Overview

For this project, you will create a simple neural network that learns to categorize objects based on their features. You will compare its behavior to that of humans learning the same categories.

Preparation

• The examples should belong to novel categories for your participants.
• Each example should be represented with at least 3 features (e.g. with value of 0 and 1).
• Examples should include relevant and irrelevant features for categorizing them.
• Examples should have a form presentable to people that have a clear correspondance to its feature representation.

Human experiment

Before you conduct any experimental trials, work out your protocol. It should include the following steps:

1. Explanation
2. Informed consent
3. Presentation of training examples
4. Testing examples
5. Debriefing

Don't forget details such as how you will order the examples and collect your data. You are strongly encouraged to script key instructions. Finally, develop all materials that you need for conducting the experiment (e.g. informed consent statement, script, stimuli, data logs).

Neural net learning

The web application for creating the neural net is available on the AI Space Neural Networks page (click for the Click Here link just below the main title).

Here are some general instructions for creating your neural network to work with this assignment:

1. At the bottom of the canvas, create a row of nodes, each corresponding to one example feature.
2. At the top of the canvas, create an output node (or multiple output node if you want to assign one category to each output node).
3. Create edges that run from the input nodes to the output nodes. Note: you are allowed to create intermediate nodes, but they are not required for this assignment.
4. Click on View/Edit Examples in the toolbar to add training examples and test examples.
5. Save your graph and data! (under File)
6. Click on the Solve tab to run your neural net. Keep track of the number of steps. One step corresponds to a training round using all training examples.

Problems? Post them to the D2L discussion board!

Report

Your report should include the following sections:

1. Overview
2. Method with human participants
3. Results with human experiments
4. Method using neural net
5. Results with neural net
6. Discussion comparing results
7. Appendix showing experimental materials (e.g. consent sheet, stimuli)

Using a single-spaced format, the main body of your report may be as small as three pages.

Submit your report to the corresponding dropbox on D2L.

Grading

This project is worth 20 points. The following grading scheme will be applied when reviewing the reports:

• Coverage (8 points) The report addresses all of the assignment requirements and discussion questions. Specific requirements include:
• Informed consent is addressed.
• The methods are clear enough so that the protocols could be replicated to achieve similar results.
• The results are a factual account of the outcomes, free of interpretation.
• The discussion is based on the results. Speculation is clearly labeled.
• Insight (8 points) The experiments are well motivated. The discussion uses concepts presented in class (e.g. typicality, prototypes). Similarities and differences between humans and the computer model are fully addressed.
• Readability (4 points) The report is well written, easy to understand and makes helpful use of formatting.