Analysis Workshop: What Comes to Mind First

Part 1 — The Big Picture

This week you learned that a concept is a mental category, that its prototype is the best example, and that the availability heuristic judges how common something is by how easily examples come to mind. Psychology only counts a claim like that as knowledge once it is measured. In this workshop you turn ease of coming to mind into an operational definition, measure it on yourself, put one number on its link with typicality using the correlation coefficient r, and design the experiment that could test a claim about the availability heuristic. A chatbot then answers questions about your work, and you catch where it goes wrong.

Time budget: Plan for about 90 minutes.

Part 2 — The Task

Guiding question: Do the most typical members of a category come to your mind first, how would you measure that honestly, and what would it take to test whether easy recall changes how common something seems?

Before you begin — hypothesis: Before you start the timer, write one sentence in this form: If I list members of one category for 60 seconds and rate how typical each is, then the items I list earliest will ___ because ___.

Workshop data: your-free-listing-run

Source: Your own free list of one category, written in the order items came to mind during 60 seconds, with your typicality rating for each of the first 10 items, recorded in this workshop; no outside file is needed.

• Rows: 10 listed items per run, one for each list position from 1 to 10; the definition table has 5 rows, and the list table has 11 rows, the 10 items plus one row for r
• Columns: List position from 1 to 10, the item you wrote, and a typicality rating from 1 to 7

Part 3 — Materials & Procedure

Tools and materials

• Google Sheets (free) or Excel
• A timer, such as a phone or a clock with a second hand
• A document or notebook for your written answers

Ways of working

• operationalize-a-construct
• design-a-correlational-or-experimental-check
• compute-and-interpret-r

Procedure

1. Choose one everyday category, such as fruit, furniture, sports, or vehicles. Fill in the first scaffold table, your operational definition, before you start: the category, the 60-second time limit, the rule that you keep the first 10 items in the order you wrote them, and the 1-to-7 typicality scale with its two end labels. Tool: A document or notebook for your written answers. Check: The first table is complete before the timer starts.
2. Start a 60-second timer and write every member of the category that comes to mind, in order, without stopping to judge. When the timer ends, copy your first 10 items into the second scaffold table, keeping their order. Tool: A timer, such as a phone or a clock with a second hand. Check: Ten items in the order they came to mind. If you listed fewer than 10, use all you listed and note the count.
3. Now rate each of the 10 items for how typical a member of the category it is, from 1 (a poor example) to 7 (the best example), and write each rating in the second table. Tool: A document or notebook for your written answers. Check: Ten typicality ratings, one beside each item.
4. Open a blank sheet. Type Position in cell A1 and Typicality in cell B1, then type the positions 1 through 10 in A2 through A11 and your ten ratings in B2 through B11. Tool: Google Sheets (free) or Excel. Check: Column A runs 1, 2, 3, and so on up to 10.
5. In an empty cell, type =CORREL(A2:A11,B2:B11) and record the result to two decimals in the last row of the second table. If every rating is the same, the formula shows an error because r cannot be computed when one variable never changes; record that and explain it in your first analysis answer. Tool: Google Sheets (free) or Excel. Check: A value between negative 1 and positive 1, or an error message if all ten ratings match.
6. Select A1 through B11 and insert a scatter chart with list position across the bottom and typicality up the side. Title both axes. Tool: Google Sheets (free) or Excel. Check: Ten dots, one per item, that you can describe in words.
7. Fill in the third scaffold table, your experiment design, then answer the analysis questions and run the critique step in Part 6. Tool: A document or notebook for your written answers. Check: Three completed tables, five written answers, and a critique.

Fallback route

When to use it: Use this route if you cannot run the free-listing task, for example if no timer or spreadsheet is available, or if a health or language reason makes timed listing unsuitable for you.

Write the operational definition in the first table for a category you know well, and fill in the second table from a list you write without a timer. Then use the model data below in place of your own ratings: compute r for the ten pairs with the same CORREL formula, make the scatter chart, and answer the first analysis question about these data. For questions 2 and 3, describe the pattern you would predict for a free-listing run and explain it with prototypes and the availability heuristic. Complete the design table, the remaining questions, and the critique step. Submit the same items for equal credit.

This route earns the same credit as the primary route.

An illustrative paired dataset this course uses for practicing r, constructed for teaching rather than collected from real students.

