The time until an approaching object passes the observer is referred to as time-to-passage (TTP). Accurate judgment of TTP is critical for visually guided navigation, such as when walking, riding a bicycle, or driving a car. Previous research has shown that observers are able to make TTP judgments in the absence of information about local retinal object expansion. In this paper we combine psychophysics and functional MRI (fMRI) to investigate the neural substrate of TTP processing. In a previous psychophysical study, we demonstrated that when local retinal expansion cues are not available, observers take advantage of multiple sources of information to judge TTP, such as optic flow and object retinal velocities, and integrate these cues through a flexible and economic strategy. To induce strategy changes, we introduced trials with motion but without coherent optic flow (0% coherence of the background), and trials with coherent, but noisy, optic flow (75% coherence of the background). In a functional magnetic resonance imaging (fMRI) study we found that coherent optic flow cues resulted in better behavioral performance as well as higher and broader cortical activations across the visual motion processing pathway. Blood oxygen-level-dependent (BOLD) signal changes showed significant involvement of optic flow processing in the precentral sulcus (PreCS), postcentral sulcus (PostCS) and middle temporal gyrus (MTG) across all conditions. Not only highly activated during motion processing, bilateral hMT areas also showed a complex pattern in TTP judgment processing, which reflected a flexible TTP response strategy.
As we drive along a road, a continuous optic flow pattern projected onto our retina is available to our visual system, and at any given point in time it can be used to compute the time remaining until we pass an object of interest. This time to passage (TTP), together with cues about motion trajectory, allows us to anticipate and judge oncoming objects regarding their path of movement and to prepare time-critical motor actions. Despite ample research on the topic, it still remains unresolved that how TTP is computed and which other optical (such as object velocity and expansion cues) are being exploited when observers are asked to provide judgments regarding the time to passage of an oncoming object. One reason why these cues have been eluding identification may lie in the adaptive nature of the visual system. In a recent psychophysical study, we have shown that the visual system appears to employ an adaptive strategy that changes with the task at hand [
Global tau is a property of coherent optic flow that relies on the systematic change of distances on the retina and between-objects. Global tau makes the assumption that 3D-distances between the objects in the world remain constant thus producing coherent optic flow, and it can be computed from the relative rate of change of the angular displacement of the target from the observer’s line of sight [
In naturalistic scenarios, in which the retinal size of the targets does expand, both the local expansion cues (local tau) and the global tau cues can be exploited by the visual system to predict TTP of the target. Studies on the utilization of local tau information report that the neural substrate involved in the extraction of local tau expansion cues is the locus rotundus in pigeons [
In the present study we used fMRI to identify such regions in human observers. Given that at the behavioral level, observers differ in the strategies they use between the case of local expansion scenarios and expansionless global optic flow, one would expect some shared but also some specific cortical areas to be involved in TTP judgment.
Imagine a cloud of fixed expansionless objects (dots) through which an observer is moving. Two dots are marked red while all the others are white. If these dots are at equal lateral distance on opposite sides of the track vector, then the dot that is sagittally farther away from the observer will project closer to the focus of expansion in the retinal flow pattern. If the observer is asked to judge which of the two marked dots is closer, she/he could base the decision on this fact. In other words, in the case of such symmetrical lateral spacing, observers might use an image-based strategy once they have discerned the track vector from the optic flow. Reducing the coherence of the optic flow makes it harder to determine the track vector, and performance should break down or resort to some other strategy. For instance, subjects may merely base their judgments on how far a target is from the center of the screen. We have previously found that observers employ flexible strategies that can use a combination of global flow analysis and image-based cues [
We have collected task-based fMRI data while observers were making TTP judgments in the absence of local tau information. By manipulating the initial positions of the target objects relative to the observers’ track vector and by changing the coherence of optic flow (75% or 0%), we used a limited number of specific information sources that observers could exploit when making TTP judgments. Based on our previous psychophysical study [
Our behavioral results are consistent with previous psychophysics findings: TTP judgments reflected the differential use and integration of multiple sources of information, including global optic flow, object retinal velocities, and other depth cues [
