What just happened with open access at the Journal of Vision?

Vision researchers recently received an email from ARVO, the publisher of Journal of Vision, that begins:

On January 1, 2016, Journal of Vision (JOV) will become open access.

But in the view of most, JoV has been open access since its inception! It’s always been an author-pays, free access journal: all articles are published on its website and can be downloaded by anyone.

But free-to-download is not enough for open access, not according to the definition of open access formulated in Budapest in 2001. Open access means (according to this definition) the right not only to download but also to

distribute, … pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers

But JoV, which has always held the copyright of the articles it publishes, says that “All companies, commercial and nonprofit, should contact ARVO directly for permission to reprint articles or parts thereof”.

Starting in 2016, such permission won’t be required.

However, paying your $1,850 for standard publication in JoV in 2016 will not get you everything. The updated Budapest declaration recommends that journals use the license CC-BY.  But JoV‘s publisher has instead chosen to use the license CC BY-NC-ND, , meaning that articles cannot be used commercially (“non-commercial”) and that you can’t distribute bits of the article (“no derivatives”). Yet increasingly today, parts of science involve mining and remixing previously-published data and content, which the ND clause of the license prohibits (unless you get special permission). Education and journalism requires re-use of bits too; think about how many textbooks and articles on the web show just one figure (a “derivative”) or illustration from a scientific paper.

And while the non-commercial, NC clause might sound rather harmless for spreading knowledge, it is sometimes unclear what non-commercial really covers. It may prevent a university, especially private universities, from distributing the article as part of course content that a student pays for (via their tuition).

For these reasons, CC BY is the way we should be going, which is why UK funders like the Wellcome Trust and RCUK require that researchers receiving grants from them publish their articles CC BY. To accommodate this, JoV as part of their new policy will license your article as CC BY, if you pay an additional fee of $500!

What ARVO has done here is only a small step forward for JoV, and unfortunately a rather confusing step. The bigger change has occurred with ARVO’s journal Investigative Ophthalmology and Vision Science (IOVS), which was only accessible via a subscription but starting in 2016, will be CC BY-NC-ND and CC BY.

As you can see, copyright is complicated. Researchers don’t have time to learn all this stuff. And that means recalcitrant publishers (not ARVO, I mean profiteers like Elsevier) can exploit this to obfuscate, complicate, and shift their policies to slow progress towards full open access.

Thanks to Tom Wallis and Irina Harris.

P.S. I think if ARVO had only been changing JoV‘s policies (rather than also the subscription journal IOVS) they wouldn’t have written “JoV will become open access” in that mass email. But because they did, it raised the issue of the full meaning of the term.

P.P.S. Partly because JoV is so expensive, at ECVP there’ll be a discussion of other avenues for open access publishing, such as PeerJ. Go! (although I’ll be stuck in Sydney).

Yellow journalism and Manhattan murders

The headline screams “You’re 45% more likely to be murdered in de Blasio’s Manhattan”.

The evidence? Sixteen people have been killed so far this year in Manhattan, against only eleven over the same period last year.

Does this evidence indicate you are more likely to be murdered, as the headline says? To find out, I tested whether a constant murder rate could explain the results. The probability of getting murdered over the same period last year may be approximately 11/Manhattan’s population = 11/1,630,000 = 0.0000674 = .00674%.

Is it likely that with the same murder rate this period this year, one would get a number as high as 16 murders? Yes.

This can be seen by calculating the 95% confidence interval for 11/1,630,000, which according to 3 different statistical methods, spans 5 to 20. That is, even with a constant murder rate, due to statistical fluctuations, the murders over this period could easily have been as low as 5 or as high as 20.  Just like if one flips a coin 10 times, one may get 3 heads the first time and 6 the next, without the chance of a head changing.

Doing this more properly means comparing the two rates directly.  I did this using three different methods, all of which found no significant difference.

