Fritz Perls is interesting to watch while he workshops his therapeutic approach and it is highly recommended to watch his “Empty Chair” technique (various videos available on YouTube). In this technique, the client and therapist are seated and there is also an empty chair, generally facing the client. This allows the client to look at the empty chair while s/he talks, with the therapist observing the client and the empty chair.
The empty chair becomes a focus for the client, as s/he is directed to speak to the chair, as if speaking to another person (or a personified problem). If the subject of the discussion is the client’s fears or insecurities, those fears or insecurities can “take form” in the empty chair and be addressed by the client. If the issue is the client’s relationship with a parent, that parent can be “placed” in the empty chair and the client addresses their parent directly. Sometimes, this involves the client talking to the empty chair and sometimes it has the client jumping from chair to chair to take on the persona (and provide responses for) the other party to the conversation.
How is this helpful?
This technique can allow the client to verbalise issues, helping to clarify problems and suggest solutions.
The client can also begin to understand the perspective of the person in the other chair, by trying to take his or her perspective.
The client can “externalise” problems (this is a powerful technique also applied in other modalities, such as Narrative Therapy). Once problems are externalised, the client can began to examine them from a distance and realise that the problem is not him or her (that is, the client is more than just the problem).
The client can be helped to move from verbalising feelings to expressing them (both in the chair as themselves and in the empty chair as the “other”).
When might the Empty Chair technique be used?
When the client insists on making the therapy session about others (the problem is not his or hers, but rests with someone else).
When the client cannot distance him/herself from problems.
When the client seems to lack empathy for others.
When the client lacks affect.
We all practice things we are going to say to others. Perhaps we are practicing how we might respond in a stressful situation. Perhaps we are visualising how we are going to respond in an interpersonal encounter. The Empty Chair makes this technique more overt and allows us to remove ourselves from the problem, to look at new ways to address it. Talking to an empty chair might help empower us to improve our relationships, so we don’t find ourselves sitting across from empty chairs outside of therapy.
Most of us spend significant parts of our lives either dwelling on the past or planning (or dreading) the future. The exception might be when we are young and are living in that moment – remember savouring an ice cream on a hot summer day, wanting nothing else and to be nowhere else?
As we get older, this changes. We propel ourselves into an imagined future, as we live our lives mentally in what we want to exist. My first experience with this was when I started university and – as I worked so hard with my studies and jobs – I imagined myself finishing university. These visions kept me going when little else would. Sadly, what started as a strategy to accomplish a goal (many of our dysfunctional actions start off to address real situations in our lives, but outlive their usefulness) became an unhealthy pattern of living in the future.
Why is this a problem? Other than losing out on your experiences now – such as the attention of your child – living in the future is not terribly productive (how much of what we imagine actually happens?) Also, such projections can lead to significant anxiety, when the future we imagine is not a positive one.
What about the opposite – living in the past? Nostalgia can be comforting, for a moment, but can quickly morph into regret, bitterness and sorrow. Living in the past offers little assistance in making it through the rest of your life.
People present to therapy who cannot stay in the moment. They are depressed about the past or anxious about the future. How can the therapist help?
There are quicker ways to come into the present:
Grounding in the body. This involves exercises within the therapy session where the client is taken through attempts to focus on what is happening in their body at the moment.
Changing speaking patterns. This involves encouraging clients to speak in the present, not the past or the future (e.g. only present tense verbs). This can be difficult and cause frustration for clients accustomed to doing otherwise.
Initial attempts at facilitating a meditative state, perhaps utilising something like Mindfulness-based Cognitive Therapy (MBCT).
There is a long-term (or perhaps better worded, a more persistent) way to come into the present:
Developing a mindfulness practice. Once a client is able to spend 20 – 60 minutes a day meditating, he or she will find that this state will start to work into other parts of life. This is the point. Mindfulness is not about a few minutes of focus, but focus that becomes normalised for the practitioner.
Do you live in the moment? Is the here-and-now your normal mode of existence?
Spend significant time reviewing the past?
Do you often find yourself feeling sorrowful about what has happened or what might have been?
Do you dread the future?
Do you find that normal things happen without you seeming to notice them (e.g. eating, spending time with family)?
Anxiety has a hard time taking hold when you focus on NOW. Depression loses it power when you find wonder around you.
This moment is all we really have. Make it your focus.
The “null hypothesis” is the hypothesis in research (and statistics) that claims there is no statistically significant relationship between the experimental (dependent) variables and the observed results or data collected.
There is an assumption that the null hypothesis is true, unless research findings indicate otherwise. Rejecting the null hypothesis can be the central task of research.
The null hypothesis can be denoted in statistics as .
First, what is a dummy variable and why do we need them?
What does machine learning do with labels like “United States” when trying to figure out how to process data? These models cannot use these labels in mathematical operations. “1 + United States” does not have a result. So, these labels (commonly referred to as “categorical” variables) need to be converted to something upon which operations can occur.
Let’s make a very simple example. You are trying to use (multiple) linear regression to figure out the effects on the salaries of workers of the following variables:
the countries in which the workers are employed
the age of the workers
the number of years the workers have been on the job
You have a list of salaries and you want plot the salaries and use machine learning to be able in future to estimate salary by country, age and years on the job. Salary is your dependent variable (the one you want to watch change in response to the other variable changes). The other variables are your independent variables.
With dummy variables:
As already noted, categorical variables need to be converted to numerical values. We do not want to do this in one column, as our machine learning model might think there is a difference in values between these variables. If “United States” is given a value of “1” and “Canada” is given a value of “2”, “United States” might be considered numerically more (or less, depending on our logic) significant. To resolve this issue, we create “dummy variables”, giving each variable its own column and providing a 0 or 1 (0 if “no” and 1 if “yes”). Our dataset which contains the dummy variables might look like the following:
Second, what is the trap?
Imagine that you have a dataset with the constant “1” and dummy columns for “male” and “female”. The male and female columns will add up to “1”, which is equal to the constant column. This “1” equals the constant regressor and the regression equation becomes unsolvable. The solution? Either remove the constant or one of the dummy variables. Back to our example – like the male versus female example, the country in our dataset must be either “United States” or “Canada”, so we can remove one of these to avoid the Dummy Variable Trap.
With constant and both dummy variables:
With constant and one dummy variable (United States dummy variable removed):
We have now avoided the Dummy Variable Trap in this dataset!