Seven subjects (5 females, 2 males, mean age = 24.42 years, SD = 4.82 years) participated in the study. They were graduate students at Boston University, recruited from our pool of subjects. All of them had normal or corrected-to-normal vision. All underwent a psychophysical testing session prior to the scan, to make sure that their performance was at least 70% correct for Δ τ = 0.5 sec for the symmetric configuration regardless of the initial x-offset and background motion coherence (0% or 75%) [
The stimuli were generated on an Intel-based Macintosh laptop and displayed at a resolution of 1024 × 768 pixels and a refresh rate of 75 Hz. Two of the dots, referred to as target dots, were red (51.20 cd/m2) and the rest of them were white (79.55 cd/m2), all displayed against a gray background (10.22 cd/m2). They were back-projected onto a translucent screen (27.3 cm × 36.5 cm) using a LCD projector. Subjects viewed the translucent screen through a mirror mounted on the head coil of a whole-body scanner. The distance between the eyes of the subject and the mirror was approximately 4 cm and the distance between the mirror and the screen was approximately 81 cm, therefore, the total viewing distance was about 85 cm. This setup provided a square viewing aperture subtending 17˚ × 17˚. fMRI data were acquired at Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, using a 3T Siemens whole body scanner and a standard 8-channel head coil. Structural images were obtained as T1 weighted magnetization prepared rapid acquisition gradient echo images (MPRAGE) (128 slices with slice thickness of 1.33 mm, voxel size: 1.00 × 1.00 × 1.33, FOV = 256, TR = 2.53 sec, TE = 3.39 msec, flip angle = 90˚). Two T1-weighted images were collected for each subject. Functional images were obtained with gradient echo, echo planar (EPI) interleaved sequence (33 slices with slices oriented along the AC-PC line, slice thickness of 3 mm with 20% distance factor, FOV = 200, TR = 2.00 sec, TE = 30 msec, flip angle = 90˚) for measurement of BOLD contrast.
A field of moving white dots simulated the observer’s forward self-motion in 3D and was presented through a square viewing aperture. The dots remained stationary with respect to one-another in the simulated space. Subjects were asked to indicate which one of two red dots would pass their eye plane first, mimicking they were moving forward through the field. All of the dots subtended 2 pixels × 2 pixels (4 arcmin × 4 arcmin) throughout the simulated approach and were placed such that they maintained a density of 2 dots/deg2. The screen size of all dots, including the targets, remained constant, thus eliminating all local tau cues. The motion of the dots simulated the subjects’ forward self-motion along a straight-line trajectory at a speed of 150 cm/s. In each trial, the direction of simulated self-motion was toward the center of the aperture. Dots that moved out of the volume behind the observer’s eye plane were randomly assigned to new locations such that the density of the dots remained constant (
The target dots were placed at different depths such that the difference between their passage times (tau difference Δtau) was set to be 0.5 sec for all trials. This value was chosen because it should be just above detection threshold, rendering the task of identifying the leading target meaningful but still allowing for errors. The initial depth of the reference target was 1200 cm. Thus, the possible TTP values from stimulus onset until both targets would have passed the observer, ranged from 7.5 s to 8.5 s. This left ample time for the 2AFC responses, which had to be made before the leading target passed the vertical eye-plane of the observer. Observers were not required to respond as quickly as possible.
The two target dots were placed such that they were always on opposite sides of the track vector. Their lateral distances from the track vector (x-offsets) were either 10 cm or 50 cm in the simulated space. This resulted in four different lateral target offset combinations: Two equidistant symmetric placements (leading target 10 cm to one side-trailing target 10 cm to the other side, or 50 cm - 50 cm). In the other two combinations, the targets were placed with asymmetric x-offsets (10 cm - 50 cm, and 50 cm - 10 cm). These 4 stimuli were paired with two coherence levels of the background dots. Remember that an entirely incoherent flow-field no longer specifies observer motion. Our previous psychophysical data [
The visual stimulus was occluded after 3 seconds. The next trial would not be presented until a decision had been made. The timing and order were randomized using optseq2 (http://www.freesurfer.net/optseq/). Inter-stimulus Intervals (ISIs) between trials varied from 1 - 7 s. Frames with static dots were presented within the ISIs, serving as a baseline condition. During the whole scanning period, subjects were required to fixate a small central cross (40 × 40 arcmin). Stimuli were presented binocularly in a two-alternative forced choice (2AFC) paradigm without feedback. The subjects’ task was to determine which of the two targets would arrive at their eye plane first. Subjects entered their responses by pressing a designated key on a magnet-compatible button box.
