
I want to walk you through the next part of our study on information processing. We're now looking at perception — a critical stage in how we handle information as pilots.
Perception is the process where sensory information gets converted into something meaningful. Think about it this way: your ears pick up vibrations in the air, but perception is what turns that pattern of vibrations into a recognized message — like understanding a specific radio call or a spoken instruction. The result of that process is called the percept — what you actually perceive. And here's the key point: the percept is not a complete, perfect copy of everything in your sensory store. It's an immediate interpretation — your brain's best guess at what's going on, based on limited information.
Let me give you a classic example. Imagine you see a notice that begins with a very familiar phrase — something like "The cat sat on the..." Most people reading it will miss an extra "THE" that's been inserted, because they think they know what's coming next. They read the first few lines, their brain jumps ahead, and they skip straight to the last word to confirm their belief. That's perception at work — your brain is actively interpreting, not just passively recording.
This leads to two important truths. First: we can only perceive what we can conceive. If you don't have a concept for something, you literally won't perceive it. Second: at any given moment, we perceive only a fraction of all the information reaching our senses. That's why the attention mechanism in our model is so important — it determines which fraction gets through.
The process of perception is greatly helped by our ability to form mental and three-dimensional visual models. As pilots, we constantly build these models in our minds — for example, picturing the aircraft's position in space or the sequence of an approach.
Now let's talk about funnelled perception. This is the idea that your perception of a situation can be completely different depending on where you start looking. Imagine two men walking through woods who see a family having a picnic. One man perceives the overall picture — a family enjoying themselves outdoors. The other man first notices details — the contrast of colours between a girl's dress and the rug she's sitting on, or the unique design of the picnic basket. Same scene, completely different initial perceptions. Over time, they might both arrive at the same conclusion — the first narrows his focus to include details, the second expands to see the big picture. But their starting points are different.
Next, we have perceived mental models — specifically, mental models themselves. We generate a mental model based on our past experience and learning. These are sometimes called the 'filters of perception'. Because our experiences differ from person to person, perception is subjective — it's not the same for everyone.
Here's how it works in practice: we run that mental model in a particular situation. The value of these models is that they reduce the need to attend to every single input. For example, a layperson listening to aircraft radio telephony — RT — conversation will find it very difficult to understand. But an experienced aviator, who has a mental model of the order of information in the message and an expectation of the potential content, will have far fewer problems understanding it. That mental model lets you fill in gaps and anticipate what's coming.
Let me show you a diagram that brings this together. That figure shows a functional model of information processing with both "bottom-up" and "top-down" processing — we'll explore those terms more as we go. And there's also Figure 8.2 referenced, which illustrates these mental models further.
So to summarise what we've covered: perception is the active interpretation of sensory information, not a perfect recording. We perceive only what we can conceive, and only a fraction of what reaches our senses — hence the critical role of attention. Funnelled perception shows how starting point shapes what we see. And mental models, built from experience, help us process information efficiently — like an experienced pilot understanding RT calls that would baffle a non-aviator.
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