Archive for the ‘perception’ Category
Below are research presentations I’m involved in for Vision Sciences Society in May. If you’re attending VSS, don’t forget about the Publishing, Open Access, and Open Science satellite which will be Friday at 11am. Let us know your opinion on the issues and what should be discussed here
Splitting attention slows attention: poor temporal resolution in multiple object tracking
Alex O. Holcombe, Wei-Ying Chen
Session Name: Attention: Tracking (Talk session)
Session Date and Time: Sunday, May 13, 2012, 10:45 am – 12:30 pm
Location: Royal Ballroom 4-5
When attention is split into foci at disparate locations, the minimum size of the selection focus at each location is larger than if only one location is targeted (Franconeri, Alvarez, & Enns, 2007)- splitting attention reduces its spatial resolution. Here we tested temporal resolution and speed limits. STIMULUS. Three concentric circular arrays (separated by large distances to avoid spatial interactions between them) of identical discs were centered on fixation. Up to three discs (one from each ring) were designated as targets. The discs orbited fixation at a constant speed, occasionally reversing direction. After the discs stopped, participants were prompted to report the location of one of the targets. DESIGN. Across trials, the speed of the discs and the number in each array was varied, which jointly determined the temporal frequency. For instance, with 9 objects in the array, a speed of 1.1 rps would be 9.9 Hz. RESULTS. With only one target, tracking was not possible above about 9 Hz, far below the limits for perceiving the direction of the motion, and consistent with Verstraten, Cavanagh, & LaBianca (2000). The data additionally suggest a speed limit, with tracking impossible above 1.8 rps, even when temporal frequency was relatively low. Tracking two targets could only be done at lower speeds (1.4 rps) and lower temporal frequencies (6 Hz). This decrease is approximately that predicted if at high speeds and high temporal frequencies, only a single target could be tracked. Tracking three yielded still lower limits. Little impairment was seen at very slow speeds, suggesting these results were not caused by a reduction in spatial resolution. CONCLUSION. Splitting attention reduces the speed limits and the temporal frequency limits on tracking. We suggest a parallel processing resource is split among targets, with less resource on a target yielding poorer spatial and temporal precision and slower maximum speed.
A hemisphere-specific attentional resource supports tracking only one fast-moving object.
Wei-Ying Chen & Alex O. Holcombe
Session Name: Attention: Tracking (Talk session)
Session Date and Time: Sunday, May 13, 2012, 10:45 am – 12:30 pm
Location: Royal Ballroom 4-5
Playing a team sport or taking children to the beach involves tracking multiple moving targets. Resource theory asserts that a limited resource is divided among targets, and performance reflects the amount available per target. Holcombe and Chen (2011) validated this with evidence that tracking a fast-moving target depletes the resource. Using slow speeds Alvarez and Cavanagh (2005) found the resource consumed by additional targets is hemisphere-specific. They didn’t test the effect of speed, and here we tested whether speed also depletes a hemisphere-specific resource. To put any speed limit cost in perspective, we modeled a “total depletion” scenario- the speed limit cost if at high speeds one could not track the additional target at all and had to guess one target. Experiment 1 found that the speed limit for tracking two targets in one hemifield was similar to that predicted by total depletion, suggesting that the resource was totally depleted. If the second target was instead placed in the opposite hemifield, little decrement in speed limit occurred. Experiment 2 extended this comparison to tracking two vs. four targets. Compared to the speed limit for tracking two targets in a single hemifield, adding two more targets in the opposite hemifield left the speed limit largely unchanged. However starting with one target in both the left and right hemifields, adding another to each hemifield had a severe cost similar to that of the total depletion model. Both experiments support the theory that an object moving very fast exhausts a hemisphere-specific attentional tracking resource.
