Can the contents of consciousness be decoded from patterns of integrated information?

[I8R5]

Consciousness is fascinating and elusive. There is “the hard problem” of how the dynamics of matter can give rise to subjective experience. “The hard problem” (Chalmers) is how some philosophers describe their own job, a job that is both appropriately glamorous and career safe, because it is not about to be taken away from them anytime soon and so difficult that lack of progress in our lifetime cannot reasonably be held against them. Brain scientists are left with “the easy problem” of explaining how the brain supports perception, cognition, and action. What’s taking so long?

Transcending this division of labour, at the intersection between philosophy and brain science, researchers are working on what Anil Seth has called “the real problem”:

“how to account for the various properties of consciousness in terms of biological mechanisms; without pretending it doesn’t exist (easy problem) and without worrying too much about explaining its existence in the first place (hard problem).”

There is a range of interesting ideas toward a theory of consciousness, from metaphors like “fame in the brain” to detailed accounts like the “neuronal global workspace” (Baars, Dehaene). In my mind, it remains unclear to what extent existing proposals are alternative theories that are mutually exclusive or complementary descriptions of the same set of phenomena.

One of the more inspiring sets of ideas about consciousness is integrated information theory (Tononi). IIT posits that consciousness arises from the interactions between the parts of a physical system and allows of degrees. The degree of consciousness of a system can be measured by an index of the overall interactivity among the parts.

States of heightened consciousness are states in which we experience an enhanced capacity to bring together in the present moment all we perceive and all we know with our needs and goals, toward adaptive action.

Our brains, mysteriously, perform an amazing feat of flexible integration of information across many scales of time (from long-term memories to our current situational model and to the momentary glimpse, in which we sense the states of motion of the objects around us), across our peripersonal space (from the scene surrounding us, in memory, to the fixated point), and across sensory modalities (as we combine what we see, hear, feel, smell and taste into an amodal percept of the scene).

And this is just the perceptual part of the process, which is integrated with our sense of current needs and goals to guide our action. It is plausible that this feat of intelligence, which is unmatched by current AI systems, requires high-bandwidth interactions between the brain components that sustain it. IIT suggests that those pieces of information, from perception or memory, that are currently most richly interrelated are the conscious ones. This doesn’t follow, but it is an interesting idea.

Intuitions about social interaction similarly suggest that interactivity is essential for efficient information processing. For example, it is difficult to imagine a team of people working together optimally efficiently on a complex task, if a subset of them is not integrated, i.e. does not interact with the rest of the group. Of course, there are simple tasks, for which independent toiling is optimal. I’m thinking here of tasks that do not require considering all the relationships between subsets of the input. But for complex tasks, like writing a paper, we might expect substantial interactivity to be required.

In computer science, NP hard tasks are those, for which no trick exists that would enable us to partition the elements into a manageable set of subsets, and tackle each in turn. Instead relationships among elements may need to be considered for all subsets, and the number of subsets is exponential in the number of the elements. The elements have to be brought into contact somehow, so we expect the system that can solve the task efficiently to be highly interactive.

A key idea of IIT is that a conscious system should be well integrated in the sense that no matter how we partition it, the partitions are highly interactive. IIT uses information theoretic measures to quantify integrated information. These measures are related to Granger causality. For two components A and B, A is said to Granger-cause B if the past values of A help predict B, beyond what can be achieved by considering only the past of B itself. For the same system composed of parts A and B, a measure of integrated information would assess to what extent taking the interactions between A and B into account enables us to better predict the state of the system (comprising both A and B) than ignoring the interactions.

For a more complex system, integrated information measures consider all subsets. The integrated information of the whole is the maximum of the integrated information values of the subsets. In other words, the system inherits its level of integrated information φmax from its most strongly interactive clique of components. Each subset’s interactivity is judged by the degree to which it cannot be partitioned (and interactions across partitions ignored) in predicting the current state from the past. A system is considered highly interactive if any partitioning greatly reduces an estimate of the mutual information between its past and present states.

Note that to achieve high integrated information, the information flow must not simply spread the information, rendering it redundant across the parts. Rather the information in different parts must be complementary and must be encoded such that it needs to be considered jointly to reveal its meaning.

For example, consider binary variables X, Y, and Z. X and Y are independent uniform random variables and Z = X xor Y, i.e. Z=1 if either X or Y is 1, but not both. Each variable then has an entropy of one bit. X and Y each singly contain no information about Z. Being told X does not tell us anything about Z, because Y is needed to interpret the information X conveys about Z. Conversely, X is needed to interpret the information Y conveys about Z. X and Y together perfectly determine Z. (The mutual information I(X;Z) = 0, the mutual information I(Y;Z)=0, but the mutual information I(X,Y;Z) = 1 bit.)

screenshot1365

Figure | The continuous flash suppression paradigm used by the authors. A stimulus presented to one eye is rendered invisible by a sequence of Mondrian images presented to the other eye.

In a new paper, Haun, Oizumi, Kovach, Kawasaki, Oya, Howard, Adolphs, and Tsuchiya (pp2016) derive some interesting predictions from integrated information theory and test them with electrocorticography, measuring neuronal activity in human patients that have implanted subdural electrodes in their brains. The authors use the established psychophysical paradigms of continuous flash suppression and backward masking to render stimuli that are processed in cortex subjectively invisible and their representations, thus, unconscious.

The paper uses the previously described measure φ* of integrated information. This measure uses estimates of mutual information between past and present states of a set of measurement channels. The mutual information is estimated on the basis of multivariate Gaussian assumptions. Computing φ* involves estimating the effects of partitioning the set of channels, by modelling the partition distributions as independent (i.e. the joint distribution obtains as the product of the partitions’ distribution). φ* is the loss in system past-to-present predictability incurred by the least destructive partitioning.

The paper introduces the concept of the φ* pattern, the pattern of φ* estimates across subsets of components of the system (where electrodes pragmatically serve to define the components). The  φ* pattern is hypothesized to reflect the compositional structure of the conscious percept.

Results suggest that stronger φ* values for certain sets of electrodes in the fusiform gyrus, which pick up on face-selective responses, are associated with conscious percepts of faces (as opposed to Mondrian images or visual noise). This association holds even across sets of trials, where the physical stimulus was identical and only the internal dynamics rendered the face representation conscious or unconscious. The authors argue that these results support IIT and suggest that the φ* pattern reflects information about the conscious percept.

