Retrieval of Autobiographical Information 

Erica C. Yu and Scott Fricker

Bureau of Labor Statistics 

2 Massachusetts Avenue, NE Washington, DC 20212 

Introduction 

Survey questions about past behavior often require respondents to retrieve autobiographical information from memory 

– a combination of personal experiences (episodic memory) and general knowledge (semantic memory; Conway, 

1996; Baddeley, 1992). An example would be recalling whether you recently purchased any sweaters and, if so, the 

price that you paid. The retrieval of autobiographical information is fundamental to the goals of many surveys and yet 

remains poorly understood. It is not known how the design of the questions may inhibit or facilitate recall, and 

ultimately affect data quality and respondent burden in recall-based surveys. 

Consider for example the Consumer Expenditure Survey (CEQ) in which respondents are led by an interviewer 

through a series of prompts to consider all of the expenses they have had in the last three months. The prompts are 

clustered by semantic similarity; for example, there is a section on home furnishings that begins with “living, family, 

or recreation room furniture,” and continues on to “dining room and kitchen furniture,” “bedroom furniture,” and so 

on. These categories are likewise composed of smaller related clusters of items, such as “sofas,” “living room chairs,” 

and “living room tables.” Designing the interview using this list method ensures that the respondent conducts an 

exhaustive search of their memory for past expenses. Despite this strength, however, the method has at least three 

limitations that may harm data quality and respondent burden. 

First, the method is optimized for a respondent that can search through their memory like a computer can search 

through an inventory. The one-size-fits-all approach assumes respondents to all have a similar organization of items 

in memory, based on the generic definitions of items rather than personal life experience. A respondent’s personal 

autobiographical associations, such as yesterday buying a pair of shoes and a suitcase from the same store, are not 

used. Rich contextual information that is naturally brought to mind with any retrieval attempt (Tulving & Thomson, 

1973) is discarded as the respondent is instead asked to consider the next prompt on the fixed order list. Burden may 

increase when the respondent must retrieve the same information again, or when confusion is created by reporting 

items when they come to mind and again when they are explicitly prompted. 

Second, the list method may suffer from retrieval-induced forgetting or part-set cueing effects, whereby the automatic 

retrieval of information related to autobiographical associations may inhibit or interfere with the retrieval of related 

expenses (Anderson, Bjork, & Bjork, 2000). The strengthened association between the suitcase and the shoes may 

mean reduced strength of associations with other related items that don’t have that shared context or immediate and 

personal connection. For example, while the associations between types of shoes is usually strong, they may be 

dampened as the respondent thinks of their personal experience. After remembering the reason for yesterday buying 

those shoes (sandals for a beach vacation) the respondent may focus on other vacation-related shoes (comfortable 

walking shoes, water shoes, and flip flops) and forget about the new work shoes bought last month. Requiring 

respondents to recall all members of an expenditure category together, despite respondent memory not being aligned 

with that expenditure category structure, may mean that items are left unreported. 

Third, asking the same prompts of all respondents regardless of their actual expenses often results in more than one 

long series of “no... no... no... no...” responses when a respondent does not have any relevant expenses to report. 

For example, the interview includes dozens of vehicle-related expense questions for all respondents, including those 

in households without vehicles. The fixed structure of the interview inevitably results in a poor fit with some 

respondent’s actual expenses. Engagement with the interview may be lost and effort reduced as respondents learn that 

the survey questions are not relevant. 

Given these and other limitations of the list method, there may exist an alternative questionnaire design that improves 

data quality or reduces respondent burden. Research is needed to understand how retrieval strategies, questionnaire 

design, and autobiographical information interact in the survey response processes. 
Strategies for Recall from Memory 

Recall from memory is widely believed to be a process that includes the activation of an item in memory based on the 

strength of its association with an initial cue. Once an item is successfully retrieved, it is believed that the newly 

retrieved item now serves as the cue for the retrieval of a subsequent item from memory. Typically, the literature on 

such models of memory assumes that the probability of recalling an item from memory is proportionate to the strength 

of its association with a cue (Raaijmakers & Shiffrin, 1981). However, in the context of targeting memories that 

include personal information, the basis on which these associations are evaluated is unclear. Personal information and 

experiences may alter the associations between memories that are objectively unrelated to each other (Greenberg & 

Verfaellie, 2010; Tulving, Schacter, McLachlan, & Moscovitch, 1988). For example, the strength of the associations 

between shoes and suitcases is typically low but an experience of purchasing a pair of shoes and a suitcase from the 

same store may strongly associate the two items together, even without explicit awareness of any association. 

