Cognitive Testing and Statistics Flashcards
What is cognition?
Cognition refers to “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses”
Including thinking, attention, language, learning, memory and perception
What cognitive mechanisms build upon each other to create more sophisticated functions?
Attention & information processing speed –>
Working memory –>
Executive functions & memory
What do the trail making and letter fluency cognitive tasks measure?
Trail making = measures processing speed and aspects of executive function
Letter fluency = measures semantic abilities, primarily word generation
Explain the trail making task
Trail making A = Connect the dots in one continuous line, starting at 1, finishing at 8, going through the numbers in regular ascending order
Trail making B = Connect the dots in one continuous line, starting at 1, finishing at D, alternating from a number to a letter and back again
Don’t lift the pen from the paper
How is the trail making task scored?
The A condition is used as a baseline measure of motor speed (which has a component processing speed)
We subtract the A time from the B time to get the main measure of higher cognitive functions like task switching, while controlling for motor speed
In the real test an administrator keeps track of any errors and corrects them during the test itself
Explain the letter fluency test
You are going to be given a letter of the alphabet and then have a minute to write down as many words you can think of that begin with that letter
Cue = C Answers = cat, car, carry, carnivore, carnival, crispy
What are the rules of the letter fluency task
Rule 1 = No proper nouns (place names, person’s names or product names
Rule 2 = no modifying of a word you’ve already said - if you said ‘carry’ you can’t say ‘carries’ or ‘carried’
Rule 3 = no gibberish
How do you score the letter fluency task?
The main score used on this is the total correct words, which is a measure of semantic abilities
MoCA = a point for 11 words or more
What is a normal distribution?
A normal distribution is where there is a clear trend for the value of most points to cluster around a central mean with equally increasing rarity on either side of this
Many things do not follow this pattern, though we tend to use it as a starting point when considering how to conduct our statistics
Why is the normal distribution important in cognitive testing?
If we are happy that our variables form a normal distribution, there are a number of mathematical assumptions we can make about them which determine how we analyse them statistically
This is a deeply complex subject, but we can largely boil the central issue down to asking if the mean value is a fair way of representing what is average
What are the characteristics of a parametric sample?
Parametric tests are those that assume that the sample data comes from a population that follows a probability distribution — the normal distribution — with a fixed set of parameters
As our sample becomes more skewed, the mean is increasingly pulled in one direction by extreme values
The median and the mode become more representative of what is most expected
What is a positive and negative skew?
Positive skew = right-skewed distribution, long tail on its right side (long, tapering end of the distribution), the mean is greater than its median
Negative skew = left-skewed distribution, long tail on its left side - e.g., a histogram showing test scores with a negative skew shows majority of pps scored above average and only a small proportion of students scored very low scores, mean is always less than its median
What is the difference in parametric and non-parametric samples when it comes to statistics?
Parametric statistics use mean values in their calculations
Non-parametric alternatives make additional corrections to try and account for the skew
What is variance?
The variance of a sample is essentially the average difference of all individual data points from the mean
It quantifies how tightly clustered (or not) the data is around the mean
It is a slight overestimation, of the sum of squares, of the difference between each individual data point and the mean
What is standard deviation?
Because the variance is a version of our data after we have squared everything, its value is greatly increased compared to our original units of measurement
The SD is just the square root of the variance, so it re-converts it back into proportion with our data