Fallback model data

Hours of sleep	Quiz score (of 20)
5	11
6	14
6.5	12
7	15
7	13
7.5	16
8	17
8	14
8.5	18
9	17

Part 4 — The Scaffold

Write the operational definition first, because a construct you have not defined as a procedure is not yet measurable.

Student scaffold table 1

Part of the definition	Your entry
The category I chose
The time limit and the listing rule	60 seconds; write every member that comes to mind, in order; keep the first 10
The typicality scale and its two end labels	1 means a poor example of the category; 7 means the best example
What list position measures, in one sentence
What one typicality rating measures, in one sentence

Record each item in the order it came to mind, because list position is the measure of ease of recall that r pairs with typicality.

Student scaffold table 2

List position	Item I wrote	Typicality rating (1 to 7)
1
2
3
4
5
6
7
8
9
10
Correlation r (two decimals)

Plan the experiment that could test a cause, because your free-listing run can only show an association.

Student scaffold table 3

Design element	Your plan
The claim being tested	Reading a vivid news story about an event makes people judge that event as more common
Independent variable (IV) and its two conditions
Dependent variable (DV) and how it is measured
How participants are assigned to conditions
How participants are chosen, and who the results could apply to
One third variable you would hold the same in both conditions
What the result would be allowed to claim

Part 5 — Analysis Questions

1. Report your r to two decimals. Describe its direction and strength in words, and write one sentence that uses the phrase associated with. If r could not be computed, explain why in terms of your ratings.
2. Describe the pattern in your ten dots: were your earliest items rated most typical, which item broke the pattern most, and do the dots look closer to a straight line or a scattered cloud?
3. Explain your result with this week's ideas. Which item on your list is closest to the prototype of your category, and why might the prototype come to mind early? Connect your answer to the availability heuristic as a memory effect: what makes an example easy to retrieve?
4. Your run is correlational: you measured list position and typicality but manipulated nothing. Name one third variable, such as how often you see, eat, or use the item, that could make an item both come to mind early and seem typical, and explain how the experiment in your third table would rule out a third variable like that.
5. Choose one and answer it in 3–4 sentences. (a) If you listed and rated the same category again next week, would you expect the same order and ratings? Say what that tells you about the reliability of your measure, and whether a reliable measure is automatically valid. (b) Could list position capture something other than ease of recall, such as what you happened to eat today or a song stuck in your head? Explain the validity problem and suggest one change that would make the measure more valid.

Part 6 — AI-Critique Moment

Choose one equal-credit critique route. Route A: ask an approved AI tool to critique your completed work, then check its response against your own evidence. Route B is the no-AI equivalent printed below.

Approved tools: Claude, Gemini, ChatGPT, Grok, Microsoft Copilot.

Prompt to paste:

I listed members of one category for 60 seconds, kept the first 10 items in the order they came to mind, and rated how typical each one is on a 1-to-7 scale. I've pasted my completed tables above, including my correlation r between list position and typicality and my plan for an experiment on news stories and frequency judgments. Please answer five things in short sentences. First, does my correlation prove that being typical causes an item to come to mind first? Second, in my planned experiment, which is the independent variable and which is the dependent variable? Third, if I want my results to apply to all adults in my country, should I use random assignment or random sampling? Fourth, my typicality ratings came out almost the same when I rated the list again a day later, so is my typicality scale valid? Fifth, is the general idea that people judge how common something is by how easily examples come to mind a theory or a hypothesis, and is my prediction that items I list earlier will get higher typicality ratings a theory or a hypothesis?

Test the response for these likely failures:

• Chatbots often say a negative r proves that typicality makes an item come to mind first, or that coming to mind first makes an item seem typical.
• Chatbots often call the frequency estimate the independent variable because it is the main measure, or call list position the independent variable in the free-listing run.
• Chatbots often say random assignment is what lets the results apply to all adults in the country.
• Chatbots often say a typicality scale that gives the same ratings twice is therefore valid.
• Chatbots often call the student's specific prediction a theory, or use theory and hypothesis as if they meant the same thing.

Write 3–5 sentences naming one thing the chatbot got right and at least two places it went wrong or overreached, quoting its words and correcting each with evidence from your own tables or this week's course definitions.