 

Strengths

  • The ideas in the paper are creative, provocative, and inspiring.
  • The paper uses well-established psychophysical paradigms to control the contents of consciousness and disentangle conscious perception from stimulus representation.
  • The φ* measure is well motivated by IIT and has been introduced in earlier work involving some of the authors – even if its relationship to consciousness is speculative.

 

Weaknesses

  • The authors introduce the φ* pattern and hypothesize that it reflects the compositional structure of conscious content. However, theoretically, it is unclear why it should be that pattern across subsets of components, rather than simply the pattern across components that reflects the compositional structure of conscious content. Empirically, results are most parsimoniously summarised by saying that φ* tends to be larger when the content represented by the underlying neuronal population is conscious. The evidence for a reflection of the compositional structure of conscious content in the φ* pattern is weak.
  • It is unclear how φ* is related to the overall activity in sets of neurons selective for the perceptual content in question (faces here). This leaves open the possibility that face selective neurons are simply more active when the face percept is conscious and this greater activity is associated with greater interactivity among the neurons, reflecting their structural connectivity.
  • The finding that the alternative measures, state entropy H and (past-present) mutual information I, are less predictive of conscious percepts does not provide strong constraints on theory, because these measures are not particularly plausible to begin with and no compelling theoretical motivation is given for them.
  • IIT suggests that integrated information across the entire brain supports consciousness. An unavoidable challenge for empirical studies, as the authors appropriately discuss, is the limitation of the φ* estimates to small sets of empirical measurements of brain activity.

 

Particular points the authors may wish to address in revision

(1) Are face-selective populations of neurons simply more active when a face is consciously perceived and φ* rises as an epiphenomenon of greater activity in the interconnected set of neurons?

It is left unclear whether the level of percept-specific neuronal activity provides a comparably good or better neural correlate of conscious content. The data presented have been analysed with more conventional activity-based pattern classification in Baroni et al. (pp2016) and results suggest that this also works. What if the substrate of consciousness is simply strong activity or activity in certain frequency bands and the φ* just happens to be a measure correlated with those simpler measures in a population of neurons? After all, we would expect an interconnected neuronal population to exhibit greater dynamic interactivity when it is strongly driven by a stimulus. The key challenge left unaddressed is to demonstrate that φ* cannot be reduced to this classical neuronal correlate of perceptual content. Do the two tend to be correlated? Can they be disentangled experimentally?

A compelling demonstration would be to show that φ* (or another IIT-motivated measure) captures variance in conscious content that is not explained by conventional decoding features. For example, two populations of neurons – one coding a face, the other a Mondrian – might be equally activated overall by a stimulus containing both a face and a Mondrian, but φ* computed for each population might enable us to predict the consciously perceived stimulus on a trial-by-trial basis.

 

(2) Does the φ* pattern reflect the conscious percept and its compositional structure?

A demonstration that the φ* pattern (across subsets) reflects the compositional structure of the content of consciousness would require an experiment eliciting a wider range of conscious percepts that are composed of a set of elements in different combinations.

The authors’ hypothesis would then have to be compared to a range of simpler hypotheses about the neural correlates of compositional conscious content (NC4) , including the following:

  • the pattern of activity across content-selective neural sites
    (rather than the across a subsets of sites)
  • the pattern of activity across subsets of sites
  • the connectivity across content-selective neural sites (where connectivity could be measured by synchrony, coherence, Granger causality or any other measure of the relationship between two sites)
  • the connectivity across content-selective neural subsets of sites

This list could be expanded indefinitely and could include a variety of IIT-inspired but distinct NC4s. There are many ideas that are similarly theoretically plausible, so empirical tests might be the best way forward.

In the discussion the authors argue that integrated information has greater a priori theoretical support than arbitrary alternative neural correlates of consciousness. There is some truth to that. However, the theoretical motivation, while plausible and and interesting, is not so uniquely compelling that it supports lowering the bar of empirical confirmation for IIT measures.

 

(3) Might the selection of channels by maximum φ* have introduced a bias to the analyses?

I understand that the selection was performed without using the conscious/unconscious trial labels. However, conscious percepts are likely to be associated with greater activity, and φ* might be confounded by greater activity. More generally, selection biases are often complicated, and without a compelling demonstration that there can be no selection bias, it is difficult to be confident. A simple way to rule out selection bias is to use independent data for selection and selective analysis.

 

– Nikolaus Kriegeskorte

 

Acknowledgement

I thank Kate Storrs for discussing integrated information theory with me.

How will the neurosciences be transformed by machine learning and big data?

[R8I7]

Machine learning and statistics have been rapidly advancing in the past decade. Boosted by big data sets, new methods for inference and prediction are transforming many fields of science and technology. How will these developments affect the neurosciences? Bzdok & Yeo (pp2016) take a stab at this question in a wide-ranging review of recent analyses of brain data with modern methods.

Their review paper is organised around four key dichotomies among approaches to data analysis. I will start by describing these dichotomies from my own perspective, which is broadly – though not exactly – consistent with Bzdok & Yeo’s.

  • Generative versus discriminative models: A generative model is a model of the process that generated the data (mapping from latent variables to data), whereas a discriminative model maps from the data to selected variables of interest.
  • Nonparametric versus parametric models: Parametric models are specified using a finite number of parameters and thus their flexibility is limited and cannot grow with the amount of data available. Nonparametric models can grow in complexity with the data: The set of numbers identifying a nonparametric model (which may still be called “parameters”) can grow without a predefined limit.
  • Bayesian versus frequentist inference: Bayesian inference starts by defining a prior over all models believed possible and then infers the posterior probability distribution over the models and their parameters on the basis of the data. Frequentist inference identifies variables of interest that can be computed from data and constructs confidence intervals and decision rules that are guaranteed to control the rate of errors across many hypothetical experimental analyses.
  • Out-of-sample prediction and generalisation versus within-sample explanation of variance: Within-sample explanation of variance attempts to best explain a given data set (relying on assumptions to account for the noise in the data and control overfitting). Out-of-sample prediction integrates empirical testing of the generalisation of the model to new data (and optionally to different experimental conditions) into the analysis, thus testing the model, including all assumptions that define it, more rigorously.

