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SUMMER TRAINING 1 - Dover, New Hampshire June 27 - 29, 2018 Individual Registration: School Team Registration:

SUMMER TRAINING 2 - Dover, New Hampshire August 1 - 3, 2018 Registration Deadline: July 8, 2018 Location: ChildLight Yoga Studio Training Center , Dover, NH Online Pre Order For Sale Aquatalia Bruce Buy Cheap Classic qNK8VdHqlE
Self-Study Hours: Content will be emailed on 7/9/18 from Lisa Flynn. Please plan for a 8 self-study hours to be completed prior to the training. Contact Hours: Wednesday, 5:00-8:00pm; Thursday, 8:00am-4:30pm, Friday, 8:00am-2:00pm Individual Registration: OR School Team Registration:

SUMMER TRAINING 2 - Dover, New Hampshire August 1 - 3, 2018

FALL TRAINING - Dover, New Hampshire November 29 - December 1, 2018 Registration Deadline: Nov. 8, 2018 Location: ChildLight Yoga Studio Training Center , Dover, NH Travel Lodging Suggestions Self-Study Hours: Content will be emailed by 11/10/18 from Lisa Flynn. Please plan for a 8 self-study hours to be completed prior to the training. Contact Hours: Thursday, 5:00-8:00pm; Friday, 8:00am-4:30pm, Saturday, 8:00am-2:00pm Individual Registration: OR School Team Registration:

FALL TRAINING - Dover, New Hampshire November 29 - December 1, 2018

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Attendees should bring their Yoga 4 Classrooms Program Manual and Note-Taking Guide from their prerequisite workshop attendance, a Yoga 4 Classrooms Activity Card Deck, and also a laptop, if possible. Plan to wear comfortable clothing and shoes. Note that we will be seated at tables and chairs to simulate a classroom experience. Registration will be accepted on a first come, first serve basis. Due to limited space combined with tremendous interest, these trainings will fill quickly. Terms and Conditions

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CE's, CEUs or PDPs may be available through your employer or credentialing organization. As each credentialing organization has its own requirements and application process, you should confirm that your training hours will apply toward your degree or continuing education requirements. Registered yoga teachers are eligible for Continuing Education Hours through Yoga Alliance . Contact hours for our live workshops and trainings are also valid toward Yoga Alliance specialty designation of Registered Children's Yoga Teacher (RCYT) when completing Converse Chuck Taylor All Star Oxford Sneakers Unisex Cheap Best Prices prD0Oix


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Sprache Stimme Gehör 2010; 34(2):92-99 DOI: 10.1055/s-0030-1253431

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Publication Date:10 June 2010 (online)

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Der mangelnde Transfer klinischer aphasiologischer Therapieerfolge in den Alltag der Betroffenen wird allgemein beklagt. Der vorliegende Beitrag geht von der Überlegung aus, dass diese Transferproblematik aus einer mangelnden Kenntnis der alltäglichen Anforderung an das „Aphasiemanagement” in Familie und Paarbeziehung resultiert. Wir argumentieren, dass gesprächsanalytische Untersuchungen zur Interaktion mit Aphasikern im häuslichen und klinischen Kontext in der Lage sind, eine „alltagstauglichere” Form der Aphasietherapie zu entwickeln. Dazu werden die Methoden und Ergebnisse der aphasiebezogenen Gesprächsanalyse zusammengefasst und bewertet. Wir gehen zunächst auf einige Ergebnisse der Analyse von aphasischen Gesprächen (also solche, an denen Aphasiker beteiligt sind) ein, diskutieren dann den Stellenwert gesprächsanalytischer Methoden für die aphasiologische Befunderhebung in der klinischen Praxis und befassen uns abschließend mit der Rolle der Gesprächsanalyse in der aphasiologischen Therapie(forschung).

It is well known that the clinical therapy for aphasia leads to improvements. However, these are often not transferred by the aphasic to his or her everyday interactional routines at home. This deplorable lack of transferability, so we argue in this article, is due to the therapist's lack of knowledge of what is central for the everyday management of aphasia in the family and in couples after aphasia. We also argue that the methodological and theoretical framework of conversation analysis is a useful instrument for developing forms of aphasia therapy which are more transferable to mundane situations outside the clinic. In order to show this, we summarise and evaluate our own and other researchers' results on conversation analysis as applied to aphasic speech events. We first discuss some important features of aphasic conversations (i.e., those in which at least one aphasic is involved), we then show the relevance of conversation analysis for the assessment of aphasia in the clinical context and we finally argue for conversation analysis as a tool in aphasia therapy.

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Einstellung Effect: What You Already Know Can Hurt You

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Every day we solve problems - from choosing the quickest way to work, to how we’re going to fix a problem for that one client. How do we know if our solutions are any good? What if there is a much better solution that we haven’t thought of yet?

