Recognition, recall, rehearsal and retrieval are important processes in both the acquisition and the use of a language. We will discuss these four processes separately, but they often operate in combination and not always in the sequence we have used. We are using the term “recognition” to refer to an awareness of familiarity when encountering a word, phrase, chunk, sound or structure, either mentally through use of the inner voice, visually when reading or auditorily when listening. By “recall”, we mean consciously accessing records in long-term memory. By “rehearsal”, we mean mental repetition of recalled or retrieved items. And by “retrieval”, we are referring to the subconscious process of bringing back information spontaneously from long-term memory in order to make use of it in acquisition, comprehension or production.

universal emotions • emotional expression • emotional experience • emotion and motivation • wheel of emotions • James–Lange theory • limbic system • amygdala • orbitofrontal cortex • lateralisation of emotions • autonomic nervous system • display rules preparedness theory • positive and negative emotions • locationist neuroscience view

life history theory • ontogenetic adaptations • deferred adaptations • conditional adaptations • attachment styles • C-F continuum • fast versus slow life history • principle of allocation • shared environment • non-shared environment • behavioural genetics • cooperation • morality

In this chapter we will discuss the potential application of the theories on intake reported in Chapter 2 by reference to the literature as well as to our and other people’s experience of applying the theories. We will also offer our own principled suggestions for application.

You want to identify hotels in a city that are good deals: underpriced for their location and quality. You have scraped the web for data on all hotels in the city, and you have cleaned the data. You have carried out exploratory data analysis that revealed that hotels closer to the city center tend to be more expensive, but there is a lot of variation in prices between hotels at the same distance. How should you identify hotels that are underpriced relative to their distance to the city center? In particular, how should you capture the average price–distance relationship that would provide you a benchmark, to which you can compare actual price to find good deals?

Your task is to help the admissions staff at a university design the online advertising for a graduate program. They have several competing ideas about how the online ad should look. How would you design an experiment that could tell which idea would work best? In particular, how many subjects would you need, and how would you assign them into groups, reach the subjects, and measure the outcome? Once you have the data, how would you examine data quality, estimate the effect of showing one version versus another version, and how would you use these results to answer the original question?

You want to know how the industrial production of your country is affected by changes in the import demand of your country’s largest trading partner. You have time series data on the industrial production of your country and total imports of its trading partner. How should you estimate this effect? Is there a way to get a reasonably precise effect estimate when your time series is not very long? In particular, can you use similar time series from similar countries to get a good and more precise estimate of the effect for your country?

Does smoking make you sick? And can smoking make you sick in late middle age even if you stopped years earlier? You have data on many healthy people in their fifties from various countries, and you know whether they stayed healthy four years later. You have variables on their smoking habits, their age, income, and many other characteristics. How can you use this data to estimate how much more likely non-smokers are to stay healthy? How can you uncover if that depends on whether they never smoked or are former smokers? And how can you tell if that association is the result of smoking itself or, instead, underlying differences in smoking by education, income, and other factors?

Your task is to predict the number of daily tickets sold for next year in a swimming pool in a large city. The swimming pool sells tickets through its sales terminal that records all transactions. You aggregate that data to daily frequency. How should you use the information on daily sales to produce your forecast? In particular, how should you model trend, and how should you model seasonality by months of the year and days of the week to produce the best prediction?

How likely is it that you will experience a large loss on your investment portfolio of company stocks? To answer this, you have collected data on past returns of your portfolio and calculated the frequency of large losses. Based on this frequency, how can you tell what likelihood to expect in the coming calendar year? And can you quantify the uncertainty about that expectation in a meaningful way?