The article also reports that the number of shooting incidents is higher this year, 50 instead of 31. Using the three different statistical methods again, this was (barely) significantly different. So here the journalist has a point. But this should be taken with a big grain of salt. Journalists are always looking for “news”, and if they repeatedly look at how many people have been murdered/shot, eventually they are guaranteed to find an apparent difference, because all possible statistical fluctuations will happen eventually.

The statistics and the code are here.

I only did all this and wrote this post because Hal Pashler saw someone tweet the NYPost piece. Hal knew I had previously looked into the statistics of proportions and asked whether the headline was justified. I invite others to disagree with my calculations if they have a better way of doing it. I don’t think different methods will give a very different result, however.

Four Reasons to Oppose the Use of Elsevier’s Services for the Medical Journal of Australia

Elsevier has a history of unethical behaviour:
  1. Elsevier created fake medical journals to promote Merck products.
  2. Elsevier sponsored arms fairs for the international sale of weapons.
  3. Elsevier sponsored a bill that would have eliminated the NIH mandate that medical research be make freely available within 12 months of publication.
  4. Elsevier requires university and medical libraries to sign agreements that prevent them from reporting the exorbitant prices the libraries pay to subscribe to Elsevier’s journals.
Thanks to such practices, Elsevier makes an outrageous level of profit, 36% of revenue- higher than BMW and higher than the mining giant Rio TintoprofitChart. While researchers and research funders are attempting to transition medical and science publishing to an open access model, Elsevier seeks to hinder this transition. It is their corporate mandate to preserve the high level of profits they make by charging subscription fees for the articles that describe taxpayer-funded research.

 


Researchers ought to be using other providers, not channeling more money into Elsevier.

 

A “tell” for researcher innumeracy?

Evaluating scientists is hard work. Assessing quality requires digging deep into a researcher’s papers, scrutinising methodological details and the numbers behind the narrative. That’s why people look for shortcuts such as the number of papers a scientist has published or the impact factor of the journals published in.

When reading a job or grant application, I frequently wonder: Does this person really take their data seriously and listen to what it’s telling them, or are they just trying to churn out papers? It can be hard to tell. But I’ve noticed an unintentional tell in the use of numbers. Some people, when reporting numbers, habitually report far more decimal places than are warranted.

For example, Thomson/ISI reports its much-derided journal impact factors to three decimal places. This is unwarranted, an example of false precision, both because of the low counts of article numbers and citations typically involved, and because their variability year to year is high. One decimal place is plenty (and given how poor a metric impact factor is, I’d prefer that impact factor simply not be used).

When I see a CV with journal impact factor reported to three decimal places, I feel pushed toward the conclusion that the CV’s owner is not very numerate. So the reporting of impact factor is useful to me; not, however, in the way the researcher intended.

I don’t necessarily expect every researcher to fully understand the sizes, variability, and distribution of the numbers that go into impact factor, so I’m more concerned by how researchers report their own numbers. When to report all the decimal places calculated can be a subtle issue however, as full reporting of some numbers is important for reproducibility.

Bottom line, researchers should understand how summaries of data behave. Reporting numbers with faux precision is a bad sign.


For references on the issue of the third decimal place of impact factor:

UPDATE 8 May: Read this blog on the topic

Bar-Ilan, J. (2012). Journal report card. Scientometrics, 92, 249–260.

Mutz, R., & Daniel, H. D. (2012). The generalized propensity score methodology for estimating unbiased journal impact factors. Scientometrics, 92, 377–390.

old (from 2011) fast-track fee protest letter

There has been renewed interest in fast-track fees, after Nature Scientific Reports began piloting their use. Back in 2011, we wrote a protest letter to seven journals that were using fast-track fees at that time (some have since discontinued). The original website where we posted the letter is defunct, so I am re-posting here.


We write to ask that you discontinue the policy of fast-tracking submissions for a fee.