Attending to one green item while ignoring another: Costly, but with curious effects of stimulus arrangement
Shih-Yu Lo & Alex O. Holcombe
Session Name: Attention: Features I (Poster session)
Session Date and Time: Monday, May 14, 2012, 8:15 am – 12:15 pm
Location: Vista Ballroom
Splitting attention between targets of different colors is not costly by itself. As we found previously, however, monitoring a target of a particular color makes one more vulnerable to interference by distracters that share the target color. Participants monitored the changing spatial frequencies of two targets of either the same (e.g., red and red) or different colors (e.g., red and green). The changing stimuli disappeared without warning and participants reported the final spatial frequency of one of the targets. In the different-colors condition, a large cost occurs if a green distracter is superposed on the red target in the first location and a red distracter is superposed on the green target in the second location. This likely reflects a difficulty with attending to a color in one location while ignoring it in another. Here we focus on a subsidiary finding regarding perceptual lags. Participants reported spatial frequency values from the past rather than the correct final value, and such lags were greater in the different-colors condition. This “perceptual lag” cost was found when the two stimuli were horizontally arrayed but not, curiously, when they were vertically arrayed. Arrangement was confounded however with processing by separate brain hemispheres (opposite hemifields). In our new study, we unconfounded arrangement and presentation in separate hemifields with a diagonal condition- targets were not horizontally arrayed but were still presented to different hemifields. No significant different-colors lag cost was found in this diagonal arrangement (5 ms) or in the vertical arrangement (86 ms), but the cost (167 ms) was significant in the horizontal arrangement, as in previous experiments. Horizontal arrangement apparently has a special effect apart from the targets being processed by different hemispheres. To speculate, this may reflect sensitivity to bilateral symmetry and its violation when the target colors are different.
Dysmetric saccades to targets moving in predictable but nonlinear trajectories
Reza Azadi, Alex Holcombe, and Jay Edelman
A saccadic eye movement to a moving object requires taking both the object’s position and velocity into account. While recent studies have demonstrated that saccades can do this quite well for linear trajectories, its ability to do so for stimuli moving in more complex, yet predictable, trajectories is unknown. With objects moving in circular trajectories, we document failures of saccades not only to compensate for target motion, but even to saccade successfully to any location on the object trajectory. While maintaining central fixation, subjects viewed a target moving in a circular trajectory at an eccentricity of 6, 9, or 12 deg for 1-2 sec. The stimulus orbited fixation at a rate of 0.375, 0.75, or 1.5 revolutions/sec. The disappearance of the central fixation point cued the saccade. Quite unexpectedly, the circularly moving stimuli substantially compromised saccade generation. Compared with saccades to non-moving targets, saccades to circularly moving targets at all eccentricities had substantially lower amplitude gains, greater curvature, and longer reaction times. Gains decreased by 20% at 0.375 cycles/sec and more than 50% at 1.5 cycles/sec. Reaction times increased by over 100ms for 1.5 cycles/sec. In contrast, the relationship between peak velocity and amplitude was unchanged. Given the delayed nature of the saccade task, the system ought to have sufficient time to program a reasonable voluntary saccade to some particular location on the trajectory. But, the abnormal gain, curvature, and increased reaction time indicate that something else is going on. The successive visual transients along the target trajectory perhaps engage elements of the reflexive system continually, possibly engaging vector averaging processes and preventing anticipation. These results indicate that motor output can be inextricably bound to sensory input even during a highly voluntary motor act, and thus suggest that current understanding of reflexive vs. voluntary saccades is incomplete.
How many of these laws do you know? The top 15, listed below, are based on psychology journal articles 1900-1999, as calculated by Teigen (2002):
LAW (REFERENCE) NUMBER OF MENTIONS
1. Weber’s law (Weber 1834) 336
2. Stevens’ power law (Stevens 1957) 241
3. Matching law (Herrnstein 1961) 183
4. Law of effect (Thorndike 1911) 177
5. Fechner’s law (Fechner 1860) 100
6. Fitts’ Law (Fitts 1954) 82
7. Law of initial values (Wilder 1957) 82
8. Law of comparative judgment (Thurstone 1927) 72
9. Yerkes-Dodson law (Yerkes & Dodson 1908) 52
10. All-or-none law (Bowditch 1871) 45
11. Emmert’s law (Emmert 1881) 43
12. Bloch’s law (Bloch 1885) 41
13. Gestalt laws (Wertheimer 1923) 41
14. Hick’s law (Hick 1952) 31
15. Listing’s law (Listing 1870) 29
Although it’s no longer in fashion in psychology to suggest that empirical generalizations are “laws”, I think the perception ones have held up fairly well. In perhaps every case exceptions have been found, but most of the laws are still useful as generalizations over a lot of empirical territory.