We hypothesize that personal information may influence retrieval outcomes, whereby the associations between items 

retrieved from memory may be based on personal experience rather than on semantic similarity alone. To explore 

these associations, we designed an online recall task in which participants were cued to consider one of several 

Consumer Expenditure Survey categories, which were chosen to elicit a range of associations during retrieval from 

memory. In the context of the Consumer Expenditure Survey, with the targeted items for retrieval from memory being 

recent expenditures, we hypothesized that retrievals would be based on personal connections related to the 

expenditures, such as being purchased at the same store (e.g., for the category “Shirts, sweaters, blouses, or tops”). 

We hypothesized also that typical autobiographical associations between items in memory would also be used, such 

as being for the same purpose or goal (“Hospital room or hospital expenses”; Reiser, Black, & Abelson, 1985). In 

addition to the autobiographical- and expenditure-related cues, we included one cue category for which we believed 

intra-semantic category associations would remain stronger than personal autobiographical associations (“Room-size 

rugs or other non-permanent floor coverings”). And finally, to explore the effect of context, we included a pair of cue 

categories for comparison: one category designed to cue a broad range of items in memory (“Sports”), and one 

category designed to cue a small subset of those items from memory (“Health clubs, fitness centers, swimming pools, 

weight loss centers, or other sports or recreational organizations”). The narrower context might reduce the recall of 

other related items or the broader context might not elicit any strong associations. A full list of the categories explored 

in the study are shown in Table 1. 

Table 1. Initial category cues and hypothesized retrieval strategies in the Consumer Expenditure Survey 

Consumer Expenditure Category Retrieval Strategy 

Shirts, sweaters, blouses, or tops Retrieval based on the store at 

which the item was purchased 

Hospital room or hospital services Retrieval based on the purpose for 

incurring the expense 

Room-size rugs or other non-permanent 

floor coverings 

Retrieval based on associations 

within the same semantic category 

Sports A broad context to use for 

searching for items in memory 

Health clubs, fitness centers, swimming 

pools, weight loss centers, or other 

sports or recreational organizations 

A narrow context within the 

“Sports” category 

Methods 

Design and Materials 

To explore recall from memory, we designed an online recall task comprised of two phases that asked participants 

first to recall a sequence of five recent purchases based on a given cue (shown in Table 1) and then to describe the 
connections between the successive recalls.1

 Although the participant may not have explicitly used the subsequently 

named connection at the time of retrieval, the reported information about the connections between successive recalls 

provides insight into the recall strategy used. Using a between-groups design, we randomly assigned each participant 

to receive one of five different cues in order to explore the use of a range of retrieval strategies. The instructions for 

recall emphasized that the recalled expenses did not need to be directly related to the cue, the indicated reference 

period of the last three months, or any previously recalled expense. 

Participants 

Participants were recruited online using the online participant recruitment platform Amazon Mechanical Turk 

(mTurk). To be eligible to participate, individuals must report being located in the United States, have an mTurk 

approval rating equal to or greater than 95%, and have completed 1,000 or more mTurk tasks. The final sample 

included 825 participants reporting a mean age of 34 years (SD = 11.3) and household size of 2.74 people (SD = 1.36). 

A total of 79 participants (9% of the sample) were excluded from analysis based on data quality concerns, such as 

leaving blank fields that were critical to analysis or clearly misunderstanding task instructions. 

Procedure 

The online study was administered between March 26 and April 1, 2014. Participants volunteered through mTurk by 

responding to the following study description: “We'll ask you to recall a few items that you have bought, and then ask 

you follow-up questions about those items.” Interested participants were directed to Qualtrics, an online survey 

platform, where the data were collected and stored. Upon providing informed consent to participate in the study, 

participants read the task instructions, completed the task, and answered debriefing questions. The task took 

approximately 10 minutes to complete and, upon completion, participants were paid $2.00. 

Coding of Expenditures and Retrieval Reasons 

The open-ended text descriptions of expenditures and reasons that were collected from the participants were coded by 

two independent coders knowledgeable about the research questions (the authors). Each case was coded by one coder 

but 20% of cases were coded by both coders in order to check inter-rater reliability. 