A separate block design employing a MT localizer task was performed by all subjects in two runs. The human middle temporal complex (hMT) has been shown to be highly involved in motion processing, including optic flow. Accordingly, area hMT was functionally localized by utilizing moving and static dot patterns [
Imaging data analysis was performed using the Statistical Parametric Mapping software package (SPM12, Wellcome Department of Cognitive Neurology, London, UK) and utilizing MATLAB (The MathWorks, Natick, Massachusetts, USA). The preprocessing steps were as follows: 1) format conversion by converting data from original DICOM files to Nifti files, 2) slice timing eliminated the time shift for all voxels of functional images, 3) realignment by motion correction, 4) co-registration of the anatomical image with the mean functional image, 5) spatial normalization normalized all images to a standard space (Montreal Neurological Institute, MNI) with a voxel size of 3 × 3 × 3 mm, and 6) smoothing: all functional data were smoothed with a FWHM (Full Width at Half Maximum) kernel of 4 mm.
For each subject, the onset and duration of each condition was modeled by a general linear model (GLM). The motion parameters from realignment were also used as multiple regressors in generating the design matrix. Trials with correct responses and incorrect responses were separated as different conditions. With the contrast images of each subject, a group level randomeffect analysis was performed for each condition. The resulting t-value maps were set as uncorrected for multiple comparisons, p < 0.05. Clusters with less than 10 contiguous voxels were excluded.
Based on group-level activation maps in normalized space, we defined several functional regions of interest (ROI) for each single subject. Every functional ROI was defined as a sphere, with its center at the respective local maximum of the activation cluster and with a 5-mm radius. Subsequently, we calculated the percent BOLD signal change for each functional ROI using Marsbar [
Bilateral hMT areas were defined using localizer tasks [
Response accuracy for each condition was first calculated per subject and then averaged across subjects (
Interestingly, when the flow field contained additional information about the track vector, performance improved. When the target dots were symmetric, subjects performed better under the 75% coherence condition compared to 0% coherence. Thus, global motion information provided by background dots
enhanced subjects’ performance only if the targets were spaced symmetrically around the track vector. However, when the target dots were asymmetric, there were no significant differences between 0% coherence and 75% coherence (p > 0.05 in paired t-test). These results replicate previous behavioral results we collected with a similar experimental design [
We contrasted activation during the trials against the activation within the static dots presentation (baseline) to obtain significance activation maps. The analysis was performed separately for 0% and 75% coherence levels in the optic flow field