Many people are generally skeptical of psychology as a science, and their voices have grown louder thanks to recent cases of fraud and to articles such as “Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant”, recently published in Psychological Science. So it’s nice to be reminded that psychological science has produced robust generalizations.
On the other hand, few question the validity of perception and psychophysics, which provides many of the laws above; the skeptics are thinking more of other areas, perhaps social psychology, clinical psychology, or developmental psychology. In those areas, effect sizes are smaller and data is harder to gather, so published results are more likely to be statistical flukes.
The “file drawer problem” is clearly one of the biggest reasons to mistrust psychological results, and I’d say it’s probably the biggest problem in all of science. The file drawer problem refers in part to the fact that when scientists can’t replicate a previously published effect, they are very likely to file the results away rather than try to publish them. So I’ve been helping create a website, psychfiledrawer.org (currently in beta), for people to report their failed replications.
Teigen, K. (2002). One Hundred Years of Laws in Psychology The American Journal of Psychology, 115 (1) DOI: 10.2307/1423676
The Derrington, Krauskopf and Lennie (1984) color space is based on the Macleod-Boynton (1979) chromaticity diagram. Colors are represented in 3 dimensions using spherical coordinates that specify the elevation from the isoluminant plane, the azimuth (the hue) and the contrast (as a fraction of the maximal modulations along the cardinal axes of the space).
It’s easier for me to think of a color in cartesian DKL coordinates with the dimensions:
- Luminance or L+M, sum of L and M cone response
- L-M, difference of L and M cone response
- S-(L+M), S cone responses minus sum of L and M cone response
The three classes of cones respond a bit to almost all colors, but some reds excite L cones the most, some greens M cones the most, and some blues S cones the most.
I’ve created the below movie (thanks Jon Peirce and PsychoPy) to show successive equiluminant slices of DKL color space, plotted with cartesian coordinates. These render correctly on my CRT screen, but the colors will be distorted on any other screen. Nevertheless it helps you get a feel for the gamut (colors that can be represented) of a typical CRT at each luminance, where -1 is the minimum luminance of the CRT and +1 is its maximum. The letters R,G,B and accompanying numbers show the coordinates of the phosphors (each gun turned on by itself).
Derrington AM, Krauskopf J, & Lennie P (1984). Chromatic mechanisms in lateral geniculate nucleus of macaque. The Journal of physiology, 357, 241-65 PMID: 6512691
MacLeod DI, & Boynton RM (1979). Chromaticity diagram showing cone excitation by stimuli of equal luminance. Journal of the Optical Society of America, 69 (8), 1183-6 PMID: 490231
We discovered that when an array of colored discs was spun so fast that attention could no longer keep up with it, people could no longer perceive which colors were adjacent.
Together with an additional attentional cueing experiment, this phenomenon suggests that a shift of attention is required to mentally link adjacent elements and apprehend their spatial relationship.
The experiments are described in:
Holcombe, A., Linares, D., & Vaziri-Pashkam, M. (2011). Perceiving Spatial Relations via Attentional Tracking and Shifting Current Biology DOI: 10.1016/j.cub.2011.05.031
The movie shows the main stimulus, but unfortunately it can’t be displayed fast enough on a webpage for you to lose the ability to apprehend the spatial relationships among the colors.
I have written a gentle introduction to perceptual speed limits such as this one.
Time is harder for humans to understand than is space. Our visual systems abound with machinery for processing extensions of space. A continuum of locations are processed in parallel, their spatial relations apprehended without cognitive effort. But for the most part, the mind represents time poorly. Our perception experience is of a very short duration- the specious present. The past is a an amalgamation of events that can be recollected, but we don’t grasp the whole series of moments together as a continuous thread.
Metaphors can make obscure material intuitive- including thoughts about time- especially if the metaphor turns time into space.
George Lakoff and his colleagues compiled a long list of common metaphors in English. I don’t know whether this list was ever formally published, but it’s floating around the web. For time, they list the below. I’ve made a few explanatory notes in brackets.
