Impact of Cognitive load on learning and bidding

Last registered on September 19, 2022

Pre-Trial

Trial Information

General Information

Title
Impact of Cognitive load on learning and bidding
RCT ID
AEARCTR-0009088
Initial registration date
March 24, 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
March 24, 2022, 4:55 PM EDT

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

Last updated
September 19, 2022, 10:19 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Primary Investigator

Affiliation
The Pennsylvania State University

Other Primary Investigator(s)

PI Affiliation
Agricultural University of Athens
PI Affiliation
Texas A&M University

Additional Trial Information

Status
In development
Start date
2022-04-01
End date
2022-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In a laboratory experiment with 240 participants, we aim to analyze the impact of cognitive load on bidding behavior in a second price sealed-bid auction. Deviations from rational behavior and consistent overbidding are often observed in second-price auctions. Cognitive load impacts bidding causing larger deviations from the rational behavior. We here aim to investigate whether the impact of cognitive load mainly impacts the understanding of the auction mechanism during the training phase, or it impacts value formation when bidding.
External Link(s)

Registration Citation

Citation
Drichoutis, Andreas C. , Rodolfo M. Nayga and Martina Vecchi. 2022. "Impact of Cognitive load on learning and bidding." AEA RCT Registry. September 19. https://doi.org/10.1257/rct.9088-1.1
Experimental Details

Interventions

Intervention(s)
Participants take part in four rounds of second price induced value auction without feedback about the profit. Participants will be allowed to express their uncertainty about their selected bid on a 0 to 10 scale.
We implement four treatments:
1. Training under NO cognitive load (CL); Bidding under NO CL
2. Training under NO CL; Bidding under CL
3. Training under CL; Bidding under NO CL
4. Training under CL; Bidding under CL
Participants are placed under CL either when undergoing the auction training, or when entering each bidding round. To induce cognitive load, we ask participants to keep a long sequence of numbers (7-digits number) in memory prior to undergoing the training or entering a bidding round. Participants are then asked to correctly report the number after the training or bidding round. If they correctly report the number they obtain the incentive, otherwise they will not receive any payoff for the task. The incentive for the cognitive load task is higher than the incentive provided for the auction, to ensure participants' effort in the task. Participants in the no cognitive load conditions are asked instead to memorize the long sequence of numbers (7-digits number) and correctly report the number prior to undergoing the training or entering the bidding round, i.e., immediately after memorization.

Intervention Start Date
2022-04-04
Intervention End Date
2022-12-31

Primary Outcomes

Primary Outcomes (end points)
1. Distance between the bid and the induced behavior
2. Extent of overbidding
3. Extent of underbidding
Primary Outcomes (explanation)
1. we calculate the difference between the actual participants' bid and the induced value assigned to the participants for the auction round
2. we separate bids into three categories: perfect demand revealing, overbid, and underbid. We calculate the share of participants in each of these three categories in the four treatments.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this study, we aim to disentangle the impact of cognitive load (CL) on the understanding of the auction mechanism vs. on value formation, by varying the timing of the CL. We use a second price sealed bid auction mechanism with induced values. This auction is often used as is considered demand revealing (i.e., each bidder should announce his true willingness to pay for the auctioned object as a dominant strategy). In a second price sealed-bid auction, each subject simultaneously submits a bid to purchase an item X, which has an induced value. The agent who submits the highest bid wins the auction and receives the item, paying an amount equal to the second-highest bid among the bidders in the auction. In each round, each subject is assigned an induced value for item X, randomly drawn from a known distribution before the first session. The induced value is the price at which the bidder could sell the item to the experimenter after the auction.

Participants are placed under CL either when undergoing the auction training, or when entering each bidding round. To induce cognitive load, we ask participants to keep a long sequence of numbers (7-digits number) in memory prior to undergoing the training or entering a bidding round. Participants are then asked to correctly report the number after the training or bidding round. If they correctly report the number they obtain the incentive, otherwise they will not receive any payoff for the task. The incentive for the cognitive load task is higher than the incentive provided for the auction to ensure participants' effort in the task. Participants in the no cognitive load conditions are asked to memorize the long sequence of numbers (7-digits number) and correctly report the number either prior to undergoing the training or prior to entering the bidding round, i.e., immediately after memorization.
This results in four treatment conditions: no CL, CL during training, CL during the bidding round, and CL both during the training and during the bidding round.
After each auction round, participants are asked their uncertainty about their selected bid on a 0 to 10 scale. To make sure we affect understanding of the mechanism, we ask participants some questions about second price auction after the four auction periods.


To identify whether the number memorization task actually manipulates cognitive load, we use the manipulation checks of Drichoutis and Nayga (2020) and Deck and Jahedi (2015). Participants are asked to solve a multiplication and an addition task either under CL or under NO CL (in this case, participants were asked to memorize and recall the 7-digits number prior to solving the arithmetic tasks, similarly to before). In the multiplication arithmetic task, subjects had to multiply two numbers. In the addition arithmetic task, subjects had to add two numbers. The tasks were meant to differ in terms of difficulty in order to assess the severity of the manipulation on decision-making.

A cognitive load manipulation might have a differential effect on subjects with varying levels of working-memory capacity. To investigate the relationship between bidding behavior, cognitive load and cognitive ability, we measure participants’ cognitive
abilities using the 9-items reduced version of the Raven’s Standard Progressive Matrices (RSPM) test (Bilker et al., 2012). This test is used to assess mental ability associated with abstract reasoning and is considered a nonverbal estimate of fluid intelligence .

Experimental Design Details
Not available
Randomization Method
Randomization is done by the experimental software
Randomization Unit
Randomization unit is done at the auction group (every 4 subjects)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
200 to 240 participants
Sample size: planned number of observations
50*4=200 participants
Sample size (or number of clusters) by treatment arms
50 to 60 participants
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using induced values in the range of $0 to $40, with N=200 we can detect a relative difference of d=0.10 for correlation ρ between observations for the same subject in the range of 0.6 to 0.8. Calculations based on standard deviations from Lee, Nayga, Deck, and Drichoutis (2020)
IRB

Institutional Review Boards (IRBs)

IRB Name
Penn State Institutional Review Board
IRB Approval Date
2022-01-20
IRB Approval Number
STUDY00019257