Experimental Conditions | Center coordinates (MNI) | t score | Region | Experimental Conditions | Center coordinates (MNI) | t score | Region | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
x | y | z | x | y | z | ||||||
Offset 10 cmvs 10 cm | −62 | −45 | 1 | 5.8782 | MTG_L | Offset 50 cm vs 10 cm | −48 | 38 | −10 | 5.3335 | IFG_L |
−45 | 30 | 31 | 5.6209 | MFG_L | 39 | 26 | −14 | 5.2095 | IFG_R | ||
38 | −9 | 61 | 5.4201 | PreCS_R | −22 | 29 | 50 | 4.3096 | MFG_L | ||
−42 | −10 | 58 | 5.0111 | PreCS_L | −43 | −43 | 50 | 4.0464 | IPG_L | ||
−30 | −23 | 68 | 4.8364 | PreCS_L | −19 | 44 | 44 | 3.8016 | SFG_L | ||
−45 | 9 | 45 | 4.6973 | PreCS_L | −51 | 3 | 37 | 3.4871 | PreCS_L | ||
52 | −76 | 22 | 4.3901 | MTG_R | −24 | 30 | 47 | 3.4871 | MFG_L | ||
−37 | −65 | 44 | 4.295 | AG_L | −52 | −70 | 28 | 3.4667 | AG_L | ||
−26 | 31 | 43 | 4.1308 | MFG_L | 52 | −62 | 19 | 2.9655 | MTG_R | ||
−43 | 15 | 46 | 3.7597 | MFG_L | −42 | −43 | 51 | 2.7081 | IPG_L | ||
24 | −48 | 73 | 3.6131 | SPG_R | 39 | −57 | 52 | 2.686 | AG_R | ||
−64 | −19 | −23 | 3.5173 | MTG_L | 32 | −70 | 57 | 2.3657 | SPG_R | ||
−50 | 13 | 25 | 3.2137 | IFG_L | −64 | −24 | −23 | 2.3448 | ITG_L | ||
−56 | −1 | −25 | 3.164 | MTG_L | −51 | −72 | 33 | 2.0063 | AG_L | ||
−25 | −6 | 65 | 3.103 | SFG_L | Offset 50 cm vs 50 cm | 32 | 2 | 59 | 10.3475 | MFG_R | |
−49 | 11 | 27 | 3.0358 | IFG_L | 50 | 42 | 6 | 10.0239 | IFG_R | ||
−27 | 32 | 42 | 2.9486 | MFG_L | 29 | 5 | 59 | 8.7421 | MFG_R | ||
45 | 12 | 31 | 2.6691 | IFG_R | 45 | 42 | 22 | 8.4385 | MFG_R | ||
11 | 39 | 42 | 2.4373 | SFG_R | 47 | 43 | 16 | 6.8521 | MFG_R | ||
62 | −40 | 25 | 2.1131 | SMG_R | −49 | −33 | 58 | 6.1049 | PostCS_L | ||
Offset 10 cm vs 50 cm | 15 | −72 | 28 | 4.9103 | Cuneus_R | 57 | −40 | 50 | 5.9746 | IPG_R | |
−41 | −41 | 57 | 4.4563 | PostCS_L | 44 | −46 | 52 | 5.9718 | IPG_R | ||
61 | −33 | 45 | 3.6884 | SMG_R | −39 | 15 | 1 | 4.6148 | Insula_L | ||
22 | 11 | 65 | 3.3684 | SFG_R | 47 | 45 | 13 | 4.3168 | MFG_R | ||
60 | −37 | 47 | 3.3336 | IPG_R | 42 | 19 | 37 | 4.1277 | MFG_R | ||
32 | 4 | 57 | 3.2927 | MFG_R | −50 | 11 | 25 | 3.9546 | IFG_L | ||
−39 | 28 | −14 | 3.2274 | IFG_L | −39 | −25 | 63 | 3.7812 | PreCS_L | ||
48 | 42 | 14 | 2.8553 | MFG_R | 27 | −86 | 42 | 3.6851 | SOG_R | ||
55 | −67 | 10 | 2.6978 | MTG_R | 55 | −61 | 31 | 3.4739 | AG_R | ||
−23 | 61 | 15 | 2.6792 | SFG_L | −44 | 48 | 18 | 3.1624 | MFG_L | ||
39 | −51 | 57 | 2.6526 | SPG_R | −20 | 25 | 56 | 3.0011 | SFG_L | ||
59 | −64 | −9 | 2.4649 | ITG_R | −50 | 34 | 11 | 2.6471 | IFG_L | ||
−46 | 36 | 19 | 2.4134 | MFG_L | −45 | 10 | 42 | 2.5428 | PreCS_L | ||
−45 | 21 | 41 | 2.2472 | MFG_L | −61 | −62 | −8 | 2.2485 | ITG_L | ||
−9 | −65 | 49 | 2.1421 | Precuneus_L | −62 | −57 | −2 | 2.1452 | MTG_L | ||
40 | 22 | −10 | 2.1283 | Insula_R | |||||||
−44 | 20 | 43 | 2.0606 | MFG_L |
