A research team recently revealed what they called a Cluster failure.

Most functional MRI studies conclusions may be erroneous because of their statistical basis. Widely used clustering inference techniques are actually pretty bad at properly inferring clusters due to “spatial autocorrelation functions that do not follow the assumed Gaussian shape”.

It means that “most common software packages for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of up to 70%”… suggesting that some results were so inaccurate they could indicate brain activity that does not exist at all!. What is interesting is that neuroscientists are interpreting what they’re told by the statistical software rather than images.

These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

Therefore, 15 years of research on brain functioning could be invalidated!

But the issue is not limited to research but also extends to clinical use that is pretty worrying.

Image credits: fMRI by OpenStax from the Textbook OpenStax Anatomy and Physiology Published May 18, 2016

I recently came across an article pretending that your brain “does not process information, retrieve knowledge or store memories” and cannot be viewed as a computer.

I personally think that this assertion is fundamentally wrong regarding the information processing.

The computer metaphor is still a valid one

First, I’d like to show you that the computer metaphor couldn’t be discarded so easily.

Of course our brain is not a computer. It is embodied and cannot be considered as an autonomous system.

Credits: Exercise Plays Vital Role Maintaining Brain Health, by a health blog

An embodied system…

“Many features of cognition are embodied in that they are deeply dependent upon characteristics of the physical body of an agent” – RA Wilson and L Foglia, Embodied Cognition in The Stanford Encyclopedia of Philosophy

A downloaded brain, if it were possible, would probably not be able to function without a body having human-like sensors and effectors… since a lot of brain activity is dedicated to monitoring sensory inputs, regulating and interacting with the environment.

A system which is itself made up of of multiple systems

Rather than, the brain should be considered as multiples systems that interact together as a whole. Each of them is specialized and is able to communicate with others through different means. The basic elements of those systems are able to transmit signals at different speed, through different pathways: electrical signaling, chemical signaling (neurohormone)… even by using important diffuse systems (glia and immune system).

A limited metaphor…

To put in a nutshell,

“Humans, along with other organisms with brains, differ from computers because they are driven by emotions and motivations. The brain is much too hot and wet to be represented by a computer. The brain is electrical, but it is also driven by fluids (blood) and chemicals (hormones and neurotransmitters). Most importantly, the brain is part of a body which it drives to action, and research from an embodiment perspective also shows that the whole body (not just the brain) affects emotion, motivation, and other psychological processes.”

So what is the adequate metaphor?

Eddie Harmon-Jones, Ph.D., Professor of Psychology at Texas A&M University suggested that

“So, how can we replace the computer metaphor with a metaphor that more accurately represents the brain of an emotion-driven, motivated organism such as a human? I like the metaphor of a car.

A car may have a computer on board, and may be able to process information. But it is driven by fluids (gasoline, oil, etc.). It is both electrical and mechanical, and it can move.”

Credits: Renault Scénic Front Cut by  Sovxx

Despite the fact that computers can be so complex using most recent advances (for instance VLSI or, simply supercomputers), the car metaphor is far more comprehensible.

We should also consider that computers have no significance if not considered through human perspective. On the contrary, all beings those have a brain exists by themselves.

But a useful metaphor

The metaphor of a brain as a computer helped scientists to gain a better understanding of our brain functioning. No more, no less. Just like both past metaphors of the brain and current ones.

Cognitivism (top down approach), connectionism (bottom up approach) and embodied cognition all succeed at explaining or modeling some aspects of our cognition. In neuroscience, several approaches contribute to explain the neural substrate functioning.

The working memory model , as well as the second revolution in linguistics, etc. were all useful for gaining a better knowledge. A knowledge that is helpful to evaluate individual’s cognitive aspects, model functions and, somehow, understand how the brain works.

These theories are still useful for natural language processing, cognitive remediation, and so on. We haven’t discarded Newtonian theory when Einstein published his general relativity. So is the computer metaphor.

The metaphor is helpful to explore the brain and to exploit its properties but it is not necessarily the ultimate metaphor. Nobody knows what the future has in store…

The brain process information

The brain is not able to recall a detailed picture you saw thousands times (a bank note for instance). That does not mean that it doesn’t store information.

As far as we know the brain stores information using synaptic plasticity (1, 2), i.e. connectivity changes, and brain network topology. The information is scattered through the brain and can be unlearned or mixed with new pieces of information.

What we exactly store is still on debate but it is synthetized and segmented information.

Actually, our brain does process information:

“Information is what is conveyed or represented by a particular arrangement or sequence of things” – Oxford dictionary

Which is exactly what the brain does.

