May 14, 2008
[ 
, Cognitive Neuroscience ]
In 2001, Yamamoto and Kitazawa showed that the perception of temporal order can be reversed when subjects cross their hands. Subjects closed their eyes and had their hands mechanically touched in quick succession (with stimuli separated in time by a variable amount - from 1500 ms to 0 ms). Subjects were asked to raise the finger of the hand that was first stimulated. The results showed that subjects were accurate in reporting the temporal order of these stimuli when separated by as little as 70ms - but when their arms were crossed, subjects showed a tendency to reverse the temporal order when the stimuli were separated by 100 to 200ms.
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Posted by Chris Chatham at 11:12 AM • 0 Comments • 0 TrackBacks
May 13, 2008
[ , Cognitive Neuroscience ]
Your ability to control thought and behavior relative to your peers - a set of capacities known as "executive functions" - is almost entirely genetic in origin, according to a newly in-press paper from Friedman et al. Over 560 twins completed tests to measure fundamental components of these executive functions, and the results were analyzed in terms of how similar identical twins performed to one another relative to fraternal twins (all twins in the study were reared together). Astonishingly, the results show that the variance common to all executive functions is correlated roughly twice as much between identical twins as between fraternal twins, and that individual variance in executive function falls directly in line with what would be expected from a perfectly heritable trait.
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Posted by Chris Chatham at 10:54 AM • 5 Comments • 0 TrackBacks
May 12, 2008
[ , Cognitive Neuroscience ]
Time pervades our understanding of the world - we use it to coordinate our movements, to perceive motion, to plan our behaviors, and perhaps even to understand causality.
But it is an under-appreciated factor in cognition. Even in the domain of the well-understood visual system, few realize that neurons in visual cortex are tuned not only for sensitivity to visual input of particular orientations, but also tuned also to time - in terms of temporal contrast.
Johnston, Arnold and Nishida were able to manipulate this temporal tuning with a relatively simple method. The authors presented a sine-wave grating which appeared to "drift" in one direction or another (at 5 or 20 Hz) on either side of a central fixation point. This was presented for 15 seconds, but the direction of drift changed every 2s to prevent motion aftereffects. Next, the authors presented a a similar grating on the same side of fixation for 600 ms, which drifted at a rate of 10 times per second. Finally, the authors presented a similar grating on the opposite side of fixation for a variable amount of time (between 300 and 1200ms) and subjects indicated whether it was presented for longer or shorter than the prior grating.
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Posted by Chris Chatham at 10:30 AM • 0 Comments • 0 TrackBacks
May 9, 2008
[ , Cognitive Neuroscience ]
Our ability to suppress unwanted thoughts and behaviors is thought to be related to a process known as "inhibition," whereby ventrolateral regions of prefrontal cortex (vlPFC) actively suppress inappropriate representations. A 2001 study by Sakagami et al. recorded firing data from neurons in the vlPFC to determine the exact mechanism by which this might occur.
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Posted by Chris Chatham at 10:28 AM • 0 Comments • 0 TrackBacks
May 8, 2008
[ , Cognitive Neuroscience, Developmental Psychology ]
Does the resolution or precision of human memory change with its available capacity? In other words, can you remember fewer items with greater precision than you can remember more items?
Contradicting intuition, a new paper from yesterday's issue of Nature shows that all items are stored in memory with equal resolution, regardless of the number of items stored. Authors Zhang & Luck first showed that subjects are equally accurate in reporting the color of a memorized item regardless of the number of other items being maintained in memory. Specifically, when subjects were asked to select the color of a square from memory by clicking on a color wheel (see image below), they were just as precise if the memorized array had included 3 squares as if it had included 6 squares.
An example from the first experiment, with a set-size of 3 squares where the preceding color of one is subsequently probed. Subjects enter their response by clicking the corresponding color on a colorwheel.
But there's an alternative explanation...
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Posted by Chris Chatham at 10:09 AM • 0 Comments • 0 TrackBacks
May 7, 2008
[ , Cognitive Neuroscience, Computational Modeling ]
Complex cognition can be predicted by remarkably simple tasks. For example, the speed with which you choose one of two possible responses can reliably predict IQ. Some theories propose that this relationship is due to differences in something called "processing speed," but more recent work has shown the effect is really due to the slowness of your slowest reaction times on such simple tasks. Known as the "worst performance rule," this can be revealed through various RT distribution decomposition techniques (e.g., "binning" of reaction times or ex-gaussian analysis).