Experimental Conditions | Center coordinates (MNI) | t score | Region | Experimental Conditions | Center coordinates (MNI) | t score | Region | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
x | y | z | x | y | z | ||||||
Offset 10 cm vs 10 cm | −67 | −25 | −21 | 6.8192 | ITG_L | Offset 10 cm vs 50 cm | −56 | −2 | −19 | 6.4753 | MTG_L |
−43 | 12 | 49 | 6.4968 | MFG_L | 34 | −44 | 63 | 5.1668 | PostCS_R | ||
−47 | 33 | −6 | 6.0027 | IFG_L | 28 | −10 | 68 | 5.1602 | SFG_R | ||
−34 | −57 | 64 | 5.5322 | SPG_L | 51 | −67 | 24 | 4.9411 | MOG_R | ||
−49 | −10 | 54 | 5.5004 | PostCS_L | 59 | 2 | 33 | 4.7903 | PreCS_R | ||
−42 | 14 | 6 | 5.317 | Insula_L | −58 | −59 | 31 | 4.7567 | AG_L | ||
−35 | −28 | 65 | 5.0485 | PreCS_L | 51 | 8 | −29 | 4.3979 | MTG_R | ||
64 | −40 | 23 | 4.822 | SMG_R | −44 | 10 | 48 | 4.3957 | PreCS_L | ||
−68 | −43 | −5 | 4.5121 | MTG_L | 52 | −74 | 22 | 4.3723 | MTG_R | ||
−40 | 18 | 49 | 4.2904 | MFG_L | 34 | −41 | 67 | 4.0675 | PostCS_R | ||
−62 | −36 | −1 | 4.1254 | MTG_L | −19 | −25 | 74 | 3.856 | ParaCL_L | ||
42 | −12 | 61 | 4.0469 | PreCS_R | −43 | 20 | 45 | 3.7685 | MFG_L | ||
−20 | 33 | 51 | 3.9973 | SFG_L | −21 | 36 | 48 | 3.3604 | SFG_L | ||
−45 | 5 | 0 | 3.8583 | Insula_L | 57 | 1 | −21 | 3.301 | MTG_R | ||
64 | −58 | 2 | 3.8517 | MTG_R | −53 | −68 | 22 | 2.6059 | MTG_L | ||
59 | −4 | −14 | 3.6064 | MTG_R | −57 | −40 | 50 | 2.2667 | IPG_L | ||
68 | −32 | 38 | 3.4659 | SMG_R | Offset 50 cm vs 10 cm | 43 | −60 | 48 | 8.139 | AG_R | |
−49 | 32 | 18 | 3.4331 | IFG_L | 65 | −8 | 30 | 6.9923 | PostCS_R | ||
−66 | −24 | −19 | 2.7305 | ITG_L | −53 | −68 | 38 | 6.8438 | AG_L | ||
−64 | −57 | −9 | 2.5988 | MTG_L | −19 | 43 | 45 | 6.1479 | SFG_L | ||
Offset 50cm vs 50 cm | −61 | −53 | 2 | 8.8346 | MTG_L | −54 | −67 | 29 | 5.4362 | AG_L | |
−49 | 5 | 35 | 7.6157 | PreCS_L | −59 | −11 | −31 | 5.381 | ITG_L | ||
−65 | −21 | −16 | 7.4735 | MTG_L | −44 | 13 | 45 | 5.3593 | MFG_L | ||
−59 | −59 | 27 | 5.4359 | AG_L | −29 | −72 | 56 | 5.2191 | SPG_L | ||
−18 | 29 | 55 | 5.2575 | SFG_L | 45 | −50 | 52 | 5.1744 | IPG_R | ||
−58 | −65 | −9 | 5.056 | ITG_L | 31 | 61 | −3 | 4.7883 | SFG_R | ||
−32 | −68 | 58 | 4.4519 | SPG_L | −22 | 24 | 58 | 4.7555 | SFG_L | ||
47 | 47 | 0 | 4.2252 | IFG_R | −42 | 6 | 52 | 4.7128 | MFG_L | ||
65 | −36 | −11 | 4.0454 | MTG_R | −44 | −50 | 52 | 4.6378 | IPG_L | ||
64 | −52 | −12 | 3.94 | ITG_R | 60 | −8 | −22 | 4.582 | MTG_R | ||
15 | 36 | 54 | 3.852 | SFG_R | −31 | −69 | 58 | 4.5712 | SPG_L | ||
−43 | 43 | 19 | 3.8006 | MFG_L | −55 | 23 | 13 | 4.5267 | IFG_L | ||
−51 | −72 | 31 | 3.0919 | AG_L | −62 | −19 | −12 | 4.4022 | MTG_L | ||
54 | 32 | 14 | 2.9253 | IFG_R | −10 | 32 | 53 | 4.2148 | SFG_L | ||
−40 | 4 | 2 | 2.7089 | Insula_L | 56 | 20 | 15 | 4.0634 | IFG_R | ||
49 | −73 | 38 | 2.6896 | AG_R | 42 | 23 | 37 | 4.02 | MFG_R | ||
34 | −1 | −32 | 2.5066 | Fusiform_R | −50 | 20 | 24 | 3.67 | IFG_L | ||
−60 | −14 | −30 | 2.2694 | ITG_L | −38 | 22 | 49 | 3.6474 | MFG_L | ||
−34 | 17 | −18 | 2.2464 | IFG_L | −63 | −41 | −1 | 3.556 | MTG_L |
51 | −26 | 55 | 2.1535 | PostCS_R | −64 | −45 | −3 | 3.4299 | MTG_L | ||