To go further in this explanation:

“Information is an abstract concept. There is a temptation to think of information as fully representable by the bits used in a digital computer. However with signals, the timing of the signal makes a difference. When precisely a signal arrives at a neuron carries information about what the signal means, and there is increasing evidence that the relative timing among neural signals carries information as well. The brain accomplishes information processing using signal processing, but there is more going on than the phrase “information processing” alone would suggest. […]All signal processing is carried out by the spontaneous “random” interactions of molecular collisions, which are loosely guided by the continuously hierarchical structural form of the brain.” – Paul King, Computational Neuroscientist, Data Scientist, Technology Entrepreneur

What is your opinion on the question?

Plants are no longer considered as insensitive and passive life forms. We even found novel means they communicate. What is more interesting is that several recent news tend to suggest some ability to count in plants.

The case of Venus flytraps

Venus flytrap by Jason

In this article, Jennifer Böhm et al. suggest that a carnivorous plant (Venus Flytrap Dionaea muscipula) is able to count the number of mechanical stimuli that trigger the production of digesting enzymes and sodium uptake modules.

The team asked the question as to “how many times trigger hairs (e.g. mechanosensors) have to be stimulated (e.g., how many APs are required) for the flytrap to recognize an encaged object as potential food, thus making it worthwhile activating the glands”.

What they found is the following:

One touch to a trigger hair sets the plant in a “ready” state. A second touch causes the trap to close around the prey. At five touches the plant begins to produce digestive enzymes and transporter molecules that take up nutrients.

It sounds like an algebraic summation that triggers different pathways.

Not yet impressed? Let me show you a more interesting computation in plants…

A more complex calculation…

A. thaliana by Alberto Salguero Quiles

Several years ago a team from the John Innes Centre found that Arabidopsis thaliana is able to perform some computations. In fact, this plant is able to have a perfect 5% of starch level after one night. Even when the night’s duration was artificially increased or decreased.

Actually, this well-known (but still unknown, in a way) plant can do arithmetic division to prevent starvation at night. The plant determines the remaining time before day then divide the starch level by this value to get the adequate velocity for consuming starch!

A novel perspective

Of course these calculations may not be conscious and are probably caused by a chemical imbalance. However, it opens really promising perspectives on the complexity of plants those are far more complex than initially thought.

I’m back with my second post of 2016 regarding consciousness. Hope it will be delightful.

Intelligences are multiple

Consciousness is not yet fully defined. As well as intelligence was until recently. The boundaries of intelligence were fluctuating across history and constantly redefined to ensure human superiority over other species. The tools to assess intelligence are often designed from our own abilities therefore fail at measuring other species intelligence.

However, we all now agree on various form of animal intelligences even in the human species. It was a long road before it became a consensus, harder than believe in multiple personalities traits which is actually intrinsically related.

What about consciousness?

Consciousness is hard to define due to lack of consensus. The definition I prefer is sentience, i.e. feeling as distinguished from perception or thought. One could also add self-awareness to the definition.

So what beings are conscious?

Basically all animals (including humans) are sentient and sometimes self-aware. Plants may also be conscious beings. Although that they don’t have neurons or glia, they possess structures that allow them to process information and react to environment or even plan actions.

“They have ways of taking all the sensory data they gather in their everyday lives … integrate it and then behave in an appropriate way in response. And they do this without brains, which, in a way, is what’s incredible about it, because we automatically assume you need a brain to process information.”; “that the line between plants and animals might be a little softer than we traditionally think of it as.” – Michael Pollan

More interestingly, we may have created artificial beings that are conscious. The neuroscientist Christof Koch speculated that the Web might have achieved sentience. Pretty freaky, right?

I personally consider that at least cluster of networked machines (with their software) or intelligent machines may be sentient. Especially ones that auto-monitor. Some people argue that any system that is unpredictable may actually be sentient.

The fact is that such system may fail at the Turing test because they are so different than us and their consciousness is so different than what we expect. Their sensory modalities may even not map what we see as inputs and outputs.

Since they process information and act, even particles may be conscious, by flashes. Hard to believe…

Are you talking about panpsychism?

Most of living scientists consider that considering non-human beings as conscious is panpsychism thus discarding it as pre-science. Panpsychism is the view that consciousness, mind or soul (psyche) is a universal and primordial feature of all things (to some degree).

This century marked a renewed interest in these hypothesis. Max Tegmark (MIT) argues that mater may be intrinsically conscious and a more restrictive scale have been invented to measure levels of consciousness of artificial agents.

An interesting conclusion

Depending of what frame of reference we consider, we may:

• Be surrounded by above suspicion conscious beings: from artificial intelligences (not that terminator-like we all think about those are maybe not conscious of us at all) and even superintelligences (if they exist) to inert chromosomes.
• Be living in a vast conscious system with a lot of subconscious entities.

The second option may be plausible since supercluster processes a lot of information as a complex system.

But the light speed limit tends to suggest a long scaled consciousness (if it exists) or consciousness at early stages of structures self-organisation only… although quantum entanglement or shared routines in a computational universe may also allow long-distance rapid information processing.

While we already know that reading fiction improves brain connectivity and function and its effects is long lasting, a new study proves that “listening to stories, greater home reading exposure is positively associated with activation of brain areas supporting mental imagery and narrative comprehension, controlling for household income”.

If you want smart kids, just don’t put your children in front of the TV for long hours. Give instead priority to active play and reading. They will develop better cognitive capabilities and be healthier.

That’s great news! Google just open-sourced TensorFlow, its deep (machine) learning library.

The engine is widely used at Google: by speech recognition systems, in the new Google photo product, in Gmail, in search, etc.

From now on startups will be able to develop systems as intelligent as a 4 year old children. More interestingly, code sharing in python between researchers or data scientists has never been easier.

The limitations of the previous system no longer exist:

[DistBelief] was narrowly targeted to [artificial] neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure — making it nearly impossible to share research code externally. […] TensorFlow has extensive built-in support for deep learning, but is far more general than that — any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some examples). Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface.

Maybe the engine will soon get available for its cloud-based service on a clustered architecture…

I’m back with a first true post regarding a discipline that I like immensely because of its tendency to be helpful to users, customers, companies… and humanity.

Today, we will assess the advantages of recruiting cognitive science specialists. You will see that such profiles are interesting not only for research but also for companies. It’s time to consider to recruit cognitive science specialists.

Cognitive science? eh?

You may wonder what I mean by “cognitive science”. That’s why I will explain a bit about this in fashion interdisciplinary field. Fashion in that is the “C” of the NBIC acronym which stands for Nanotechnology, Biotechnology, Information technology and Cognitive science that refers to the converging technologies (and sciences) for improving human performance.

A definition

I will first introduce a definition by Jean-Pierre Desclés:

This field is the study of the Mind in relation to its material substrate, the brain. It is understood through both the comprehension of the neurobiological aspects and the modeling of observable “intelligent” behaviors [such as perception, memory or planning].

This study of cognition, by the way of models (logical, mathematical, probabilistic and computer/information model) encompasses the mind-brain relations not only in humans but also in animals and complex computers […].

In a broader way, I would define the cognitive science as the study of the cognitive processing of information (according to the prevailing information paradigm), in either natural or artificial systems.

A little bit of history

Disclaimer: This section is related to history of science and can easily be skipped… but at the cost of  lacking of some interesting deeper understanding of the essence of cognitive science!

This field, by nature interdisciplinary, developed through 3 ages and their respective complementary paradigms:

Cybernetics and the formalisation of the “self” idea, from the Macy’s conferences (1946-1953) is the starting point of cognitive studies. It “studies the flow of information in complex artificial systems and natural ones”.

Followed by cognitive science emergence in reaction to the “black box model” of behaviorism (where “psychology can be accurately studied only through the examination and analysis of objectively observable and quantifiable behavioral events, in contrast with subjective mental states“):

1. Cognitivism (top down approach) and the introduction of two work hypothesis (representationalism and computationalism), 1950-1980 – “the thought is a symbolic process and knowledge is readily shifted between different memory registers”
2. Connectionism (bottom up approach), which models mental states as the emergent processes of interconnected networks of simple processing units, early 1980 – knowledge is represented in the weights of the connections between neurons and is shaped by weighting during learning
3. Embodied cognition where “Many features of cognition are embodied in that they are deeply dependent upon characteristics of the physical body of an agent” (RA Wilson and L Foglia, Embodied Cognition in The Stanford Encyclopedia of Philosophy) … grounded in an environment – “the mind and body work together to form cognition”

I must confess, this description of the evolution is, on purpose, simplistic. Actually it is far more complicated with mutual feedback loop, co-occurence of paradigms, origination from several other theories like structuralism, mediated reference theory and many more. If it was so simple it would not be studied as a standalone speciality!

5-keys insights

To summarize, in his book The Blank Slate (2002), psychologist Steven Pinker identified five key ideas that made up the cognitive revolution:

• “The mental world can be grounded in the physical world by the concepts of information, computation, and feedback.”
• “The mind cannot be a blank slate because blank slates don’t do anything.”
• “An infinite range of behavior can be generated by finite combinatorial programs in the mind.”
• “Universal mental mechanisms can underlie superficial variation across cultures.”
• “The mind is a complex system composed of many interacting parts.”

Interdisciplinarity required

From the early stages of its precursor cybernetics, the interdisciplinarity is inherent to the cognitive science. Since its emergence, the field strengthened relations between several fields and generated new topics of research to in fine create new relations between distant fields.

At the academic level, cognitive science is constituted of legacy core disciplines those are:

• analytic philosophy
  •  of language
  •  of mind
  • epistemology
• psychology
  • cognitive ψ
  • engineering ψ
• linguistics
  • psycholinguistics
  • cognitive linguistics
  • transformational grammar
  • …
• neuroscience (not only computational)
• artificial intelligence
• anthropology

At all levels, the use of formal sciences for description, modeling and simulation (i.e. logic, mathematics, computer science) is compulsory.

In addition to the prominent use of bayesian inference, I should point out the emergence of a bayesian cognitive science where cognitive systems are assumed to behave like rational Bayesian agents in particular types of tasks : “The Bayesian approach assumes that cognition is approximately optimal in accord with probability theory” (Thagard, Paul, Cognitive Science in The Stanford Encyclopedia of Philosophy (Fall 2014 Edition)).

Depending of the topic, more fields can be adjoined such as ethology, information science or educational sciences.

Applied cognitive science

Since its main subject is the study of information processing in either artificial or natural systems, the applications are infinite in this informational world (even intrinsically)!

You may now suppose that fields of application range from improving the coupling between humans and artificial systems to sensory substitution and you are (almost) right.

Professional areas

Actually, cognitive science specialist can encompass careers not only in:

• Data/Information representation, filtering and retrieval
  • semantic Web
  • recommender systems
  • human-computer information retrieval
  • …
• Intelligence analysis (in order to help to make key business decisions through the use of description, modeling and prediction)
  • behavioral analytics
  • customer analytics
  • business intelligence
• Computer-human interaction and human factors (for a better experience and efficiency at using services)
  • interaction design
  • User eXperience design
  • information architecture
• Multimedia design and multimodal interaction
  • for instance ubiquity
• Artificial intelligence
  • Web intelligence
  • ambient intelligence
  • robotics
  • …
• Natural Language Processing
  • text mining
  • speech recognition
  • …

But also in deviated fields:

• Knowledge engineering and knowledge management
  • expert systems to help doctors
  • enterprise collaboration systems
  • …
• Public health and pharmaceutics
  • behavioral neuroscience in preclinical studies
  • biostatistics
• Social intervention and remediation
• Education
  • innovative teaching
    • serious games
    • e-learning
    • …
• Innovation
  • for instance by applying:
    • C-K theory of creativity
    • along with their mediation and catalyst skills
• Science journalism

And much more!

Effectiveness & efficiency enhancement

Another thing I should mention is that such professional profiles are especially valuable in either industry or professional services company. For instance, a cognitive science specialist would make an excellent (techno) functional business analyst in a standard company.

In a well-suited environment, the point is that the cognitive science specialist has the ability to improve both the creativity process and the project efficiency. User-centered design (design thinking, …) allows one to design efficiently. By means of considering human needs at each stage of a project, such specialists reduce the risks of taking the wrong turn (leading to cost overruns).

Skills

Let’s tackle the most awaited part of this article!

Due to the diversity of academic curriculum, cognitive scientists and analysts may develop diverse skills. One professional who had a major in cognitive science may become specialized in recommender systems when another may be a specialist in Natural language processing.

Specialization apart they share a common set of skills which is, by itself, uncommon right out of university:

• Technical background
• Analysis skills
  • Excellent reading comprehension, writing, and speaking skills
  • Talented at evaluating and interpreting ideas
  • Skills at explaining complex scientific research
• Formalization skills
  • Skills at acquiring data, modeling and predicting
  • Ability to solve problems through the use of formal methods (statistics, logic, …)
• Strong interdisciplinarity
  • Ability to act as catalysts of problem solving and innovation
  • Ability to act as mediators in teams
  • Ability to perform problem management
• Project management and decision-making skills
• Understanding of human needs in multiple professional areas

As you may conclude, such professionals acquired a specialized theoretical and practical background with a set of well-developed professional skills which allow them to hold critical and leading positions in teams.

With them, your business efficiency will improve and either users or consumers will enjoy a valuable experience.

Sounds interesting, right? Why don’t you try to hire several cognitive science analysts or project manager?

I hope you really enjoyed this first article. See you soon for a new article.