TIME IS SOMETHING MOVING TOWARD YOU [time-moving metaphor]
“Thursday passed without incident.” [Usually future events are in front, and past events are behind]
Special case : Foreseeable Future Events are Up [I don't understand why they say "future events are up", since the examples seem to indicate that future events start below and then move upward]
“Upcoming events. What’s coming up this week? What events are up ahead?”
TIME IS A LANDSCAPE WE MOVE THROUGH [ego-moving metaphor]
“Thanksgiving is looming on the horizon.”
TIME IS MONEY: “She spends her time unwisely.”
TIME IS A RESOURCE: “We’re almost out of time.”
(BOUNDED) TIME IS A CONTAINER: “He did it in three minutes.”
TIME IS A PURSUER: “Time will catch up with him.”
TIME IS A CHANGER: “Time heals all wounds.”
A SCHEDULE IS A MOVING OBJECT: “He was behind (the) schedule.”
from George Lakoff, Jane Espenson, and Alan Schwartz (1991 unpublished manuscript). Master Metaphor List. 2nd edition, second draft copy.
Studying these metaphors and other linguistic artifacts can illuminate the limitations of our processing of matters temporal. Recently I’ve been wondering why we say “all the time” as in “getting better all the time” rather than “all of time” or “everywhen”. I haven’t been able to find any literature on this (please tell me if you know of any) and I hope to post about it later.
Applicants to the lab postdoc / advanced RA position wanna know what they’d be in for if they took the job. Below are some recent conference abstracts from the lab, along the lines of the funded project.
In Multiple Object Tracking, At High Speeds One May Only Be Able To Track A Single Target—Even If No Crowding Occurs
Alex Holcombe, Wei-Ying Chen [VSS 2011 abstract]
To assess the speed limit for tracking moving objects with attention, first some blobs are designated as targets, then they and other identical blobs travel about. After a variable tracking period, participants must indicate which blobs had been designated as targets. If more blobs are designated for tracking, the maximum speed yielding accurate performance decreases. However, Franconeri et al. (2008; 2010) suggested that the decrease in high-speed performance with more targets is entirely attributable to crowding—in most studies, at higher speeds objects pass near each other more frequently. We assessed the speed limit for tracking one and for tracking two targets. In each of two concentric circular trajectories, two blobs traveled. Within a trajectory, the two blobs were always on opposite sides of fixation. One blob in one trajectory (one-target condition) or one blob in each trajectory (two-target condition) was precued. Separation between the trajectories was varied to assess any effect of crowding. RESULTS. The average speed limit (68% threshold) of six participants was substantially higher for tracking one target (1.9 rps) than for tracking two targets (1.5 rps), even when crowding was avoided with large separation. The slowness of the two-target limit found is similar to that predicted (1.6 rps) if each participant tracked only one target at high speeds, guessing when they picked the wrong one to track. To further investigate what causes the speed limits, we exploited the finding of hemisphere-specific tracking resources (Alvarez & Cavanagh 2005). Two targets were in either the same hemifield or different hemifields. The speed limit was significantly slower (six participants) for targets in the same hemifield than in opposite hemifields, consistent with the involvement of independent resources. Availability of such resources may set the severe speed limits on tracking documented here.
Time to contact does not pop out
Eli Brenner & Alex Holcombe [VSS 2011 abstract]
In visual search, items differing markedly from the others in a basic visual feature are quickly localized, irrespective of the number of distracters. Is time to contact such a basic visual feature? This question cannot be answered with conventional search tasks because time to contact necessarily changes continuously. We therefore developed two alternative tasks in which items converged towards a single point. In the first, before reaching the point, all items disappeared simultaneously. Participants indicated which they thought would have reached the point first. The items were assigned random speeds, with initial distance set so all except the target would have reached the point at the same time. The number of items strongly influenced the difference in time to contact required for the target to be picked reliably. Performance was only slightly better than if participants had simply picked the item that was nearest to the point when the items disappeared. On half the trials of the second experiment one item had a shorter time to contact than the others and on the other half all items had the same time to contact. Participants indicated as quickly as possible whether all items would arrive at the same time. Reaction time hardly depended on the number of items, but on average participants did not respond until the target’s time to contact was less than half that of the other items. For trials with no target, they usually did not respond until when a target would have arrived. The results were similar for simulated motion towards the participant. When the speed heterogeneity was eliminated to make proximity a reliable cue, search was much faster. Apparently having a shorter time to contact does not make an item easy to detect. How then do we cross a busy intersection or negotiate a busy plaza?