Equivalent route (no AI required)

No AI account is required for this workshop. Instead of the chatbot, critique this flawed analysis, printed here in full: "My earliest items got the highest typicality ratings and my correlation was strongly negative, which proves that being typical makes an item pop into memory first. In my experiment, the frequency estimate is the independent variable and the news story is the dependent variable. I will randomly assign people to the vivid-story and neutral-story groups so that my results apply to every adult in the country. When I rated my list again the next day, my ratings came out almost the same, so my typicality scale is valid. The availability heuristic is only a hypothesis, while my prediction that earlier items will be rated more typical is a theory." Mark it up: quote each flawed sentence, name what is wrong (cause read from a correlation, the IV and DV swapped, random assignment confused with random sampling, reliability confused with validity, or theory confused with hypothesis), cite the evidence from your tables or the course definitions, and write a corrected sentence beneath it. This route assesses the same objectives (Objective 6 and Objective 5) as the chatbot critique, takes comparable effort (about 30 minutes and 200–300 words), is scored by the same scoring criteria for the same credit, and its own submission artifact is the marked-up flawed analysis, posted in place of the chatbot share link and report.

Part 7 — What to Submit

• All three completed scaffold tables
• Your scatter chart with titled axes, or a labelled sketch of it
• Your hypothesis sentence and written answers to all five analysis questions
• The chatbot critique report with its share link, or the equivalent route's marked-up flawed analysis

Deliverable:

A short Analysis Workshop report: three completed tables (your operational definition of ease of recall and typicality, your ten listed items with ratings and r, and an experiment design), a scatter chart with titled axes or a labelled sketch, a hypothesis sentence and written answers to five analysis questions, and a critique of a chatbot's answers about your work, or, on the equivalent route, a marked-up correction of the printed flawed analysis. Submit in any accessible file type or as a link; a recorded response may be submitted as audio or video with a transcript, or as text. Choose a written, recorded, or visual form for your answers; every form is graded by the same scoring criteria.

Rubric

The rubric below shows how each submitted part is evaluated; bands are full, partial, or none.

Workshop evaluation criteria

Criterion	Full	Partial	None
Operational definition of ease of recall and typicality	The first table names a category, the time limit and listing rule, a typicality scale with both end labels, and one-sentence statements of what list position and one rating each measure.	A definition is given, but the listing rule, the scale's end labels, or one of the two one-sentence statements is missing or vague.	No operational definition is written.
Data, r, and its interpretation	Ten items with ratings or the fallback data are recorded, r is computed and reported to two decimals or its error is explained, the scatter chart or sketch has titled axes, and the interpretation says associated with, gives direction and strength, and names a third variable.	The data and r are present, but the chart, the direction and strength, or the third variable is missing, or the sentence slips into cause.	No items or correlation are recorded, or the values are invented.
Experimental design check	The design table puts the manipulated story condition as the IV and the measured frequency estimate as the DV, uses random assignment for the conditions and random sampling for who could be studied, holds a third variable constant, and states what the result could claim.	The design is attempted, but the IV and DV are swapped, or assignment and sampling are confused, or the claim goes beyond the design.	No experiment design is submitted.
Prototype and availability reasoning and the limitation	Questions 2 to 5 describe the pattern, explain it with the prototype and with ease of retrieval as the memory effect behind the availability heuristic, connect the third variable to the experiment, and answer the chosen limitation question with an accurate account of reliability or validity.	Most questions are answered, but the prototype is treated as the definition of the category, availability is confused with representativeness, reliability and validity are blurred, or a reason is missing.	The analysis questions are missing or do not address the measurement.
Critique of the chatbot or of the printed flawed analysis	On either route, at least two errors are quoted and corrected with evidence from the student's own tables or the course definitions, and one correct point is acknowledged; both routes earn this band equally.	On either route, errors are named but corrected without evidence, or only one error is caught.	No critique appears, or pasted output is submitted without any checking.

Quality Gate

• The operational definition is written before the timer starts, and it names the category, the time limit and listing rule, and the scale with both end labels.
• Every item, rating, and the correlation come from your own run and formula, or from the fallback model data, never from memory or a chatbot.
• The scatter chart puts list position across and typicality up, with both axes titled.
• Every sentence about r says associated with and names a third variable; no sentence claims that typicality causes an item to come to mind first.
• The design table names the IV, the DV, random assignment, and random sampling, each in its correct role.
• No statistic, rating, or source is invented anywhere in the submission.
• The critique quotes the chatbot or the flawed analysis and corrects at least two errors with evidence from your tables or the course definitions.