What are some effective study techniques and why do they work?
To talk about effective study strategies, it is first important to note some not-so-effective study strategies that students often try. These may include rote memorization, re-reading, or highlighting. These can be done without much cognitive effort, and thus result in a very surface-level understanding of the material. If you are simply memorizing definitions but not applying any other meaning to it, you may be able to remember it in a few minutes, but it is unlikely that you will remember it a week from now. Similarly, re-reading material without critically thinking about the information can just be a waste of time. So, what study strategies do work? There are many, but the ones I have personally found the most helpful are are strongly backed by scientific evidence are distributed practice and testing yourself. For distributed practice, it is better to space out study sessions across a period of time rather than to study in one large block - even if they both add up to the same amount of time! This leads to faster improvement rates and longer retention because you do not get fatigued, and you are giving your brain time in-between to process the information. In other words, try to create a study schedule before your next big test to study an hour or two each day leading up to it rather than trying to cram it all in the day before. Another study technique that is very useful is testing yourself. Research studies have shown that students had much better retrieval of material a week later when they were tested on it when compared with students who simply re-read the material. Even if you test yourself and can't remember the answer, the action of at least trying to think of the answer will increase retention in the long-term by increasing storage strength. Therefore, creating a practice quiz for yourself after each study session or having a friend ask you questions leads to the best long-term retention of material.
How does a neuron fire an action potential?
One of the first important rules to remember about a neuron's action potential is that it is an all-or-nothing response. This means that, in a single neuron, it either fires or does not - there is no way it could only partially fire. The way neurons create a weaker or stronger stimulus is by the number of times (frequency) that is fires, rather than the strength of the firing itself. Each neuron has a resting state around -70mV, which means that the overall voltage in the inside of the neuron is more negative than the outside. Ions move in and out of the cell, resulting in slight increases or decreases of this membrane potential. If enough positive ions enter to increase the membrane potential to the threshold (usually around -55mV), then the neuron fires an action potential. If it does not reach this threshold, no action potential is fired (remember - it is an all-or-nothing response). If this threshold is reached, sodium (Na+) channels are opened, allowing for a large influx of Na+ into the neuron. This is called a depolarization. After this, potassium (K+) channels are opened, allowing K+ ions to flow out of the neuron. At the peak of an action potential amplitude (around +50mV), the Na+ channels close, stopping the Na+ ions from flowing into the neuron. Because positive K+ ions are still flowing out of the neuron, the membrane potential starts to rapidly fall back to its resting state. When it starts to reach this state, the K+ channels close again. The membrane potential overshoots a little past (more negative) than the resting potential of -70mV. This is called hyperpolarization. The ions diffuse back to their original places through sodium/potassium pumps, and the neuron returns to its resting potential - ready to fire another action potential.
What's the difference between a representativeness heuristic and an availability heuristic?
First, it is important to define what a heuristic is in general. A heuristic is a "rule of thumb" or mental shortcut that we take to get to an answer to a problem or make a decision. This is often done subconsciously as our brain interprets the world around us. There are many different types of heuristics, but two of the most popular are representativeness and availability heuristics. A representativeness heuristic demonstrates when we make a decision based on how *representative* something is to the real-life situations we have experienced or our mental prototypes. For example, if asked which outcome is more probable when flipping a coin, where H stands for heads and T stands for tails - H, H, H, H, H, H versus H, T, T, H, T - people will often report that the second one is more probably because it "looks" more random, despite being just as likely. Another example would be that if someone was describes as wearing glasses, being a middle-aged female, and having a kind a quiet demeanor, you would more likely assume they are a school librarian than a construction worker. The second one is more representative of what we see in real-life. Another heuristic is the availability heuristic, in which we make decisions based on how easily it is to access something from memory (i.e. how readily *available* the information is to us). The more easily something comes to mind, the more probable it is thought to be. A common example of this is people thinking that airplanes crash all the time. This is because every time it does happen, it makes headlines in the news, making a vivid picture in our mind. Although, what many people fail to realize is that, statistically speaking, airplanes actually crash very rarely, and they are MUCH safer than cars. A similar comparison can be made to people thinking that shark attacks occur much more frequently than they actually do. Overall, representativeness and availability heuristics both allow us to quickly make decisions without much mental effort. Representativeness heuristics are based on a situation representing our given prototype and what we think is average/normal in that situation whereas an availability heuristic is based on how available something is in our memory.