|Inability to perceive the spatial relationship of objects revolving too quickly to attentively track|
|Alex Holcombe1, Daniel Linares1, Maryam Vaziri-Pashkam2 [VSS 2010 abstract]
1School of Psychology, University of Sydney
2Department of Psychology, Harvard University
|What is perception missing when one cannot attentively track? To find out, we exploit the finding that an object revolving about fixation faster than 1.5 revolutions per second (rps) cannot be tracked (Verstraten, Cavanagh, Labianca 2000). METHODS. Six Gaussian blobs were evenly spaced along a circular orbit (radius 2 deg). Three colors were used in two identically-ordered triplets, e.g. red-green-cyan-red-green-cyan. The triplet of colors was chosen pseudorandomly on each trial. The blobs moved for three seconds. Observers fixated the center of the rotating ring and subsequently attempted to report the colors’ relative order. In a second experiment, an outer (radius 4 deg) ring of blobs with three new colors, e.g. yellow-blue-fuchsia-yellow-blue-fuchsia, was added. Each blob in the inner ring was aligned with another in the outer ring and observers judged, for any color they chose of the inner ring, which color was aligned with it in the outer ring. In an identification control experiment, observers reported which colors were presented. To confirm the tracking limit, with all blobs set to the same color observers were cued to track one blob and at the end are tested on which blob it was. RESULTS. Observers could identify the colors (>90% correct) at rates over 2.5 rps. The limit on attentive tracking was much lower with average 75% threshold <1.5 rps. For the two experiments eliciting judgments of spatial relationships, 75% threshold rates were again 1.5 rps or lower and participants were near chance at rates for which the colors could easily be identified. Indeed, when viewing the display rotating at 2 rps, most observers are struck by their inability to grasp the relative location of any two colors, despite clear perception of the colors’ identity. CONCLUSIONS. Coordinated individuation by attention may be necessary to extract most spatial relationships.|
We invite applications for a research fellowship/postdoctoral research fellowship working with Dr. Alex Holcombe in the School of Psychology at the University of Sydney. The research area is visual psychophysics, and the project involves the perception and attentive tracking of moving objects. One line of experiments will investigate the limits on judging the spatial relationship of moving objects. See http://www.psych.usyd.edu.au/staff/alexh/ for more on the laboratory’s research.
The University of Sydney is Australia’s first university with an outstanding global reputation for academic and research excellence. The School of Psychology is Australia’s first established psychology department and boasts a proud history of excellence that characterises the entirety of its research and educational activities. You would be working with a dynamic community of local vision researchers (see http://www.physiol.usyd.edu.au/span/ ) and attend seminars and colloquia in perception and related fields.
An essential requirement for the Postdoctoral Research Associate position is a PhD in psychology, vision science, or similar field, and a demonstrated ability to conduct vision research (BSc Honours or equivalent if at the Research Associate level). An understanding of psychophysical and psychology experiment design is also essential. Conducting the experiments will require skill in programming visual perception experiments and experience analysing data from psychophysical and/or psychology experiments, using a command-line tool (as opposed to Excel or SPSS) such as R, MATLAB, or program code in Python, C, etc. Preference may be given to individuals with experience with PsychoPy and R.
The position is full-time fixed term for 16 months subject to the completion of a satisfactory probation period for new appointees. There is the possibility of further offers of employment of up to 12 months, subject to funding and need. Membership of a University approved superannuation scheme is a condition of employment for new appointees.
Remuneration package: up to $92k p.a. (currently ~$90k USD), consisting of a base salary, leave loading and up to 17% employer’s contribution to superannuation). Some assistance towards relocation cost and visa sponsorship may be available for the successful appointee if required. Level of appointment will be commensurate with experience and qualifications.