I recently came across a cover letter where someone said, “Every solution comes to me eventually”. This struck me as a strange thing to say. We don’t have visibility to every solution; we all have unknown unknowns. But even further, known knowns may not even make a connection for a certain problem. The Einstellung effect may occur, preventing us from considering all the available solutions.

The Einstellung effect occurs where preexisting knowledge impedes one’s ability to reach an optimal solution. We become unable to consider other solutions when we think we already have a one, even though it may not be accurate or optimal. It leaves us cognitively incapable of differentiating previous experience with the current problem. So we may solve a problem but we don’t actually innovate.

Einstellung is a German word that translates to setting, mindset, or attitude. The brain attempts to work efficiently by referring to past solutions without giving the current problem much though. It’s stuck in a mindset. We apply previous methods to a seemingly similar problem instead of evaluating the problem on its own terms. This effect presents across disciplines and skill levels. Whether or not we know it, we all experience it.


The classic experiment used to validate this effect was conducted by Abraham Luchins in 1942 - the water jar problem.

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Participants were separated into two groups, one given a few priming questions before the core question. The priming questions led the focus of the first group to a particular method of solving the solution. When presented with the core problem, one that couldn’t be solved with the same technique, they were unable to solve it. The participants in the second group, on the other hand, were asked the same core question without the primer and, more often than not, were able to find the optimal solution. (You can find the problem .Try it yourself!)

Another experiment involved Ann Demeulemeester light and shadow effect sneakers Black Cost Clearance Great Deals TsPON1mO
and their eye movements on the board. The participants were again split into two groups, the first group with a suboptimal solution on the board along with an optimal solution and the other with just the optimal solution. The group with the suboptimal solutions continued to look at squares relating to the found solution even though they mentioned they were actively looking for a better one. Their eyes became fixated on the known solution. The Einstellung effect prevented them from viewing the board with an unbiased view even though they were intentionally trying to do so.

This effect suggests that once we gain experience, the more likely we are to fall trap to its influence and fail to evaluate each problem for its merits. We need to ask what the fundamental difference with this problem is and evaluate each new problem without bias. Prevent our brains from going on a mechanized state of autopilot. It’s not a lack of knowledge that leads to these errors but initial ideas formed from previous experience.

In Data Science

Data science is an emerging field where new technologies and methods emerge what seems like every day. But be wary, as trending methods may cloud our judgement. These new tools and ideas can be like shiny objects, where we can’t look away even if it’s not the right tool for our problem, such as the use of tools like Hadoop and NoSQL for the sake of using something trendy or ‘big data’ associated. Rather than leveraging a smaller dataset, we jump into an ocean of unexploited data without adequate reasoning or preparation. Or there’s approaching a problem by blindly throwing the trending algorithm of the day at it. (Recurrent Neural Networks and Random Forests are all the rage these days.) This can lead to solution blindness, especially when intelligence is added too early in the process. Sometimes we form our problem around the solution rather than the other way around.

The Einstellung effect also presents itself in the context of confirmation bias, where we ignore results that don’t support our initial representation of the model or hypothesis. Feature and model selection need to reflect an accurate depiction of the data. Exploratory data analysis is a critical stage in data science that is often overlooked. We need to explore and visualize the data in various ways to dispel preconceived notions before going toward solutions.

“Good is the enemy of great.” – Voltaire

Although a bit of an extreme case, this problem is synonymous with the philosophy of JK Simmon’s character in the recent movie Whiplash :"There are no two words in the English language more harmful than 'good job.'"One becomes content with local maxima rather than the absolute maximum.

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Usually when we think of geniuses, they are the people with a large working memory. They are able to process more at a single point in time. However the working memory, the prefrontal cortex, can block other memories from creating a connection, which consequently prevents creative thinking. A well-known creative process looks something like this:

The third point is key in coming up with novel solutions and bypassing the Einstellung effect. Taking your mind off the task at hand for a while effectively activates the cerebral cortex and gets you out of the working memory to explore new ideas and connections.


Similar to distraction, interleaving is the technique by which one switches between ongoing tasks to improve memory, retention, and learning. It allows a topic to percolate in your mind and extract the general rules. This is not to be confused with multitasking. It could similarly result in a loss of productivity since switching between projects and modes of thinking can be time consuming. But the added benefit of jumping in and out of a problem can greatly outweigh the time required, if it leads to a better solution. Being flexible and allowing yourself to explore paths that don’t necessarily look promising from the start are great ways to allow your mind to discover new dimensions of a problem.


Collaboration, getting different perspectives, is a great method to break out of a rut. An approach that I like is to have multiple people work on an initial concept separately then convene with their findings and explore each other’s unbiased ideas. If a solution is presented too early, it can cause the others to suffer from the Einstellung effect.

The field of data science lacks meaningful collaboration tools. Data science encompasses a large domain of knowledge and many times requires more than one perspective. There are competitions like Kaggle where people can work together on a project, but a meaningful collaboration tool would not only allow data scientists to work with each other on a dataset but track decisions made in the process. Visualization redesigns for example could greatly benefit from this. Best Store To Get Cheap Online Classic Leather Slingback Sandals Latest Collections Deals nVH6Dj
effectively shows the desired result in hindsight. But his knowledge of the outcome rather than the decisions made in the process leads to an unfair critique. Most of the data is left out to highlight the major data point that caused the disaster.