Generative models are more ambitious than discriminative models in that they attempt to account for the process that generated the data. Discriminative models are often leaner – designed to map directly from data to variables of interest, without engaging all the complexities of the data-generating process.

Nonparametric models are more flexible than parametric models and can adapt their complexity to the amount of information contained in the data. Parametric models can be more stable when estimated with limited data and can sometimes support more sensitive inference when their strong assumptions hold.

Philosophically, Bayesian inference is more attractive than frequentist inference because it computes the probability of models (and model parameters) given the givens (or in Latin: the data). In real life, also, Bayesian inference is what I would aim to roughly approximate to make important decisions, combining my prior beliefs with current evidence. Full Bayesian inference on a comprehensive generative model is the most rigorous (and glamorous) way of making inferences. Explicate all your prior knowledge and uncertainties in the model, then infer the probability distribution over all states of the world deemed possible given what you’ve been given: the data. I am totally in favour of Bayesian analysis for scientific data analysis from the armchair in front of the fireplace. It is only when I actually have to analyse data, at the very last moment, that I revert to frequentist methods.

My only problem with Bayesian inference is my lack of skill. I never finish enumerating the possible processes that might have given rise to the data. When I force myself to stop enumerating, I don’t know how to implement the (incomplete) list of possible processes in models. And if I forced myself to make the painful compromises to implement some of these processes in models, I wouldn’t know how to do approximate inference on the incomplete list of badly implemented models. I would know that many of the decisions I made along the way were haphazard and inevitably subjective, that all them constrain and shape the posterior, and that few of them will be transparent to other researchers. At that point frequentist inference with discriminative models starts looking attractive. Just define easy-to-understand statistics of interest that can efficiently be computed from the data and estimate them with confidence intervals, controlling error probability without relying on subjective priors. Generative-model comparisons, as well, are often easier to implement in the frequentist framework.

Regarding the final dichotomy, out-of-sample prediction using fitted models provides a simple empirical test of generalisation. It can be used to test for generalisation to new measurements (e.g. responses to the same stimuli as in decoding) or to new conditions (as in cross-decoding and in encoding models, e.g. Kay et al. 2008; Naselaris et al. 2009). Out-of-sample prediction can be applied in crossvalidation, where the data are repeatedly split to maximise statistical efficiency (trading off computational efficiency).

Out-of-sample prediction tests are useful because they put assumptions to the test, erring on the safe side. Let’s say we want to test if two patterns are distinct and we believe the noise is multinormal and equal for both conditions. We could use a within-sample method like multivariate analysis of variance (MANOVA) to perform this test, relying on the assumption of multinormal noise. Alternatively, we could use out-of-sample prediction. Since we believe that the noise is multinormal, we might fit a Fisher linear discriminant, which is the Bayes-optimal classifier in this scenario. This enables us to project held-out data onto a single dimension, the discriminant, and use simpler inference statistics and fewer assumptions to perform the test. If multinormality were violated, the classifier would no longer be optimal, making prediction of the labels for held-out data work worse. We would more frequently err on the safe side of concluding that there is no difference, and the false-positives rate would still be controlled. MANOVA, by contrast, relies on multinormality for the validity of the test and violations might inflate the false-positives rate.

More generally, using separate training and test sets is a great way to integrate the cycle of exploration (hypothesis generation, fitting) and confirmation (testing) into the analysis of a single data set. The training set is used to select or fit, thus restricting the space of hypotheses to be tested. We can think of this as generating testable hypotheses. The training set helps us go from a vague hypothesis we don’t know how to test to a specifically defined hypothesis that is easy to test. The reason separate training and test sets are standard practice in machine learning and less widely used in statistics is that machine learning has more aggressively explored complex models that cannot be tested rigorously any other way. More on this here.

 

Out-of-sample prediction is not the alternative to p values

One thing I found uncompelling (though I’ve encountered it before, see Fig. 1) is the authors’ suggestion that out-of-sample prediction provides an alternative to null-hypothesis significance testing (NHST). Perhaps I’m missing something. In my view, as outlined above, out-of-sample prediction (the use of independent test sets) enables us to use one data set to restrict the hypothesis space  (training) and another data set to do inference on the more specific hypotheses, which are easier to test with fewer assumptions. The prediction is the restricted hypothesis space. Just like within-sample analyses, out-of-sample prediction requires a framework for performing inference on the hypothesis space. This framework can be either frequentist (e.g. NHST) or Bayesian.

For example, after fitting encoding models to a training data set, we can measure the accuracy with which they predict the test set. The fitted models have all their parameters fixed, so are easy to test. However, we still need to assess whether the accuracy is greater than 0 for each model and whether one model has greater accuracy than another.

Using up one part of the data to restrict the hypothesis space (fitting) and then using another to perform inference on the restricted hypothesis space (so as to avoid the bias of training set accuracy that results from overfitting) could be viewed as a crude approximation (vacillating between overfitting on one set and using another to correct) to the rigorous thing to do: updating the current probability distribution over all possibilities as each data point is encountered.

 

ScreenShot1269.png

Figure 1: I don’t understand why some people think of out-of-sample prediction as an alternative to p values.

 

Bzdok & Yeo do a good job of appreciating the strengths and weaknesses of either choice of each of these dichotomies and considering ways to combine the strengths even of apparently opposed choices. Four boxes help introduce the key dichotomies to the uninitiated neuroscientist.

The paper provides a useful tour through recent neuroscience research using advanced analysis methods, with a particular focus on neuroimaging. The authors make several reasonable suggestions about where things might be going, suggesting that future data analyses will…

  • leverage big data sets and be more adaptive (using nonparametric models)
  • incorporate biological structure
  • combine the strengths of Bayesian and frequentist techniques
  • integrate out-of-sample generalisation (e.g. implemented in crossvalidation)

 

Weaknesses of the paper in its current form

The main text moves over many example studies and adds remarks on analysis methodology that will not be entirely comprehensible to a broad audience, because they presuppose very specialised knowledge.

Too dense: The paper covers a lot of ground, making it dense in parts. Some of the points made are not sufficiently developed to be fully compelling. It would also be good to reflect on the audience. If the audience is supposed to be neuroscientists, then many of the technical concepts would require substantial unpacking. If the audience were mainly experts in machine learning, then the neuroscientific concepts would need to be more fully explained. This is not easy to get right. I will try to illustrate these concerns further below in the “particular comments” section.

