DFG-1B: Lack Of Information Vs. Information Overload - Info and Reading Options

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**Context** One way to assess people's distribution perception is MIQEs: Mean Interquantile Estimations (Deutsch et al., 2023). Prior studies have shown that participants very reliably give overly linear MIQEs, an effect called Linear Smoothing (Galesic et al., 2012). Linear Smoothing can be explained in several different ways. Two competing explanations for this linearity bias are: 1. A lack of information: Subjects have to little information which could lead to linear judgements via two cognitive processes: Due to the lack of information subjects have problems updating a prior linear expectation. Their final representation of the distribution is biased towards linearity because of this incomplete updating (updating inertia). On the other hand, the lack of information could lead to the use of a heuristic strategy that uses linearity to “fill in the blanks”. In both cases linearity is caused by a lack of information. 2. An excess of information: Subjects receive too much information which leads to information overload. Remembering and using all pieces of information is so difficult that subjects fall back on heuristic processing and use linearity either as an easy “best guess” to represent the distribution or as a shortcut to produce answers for the MIQEs. In both cases, linearity is caused by an excess of information. Own studies trying to test these explanations against each other manipulated the amount of information different groups of participants got before producing MIQEs. No significant differences due to amount of information were found. Additionally, own studies have shown that giving participants feedback about the distribution did not improve MIQEs but did improve frequency estimations. This finding suggests that MIQEs are not based on frequency judgements. --- **Goal** The current experiment will replicate a prior study that found no significant changes due to amount of information. To test the idea that MIQEs indeed are not based on frequency of values we will add a condition in which subjects will give different estimations: frequency estimations. --- **Method** At the beginning of the experiment, subjects will be informed that they will see a sequence of numbers and are told which estimation task they will have to perform (MIQE of Frequency Estimations). They will receive a detailed explanation of the task, see an example distribution, give answers and receive the correct solutions for the example distribution. Afterwards they will then enter three experimental blocks. In each block, subjects will see a sequence of 40 numbers (1s each). The numbers in each block will be the same but in a randomized order. Afterwards we will ask participants to give estimations regarding the distribution (see Manipulation for more Information). --- **Manipulation** Distribution Form: In contrast to our prior study, we will not manipulate distribution form. All participants will see a u-shaped distribution. Measurement: As our first manipulation we will change the measurement after the number presentation. Participants will be asked to either give MIQEs or Frequency Estimations. Information Amount: As our second manipulation, we will manipulate how often we ask for estimations. Group 1 will be asked at the end of the 3rd block only, Group 2 both after the 2nd and 3rd block, and Group 3 after all three blocks. --- **Hypothesis** Different processes make different predictions towards subjects’ MIQEs and frequency judgements: If MIQEs are based on a simple endpoint linearity heuristic due to information overload, then increased learning experience will decrease accuracy of MIQE judgments. However, if MIQEs are based on a memory representation of the distribution, then increased learning experience will increase MIQE accuracy as errors occur due to a lack of information. If frequency estimations are based on a simple endpoint linearity heuristic due to information overload, then increased learning experience will decrease accuracy of frequency judgments. However, if frequency judgments are based on a memory representation of the distribution, then increased learning experience will increase the accuracy of frequency judgements as errors occur due to a lack of information. Based on prior studies, we expect different cognitive processes to underly Frequency Estimations and MIQEs. Therefore, we expect increased learning experience to have a different effect on frequency judgements than on MIQEs. ---

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