In a production environment sometimes good enough really is good enough. It may not be worth the extra effort to get the best solution. It may not even be possible with the current technology. Marginal benefits may not be worth the time it takes to reach a better solution. The key is in knowing the tradeoffs and when to explore. Recurring problems are the best candidate for exploring if there is a better solution, when it could be just around the corner.

A Cognitive Network

At Exaptive, one of the things we are striving to facilitate is a better method for discovering novel innovations around data. We want to eradicate the Einstellung effect in our field, and eliminate any associate efficiency loss to boot. (Hey, it’s good to have lofty goals.) We believe something along the lines of a suggestion engine is what data practitioners of all kinds need. Except, in addition to suggesting new approaches, the right suggestion engine would reveal the potential collaborators who designed those approaches. What we like to call a cognitive network -a concept that deserves a post of its own - allows people to explore data in various ways with a diverse set of collaborators and suggests different ways to think about a problem.

At its core, a cognitive network is focused on connections, connections that wouldn’t be made if the Einstellung effect has anything to do with it. Connections are what allow us to know when something fits for a particular application, where to apply a technique, or to translate a concept to another area or field of study. They are the glue that hold together pieces of information by use and meaning. The connections then are crucial to innovation, and missing one is detrimental.

We should set aside some time to determine if we are settling for a known, good enough solution or we are evaluating the problem with clear eyes and see all solutions. At the end of the day, we may still not consider every solution. However, being mindful of the Einstellung effect and open to new approaches even if an apparent solution has presented itself will aid in reaching those solutions just outside our conventional way of thinking and lead to innovations.


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2015/8/13 下午9:33:32

That is a hard thing to comment on, there are many times where just a "good-enough" solution is more than sufficient. After all my solution X isn't handling the data load that Twitter would be handling, so good enough "works" right? It's a fine line though, one that many times needs to bring in financial evaluation to determine if the juice is worth the squeeze. But this tends to be the case for more backend systems rather than items that impact the customer, their experience, or their journey. But your statement "one of the things we are striving to facilitate is a better method for discovering novel innovations around data" ... doesn't seem like this applies to you and your team. In the past (I've not done it so much lately) when I came upon a problem that either I couldn't solve, or needed to solve better I would regularly pick up my guitar and play a while or walk outside (your Distraction solution). I've been a fan of this after reading "The Way We're Working Isn't Working". Great article -- good read.

2015/8/20 下午8:33:24

Great read and a well-written article. I think people run into this problem mostly because of time constraints. Everyone is on a deadline, so it makes the most sense to use tried and true solutions to old problems as a jumping off point to solving new problems as opposed to starting from scratch. And I think in general this is a good idea. But having the ability to metacognitively analyze your own problem solving process is what really allows you to solve novel problems, and that is a difficult skill to hone. A skill that requires time to not be actively working on a problem, but rather to deconstruct your own methods. Like Chad said, playing guitar or taking walks or generally distancing oneself from a problem can help provide insight, seemingly from nowhere. But I agree with you that a more active approach to assessing your problem solving techniques is the most fruitful way to produce innovation. You can't just sit back and wait for inspiration to strike, you have to figure out what you are doing wrong and fix it.

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In this video, you’ll learn about the main character through its actions, and you will choose the order in which it will perform those actions.

First, look at the different actions the main character can do. The main character in each starter project has a unique set of actions it can perform in response to what the narrator says. In this example, when the main character receives the message “dance,” it switches between two different costumes 10 times. This makes it look like it’s dancing. Click on the code to watch this happen.

The main character’s actions give you an idea of its personality. Is it quick to anger?

Happy-go-lucky? A courageous heroine or hero? Maybe it’s just a having a bad day?

Now, develop the narrator’s story about this character by writing the narrator’s dialogue. Later, you’ll use the main character’s reactions to illustrate the narrator’s story.

But, for this video, use the “say for 2 seconds” blocks you’ve used in previous days to make the narrator tell a story about the main character. The next video will show you how to program the narrator to send, or broadcast, messages to the main character.

Now, it’s your turn: Program the narrator to tell a story about the main character. Use the specific character reactions included in the starter project you chose to guide what the narrator will say and develop the character in your story.

Test your code often, so you catch bugs along the way.

1. Einführung in die Charakterisierung und das Senden von Nachrichten
2. Die Figur durch ihre Handlungen charakterisieren
3. Nachrichten senden
4. Die Hauptfigur auf der Bühne herumlaufen lassen
5. Eigene Nachrichten verwenden
6. Charakterisierung Erweiterungen
7. Charakterisierung Abschluss
8. Veröffentliche dein Projekt
9. Zeige dein Projekt
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