Too uncritical: A positive tone is a good thing, but I feel that the paper is a little too uncritical of claims in the literature. Many of the results cited, while exciting for the sophisticated models that are being used, stand and fall with the model assumptions they are based on. Model checking and comparison of many alternative models are not standard practice yet. It would be good to carefully revise the language, so as not to make highly speculative results sound definitive.

No discussion of task-performing models: The paper doesn’t explain what from my perspective is the most important distinction among neuroscience models: Do they perform cognitive tasks? That this distinction is not discussed in detail reflects the fact that such models are still rare in neuroscience. We use a lot of different kinds of model, but even when they are generative and causal and constrained by biological knowledge, they are still just data-descriptive models in the sense that they do not perform any interesting brain information processing. Although they may be stepping stones toward computational theory, such models do not really explain brain computation at any level of abstraction. Task-performing computational models, as introduced by cognitive science, are first evaluated by their ability to perform an information-processing function. Recently, deep neural networks that can perform feats of intelligence such as object recognition have been used to explain brain and behavioural data (Yamins et al. 2013; 2014: Khaligh-Razavi et al. 2014; Cadieu et al. 2014; Guclu & van Gerven 2015). For all their abstractions, many of their architectural features are biologically plausible and at least they pass the most basic test for a computational model of brain function: explaining a computational function (for reviews, see Kriegeskorte 2015; Yamins & DiCarlo 2015).

 

 

 

screenshot1271Figure 2: Shades of Bayes. The authors follow Kevin Murphy’s textbook in defining degrees of Bayesianity of inference, ranging from maximum likelihood estimation (top) to full Bayesian inference on parameters and hyperparameters (bottom). Above is my slightly modified version.

 

 

 

Comments on specific statements

“Following many new opportunities to generate digitized brain data, uncertainties about neurobiological phenomena henceforth required assessment in the statistical arena.”

Noise in the measurements, not uncertainties about neurobiological phenomena, created the need for statistical inference.

 

“Finally, it is currently debated whether increasingly used “deep” neural network algorithms with many non-linear hidden layers are more accurately viewed as parametric or non-parametric.”

This is an interesting point. Perhaps the distinction between nonparametric and parametric becomes unhelpful when a model with a finite, fixed, but huge number of parameters is tempered by flexible regularisation. It would be good to add a reference on where this is “debated”.

 

“neuroscientists often conceptualize behavioral tasks as recruiting multiple neural processes supported by multiple brain regions. This century-old notion (Walton and Paul, 1901) was lacking a formal mathematical model. The conceptual premise was recently encoded with a generative model (Yeo et al., 2015). Applying the model to 10,449 experiments across 83 behavioral tasks revealed heterogeneity in the degree of functional specialization within association cortices to execute diverse tasks by flexible brain regions integration across specialized networks (Bertolero et al., 2015a; Yeo et al., 2015).”

This is an example of a passage that is too dense and lacks the information required for a functional understanding of what was achieved here. The model somehow captures recruitment of multiple regions, but how? “Heterogeneity in the degree of functional specialisation”, i.e. not every region is functionally specialised to exactly the same degree, sounds both plausible and vacuous. I’m not coming away with any insight here.

 

“Moreover, generative approaches to fitting biological data have successfully reverse engineered i) human facial variation related to gender and ethnicity based on genetic information alone (Claes et al., 2014)”

Fitting a model doesn’t amount to reverse engineering.

 

“Finally, discriminative models may be less potent to characterize the neural mechanisms of information processing up to the ultimate goal of recovering subjective mental experience from brain recordings (Brodersen et al., 2011; Bzdok et al., 2016; Lake et al., 2015; Yanga et al., 2014).”

Is the ultimate goal to “recover mental experience”? What does that even mean? Do any of the cited studies attempt this?

“Bayesian inference is an appealing framework by its intimate relationship to properties of firing in neuronal populations (Ma et al., 2006) and the learning human mind (Lake et al., 2015).”

Uncompelling. If the brain and mind did not rely on Bayesian inference, Bayesian inference would be no less attractive for analysing data from the brain and mind.

 

“Parametric linear regression cannot grow more complex than a stiff plane (or hyperplane when more input dimensions Xn) as decision boundary, which entails big regions with identical predictions Y.”

The concept of decision boundary does not make sense in a regression setting.

 

“Typical discriminative models include linear regression, support vector machines, decision-tree algorithms, and logistic regression, while generative models include hidden Markov models, modern neural network algorithms, dictionary learning methods, and many non-parametric statistical models (Teh and Jordan, 2010).”

Linear regression is not inherently either discriminative or generative, nor are neural networks. A linear regression model is generative when it predicts the data (either in a within-sample framework, such as classical univariate activation-based brain mapping, or in an out-of-sample predictive framework, such as encoding models). It is discriminative when it takes the data as input to predict other variables of interest (e.g. stimulus properties in decoding, or subject covariates).

 

“Box 4: Null-hypothesis testing and out-of-sample prediction”

As discussed above, this seems to me a false dichotomy. We can perform out-of-sample predictions and test them with null-hypothesis significance testing (NHST). Moreover, Bayesian inference (the counterpart to NHST) can operate on a single data set. It makes more sense to me to contrast out-of-sample prediction versus within-sample explanation of variance.

 

 

The selfish scientist’s guide to preprint posting

Preprint posting is the right thing to do for science and society. It enables us to share our results earlier, speeding up the pace of science. It also enables us to catch errors earlier, minimising the risk of alerting the world to our findings (through a high-impact publication) before the science is solid. Importantly, preprints ensure long-term open access to our results for scientists and for the public. Preprints can be rapidly posted for free on arXiv and bioRxiv, enabling instant open access.

Confusingly for any newcomer to science who is familiar with the internet, scientific journals don’t provide open access to papers in general. They restrict access with paywalls and only really publish (in the sense of to make publicly available) a subset of papers. The cost of access is so high that even institutions like Harvard and the UK’s Medical Research Council (MRC) cannot afford paying for general access to all the relevant scientific literature. For example, as MRC employees, members of my lab do not have access to the Journal of Neuroscience, because our MRC Unit, the Cognition and Brain Sciences Unit in Cambridge, cannot afford to subscribe to it. The University of Cambridge pays more than one million pounds in annual subscription fees to Elsevier alone, a single major publishing company, as do several other UK universities. Researchers who are not at well-funded institutions in rich countries are severely restricted in their access to the literature and cannot fully participate in science under the present system.