---|---|---|---|---|---|---|---|---|---|---|---|
−58 | −12 | 40 | 3.2885 | PostCS_L | |||||||
−46 | 46 | −5 | 3.2316 | IFG_L | |||||||
47 | −69 | 47 | 3.0358 | AG_R | |||||||
66 | −30 | −20 | 2.7127 | ITG_R | |||||||
52 | −26 | 53 | 2.5203 | PostCS_R | |||||||
−59 | −11 | −11 | 2.1069 | MTG_L |
AG: angular gyrus, IFG: inferior frontal gyrus, IPG: inferior parietal gyrus, ITG: inferior temporal gyrus, MFG: middle frontal gyrus, MOG: middle occipital gyrus, MTG: middle temporal gyrus, ParaCL: paracentral lobule, PostCS: postcentral cortex, PreCS: precentral cortex, SFG: superior frontal gyrus, SMG: supramarginal gyrus, SOG: superior occipital gyrus, SPG: superior parietal gyrus.
In general, across all the subjects, the activation areas were distributed along the motion processing pathway [
For all x-offset target conditions, when we compared the activation for 0% coherence and 75% coherence conditions, the latter resulted in more distributed activation aroundthe bilateral pre-central and post-central temporal areas, MTG, IFG and IPS.
Based on previous functional imaging data related to TTP and TTC processing (e.g. Field & Wann, 2005), we defined several functional regions of interest based on the Automated Anatomical Labeling (AAL) atlas.
The percent signal changes suggest that bilateral precentral and postcentral sulci as well as a MTG are highly involved in the processing of global optic flow. The activation in hMT bilaterally suggests that more complicated visual processing is performed when there is more than one cue that subjects might use (e.g. global
optic flow, object velocities, and symmetry heuristics) for their TTP judgments. The activation of IFG and MFG may be underlying the process of decision making for solving the task.
For each condition described above, we also compared the trials in which subjects gave the correct responses to those with incorrect responses. For the symmetric conditions (offsets of 10 cm vs 10 cm and 50 cm vs 50 cm), the activations were more extended for trials with correct responses than for those with incorrect responses. The activations in precentral postcentral, IPS, MTG and MFG regions, in both correct and incorrect trials, suggest the involvement of these areas in solving the underlying task. For asymmetric conditions with the leading target at 10 cm x-offset (10 vs 50), invalid image velocity information was provided to the subjects, resulting in more distributed activation in incorrect trials than in correct trials, at 0% coherence. When coherent background dots were presented (75% coherence), thereby providing global motion information, subjects exploited multiple cues, resulting in more distributed activation in correct trials than in incorrect trials. Conversely, for the asymmetric conditions with the leading target at 50 cm x-offset (50 vs 10), valid image velocity information was provided, and activations were more distributed in correct trials than incorrect ones in both 0% and 75% coherence level.