We have two objections to the policy. First is that we are against any form of preferential treatment for those who can pay. Fast-tracking for a fee creates a two-tier system, wherein the well-funded have an unfair advantage over the less well-to-do; in particular, it exacerbates the differences between developed and developing nations. The fast-track policy at the least allows faster publication by those with funds, improving the chance for the funded to win subsequent grants and to publish before other labs working on the same topic.

Our second objection to the policy stems from our concern that fast-tracked manuscripts will receive an advantage above and beyond just faster publication. Your policy requires that reviewers review more rapidly and editors make their decision in a shorter time than for non-fast-tracked manuscripts. There are three possible negative effects of this. First is that the reduced time for reviewers to spend on their work may lead to more superficial and less stringent reviews. Second is that the editor may sometimes have to complete their action letter on the basis of fewer reviews, when the reviewers do not finish by the deadline. The consequence is that at least some fast-tracked articles will receive less critical reviewing than those by author teams who do not pay for fast-track. The third possible negative effect reflects the linkage between fast-tracked articles and the journals finances. Your journal would receive more money if it evaluates fast-tracked articles less stringently, and even if it does not succumb to this incentive the readers may always have that perception.

Overall, the association of author fees with preferential treatment may eventually imperil sciences reputation among governments and the public. Science traditionally has been something of a refuge from the injustice of rich vs. poor, and previously in publishing there has always been the expectation that publication of an article is a mark of the quality of the work, not the depth of the pockets behind it.

Superficially, the policy of fees for fast-tracking seems similar to the Gold Open Access model, in which authors pay a fee to have their article published if it passes peer review. In most of those journals, however, the policy is set so that authors who pay are treated the same as those who dont. Most Gold OA journals offer a waiver for authors who cannot afford the usual fee, and reviewers and editors do not know whose fees are waived and whose are not. And in those unfortunate cases of journals that require a fee for all, at least there is no difference within the journal with some articles receiving preferential treatment.

We, the undersigned, will not submit work to a journal which offers competitive advantages at a financial premium; nor will we review for any such journal.

Alex O. Holcombe, PhD, Senior Lecturer, School of Psychology, University of Sydney (alex.holcombe@sydney.edu.au)
Claudia Koltzenburg, Managing editor, Cellular Therapy and Transplantation (an open access journal in Western/Russian cooperation), University Medical Center Hamburg-Eppendorf, Germany (managingeditor@ctt-journal.com)
Kaan Цztьrk, Dept. of Information Systems and Technologies, Yeditepe University, Istanbul, Turkey. (kaan.ozturk@yeditepe.edu.tr)
Ayşe Karasu, METU, Dept. of Physics, Ankara, Turkey (akarasu@metu.edu.tr)
Arman Abrahamyan, PhD, Postdoctoral Research Fellow, School of Psychology, University of Sydney (arman.abrahamyan@sydney.edu.au)
Bill Hooker, Portland, OR (cwhooker@fastmail.fm)
William Gunn, San Diego CA
Daniel Mietchen, PhD, Jena, Germany (daniel.mietchen@science3point0.com)
Daniel Linares, PhD, Generalitat de Catalunya, Spain (danilinares@gmail.com)
Barton L. Anderson, School of Psychology, University of Sydney
Kiley Seymour, PhD, Alexander von Humboldt postdoctoral fellow, Berlin, Germany
Bjorn Brembs, PhD, Heisenberg Fellow, Freie Universitдt Berlin, Germany
M Fabiana Kubke, PhD, University of Auckland, New Zealand
Graham Steel, Glasgow, Scotland ( graham at science3point0.com )
Matthew Davidson, Psychology Dept, Columbia University (matthew@psych.columbia.edu)
Richard Badge, PhD, Lecturer, Department of Genetics, University of Leicester, UK (rmb19@leicester.ac.uk)
Pedro Mendes, PhD, Professor, School of Computer Science, The University of Manchester, UK (mendes.uniman@googlemail.com)
R. Steven Kurti, PhD, Director Biomaterials and Photonics Laboratory, Loma Linda University School of Dentistry, California (skurti@llu.edu)

The above are the original authors and signatories. The link [now dead] will reveal new (post 25 April 2011) signatories.