Journals administer peer review and provide pretty layouts and in some cases editing services. Preprints complement journals, enabling us to read about each other’s work as soon as it’s written up and without paywall restrictions. With the current revival of interest in preprints (check out ASAPbio), more and more scientists choose to post their papers as preprints.

All major journals including Nature, Science, and most high-impact field-specific journals support the posting of preprints. Preprint posting is in the interest of journals because they, too, would like to avoid publication of papers with errors and false claims. Moreover, the early availability of the results boosts early citations and thus the journal’s impact factor. Check out Wikipedia’s useful overview of journal preprint policies. For detailed information on each journal’s precise preprint policy, refer to the excellent ROMEO website at the University of Nottingham’s SHERPA project on the future of scholarly communication (thanks to Carsten Allefeld for pointing this out).

All the advantages of using preprints to science and society are good and well. However, we also need to think about ourselves. Does preprint posting mean that we give away our results to competitors, potentially suffering a personal cost for the common good? What is the selfish scientist’s best move to advance her personal impact and career? There is a risk of getting scooped. However, this risk can be reduced by not posting too early. It turns out that posting a preprint, in addition to publication in a journal, is advisable from a purely selfish perspective, because it brings the following benefits to the authors:

  • Open access: Preprints guarantee open access, enhancing the impact and ultimate citation success of our work. This is a win for the authors personally, as well as for science and society.
  • Errors caught: Preprints help us catch errors before wider reception of the work. Again this is a major benefit not only to science, society, and journals, but also to the authors, who may avoid having to correct or retract their work at a later stage.
  • Earlier citation: Preprints grant access to our work earlier, leading to earlier citation. This is beneficial to our near-term citation success, thus improving our bibliometrics and helping our careers — as well as boosting the impact factor of the journal, where the paper appears.
  • Preprint precedence: Finally, preprints can help establish the precedence of findings. A preprint is part of the scientific record and, though the paper still awaits peer review, it can help establish scientific precedence. This boosts the long-term citation count of the paper.

In computer science, math, and physics, reading preprints is already required to stay abreast of the literature. The life sciences will follow this trend. As brain scientists working with models from computer science, we read preprints and, if we judge them to be of high-quality and relevance, we cite them.

My lab came around to routine preprint posting for entirely selfish reasons. Our decision was triggered by an experience that drove home the power of preprints. A competing lab had posted a paper closely related to one of our projects as a preprint. We did not post preprints at the time, but we cited their preprint in the paper on our project. Our paper appeared before theirs in the same journal. Although we were first, by a few months, with a peer-reviewed journal paper, they were first with their preprint. Moreover, our competitors could not cite us, because we had not posted a preprint and their paper had already been finalised when ours appeared. Appropriately, they took precedence in the citation graph – with us citing them, but not vice versa.

Posting preprints doesn’t only have advantages. It is also risky. What if another group reads the preprint, steals the idea, and publishes it first in a high-impact journal? This could be a personal catastrophe for the first author, with the credit for years of original work diminished to a footnote in the scientific record. Dishonorable scooping of this kind is not unheard of. Even if we believe that our colleagues are all trustworthy and outright stealing is rare, there is a risk of being scooped by honorable competitors. Competing labs are likely to be independently working on related issues. Seeing our preprint might help them improve their ongoing work; and they may not feel the need to cite our preprint for the ideas it provided. Even if our competitors do not take any idea from our preprint, just knowing that our project is ready to enter the year-long (or multiple-year) publication fight might motivate them to accelerate progress with their competing project. This might enable them to publish first in a journal.

The risk of being scooped and the various benefits vary as a function of the time of preprint posting. If we post at the time of publication in a journal, the risk of being scooped is 0 and the benefit of OA remains. However, the other benefits grow with earlier posting. How do benefits and costs trade off and what is the optimal time for posting a preprint?

As illustrated in the figure below, this selfish scientist believes that the optimal posting time for his lab is around the time of the first submission of the paper. At this point, the risk of being scooped is small, while the benefits of preprint precedence and early citation are still substantial. I therefore encourage the first authors in my lab to post at the time of first submission. Conveniently, this also minimises the extra workload required for the posting of the preprint. The preprint is the version of the paper to be submitted to a journal, so no additional writing or formatting is required. Posting a preprint takes less than half an hour.

I expect that as preprints become more widely used, incentives will shift. Preprints will more often be cited, enhancing the preprint-precedence and early-citation benefits. This will shift the selfish scientist’s optimal time of preprint posting to an earlier point, where an initial round of responses can help improve the paper before a journal vets it for a place in its pages. For now, we post at the time of the first submission.

 

 

preprint benefits afo posting time

Benefits and costs to the authors of posting preprints as a function of the time of posting. This figure considers the benefits and costs of posting a preprint at a point in time ranging from a year before (-1) to a year after (1, around the time of appearance in a journal) initial submission (0). The OA benefit (green) of posting a preprint is independent of the time of posting. This benefit is also available by posting the preprint after publication of the paper in a journal. The preprint-precedence (blue) and early-citation (cyan) benefits grow by an equal amount with every month prior to journal publication that the paper is out as a preprint. This is based on the assumption that the rest of the scientific community, acting independently, is chipping away at the novelty and citations of the paper at a constant rate. When the paper is published in a journal (assumed at 1 year after initial submission), the preprint no longer accrues these benefits, so the lines reach 0 benefit at the time of the journal publication. Finally, the risk of being scooped (red) is large when the preprint is posted long before initial submission. At the time of submission, it is unlikely that a competitor starting from scratch can publish first in a journal. However, there is still the risk that competitors who were already working on related projects accelerate these and achieve precedence in terms of journal publication as a result. The sum total (black) of the benefits and the cost associated with the risk of being scooped peaks slightly before the time of the first submission to a journal. The figure serves to illustrate my own rationale for posting around the time of the first submission of a paper to a journal. It is not based on objective data, but on subjective estimation of the costs and benefits for a typical paper from my own lab.

