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I ran across this connect to Paul Krugman being insightful and thoughtful in regards to the general question of What is usually a Model and What do we utilize them for in Science? It s about economics and specifically kinds of development economics, though the general questions of methodology affect social sciences more broadly. It is within a
The following ad should can be found in the Cognitive Neuroscience Newsletter soon: Postdoctoral Positions at Northwestern University Memory Systems, Intuition and Modeling Department of Psychology Laboratories of Paul Reber Ken Paller Multiple postdoctoral openings now available on two new projects aimed towards accelerating expertise development from training using memory systems theory. One project will build up
I got another request to discuss yet another media are convinced that technology is not good for our brains. It s actually another good instance of really poor science reporting in the news, so I won t link it, though the topic seems generally of great interest and it seems to be based over a curious underlying folk
I was inspired to answer some questions from your middle school student performing a research project on game titles. Since I am enthusiastic about the topic generally, I should probably understand how to answer these kind of questions with an age-appropriate level. My attempt: Jose asks: 1. Do game titles affect the neural?
This is definitely an interesting piece around the philosophy of science and popular understandings of science: How our botched comprehension of science ruins everything /article/index/268360/how-our-botched-understanding-of-science-ruins-everything As an exercise towards the reader, explain what's wrong with his complaint that what many people think of science is really the opposite of science. Some helpful ideas
How and where memory occur in the brain, particularly memory acquired through practice
How experience shapes action, perception and weighed pervasive mechanisms of plasticity throughout the mind
Investigating memory system interactions and intuitive making decisions using visual category learning.
Check the Presentations link within the right side bar to discover the most recent ideas and reports as presented as posters and talks at recent conferences.
I ran across this hyperlink to Paul Krugman being insightful and thoughtful around the general question of What is often a Model and What do we utilize them for in Science?
It s about economics and specifically types of development economics, however the general questions of methodology pertain to social sciences more broadly.
It is in a very way unfortunate that for several of us the whole picture of a successful field of scientific endeavor is basic physics. The objective on the most basic physics is really a complete description of the happens. In principle and apparently in reality, quantum mechanics offers a complete account of the items goes on inside, say, a hydrogen atom. But most things we should analyze, even just in physical science, is not dealt with during that level of completeness. The only exact kind of the global weather method is that system itself. Any kind of that method is therefore to varying degrees a falsification: it leaves out some many facets of reality.
How, then, does the meteorological researcher decide what things to put into his model? And how does he decide whether his model is often a good one? The answer for the first question is the choice of model represents a blend of judgement and compromise. The model should be something you probably know how to make which is, you're constrained because of your modeling techniques. And the model need to be something it is possible to construct given your resources time, money, and patience are certainly not unlimited. There may be lots of models possible given those constraints; what type or ones you select actually to construct depends on educated guessing.
And can you be sure that the model is great? It will not be right in the way in which quantum electrodynamics is correct. At a certain point you will be good enough at predicting your results can be used to repeated practical use, such as the giant weather-forecasting models that run on today s supercomputers; therefore predictive success is usually measured when it comes to dollars and cents, plus the improvement of models turns into a quantifiable matter. In the early stages of your complex science, however, the criterion for any good model might be more subjective: it is usually a good model when it succeeds in explaining or rationalizing some of the you see within the world inside a way that you do not have expected.
There is fashionable nice description of any Dishpan model by David Fultz as an demonstration of a hyper-simplified model that illustrated some emergent properties ideal for meteorology.
What resonates beside me about Krugman s description is usually a common fascination with building the most convenient, descriptive models we hope illuminate underlying principles in complex processes. In Economics, particularly Macro, the scientific goal is usually to understand systems of unmanageable complexity interactions among every one of the people and institutions that produce economic activity. In Neuroscience and Psychology, we try to understand the neural, another system of unmanageable complexity.
I also prefer simple models using a small small amount of parameters as an instance concepts, with a lot of admiration and respect for modelers who take around the complexity to construct up from individual neurons each themselves having nearly unmanageable complexity, fwiw. The simple models also is not right inside the same sense Krugman describes above, nevertheless they can take into account some useful fraction on the variance we try and explain and hopefully expose some deeper principles which may even eventually direct neural-level modeling.