All applications must be submitted via The University of Sydney careers website. Visit http://sydney.edu.au/positions/ and search by the reference number,
3856/1110 3846/1110, for more information and to apply.
CLOSING DATE: 13 January 2011 (11:30PM Sydney time) with interview to be scheduled in the beginning of February.
The University is an Equal Opportunity employer committed to equity, diversity and social inclusion. Applications from equity target groups and women are encouraged. The University reserves the right not to proceed with any appointment.
UPDATE: human resources here gave me the wrong reference number, which has now been corrected above
If you don’t understand the title of this post, you almost certainly will regret reading further.
We’re doing an experiment in which one target is presented along with m distracters. The participant tries to determine which is the target, and must respond with their best guess regarding which is it. Together the m distracters + 1 target = “number of alternatives”.
In the plots shown are the predictions from vanilla signal detection theory for the relationship between probability correct, d-prime, and number of alternatives. Each distracter is assumed to have a discriminability of d-prime from the target.
The two plots are essentially the inverse of each other.
Note that many studies use two-interval forced choice wherein the basic stimulus containing distracters are presented twice, one with the signal and the participants has to choose which contained the signal. In contrast, here I’m showing predictions for an experiment wherein the target with all its distracters is only presented once, and the participant reports which location contained the target.
I should probably add a lapse rate to these models, and generate curves using a reasonable lapse rate like .01.
I’ll post the R code using ggplot that I made to generate these later; email me if I don’t or you want it now. UPDATE: the code, including a parameter for lapse rate.
reference: Hacker, M. J., & Ratcliff, R. (1979). A revised table for d’ for M-alternative forced choice, 26(2), 168-170.
#To determine the probability of target winning, A, use the law of total probability:
# p(A) = Sum (p(A|B)p(B)) over all B
# Here, B will be all possible target TTC estimates and p(A|B) will be probability distracters
# are all lower than that target TTC estimate, B
# x is TTC estimate of distracter
# Probability that distracter TTC estimate less than target is pnorm(x): area under curve
# less than x.
# m: number of objects, m-1 of which are distracters
# p(A|B)*p(B) = pnorm(x)^(m-1) * dnorm(x-dprime)
# Hacker & Ratcliff, 1979 and Eliot 1964 derive this as did I
# Jakel & Wichmann say that "numerous assumptions necessary for mAFC" where m>2 but not clear
# whether talking about bias only or also about d'
To what extent should we have confidence in the average published scientific result? The news that Professor Marc Hauser of Harvard University may have been involved in scientific misconduct, yielding irreproducible results, prompts us to reflect on whether irreproducible results are usually caught, or not. Investigations of scientific misconduct are *extremely* rare, so I don’t think irreproducible results are usually caught through the official-investigation route. Irreproducible results might be flagged if people tried to replicate them, failed, and then published a paper describing the non-replication. Unfortunately, publishing a non-replication is usually difficult. Those that know this, shy away from the attempt (the “file-drawer problem
“). Those scientists naive enough to not know this, or idealistic enough to attempt to publish a non-replication anyway, sometimes succeed but the successful numbers will be low.
If irreproducible results are unlikely to be flagged as such in the scientific literature, things look very bad for science generally. However, some scientists completely disagree, and think that irreproducible results are usually corrected.
Over at Psychology Today, Professor Art Markam wrote a blog post entitled “Why science is self-correcting“, subtitled “There’s no point in scientific misconduct; it is always found”:
If a result piques the interest of other scientists, then their first step is usually to try to repeat the experiment, perhaps with a few changes to test alternative explanations for a finding. Because scientists are always repeating each other’s experiments, it is hard for a fictitious result to hang on for very long.
As should be clear from what I wrote above, I think this is a misleading picture of the vast majority of fields. I do believe Art is correct for some corners of science. An example is some subfields of vision science, especially those that revolve around visual illusions that can be replicated simply by looking at the display and asking yourself whether you see the illusion! Unfortunately, in most areas of science, experiments are not free and instantaneous. Instead, they are costly and time-consuming.