 

The four pillars of open science

An open review of Gorgolewski & Poldrack (PP2016)

the 4 pillars of open science.png

The four pillars of open science are open data, open code, open papers (open access), and open reviews (open evaluation). A practical guide to the first three of these is provided by Gorgolewski & Poldrack (PP2016). In this open review, I suggest a major revision in which the authors add treatment of the essential fourth pillar: open review. Image: The Porch of the Caryatids (Porch of the Maidens) of the ancient Greek temple Erechtheion on the north side of the Acropolis of Athens.

 

Open science is a major buzz word. Is all the talk about it just hype? Or is there a substantial vision that has a chance of becoming a reality? Many of us feel that science can be made more efficient, more reliable, and more creative through a more open flow of information within the scientific community and beyond. The internet provides the technological basis for implementing open science. However, making real progress with this positive vision requires us to reinvent much of our culture and technology. We should not expect this to be easy or quick. It might take a decade or two. However, the arguments for openness are compelling and open science will prevail eventually.

The major barriers to progress are not technological, but psychological, cultural, and political: individual habits, institutional inertia, unhealthy incentives, and vested interests. The biggest challenge is the fact that the present way of doing science does work (albeit suboptimally) and our vision for open science has not merely not yet been implemented, but has yet to be fully conceived. We will need to find ways to gradually evolve our individual workflows and our scientific culture.

Gorgolewski & Poldrack (PP2016) offer a brief practical guide to open science for researchers in brain imaging. I was expecting a commentary reiterating the arguments for open science most of us have heard before. However, the paper instead makes good on its promise to provide a practical guide for brain imaging and it contains many pointers that I will share with my lab and likely refer to in the future.

The paper discusses open data, open code, and open publications – describing tools and standards that can help make science more transparent and efficient. My main criticism is that it leaves out what I think of as a fourth essential pillar of open science: open peer review. Below I first summarise some of the main points and pointers to resources that I took from the paper. Along the way, I add some further points overlooked in the paper that I feel deserve consideration. In the final section, I address the fourth pillar: open review. In the spirit of a practical guide, I suggest what each of us can easily do now to help open up the review process.

 

1 Open data

  • Open-data papers more cited, more correct: If data for a paper are published, the community can reanalyse the data to confirm results and to address additional questions. Papers with open data are cited more (Piwowar et al. 2007, Piwowar & Vision 2013) and tend to make more correct use of statistics (Wicherts et al. 2011).
  • Participant consent: Deidentified data can be freely shared without consent from the participants in the US. However, rules differ in other countries. Ideally, participants should consent to their data being shared. Template text for consent forms is offered by the authors.
  • Data description: The Brain Imaging Data Structure (BIDS) (Gorgolewski et al. 2015) provides a standard (evolved from the authors’ OpenfMRI project; Poldrack et al. 2013) for file naming and folder organisation, using file formats such as NifTI, TSV and JSON.
  • Field-specific brain-imaging data repositories: Two repositories accept brain imaging data from any researcher: FCP/INDI (for resting state fMRI only) and OpenfMRI (for any datasets that includes MRI data).
  • Field-general repositories: Field-specific repositories like those mentioned help standardise sharing for particular types of data. If the formats offered are not appropriate for the data to be shared, field-general repositories, including FigShare, Dryad, or DataVerse can be used.
  • Data papers: A data paper is a paper that focusses on the description of a particular data set that is publicly accessible. This helps create incentives for ambitious data acquisitions and to enable researchers to specialise in data acquisition. Journals publishing data papers include: Scientific Data, Gigascience, Data in Brief, F1000Research, Neuroinformatics, and Frontiers in Neuroscience.
  • Processed-data sharing: It can be useful to share intermediate or final results of data analysis. With the initial (and often somewhat more standardised) steps of data processing out of the way, processed data are often much smaller in volume and more immediately amenable to further analyses by others. Statistical brain-imaging maps can be shared via the authors’ NeuroVault.org website.

 

2 Open code

  • Code sharing for transparency and reuse: Data-analysis details are complex in brain imaging, often specific to a particular study, and seldom fully defined in the methods section. Sharing code is the only realistic way of fully defining how the data have been analysed and enabling others to check the correctness of the code and effects of adjustments. In addition, the code can be used as a starting point for the development of further analyses.
  • Your code is good enough to share: A barrier to sharing is the perception among authors that their code might not be good enough. It might be incompletely documented, suboptimal, or even contain errors. Until the field finds ways to incentivise greater investment in code development and documentation for sharing, it is important to lower the barriers to sharing. Sharing imperfect code is preferable to not sharing code (Barnes 2010).
  • Sharing does not imply provision of user support: Sharing one’s code does not imply that one will be available to provide support to users. Websites like org can help users ask and answer questions independently (or with only occasional involvement) of the authors.
  • Version Control System (VCS) essential to code sharing: VCS software enables maintenance of complex code bases with multiple programmers and versions, including the ability to merge independent developments, revert to previous versions when a change causes errors, and to share code among collaborators or publicly. An excellent, freely accessible, widely used, web-based VCS platform is com, introduced in Blischak et al. (2016).
  • Literate programming combines code and results and text narrative: Scripted automatic analyses have the advantage of automaticity and reproducibility (Cusack et al. 2014), compared to point-and-click analysis in an application with a graphical user interface. However, the latter enables more interactive interrogation of the data. Literate programming (Knuth 1992) attempts to make coding more interactive and provides a full and integrated record of the code, results, and text explanations. This provides a fully computationally transparent presentation of results, makes the code accessible to oneself later in time, and to collaborators and third parties, with whom literate programs can be shared (e.g. via GitHub). Software supporting this includes: Jupyter (for R, Python and Julia), R Markdown (for R) and matlabweb (for MATLAB).

