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Category: October 2020–Predictive Processing
English in a Time of Corona – A Semester with COVID
At first COVID19 was news that my already saturated brain just learned to tune out. Japan was used to its fair share of calamities, so I assumed the coronavirus would simply blow over with the collective bubble power of gaman (bearing it out). I exercised the kind of exceptionalist thinking sometimes common in this country. But then there were fewer jokes and more people started talking in somber tones. Slowly the sense of security disappeared as I heard more stories and anecdotes from people I knew. Finally, in April, a state of emergency was declared. The bubble had popped and the coronavirus was here to stay.
Maximising the Self by Minimising Surprise
The idea that we remember past experiences in order to predict future ones can be traced back as least as far as Tolman (1948). His research, with various other scholars, conducted at a time when behaviourism was the dominant theory in psychology, led him to propose that learning by association did not result in conditioned stimulus-response, but rather that animals (including humans) developed expectancies based on prior experience. He coined the term “cognitive map” to describe the higher-order representations that were formed during learning. For example, in one experiment, summarised in Tolman (1948), rats trained to run down one arm of a maze for food, were more likely to run down an arm in a similar direction when the original arm was blocked, showing that they had not only learned to associate the arm with the food reward, but had formed a cognitive map of the orientation of the maze. This cognitive map represented the task at a level of abstraction, allowing the rats to generalise from prior learning and transfer that knowledge to new stimuli. More recent research suggests that animals and humans form cognitive maps of a variety of tasks and events, not just spatial maps, enabling them to make predictions that optimise reward in new situations (Behrens et al., 2018).
Building a Better Generative Model
Graded readers and other forms of controlled linguistic input have been widely accepted tools to facilitate language learning; however, this controlled input is only part of the equation. Language learners also need clear and accurate internal models of correct linguistic forms to allow them to parse the language they encounter. This seems obvious enough, and I do not believe anyone would consider it a controversial statement. However, if asked why this is true, I’m not sure that many could offer a theory to support their gut feelings. Once again, we turn to the theory of predictive processing to answer this question.
At the Bat: A Predictive Language Processing Primer
Imagine a major-league baseball game. Your favorite batter steps up to the plate. The pitcher rears back and throws. From the moment the ball leaves the pitcher’s hand to the moment it crosses the plate, approximately 500 milliseconds (ms) will elapse. That’s half a second. In that time, the batter must evaluate the pitch headed his way, adjust his stance (150ms), and swing the bat (200ms). He is left with 150ms—half the time it takes to blink his eyes—to determine what kind of pitch it is and to decide whether and how he should swing, or indeed if he should fling his body out of the way of a wild pitch.
Multi-word Expressions Made Easy or Difficult: What L1 and L2 Processing Tells Us
When I wrote an article for the “Emotion” issue of the Think Tank in 2018, I learned that emotions are predictions our brain makes, not reactions to some stimuli from the outside world (Barrett, 2017). Although this claim sounded somewhat surprising and went against common beliefs about how our minds work, neuroscientific evidence has accumulated that our brain is, in fact, a prediction-making machine. The brain constantly creates predictions about our senses, cognition, and behaviors, let alone our emotions, and creating predictions is indeed its main function. Simply put, our brain is using statistics in order to make better and more fine-tuned future predictions, and when the predictions are found to be wrong, the brain adjusts and renews the model. This is indeed learning.
Predictive Processing: The Grand Unifying Theory of the Brain
The brain does so many amazing things. So, deciding which is the most amazing is not easy. Is it memory, in which even its faults are part of the design (see my piece in Think Tank on Forgetting), or emotion, the mechanism that steers us through life (see Think Tank on Emotion), or maybe even language, the tool that allowed our species to exploit the social environment (see Pagel’s TED Talk)? Any of these choices would be good, but I am going to select something else: the miraculous way the brain changes raw sensory signals into Picassos, parties, and poems.
Eggs to Dopamine: The Shock of the New
New city. New country. Time to explore. This is one of the best feelings I know: arriving in a place I’ve never been to before, checking into my accommodation, and heading out to wander the streets and see what I can see. The fewer preconceptions the better. Let the new place speak to me, teach me, surprise me.
The plane arrived in Manila just after lunch time. The taxi didn’t take long to reach my hotel. Shower, change of clothes, camera in my pocket. Past the hotel security check and out onto the streets. What did this new city have in store for me? Sights. Sounds. Traffic rules. All worth leaving home for. A Japanese department store, a soccer game in the middle of the street, Christmas decorations in September, and purple eggs. Purple eggs? That made me pause. I knew the white kind and the brown kind (my wife insists they taste different) but what was with the purple ones? What mysteries did this new experience hold?
The Think Tank We Always Wanted To Do
This is the issue we have been wanting to do for years. Predictive processing (also called “predictive coding”) is one of the most interesting perspectives on what the brain does, and it veers heavily away from traditional views. You only get an inkling of that if you watch the Lite intro video choice, but if that short Part 1 catches your fancy, continue on with the next two or three in Jones’ set. Then, if your level of interest keeps rising and you feel brain-strong, go on to the master himself, Andy Clark, in the Deep intro video choice. As you will discover, Andy and his colleague, Karl Friston, are not the easiest people to understand, and that may be why the theory is not more widely known. I don’t think I would have looked into it had I not had my curiosity piqued years before by a short statement from a psychologist: “Our brains are always predicting.”