There s a fantastic question within the other end from the complexity spectrum likewise, about why it truly is worth even building simple models using a few parameters above simply making theoretical statements like changing x leads to a change in y. Such theoretical statements are definitely the bread and butter of ordinary approaches to Psychological Science, especially experimental work, but I ll leave a better solution as an exercise, perhaps to become tackled within my graduate seminar the very next time I teach modeling hints: quantification and prediction are crucial.
Memory Systems, Intuition and Modeling
Multiple postdoctoral openings available today on two new projects directed at accelerating expertise development from training using memory systems theory. One project will establish methods to help the use of intuition in making decisions. The second project make use of targeted memory reactivation to further improve consolidation processes and speed learning. Both projects reflect collaborative research between laboratories of Professor Paul Reber and Professor Ken Paller /. Also see for additional information about the local cognitive neuroscience environment.
We are trying to find postdoctoral candidates using a strong involvement in human memory research along with expertise in several of the following areas: memory systems research, experimental behavioral methods, computational simulation modeling, multivariate pattern analysis, EEG recording and analysis.
Interested candidates can send inquiries and application materials to Susan Florczak. Applications will likely be evaluated when received and hiring decisions made over a rolling basis. Multiple two-year appointments are available. Applications includes a cover letter, CV, and names for a minimum of three references.
We will also be looking to work with a new Research Assistant with the lab. Applications for your RA position is going through NU Human Resources.
I got another request to discuss yet another media declare that technology is not good for our brains. It s actually additionally a good instance of really poor science reporting in the news, so I won t link it, even so the topic seems generally interesting and it seems to be based on the curious underlying folk kind of cognition worth thinking of.
How would this work? How could technology make us less smart? The core idea is always that be looking things up, we memorize less and for that reason we are less smart than we will be otherwise. But this misses the problem of substitution. If you aren t memorizing something you are able to look up, does one learn something different instead?
To me, the interesting underlying idea is: Memory doesn t produce an off switch
We are constantly recording experiences from environment. Of course, not everything gets remembered, so maybe we focus too much within the memory failures. But we aren t consciously turning our memories don and doff through the day. So if we're trying to memorize arbitrary facts that people could lookup on google instead, during that time we aren t doing something different that could have gone a useful memory trace. Note that I m describing this for an attention/perception bottleneck, nevertheless it could certainly be a memory consolidation level bottleneck at the same time which is possibly the actual constraint that keeps us from remembering everything we all experience.
The best way for this argument to completely make sense would be to have a strong theory that everything we may have memorized rather than relying on google is a lot more valuable to the internal knowledge state than everything we learn instead. I think which is going to get a hard case to produce. And it won t actually be about technology.
There s another way to create a possible technology hurts as their pharmicudical counterpart case determined by skill learning/strengthening. If memory is usually a skill that is usually improved by intensive practice, then concentrated tries to memorize arbitrary information could theoretically allow you to be better at remembering as well as over time, you d just get smarter. But there is no evidence anywhere that long-term memory may be strengthened by doing this and many individuals have tried to try this.
Working memory looks being trainable, however if anything, technology which makes you hold something in mind while investing in the search phrases to look it down is going to expand your WM as opposed to causing it to atrophy.
So no, technology is not going to produce us less smart. It s almost certain being overwhelmingly from the other direction the access offered by the internet to incredibly rich and diverse types of information means the typical knowledge content with the average brain in the 21st century is really a lot more as opposed to 20th or some other prior time.
I was inspired to answer some questions from your middle school student conducting a research project on game titles. Since I am thinking about the topic generally, I should probably discover how to answer these kind of questions in an age-appropriate level. My attempt:
1. Do video gaming affect the neural? Do game titles affect the state of mind? Do video gaming damage the thinking part of as their pharmicudical counterpart?
Yes, game titles can affect your head, like everything else that you do many. However, these changes can sometimes be to the better. There is recent proof of improvements in 822
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