Associative Memory and Belief Formation

Last registered on April 14, 2022

Pre-Trial

Trial Information

General Information

Title
Associative Memory and Belief Formation
RCT ID
AEARCTR-0009215
Initial registration date
April 12, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
April 14, 2022, 11:51 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
briq and University of Bonn

Other Primary Investigator(s)

PI Affiliation
Harvard University
PI Affiliation
Frankfurt School of Finance

Additional Trial Information

Status
In development
Start date
2022-04-13
End date
2022-05-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Laboratory experiments to study the effect of (associative) memory on belief formation.
External Link(s)

Registration Citation

Citation
Enke, Benjamin, Frederik Schwerter and Florian Zimmermann. 2022. "Associative Memory and Belief Formation." AEA RCT Registry. April 14. https://doi.org/10.1257/rct.9215-1.0
Experimental Details

Interventions

Intervention(s)
Laboratory experiments to study effect of associative memory on belief formation
Intervention Start Date
2022-04-13
Intervention End Date
2022-05-30

Primary Outcomes

Primary Outcomes (end points)
The study contains the following outcome variables, explained in the attachment:

Individual

• Subjects’ second belief, i.e., their guess about the value of a company in Part 2.
• Subjects’ recall, i.e., the number of positive and negative signals that they recall having seen in Part 1.

Market
• Subjects’ second belief, i.e., their guess about the value of a company in Part 2.
• Subjects’ total betting amount in Part 2.
• Subjects’ share of their total betting that they bet on the company being good in Part 2.
• Market price, defined as the inverse odds ratio for a given market on the state being good.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Different treatment variations to manipulate subjects' memory constraints when they form beliefs about whether hypothetical companies are good or bad.
Experimental Design Details
Also see attachment:

I. DATA COLLECTION:

In the BonnEconLab at the University of Bonn, we conduct laboratory experiments. We conduct individual belief formation experiments as well as market experiments.

Individual

The basic structure of the experimental setup is as follows. Subjects are asked to estimate the quality of 14 hypothetical companies. Each company is either good or bad. The computer determines, independently for each company and with 50-50 probability whether the company is good or bad. Participants do not learn about the actual realization of the computer draw, but receive multiple pieces of news. News are symmetric and correct with 65%, so Pr(news=good|company=good) = Pr(news=bad|company=bad) = 65%. News are independently drawn conditional on the state of the company (good or bad).

The experiment consists of the following stages:

Part 1:
• For each of the 14 companies, a subject first observes a sequence of news. The news appear sequentially on a subject’s computer screen. The number of signals per company is 0, 1, 2, 3 or 4.
• The subject is then asked to guess whether the company is good or bad (we will henceforth refer to this as “first belief”). This belief is financially incentivized through a binarized scoring rule.
• This procedure is repeated for all 14 companies.
• The subject completes real effort tasks for 15 minutes.

Part 2:
• A subject observes a final piece of news about the company.
• The subject then guesses whether the company is good or bad (we will henceforth refer to this as “second belief”). This belief is financially incentivized through a binarized scoring rule.
• This procedure is repeated for all 14 companies.

Part 3: Questionnaire.

Markets

The basic structure of the market experiments is identical to the individual experiments, except that now subjects participate in a version of a parimutuel betting market. Subjects are matched into groups of three. In both part 1 and part 2, after seeing the pieces of news and stating their individual estimates, the three matched subjects bet in a market. Subjects know that all participants in their market saw exactly the same pieces of information as they did. For their betting decision, subjects obtain a budget of 10 Euros. Betting occurs in two steps:
1. Subjects state which part of the total amount they are willing to bet they want to bet on the company being good. 0% means the entire amount they bet is bet on the company being bad, 100% means the entire amount they bet is bet on the company being good.
2. Subjects state the total amount they want to bet (maximum 10€, minimum 0€).



II. TREATMENTS:

There are three pairs of treatments, Main Individual and No Cue Individual, Recall Main and Recall No Cue, Main Market and No Cue Market, where each pair is implemented in a within-subject design.

1. Treatment Main Individual

In treatment Main Individual, the pieces of news are communicated on subjects’ computer screens along with a context. The context consists of a story and an image. Importantly, there is a one-to-one mapping between type of news for a given company and context. That is, every positive news for company A is communicated with the same context (image and story). Likewise, every negative news for company A is communicated with the same context (albeit a different one than the positive news of course). The same logic holds for all other companies.

2. Treatment No Cue Individual

In treatment No Cue Individual, the setup is exactly the same as in Main, except that each piece of news is communicated with a different context. That is, a given context (image and story) never appears twice, even if the company and type of news is identical.

3. Treatment Recall Main

In treatment Recall Main, the setup is exactly the same as in Main Individual, except that, before eliciting beliefs in period 2, we ask subjects to recall the pieces of news they saw.

4. Treatment Recall No Cue

In treatment Recall No Cue, the setup is exactly the same as in No Cue Individual, except that, before eliciting beliefs in period 2, we ask subjects to recall the pieces of news they saw.

5. Treatment Main Market

As in treatment Main Individual, there is a one-to-one mapping between type of news for a given company and context. That is, every positive news for company A is communicated with the same context (image and story). Likewise, every negative news for company A is communicated with the same context (albeit a different one than the positive news of course).