In such expensive and time-consuming areas of science, even results that attract lots of attempted replications may often end up with published replications when the original result was actually a statistical fluke rather than a true portrait of reality. There’s a vicious cycle: amazing results (which are sometimes reflect a mistake or a statistical fluke) often attract many scientists to pursue them further. If the original result was a mistake or a scientific fluke, most of these attempted replications will fail and never be published (the file-drawer problem). However, a few will succeed via a statistical fluke, and these are likely to be published as replications. John P.A. Joannides has done a fascinating analysis of where this and other sources of systematic distortion in the scientific literature may lead. His provocative conclusion provided the title of his paper: “Why Most Published Research Findings Are False”.
It is certainly appropriate to argue with whether Joannides is right that more than 50% of published findings are actually false, and I’d love to see an attempt to test the title of this blog post—to estimate the proportion of scientific results that are actually replicated. But even without these issues fully settled, I think it does science a disservice to say as Professor Markram did that science always self-corrects. For science to contribute as much as it should to solving society’s problems, society needs to have confidence in science. We need to face science’s problems head on and work towards resolving them. Public confidence in science, and the extent to which the public looks to science, has got to increase! Otherwise, decisions by elected officials may continue to be more faith-based than science-based.
A small part of the solution, I believe, is to lower the barriers to publication of negative results so that more non-replications escape from the file drawer and see the light of day. It’s true that this could create an unmanageable burden of papers that need to be reviewed, but this can be avoided by moving some fraction of the scientific literature towards post-publication review. Another important step will be to make publishing one’s original data the norm. There are objections to these suggestions which I won’t go in to here, for now suffice it to say that I think that on balance, a more prolific science would lead us closer to truth.
Most people are confused about temporal resolution. That includes my students, and even BBC science programmes. So I created this diagram to communicate the basic concept, with the example of human visual processing, using a water-works metaphor.
Why water-works? I’m trying to explain an unfamiliar concept in terms that everyone can understand intuitively. By using a hydraulic metaphor for the nervous system, I’m following in the footsteps of Descartes, who in the 1600s knew almost nothing about the brain or even nerves but nevertheless had a pretty good notion of what was going on:
And truly one can well compare the nerves of the machine that I am describing to the tubes of the mechanisms of these fountains, its muscles and tendons to diverse other engines and springs which serve to move these mechanisms, its animal spirits to the water which drives them, of which the heart is the source and the brain’s cavities the water main.
Here’s my more modern, yet still hydraulic, take on temporal resolution of the visual brain:
At top is the stimulus, which consists of two patches. The top patch alternates between green and red, and the one below alternates between leftward-tilted and rightward-tilted. This image is projected onto the retina. The retina processes the stimulus somewhat before passing it on to the cortex (via the thalamus’ lateral geniculate nucleus), where one population of neurons determines the stimulus color, and another set of neurons determines the stimulus orientation. These processes have high temporal resolution, meaning that they determine color based on a relatively short interval of signals. This is why we can perceive colors correctly even when they are presented at a rapid rate, say 9 colors per second.
The resulting color signals ‘pour’ into the binding process, which has poor temporal resolution. A long interval of signals must accumulate before the process can compute the feature pairing. For the presentation rate depicted, the consequence of the long integration time is that multiple colors and orientations fall within an interval that is essentially simultaneous from the perspective of the binding process. The binding process cannot determine which color and orientation were presented at the same time. At this rate, we can perceive which colors were presented and which orientations were presented, but not the pairing between them (Holcombe & Cavanagh 2001). Click here to see this for yourself.
Large bucket = long interval of signals mixed together before the output is determined= poor temporal resolution = long integration interval = “slow” process.
But the last term in this equation can get us into trouble, because in everyday language the word ‘slow’ conflates temporal resolution with latency. My next post will add to the illustration in an attempt to make the distinction clear.
Rene Descartes (1664). Traite de l’Homme (Treatise of Man) Harvard University Press (1972)
Holcombe AO, & Cavanagh P (2001). Early binding of feature pairs for visual perception. Nature Neuroscience, 4 (2), 127-8 PMID: 11175871
[UPDATED 9 August 2011 to remove reference to non-existent movie link]