each bar is colored green or purple (of the same luminance in the behavioral experiment), tilted to the left or right from vertical by the same absolute tilt angle, moving to the left or right (indicated by an arrow pointing to left or right) by the same motion speed. Under each schematic, the non-trivial neural responses (e.g., these responses are expected to be substantially higher than the responses to the background bars) evoked by the singleton are listed. Each singleton scene here is called a purple scene (in this paper) to denote that the color of the background bars are purple. Swapping between the green and purple colors changes a purple scene into a green scene. All purple scenes are assumed to share an invariant background response distribution, so are all the green scenes. The behavioral experiment [29] randomly interleaved purple and green scenes between trials.
Fig 5.
The observed and predicted distributions of reaction times for a double- or triple-feature singleton, using four different race models (race equalities), (in panel A),
(in panel B),
(in panel C), or
(in panel D), in a race between the corresponding racers whose reaction times are those of the corresponding single-feature singletons.
The data are from the same subject SA already shown in Fig 3, panel A shows the same information as that in the bottom panel of Fig 3. The predicted and observed distributions are significantly different from each other except in panel C.
Fig 6.
The observed distributions of RTC, RTM, RTO, RTCM, RTCO, and RTMO for an observer are used to predict the distribution of RTC M O for the same observer (SA who was also in Figs 3 and 5) by the non-spurious race equality .
The predicted and observed distributions of RTC M O are statistically indistinguishable from each other (p = 0.094). This figure has the same format as Fig 3.
Fig 7.
Observed and predicted distributions of RTC M O using the non-spurious race equality for six observers, including observer SA whose details are shown in Fig 6.
The predictions agree with data (indicated by p > 0.05) for all observers.
Fig 8.
The predicted and observed P(RTC M O) from the non-spurious equality and the three spurious ones, listed in Eqs (31)–(34), are plotted in A, B, C, and D, respectively.
These four equalities share a similar complexity and are also denoted as RE1, RE6, RE7, and RE8, respectively, in Table 1.
Table 1.
race equalities considered in this paper.
Fig 9.
The fraction of the tests of each race equality that falsify the equality for each observer.
Each observer is color coded by: red, white, green, blue, cyan, magenta, yellow, or black (red for our example observer SA). Different tests of an equality use different sets of parameters in the testing method to include all possible combinations of the parameter values. Each race equality is tested on six or eight observers as indicated. Results for REi for i = 2–4 is placed above that of its corollary equality REi+4 for easy of comparison.
Fig 10.
Average numbers of observers to break various race equalities, as shown in blue or black data points whose error bars denote standard deviations.
The non-spurious race equality is RE1. Data from 6 observers were tested for race equalities RE1 and REi for i ≥ 5 and data from 8 observers were tested for RE2, RE3, and RE4. Applying a test of a given race equality to all the observers gives a number of observers breaking this equality, and the average of this number over 80 (for REi with i = 2–5) or 320 (for RE1 and REi with i > 5) tests, each characterized by a unique set of parameters in the testing method, gives a data point (blue cross or black square). The background shadings visualize the probabilities of at least a certain number of observers breaking a true race equality accidentally, shadings in red hue indicate probabilities larger than 0.05. Note that the number of observers in this probability representation is an integer number, whereas the data points are generally non-integers since they are averages of integer numbers.
Fig 11.
Diagram outlining the methods to test each of our race equalities, e.g., .
The details of various components, in boxes (1)-(8), are described in the text.