In this study, we have used fMRI to record observers’ TTP judgments in the absence of local expansion information. During simulated forward motion, the observer had to judge, which of two red dots would pass him/her first. We have presented the information indicative of forward motion of the observer (global flow information) by manipulating the coherence of the flow field (no coherence vs. 75% coherence). We also manipulated the lateral offsets of the targets from the track vector and the initial target depths from the observer. Since local expansion information was not present in the optic flow, only global flow information could be used for the task. From this global flow information observers could, in principle, utilize simple image cues and/or the complex pattern provided by the entire dot field, but they could do so only when the dot field (RDK) moved coherently. In the case of incoherent motion of the dots, observers could have used other cues, such as relative screen velocity of the targets, which remained available in the display. However, the latter image cues were only valid when the targets were spaced symmetrically around the track vector. In cases of asymmetric spacing and incoherent optic flow, no useful information about relative TTP remained and, as expected, subjects’ behavioral performance was at chance.
For scenarios with asymmetric target spacing and coherent optic flow, we expected above chance performance if and only if the global flow information could be fully exploited. However, this was not the case. Coherence of optic flow had a small positive effect, only when the targets were spaced symmetrically around the track vector. In the presence of coherent optic flow in the background, this produced stronger and temporally extended cortical activation along the middle temporal gyrus, the precentral, and postcentral sulcus regions.
When the two targets were spaced asymmetrically, coherent optic-flow could in principle produce good performance if the relative rates of change of the respective angle between the target object and the track vector are considered. However, our results showed that this was not the case suggesting that in the presence of asymmetric targets, observers failed to exploit the global flow information for judging TTP. Simpler image cues, such as the targets’ positions and relative velocities would only provide valid information if the targets are spaced symmetrically to either side of the track vector (or the center of the aperture). Thus, only in the presence of symmetric targets, could above-chance performance be reached with incoherent flow. This did in fact improve performance but failed to approach perfection. Thus, with multiple sources of information, when judging TTP, subjects appear to integrate several cues through an economic strategy that mostly rely on image cues. This strategy becomes clearly noticed when local tau information is missing and the symmetry assumption holds.
The cortical activities during TTP judgments reflect this economic strategy. In general, subjects showed higher and broader activations on trials with 75% coherence than on those with 0% coherence. This suggests that they did processoptic flow information when making TTP judgments, which is consistent with previous studies [
Previous retinotopic mapping and fMRI studies in humans have established a continuum of several motion-selective regions, including cortical areas hMT and superior parietal gyrus [
Significant differences in percent signal change were found bilaterally in hMT when the two targets were asymmetric. Activity during stimuli with 0% coherence was higher than during stimuli with 75% coherence in the 10 vs 50 condition, whereas activity during stimuli with 75% coherence was higher than during stimuli with 0% coherence in the 50 vs 10 condition. This points to lateralized differences that reflect the complex reaction of hMT to changes in global and local information. Remember that only when global cues were unavailable, subjects based their TTP judgments on the velocity discrepancy between the targets [
Consistent with previous research, we also found activation in bilateral superior colliculus (SC), which is an area involved in motor preparation and attention [
In summary, in this study we investigated the neural substrate of the mechanisms involved in TTP judgments in the absence of local expansion cues. Previous behavioral results suggested that the subjects base their TTP judgments on the integration of multiple sources of information, with emphasis on image cues, such as target velocity, which are supplemented by global optic flow information, if the latter is coherent. Accordingly, and consistent with previous studies [
The authors declare that they have no conflict of interest.
Geng, Y., Sikoglu, E.M., Hecht, H. and Vaina, L.M. (2018) Functional Neuroanatomy of Time-To-Passage Perception. Journal of Behavioral and Brain Science, 8, 622-640. https://doi.org/10.4236/jbbs.2018.811039