A particular class of computational models provides an elegant explanation for the cognitive processes that generate this "worst performance rule." As described by Usher & McClelland, Ratcliff's diffusion model posits that information processing occurs gradually and unfolds continuously over time, that a response is provided once information has accumulated past a threshold, and that information cannot be accumulated prior to some startup time. Almost all parts of this model are subject to variability, including the rate and direction of information accumulation (e.g., "drift"), and the starting point of information accumulation. This variability is critical to the predictive power of these models - e.g., all source of variability are important for capturing asymmetries in the relationship of stimulus discriminability to reaction time, and variability in drift or information accumulation rate is particularly important for capturing skew in reaction time. It is this "skew" which makes up the slowest portion of reaction times, and which lead to the worst performance rule.
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Posted by Chris Chatham at 12:47 PM • 0 Comments • 0 TrackBacks
April 10, 2008
[ Artificial Intelligence, Cognitive Neuroscience, Computational Modeling ]
Peter Hankins has written an excellent commentary criticizing the "positive comparisons" I make after contrasting brains with computers.
Peter says:
"... the concept of processing speed has no useful application in the brain rather than that it isn't fixed."
While this statement may intuitively appeal to some philosophers, temporal limitations in neural processing are both critical for neuronal function and well accepted in both neuroscience and psychometrics. At the biological level, the membrane capacitance of neurons is important for regulating the firing rate of neurons, which itself has an upper limit. Myelination is another feature of neurons which is clearly critical for the speed that information can be processed. At the level of individual differences, "processing speed" is a well-established psychometric construct which can be reliably measured, can reliably predict higher cognitive function, may be related to myelination, and is thought by some to be a parameter that is critical for capturing age-related change in cognition.
Peter Hankins says:
"Are brains analogue? Granted they're not digital...I take digital and analogue to be two different ways of representing real-world quantities; I don't think we really know exactly how the brain represents things at the moment."
It's increasingly accepted that the brain uses a sparse, distributed code for representing information. Computational models based on these principles are able to account for an increasingly wide variety of interactions between cognition, pharmacology, and deep brain stimulation. Work on the sparse distributed nature of these representations, and the learning processes which generate them, has driven the development and partial success of pattern classifiers for deciphering fMRI data - providing converging evidence that we do have an increasingly good idea of how brains represent information. Peter is probably uncomfortable with equating "sparse distributed representation" with "analog," as I may have in my original post.
Of course, there are arguments that portions of the brain do represent information in a digital fashion, though in my opinion these similarities are somewhat superficial.
Peter says:
"... are brains 'massively parallel'? ... Chris is really warning against an excessively modular view."
It's true I abhor modular views of cognition, but it's hard for me to imagine how the brain is not a massively parallel device - in the intuitive use of that phrase. Most attempts to identify the flow of information processing through cortical networks end up positing bidirectional connectivity between most cortical regions. I think Peter's derision of the "massively parallel" phrase actually confounds the issue of "massively parallel" with "massively parallel computing". And what we mean by "computing" is, of course, the crux of the issue.
Posted by Chris Chatham at 11:02 AM • 7 Comments • 0 TrackBacks
March 4, 2008
[ , Developmental Psychology ]
Almost everyone tries to lose weight at some point, but we are remarkably bad at it; most people quickly return to their original weight after cessation of exercise or resumption of a normal diet. A review article by Patterson & Levin elucidates the pathways for this effect, and in the process finds a special role for juvenile exercise in guarding against obesity throughout the lifespan.
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Posted by Chris Chatham at 4:08 PM • 1 Comments • 0 TrackBacks
February 25, 2008
[ , Artificial Intelligence, Cognitive Neuroscience ]
How does the human brain construct intelligent behavior? Computational models have proposed several mechanisms to accomplish this: the most well known is "Hebbian learning," a process mathematically similar to both principal components analysis and Bayesian statistics. But other neural learning algorithms must exist - how else could the brain disentangle mere correlations from true causation?
Temporal precedence helps to some extent - and does seem to play a large role in Hebbian learning (e.g., spike-timing dependent plasticity). But the smell of rain does not actually cause rain - although it can both precede and follow it - so temporal precedence is not a complete answer to disambiguating causality from correlation.
Environmental interaction is another possibility - and one function of children's exploratory play does appear to be the resolution of causally-ambiguous events.
But exploratory play is just a specific example of a larger class of learning situations, where the environment itself can provide an "error signal" in testing causal inferences - appropriately known as "error-driven learning" in computational models. Errors in predictions about the environment can be used to better extract causation from correlation.
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Posted by Chris Chatham at 11:54 AM • 6 Comments • 0 TrackBacks
February 14, 2008
[ , Cognitive Neuroscience ]
Well, it's not quite as erotic as it sounds, but they could break the ice on more than a few Valentine's dates. Hayward's new article in Brain Research Bulletin describes all known tactile illusions. Some can be tried easily at home, but can work better when your gaze is averted and if someone else is performing these illusions on you (to reduce proprioceptive feedback):
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Posted by Chris Chatham at 11:41 AM • 4 Comments • 0 TrackBacks