Reporting items from a stream, and mixture modeling to reveal buffering and a bottleneck

In our basic task, one or two streams of stimuli are rapidly presented. The target(s) to be reported are highlighted with cues that encircle them. On half of trials, participants are first queried about the left target, and in half they are first queried about the right target. This has no significant effect on the main result- a substantial disadvantage in reporting the right target, if the left target must also be reported.

In our basic task, one or two streams of stimuli are rapidly presented. The target(s) to be reported are highlighted with cues that encircle them. On half of trials, participants are first queried about the left target, and in half they are first queried about the right target. This has no significant effect on the main result- a substantial disadvantage in reporting the right target, if the left target must also be reported.

My collaborators and I have started using a new behavioural technique to better understand attentional selection from a rapid stream of stimuli. We have applied this to gain insights into the effect of naps on learning (Cellini et al., in press), the nature of the attentional blink (Goodbourn et al., in preparation), and function in parietal patients.

Here I explain the technique in the context of our study of a particular attentional phenomenon called pseudoextinction (Goodbourn & Holcombe, 2015).

The technique dissociates time of sampling visual information from the nature of subsequent processing. Stimuli are presented rapidly in series (a “stream”), shown here with one stream of letters on the left and a second stream on the right.

On an unpredictable frame in the sequence, the stimuli on that frame are cued by two circles, which enclose the stimuli. The participants’ task is to report the cued stimuli, letters in this case.

Accuracy is much poorer for the cued stimulus on the right than for the cued stimulus on the left. But if only one of the streams is cued, accuracy is equally high whether the cue is on the left or the right. This deficit specific to two-target conditions is pseudoextinction. The deficit is unaffected by which stream the participant is asked to report first. It likely reflects a severe capacity limit.

a. Each response of the participant corresponds to a particular item in the stream (because all items are presented on each trial). The distribution of the positions of these items is usually centred around the time of the cue, denoted as zero. b.  Mixture modelling fits the data with a combination of two distributions, the guessing distribution shown in light grey and a Gaussian, shown in dark grey. This fit yields the latency (mean) and temporal precision (standard deviation) of the Gaussian as well as the proportion of guessing trials.

a. Each response of the participant corresponds to a particular item in the stream (because all items are presented on each trial). The distribution of the positions of these items is usually centred around the time of the cue, denoted as zero. b. Mixture modelling fits the data with a combination of two distributions, the guessing distribution shown in light grey and a Gaussian, shown in dark grey. This fit yields the latency (mean) and temporal precision (standard deviation) of the Gaussian as well as the proportion of guessing trials.

Participants’ responses were coded in terms of the serial position of the corresponding item in the stream. For example, if a participant reports the letter ‘A’ for the left stream and it was presented not at the time of the cue but two frames later, that response is coded as +2. If their report corresponds to the item immediately preceding the cued stimulus, it is coded as -1, and a report of the cued item is coded as 0. Random guesses thus will contribute an approximately uniform distribution to the histogram of serial position errors . This is quantified by mixture modelling, which determines the relative proportion of guesses and cue-related reports that best fit the data. We model the cue-related responses as a Gaussian distribution. The mixture modeling procedure yields its latency (position of the peak of the distribution relative to the time of the cue) and precision (standard deviation). It also estimates the proportion of trials that participants guessed or misperceived the letter versus the complementary proportion, which we call efficacy, of trials that participants reported a letter from around the time of the cue.

In cuing experiments, researchers typically conceive of the appearance of the cue as triggering attention to begin sampling from the scene. However, we have consistently observed that the distribution is symmetric and centred near the time of the cue. This indicates that rather than the cue triggering the intake of information from the letter stream, the letters are taken into a buffer before the cue is even presented. If letters were not already in a buffer at the time of the cue, responses from the left (earlier) side of the distribution would be relatively uncommon, skewing the distribution towards later responses.