6. Treatment No Cue Market

As in treatment No Cue Individual, each piece of news is communicated with a different context. That is, a given context (image and story) never appears twice, even if the company and type of news is identical. As described above, after stating beliefs, subjects participate in a betting market.



III. NATURE OF ANALYSES

Individual

We analyze our experimental data by means of OLS regressions:
• The main dependent variable will be log belief odds of subject’s second period belief.
• The main independent variable is the realization of the last signal, as well as its interaction with a treatment dummy (Main versus No Cue).
• Because we have multiple observations per subject, we cluster the standard errors at the subject level.

Market

We analyze our experimental data by means of OLS regressions:
• The main dependent variable will be log belief odds of second period market prices.
• The main independent variable is the realization of the last signal, as well as its interaction with a treatment dummy (Main Market versus No Cue Market).
• Because we have multiple observations per market, we cluster the standard errors at the market level.


IV. HYPOTHESES:

Individual

1. Overreaction in beliefs

Restrict attention to treatment Main Individual. Take as dependent variable log belief odds of a subject’s second belief, divided by ln(0,65/(1-0.65)). Regress on the last piece of news, controlling for number of positive news from stage 1 minus the number of negative news from stage 1. We hypothesize that the OLS coefficient on the last piece of new is significantly larger than one.


2. The role of associative memory

Restrict attention to treatments Main Individual and No cue Individual. Take as dependent variable log belief odds of a subject’s second belief, divided by ln(0,65/(1-0.65)). Regress on (i) the last piece of news, (ii) a treatment dummy, and (iii) the interaction of the value of the last signal and a treatment dummy (where treatment Main is coded as 1), controlling for number of positive news from stage 1 minus the number of negative news from stage 1. We hypothesize that the OLS coefficient of the interaction term is significantly larger than zero.


3. Variation in the number of cued signals

Restrict attention to treatment Main Individual. Take as dependent variable log belief odds of a subject’s second belief, divided by ln(0,65/(1-0.65)). Regress each of these on (i) the last news, (ii) the number of signals in the first part that took on the same value as the signal in the second part, and (iii) a corresponding interaction term, controlling for number of positive news from stage 1 minus the number of negative news from stage 1. We hypothesize that the OLS coefficient of the interaction term is significantly larger than zero.


4. Weight on cued versus non-cued signals

Restrict attention to treatments Main Individual and No cue Individual. Compute the sum of cued signals as well as the sum non-cued signals from stage 1. Take as dependent variable log belief odds of a subject’s second belief, divided by ln(0,65/(1-0.65)). Regress on (i) the last piece of news, (ii) a treatment dummy, (iii) the sum of cued signals, (iv) the sum of non-cued signals (v) the interaction of the sum of cued signals and a treatment dummy (where treatment Main is coded as 1), and (vi) the interaction of the sum of non-cued signals and a treatment dummy (where treatment Main is coded as 1). We hypothesize that the OLS coefficient of the interaction term with cued signals is significantly larger than zero, whereas the OLS coefficient of the interaction term with non-cued signals is not statistically different from zero.

Markets

5. Overreaction in market prizes

Restrict attention to treatment Main Market. Take as dependent variable ln(market price/1-market price), divided by ln(0,65/(1-0.65)). Regress on the last piece of news, controlling for number of positive news from stage 1 minus the number of negative news from stage 1. We hypothesize that the OLS coefficient on the last piece of new is significantly larger than one.

6. The role of associative memory

Restrict attention to treatments Main Market and No Cue Market. Take as dependent variable ln(market price/1-market price), divided by ln(0,65/(1-0.65)). Regress on (i) the last piece of news, (ii) a treatment dummy, and (iii) the interaction of the value of the last signal and a treatment dummy (where treatment Main is coded as 1), controlling for number of positive news from stage 1 minus the number of negative news from stage 1. We hypothesize that the OLS coefficient of the interaction term is significantly larger than zero.

7. Variation in the number of cued signals

Restrict attention to treatment Main Market. Take as dependent variable ln(market price/1-market price), divided by ln(0,65/(1-0.65)). Regress each of these on (i) the last news, (ii) the number of signals in the first part that took on the same value as the signal in the second part, and (iii) a corresponding interaction term, controlling for number of positive news from stage 1 minus the number of negative news from stage 1. We hypothesize that the OLS coefficient of the interaction term is significantly larger than zero.
Randomization Method
Randomization done by computer
Randomization Unit

Treatments are randomized within-subject according to the following logic:
• Block A: Treatments Main Individual, No cue Individual - individual level
• Block B: Treatments Recall Main, Recall No Cue - individual level
• Block C: Treatments Main Market, No cue Market - market level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
n/a
Sample size: planned number of observations
410 subjects
Sample size (or number of clusters) by treatment arms
The sample size will be given by:
• Treatments Main Individual and No cue Individual:100 subjects total
• Treatments Recall Main and Recall No Cue: 70 subjects total
• Treatments Main Market and No cue Market: 240 subjects total
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard IRB
IRB Approval Date
2022-01-03
IRB Approval Number
IRB16-1753
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials