neurodudes

This past May, the Almaden Research center, part of IBM research, invited some provocative speakers on the topic of “Cognitive Computing” to come and speak. Since IBM recently invested a lot of money into understanding the brain with the Blue Brain project, it seems like this meeting was a way to figure out the next step along this path.

Powerpoint presentations and videos of the event are available online.

From the synopsis:

The 2006 Almaden Institute will focus on the theme of “Cognitive Computing” and will examine scientific and technological issues around the quest to understand how the human brain works. We will examine approaches to understanding cognition that unify neurological, biological, psychological, mathematical, computational, and information-theoretic insights. We focus on the search for global, top-down theories of cognition that are consistent with known bottom-up, neurobiological facts and serve to explain a broad range of observed cognitive phenomena. The ultimate goal is to understand how and when can we mechanize cognition.

Confirmed speakers include Toby Berger (Cornell), Gerald Edelman (The Neurosciences Institute), Joaquin Fuster (UCLA), Jeff Hawkins (Palm/Numenta), Robert Hecht-Nielsen (UCSD), Christof Koch (CalTech), Henry Markram (EPFL/BlueBrain), V. S. Ramachandran (UCSD), John Searle (UC Berkeley) and Leslie Valiant (Harvard). Confirmed panelists include: James Albus (NIST), Theodore Berger (USC), Kwabena Boahen (Stanford), Ralph Linsker (IBM), and Jerry Swartz (The Swartz Foundation).

Backs to the Future

Cool stuff:

New analysis of the language and gesture of South America’s indigenous Aymara people indicates a reverse concept of time.

Contrary to what had been thought a cognitive universal among humans – a spatial metaphor for chronology, based partly on our bodies’ orientation and locomotion, that places the future ahead of oneself and the past behind – the Amerindian group locates this imaginary abstraction the other way around: with the past ahead and the future behind.

Appearing in the current issue of the journal Cognitive Science, the study is coauthored, with Berkeley linguistics professor Eve Sweetser, by Rafael Nunez, associate professor of cognitive Science and director of the Embodied Cognition Laboratory at the University of California, San Diego.

On Making the Right Choice: The Deliberation-Without-Attention Effect — Dijksterhuis et al. 311 (5763): 1005 — Science

I don’t know quite what to make of this. In fact, I just don’t understand what is going on. But I can definitely think of examples from my own life where this is true. Sometimes not thinking about a problem really does lead to its solution and it’s fascinating to think about why this may be.

Also, the authors draw a connection between what they call unconscious thought (as performed in their experiments) and insights that can come “after sleeping on it”; I’m not sure these phenomena are the same. I think sleep taps into deeper organization processes that are not available on the timescale of unconscious thought, as given in the experiment.

Abstract:

Contrary to conventional wisdom, it is not always advantageous to engage in thorough conscious deliberation before choosing. On the basis of recent insights into the characteristics of conscious and unconscious thought, we tested the hypothesis that simple choices (such as between different towels or different sets of oven mitts) indeed produce better results after conscious thought, but that choices in complex matters (such as between different houses or different cars) should be left to unconscious thought. Named the “deliberation-without-attention” hypothesis, it was confirmed in four studies on consumer choice, both in the laboratory as well as among actual shoppers, that purchases of complex products were viewed more favorably when decisions had been made in the absence of attentive deliberation.

Last year, the Redwood Center for Theoretical Neuroscience moved from the Redwood Neuroscience Institute in Meno Park to the Helen Wills Neuroscience Institute at Berkeley. In October they held a symposium with several interesting speakers presenting on various topics within Theoretical Neuroscience.

The videos are now online for your perusal, or you can buy a DVD of the whole symposium for a paltry $5.

• Horace Barlow, Cambridge University: The Roles of Theory, Commonsense, and Guesswork in Neuroscience
• Dan Kersten, University of Minnesota: Human Object Perception: Theory, Psychophysics & Imaging
• Sue Becker, McMaster University: The role of the hippocampus in memory, contextual gating, stress and depression
• Florentin Worgotter, University of Goettingen: Learning in Neurons and Robots
• Panel Discussion: The Role and Future Prospects for Math/Computational Theories in Neuroscience
• David Heeger, New York University: What fMRI Can Tell Us about How Visual Cortex Works
• Kevan Martin, ETH/UNI Zurich: Canonical Circuits for Neocortex
• Terry Sejnowski, Salk Institute: Dendritic Darwinism
• Jeff Hawkins, Numenta: Prospects and Problems of Cortical Theory

A forthcoming issue of the journal Neuroscience is devoted to an examination of multidisciplinary approaches to the study of working memory within the field of Cognitive Neuroscience. Although the issue will not be released until late April, a detailed press release is available from the University of Washington at St. Louis.

From the article:

“Multidisciplinary research within cognitive neuroscience has established itself as a promising approach to answering the question of how the mind emerges from the working of the brain” [...] “One of the fields that has gained substantially by successfully combining the theoretical frameworks, methodologies, empirical results and insights of the varied disciplines within cognitive neuroscience, is the study of working memory”

It goes on to describe a “pyramid approach” to multidisciplinary work in this area, which chiefly involves the merging of cognitive psychology, computational science, neuroscience, and cognitive neuropsychiatry.

Shannon Moffett, author of The Three Pound Enigma [book website; Amazon], was kind enough to send us a copy of her book to review. To be honest, when I first took a look at the book, I was pretty sure that — while it might be a great, general-neuroscience-interest book for the public — it would certainly not appeal or be informative for the specialist in our Neurodudes audience. Now, after reading her wonderful book, I realize how wrong I was.

Full review is after the jump.
(more…)

This article in the latest issue of the Journal of Neuroscience is interesting in the sense that they are do human brain stimulation of the hypothalamus, for the treatment of cluster headaches - but they then do positron emission tomography (PET) to examine the downstream neural circuits responsible for the abolition of the perception of headache.

Hypothalamic Deep Brain Stimulation in Positron Emission Tomography

This moves the field of brain stimulation from simple stimulate-and-see-what-happens, towards more of a study of human neural circuitry and how stimulation drives activity in connected locations. It’s possible this will lead, in the future, to better and more focal stimulation protocols, as people figure out what the “circuit-level” phenomena are that correct particular aspects of neural dysfunction. Perhaps someday we will have a map of the “hot spots” where stimulation of a small chunk of matter can modulate a wide degree of neural circuitry for the better.

(Last year, Helen Mayberg and colleagues’ deep-brain-stimulation-and-depression paper got at this issue as well, in which they stimulate the cingulate and (perhaps surprisingly) sent depressed patients into remission, and furthermore changed the activity of frontal structures from the abnormal state, back to a more normal pattern of activity.)

These studies are perhaps setting a good precedent for brain-stimulating neuroclinicians to follow.

— Ed

What gives humans the unique ability to construct novel sentences from the building blocks of language? A recent article in Behavioral and Brain Sciences proposes a “neural blackboard architecture” is capable of just this.

From the article (doi: 10.1017/S0140525X06009022):

“This paper aims to show that neural “blackboard” architectures can provide an adequate theoretical basis for a neural instantiation of combinatorial cognitive structures. [...] We also discuss the similarities between the neural blackboard architecture of sentence structure and neural blackboard architectures of combinatorial structures in visual cognition and visual working memory [...]”

As with all main articles in Behavioral and Brain Sciences, this one is followed by extensive comment and criticism from colleagues, and finally a reply by the authors. This provides a very deep look at the article and the issues surrounding it.

An older, but freely available, version of the article is available here.

A recent study in the Journal of Cognitive Neuroscience has isolated activation in the brain during a 3-stage model of deductive reasoning.

The study shows that during the ‘premise processing’ stage, there is more activity in occipito-temporal areas. During the ‘integration phase’, anterior prefrontal cortex is more active. During the final ‘valdiation phase’, the find more activity in posterior parietal and prefrontal areas.

AI started working on reasoning early on. Will studies like this lead us to the next advance in building models of reasoning?

Looking at static pictures of people running versus pictures of people standing still “evokes a delayed response in an area that overlaps with motionsensitive cortex (hMT+)”. Past studies have indicated a similar response for images depicting a falling cup versus a cup resting on a table.

The paper discusses the role of top-down influence from the temporal lobe as a possible cause for the response. How could this kind of brain activity be influencing our ability to recognize objects in scenes? Is this evidence of the activation of a distributed cortical representation of a moving object?

Should the field of AI be trying to figure out how to replicate a similar top-down influence in next-generation object recognition algorithms?

Abstract from the Journal of Cognitive Neuroscience is available here.