When two streams are presented, participants perform much better for the stream on the left (if the streams are in a horizontal configuration) or much better for the stream on the top (if the streams are vertically arrayed). If only one stream is presented, participants perform approximately equally in all four positions (data not shown).

When two streams are presented, participants perform much better for the stream on the left (if the streams are in a horizontal configuration) or much better for the stream on the top (if the streams are vertically arrayed). If only one stream is presented, participants perform approximately equally in all four positions (data not shown).

The pseudoextinction phenomenon, a right-side deficit when both streams are cued, manifests both in raw accuracy and also in the accuracy-related parameter of the mixture modelling. This is the efficacy parameter – the proportion of trials captured by the cue-related Gaussian distribution. Whereas efficacy when only one stream is presented or cued is similar on both the left and the right of fixation, and above and below fixation (not shown), when two streams are presented one stream suffers. The right stream suffers in a horizontal arrangement and in a vertical arrangement the inferior stream suffers, consistent with preferred reading order.

The decrease in efficacy for the extinguished stream is not accompanied by a change in latency or standard deviation of the Gaussian distribution of cue-related responses. Moreover, the correlogram of the serial position error for the two streams reveals that the two streams are sampled independently, indicating that the items are buffered independently, without regard to reading order or which hemisphere they are processed by. Together these results suggest that items are always sampled from the stream in the same way, but a subsequent processing limitation results in pseudoextinction if two targets must be processed.

Related patterns of performance have arisen in previous literature, and typically have been attributed to a difference between the left and right hemisphere (e.g. Scalf, Banich, Kramer, Narechania, & Simon, 2007). That however cannot explain the superior/inferior difference, so researchers sometimes then appeal to a difference in dorsal vs. ventral cerebral functioning. We suspect it instead reflects attentional prioritisation of the left item for serial high-level processing, for tokenisation or memory consolidation.

References

Cellini, N., Goodbourn, P.T., McDevitt, E.A., Martini, P., Holcombe, A.O., & Mednick, S.C. (in press). A daytime nap reduces the attentional blink. Attention, Perception, & Psychophysics.

Goodbourn, P.T. & Holcombe, A.O. (2015). ‘Pseudoextinction’: Asymmetries in simultaneous attentional selectionJournal of Experimental Psychology: Human Perception and Performance, 41(2), 364–84.

Martini, P. (2013) “Sources of bias and uncertainty in a visual temporal individuation task.” Attention, Perception, & Psychophysics 75: 168-181.

Scalf, P. E., Banich, M. T., Kramer, A. F., Narechania, K., & Simon, C. D. (2007). Double take: parallel processing by the cerebral hemispheres reduces attentional blink. Journal of Experimental Psychology. Human Perception and Performance, 33(2), 298–329. doi:10.1037/0096-1523.33.2.298

Nature Scientific Reports. Fast-tracking fees history and concerns.

Nature Scientific Reports has adopted is piloting fast-tracking for a fee.

Four years ago, I noticed that several journals had adopted such a policy. I raised a number of concerns, such as

  • What happens if the fast-tracking period elapses and a reviewer hasn’t gotten their review in yet? Will the decision about the manuscript be made without that review?
  • How is the additional money used? Does any go to reviewers?
  • Does the action editor know when a particular manuscript is being fast-tracked? Do the reviewers? To avoid monetary influence, both should be blind to this, but that seems impossible if these things are to be expedited.
  • Will articles which benefited from fast-tracking be indicated in a note associated with those articles? Without such a policy, all articles in the journal may be sullied, at least in the minds of cynics.
  • Are the fees worth risking the appearance of favoritism for money, the disadvantage in speed to scientists with fewer resources, and the possible loss of public trust in science?

We started a petition against the policy, and our complaints seem to have led to the demise of the policy at a few journals. For details, see my previous posts on the topic.

I suggest that the tag #fastTrackFee be used on social media to discuss this.