Intro

Overview

During the first day, you learned that different models can answer different questions. That means that depending on your question, goals, and hypotheses, you will need to develop different kinds of models. How to best approach this is the goal of today. We will use this as an opportunity to kick off your group projects simultaneously. To do so, we will start walking you through a step-by-step guide of how-to-model. Today, we will start with the following steps: how to develop a good question and goal, how to do a literature review, how to determine what ingredients you need, and what hypotheses you would like to evaluate.

Today’s tutorial focuses on the first 4 steps of how-to-model by demonstrating the thought process based on a simple phenomenon known as the train illusion. We will first introduce the phenomenon and then provide a step-by-step guide on thinking about and developing the 4 first steps of framing your project. Groups will then think about their own projects and develop answers to each step’s questions. We will introduce an online project planner based on a large language model that can give you feedback to these answers, which you can use iteratively to develop the full plan of your project. Different types of projects will probably go through the steps in a different order, but at the end, all projects should complete all steps.

How to model is rarely, if ever, taught systematically. Our guide and project planner is not the only way to approach modeling, but it’s one way to ensure you don’t miss anything important. Going through all the steps also makes publication much easier because you have already explicitly thought about all the elements you will ultimately need to communicate (see Abstract section later for example).

Install and import feedback gadget

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# @title Install and import feedback gadget

!pip3 install vibecheck datatops --quiet

from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
    return DatatopsContentReviewContainer(
        "",  # No text prompt
        notebook_section,
        {
            "url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
            "name": "neuromatch_cn",
            "user_key": "y1x3mpx5",
        },
    ).render()


feedback_prefix = "W2D1_Intro"

Submit your feedback

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# @title Submit your feedback
content_review(f"{feedback_prefix}_Video")