 

3 Open papers

  • Open notebook science: Open science is about enhancing the bandwidth and reducing the latency in our communication network. This means sharing more and at earlier stages, not only our data and code, but ultimately also our day-to-day incremental progress. This is called open notebook science and has been explored, by Cameron Neylon and Michael Nielson among others. Gorgolewski & Poldrack don’t comment on this beautiful vision for an entirely different workflow and culture at all. Perhaps open notebook science is too far in the future? However, some are already practicing it. Surely, we should start exploring it in theory and considering what aspects of open notebook science we can integrate into our workflow. It would be great to have some pointers to practices and tools that help us move in this direction.
  • The scientific paper remains a critical component of scientific communication: Data and code sharing are essential, but will not replace communication through permanently citable scientific papers that link (increasingly accessible) data through analyses to novel insights and relate these insights to the literature.
  • Papers should simultaneously achieve clarity and transparency: The conceptual clarity of the argument leading to an insight is often at a tension with the transparency of all the methodological details. Ideally, a paper will achieve both clarity and transparency, providing multiple levels of description: a main narrative that abstracts from the details, more detailed descriptions in the methods section, additional detail in the supplementary information, and full detail in the links to the open data and code, which together enable exact reproduction of the results in the figures. This is an ideal to aspire to. I wonder if any paper in our field has fully achieved it. If there is one, it should surely be cited.
  • Open access: Papers need to be openly accessible, so their insights can have the greatest positive impact on science and society. This is really a no brainer. The internet has greatly lowered the cost of publication, but the publishing industry has found ways to charge higher prices through a combination of paywalls and unreasonable open-access charges. I would add that every journal contains unique content, so the publishing industry runs hundreds of thousands of little monopolies – safe from competition. Many funding bodies require that studies they funded be published with open access. We need political initiatives that simply require all publicly funded research to be publicly accessible. In addition, we need publicly funded publication platforms that provide cost-effective alternatives to private publishing companies for editorial boards that run journals. Many journals are currently run by scientists whose salaries are funded by academic institutions and the public, but whose editorial work contributes to the profits of private publishers. In historical retrospect, future generations will marvel at the genius of an industry that managed for decades to employ a community without payment, take the fruits of their labour, and sell them back to that very community at exorbitant prices – or perhaps they will just note the idiocy of that community for playing along with this racket.
  • Preprint servers provide open access for free: Preprint servers like bioRxiv and arXiv host papers before and after peer review. Publishing each paper on a preprint server ensures immediate and permanent open access.
  • Preprints have digital object identifiers (DOIs) and are citable: Unlike blog posts and other more fleeting forms of publication, preprints can thus be cited with assurance of permanent accessibility. In my lab, we cite preprints we believe to be of high quality even before peer review.
  • Preprint posting enables community feedback and can help establish precedence: If a paper is accessible before it is finalised the community can respond to it and help catch errors and improve the final version. In addition, it can help the authors establish the precedence of their work. I would add that this potential advantage will be weighed against the risk of getting scooped by a competitor who benefits from the preprint and is first to publish a peer-reviewed journal version. Incentives are shifting and will encourage earlier and earlier posting. In my lab, we typically post at the time of initial submission. At this point getting scooped is unlikely, and the benefits of getting earlier feedback, catching errors, and bringing the work to the attention of the community outweighs any risks of early posting.
  • Almost all journals support the posting of preprints: Although this is not widely known in the brain imaging and neuroscience communities, almost all major journals (including Nature, Science, Nature Neuroscience and most others) have preprint policies supportive of posting preprints. Gorgolewski & Poldrack note that they “are not aware of any neuroscience journals that do not allow authors to deposit preprints before submission, although some journals such as Neuron and Current Biology consider each submission independently and thus one should contact the editor prior to submission.” I would add that this reflects the fact that preprints are also advantageous to journals: They help catch errors and get the reception process and citation of the paper going earlier, boosting citations in the two-year window that matters for a journal’s impact factor.

 

4 Open reviews

The fourth pillar of open science is the open evaluation (OE, i.e. open peer review and rating) of scientific papers. This pillar is entirely overlooked in the present version of the Gorgolewski & Poldrack’s commentary. However, peer review is an essential component of communication in science. Peer review is the process by which we prioritise the literature, guiding each field’s attention, and steering scientific progress. Like other components of science, peer review is currently compromised by a lack of transparency, by inefficiency of information flow, and by unhealthy incentives. The movement for opening the peer review process is growing.

In traditional peer review, we judge anonymously, making inherently subjective decisions that decide about the publication of our competitors’ work, under a cloak of secrecy and without ever having to answer for our judgments. It is easy to see that this does not provide ideal incentives for objectivity and constructive criticism. We’ve inherited secret peer review from the pre-internet age (when perhaps it made sense). Now we need to overcome this dysfunctional system. However, we’ve grown used to it and may be somewhat comfortable with it.

Transparent review means (1) that reviews are public communications and (2) that many of them are signed by their authors. Anonymous reviewing must remain an option, to enable scientists to avoid social consequences of negative judgments in certain scenarios. However, if our judgment is sound and constructively communicated, we should be able to stand by it. Just like in other domains, transparency is the antidote to corruption. Self-serving arguments won’t fly in open reviewing, and even less so when the review is signed. Signing adds weight to a review. The reviewer’s reputation is on the line, creating a strong incentive to be objective, to avoid any impression of self-serving judgment, and to attempt to be on the right side of history in one’s judgment of another scientist’s work. Signing also enables the reviewer to take credit for the hard work of reviewing.

The arguments for OE and a synopsis of 18 visions for how OE might be implemented are given in Kriegeskorte, Walther & Deca (2012). As for other components of open science, the primary obstacles to more open practices are not technological, but psychological, cultural, and political. Important journals like eLife and those of the PLoS family are experimenting with steps toward opening the review process. New journals including, the Winnower, ScienceOpen, and F1000 Research already rely on postpublication peer review.

We don’t have to wait for journals to lead us. We have all the tools to reinvent the culture of peer review. The question is whether we can handle the challenges this poses. Here, in the spirit of Gorgolewki & Poldrack’s practical guide, are some ways that we can make progress toward OE now by doing things a little differently.

  • Sign peer reviews you author: Signing our reviews is a major step out of the dark ages of peer review. It’s easier said than done. How can we be as critical as we sometimes have to be and stand by our judgment? We can focus first on the strengths of a paper, then communicate all our critical arguments in a constructive manner. Some people feel that we must sign either all or none of our reviews. I think that position is unwise. It discourages beginning to sign and thus de facto cements the status quo. In addition, there are cases where the option to remain anonymous is needed, and as long as this option exists we cannot enforce signing anyway. What we can do is take anonymous comments with a grain of salt and give greater credence to signed reviews. It is better to sign sometimes than never. When I started to sign my reviews, I initially reserved the right to anonymity for myself. After all this was a unilateral act of openness; most of my peers do not sign their reviews. However, after a while, I decided to sign all of my reviews, including negative ones.
  • Openly review papers that have preprints: When we read important papers as preprints, let’s consider reviewing them openly. This can simultaneously serve our own and our collective thought process: an open notebook distilling the meaning of a paper, why its claims might or might not be reliable, how it relates to the literature, and what future steps it suggests. I use a blog. Alternatively or additionally, we can use PubMed Commons or PubPeer.
  • Make the reviews you write for journals open: When we are invited to do a review, we can check if the paper has been posted as a preprint. If not, we can contact the authors, asking them to consider posting. At the time of initial submission, the benefits tend to outweigh the risks of posting, so many authors will be open to this. Preprint posting is essential to open review. If a preprint is available, we can openly review it immediately and make the same review available to the journal to contribute to their decision process.
  • Reinvent peer review: What is an open review? For example, what is this thing you’re reading? A blog post? A peer review? Open notes on the essential points I would like to remember from the paper with my own ideas interwoven? All of the above. Ideally, an open review helps the reviewer, the authors, and the community think – by explaining the meaning of a paper in the context of the literature, judging the reliability of its claims, and suggesting future improvements. As we begin to review openly, we are reinventing peer review and the evaluation of scientific papers.
  • Invent peer rating: Eventually we will need quantitative measures evaluating papers. These should not be based on buzz and usage statistics, but reflect the careful judgement of peers who are experts in the field, have considered the paper in detail, and ideally stand by their judgment. Quantitative judgments can be captured in ratings. Multidimensional peer ratings can be used to build a plurality of paper evaluation functions (Kriegeskorte 2012) that prioritise the literature from different perspectives. We need to invent suitable rating systems. For primary research papers, I use single-digit ratings on multiple scales including reliability, importance, and novelty, using capital letters to indicate the scale in the following format: [R7I5].

 

Errors are normal

As we open our science and share more of it with the community, we run the risk of revealing more of our errors. From an idealistic perspective that’s a good thing, enabling us learn more efficiently as individuals and as a community. However, in the current game of high-impact biomedical science there is an implicit pretense that major errors are unlikely. This is the reason why, in the rare case that a major error is revealed despite our lack of transparent practices, the current culture requires that everyone act surprised and the author be humiliated. Open science will teach us to drop these pretenses. We need to learn to own our mistakes (Marder 2015) and to be protective of others when errors are revealed. Opening science is an exciting creative challenge at many levels. It’s about reinventing our culture to optimise our collective cognitive process. What could be more important or glamorous?

 

Additional suggestions for improvements in revision

  • A major relevant development regarding open science in the brain imaging community is the OHBM’s Committee on Best Practices in Data Analysis and Sharing (COBIDAS), of which author Russ Poldrack and I are members. COBIDAS is attempting to define recommended practices for the neuroimaging community and has begun a broad dialogue with the community of researchers (see weblink above). It would be good to explain how COBIDAS fits in with the other developments.
  • About a third of the cited papers are by the authors. This illustrates their substantial contribution and expertise in this field. I found all these papers worthy of citation in this context. However, I wonder if other groups that have made important contributions to this field should be more broadly cited. I haven’t followed this literature closely enough to give specific suggestions, but perhaps it’s worth considering whether references should be added to important work by others.
  • As for the papers, the authors are directly involved in most of the cited web resources OpenfMRI, NeuroVault, NeuroStars.org. This is absolutely wonderful, and it might just be that there is not much else out there. Perhaps readers of this open review can leave pointers in the comments in case they are aware of other relevant resources. I would share these with the authors, so they can consider whether to include them in revision.
  • Can the practical pointers be distilled into a table or figure that summarises the essentials? This would be a useful thing to print out and post next to our screens.
  • “more than fair” -> “only fair”

 

Disclosures

I have the following relationships with the authors.

relationship number of authors
acquainted 2
collaborated on committee 1
collaborated on scientific project 0

 

References

Barnes N (2010) Publish your computer code: it is good enough. Nature. 467: 753. doi: 10.1038/467753a

Blischak JD, Davenport ER, Wilson G. (2016) A Quick Introduction to Version Control with Git and GitHub. PLoS Comput Biol. 12: e1004668. doi: 10.1371/journal.pcbi.1004668

Cusack R, Vicente-Grabovetsky A, Mitchell DJ, Wild CJ, Auer T, Linke AC, et al. (2014) Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML. Front Neuroinform. 2014;8: 90. doi: 10.3389/fninf.2014.00090

Gorgolewski KJ, Auer T, Calhoun VD, Cameron Craddock R, Das S, Duff EP, et al. (2015) The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments [Internet]. bioRxiv. 2015. p. 034561. doi: 10.1101/034561

Gorgolewski KJ, Varoquaux G, Rivera G, Schwarz Y, Ghosh SS, Maumet C, et al. (2015) NeuroVault.org: a webbased repository for collecting and sharing unthresholded statistical maps of the human brain. Front Neuroinform. Frontiers. 9. doi: 10.3389/fninf.2015.00008

Knuth DE (1992) Literate programming. CSLI Lecture Notes, Stanford, CA: Center for the Study of Language and Information (CSLI).

Kriegeskorte N, Walther A, Deca D (2012) An emerging consensus for open evaluation: 18 visions for the future of scientific publishing Front. Comput. Neurosci http://dx.doi.org/10.3389/fncom.2012.00094

Kriegeskorte N (2012) Open evaluation: a vision for entirely transparent post-publication peer review and rating for science. Front. Comput. Neurosci., 17 http://dx.doi.org/10.3389/fncom.2012.00079

Marder E (2015) Living Science: Owning your mistakes DOI: http://dx.doi.org/10.7554/eLife.11628 eLife 2015;4:e11628

Piwowar HA, Day RS, Fridsma DB (2007) Sharing detailed research data is associated with increased citation rate. PLoS One. 2007;2: e308. doi: 10.1371/journal.pone.0000308

Piwowar HA, Vision TJ (2013) Data reuse and the open data citation advantage. PeerJ. 1: e175. doi: 10.7717/peerj.175

Poldrack RA, Barch DM, Mitchell JP, Wager TD, Wagner AD, Devlin JT, et al. (2013) Toward open sharing of taskbased fMRI data: the OpenfMRI project. Front Neuroinform. 2013;7: 1–12. doi: 10.3389/fninf.2013.00012

Wicherts JM, Bakker M, Molenaar D (2011) Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results. Tractenberg RE, editor. PLoS One. 6: e26828. doi: 10.1371/journal.pone.0026828