Expenditure category codes were based on the Consumer Expenditure Survey’s expenditure categories. Coders were 

instructed to categorize each open-ended description as belonging to one expenditure category. The CE Information 

Booklet, which contains a list of categories collected during the CE interview and examples of the items belonging to 

those categories, was used to identify the majority of expenditure categories. Expenditures that would otherwise be 

collected from the CE diary, which does not have a categorical structure, were coded as belonging to one of the 

following ad hoc created categories: groceries (e.g., milk), household goods (cleaning spray), or personal care 

(cosmetics). A total of 53 codes were used. The overall inter-rater agreement was κ = 0.894. 

Reason codes were created through an iterative bottom-up coding process and a coding manual with definitions and 

examples of reasons was developed. Across several rounds, the coders double-coded small batches of cases and 

discussed the classification scheme. To assign a code, coders were to consider an ordered list of seven codes and 

assign the first code that applied to the target text; in other words, the last code of “Similarity” was only to be assigned 

if none of the other codes applied to the text. A list of the codes and short descriptions are shown in Table 2. The 

overall inter-rater agreement was κ = 0.633. 

Results 

Expenditures Retrieved 

Across the 53 expenditure categories represented in the data, the most frequently retrieved expenditure categories 

were clothing (1,230 retrieved expenses), followed by personal care goods (343), household goods (333), and groceries 

(333). 

 

1

 A third phase was excluded from the analysis. In the third phase, each participant was shown the open-ended 

descriptions of the associations between recalled items he or she had typed during the second phase and asked to 

self-categorize those open-ended responses into one of several a priori defined categories. The data of the third 

phase were excluded due to the limited representativeness of the a priori defined categories. The first and second 

phases were completed before the third phase began and were not impacted by the third phase. 
Table 2. Reason codes 

Reason Code Dominant characteristic of the reason given 

Visualization Visual re-imagining of an experience related to the item 

Same Shopping Episode A specific shopping trip, day, or store 

Narrative Sequence Temporal or causal connection between retrievals 

By or for the Same Person A specific person 

Shared Activity A specific action, function, or event, which may include subtasks and one-time or repeated activities 

Shared Goal A specific goal, which may encompass multiple activities 

over an extended period of time 

Similarity An exemplar of the cued category or a previously reported 

expense, a feature similarity, a repetition of the reason 

provided for the previous item, or an unspecified association 

Analysis of the first expense retrieved shows that participants often began recall by reporting items related to the cued 

category. In-category rates ranged from 50.3% (SD = 3%; Health clubs) to 97.6% (3%; Shirts). Overall, however, 

participants’ recall sequences did not follow the CE expenditure category structure. Participants cued to consider 

hospital expenses reported a mean of 1.5 out of 5 possible expenses (SD = 0.1) that would be coded as hospital 

expenses. The mean was highest for participants cued to recall expenses related to shirts, with a mean of 3.4 out of 5 

possible expenses (SD = 0.1) that would be coded as clothing. While some of these retrievals outside of the cued 

expenditure category are likely due to the participant simply not having such expenses, there is evidence that some 

portion of the retrieval of unrelated items is due to the nature of recall itself. The data suggest that participants did 

have related expenses to report but that their recall sequence did not align with the CE structure by expenditure 

category. To examine this, we looked at how the retrieved expenses aligned with expenditure categories. Analysis 

shows that at least 4.3% of reported expenses were related to the original cue category but were retrieved only after 

the participant had already begun to report unrelated expenses. This behavior of recalling an expense related to the 

original cue after exiting the category occurred at least once for 12.1% of all participants. In other words, these 

expenses may have gone unreported if the conventional list method had been used, where the participant would have 

been instructed to move on to another expenditure category prompt. 

Reasons for Retrievals 

Across the seven reason codes used to summarize the data, the most frequently coded reasons were Similarity (2,499 

reason codes), Shared activity (681), Shared goal (299), Same shopping episode (281), and Narrative sequence (151). 

The high proportion of reasons assigned the Similarity code is as expected, given that the code was designed to capture 

all unspecified associations and associations based on recall of a cluster of expenses using a single retrieval strategy. 

Analysis of the reasons for retrievals shows that participants used more than one retrieval strategy during a recall 

sequence. The average number of unique reason codes ranged from 2.3 reasons (SD = 0.1; Shirts) to 2.9 (SD = 0.1; 

Hospitals). The distribution of reasons used also varied depending on the cued category, as shown in Table 3. As 

expected, participants cued to consider Hospital expenses used goals, narrative sequences, and people to retrieve 

expenses, at rates higher than participants of other cued categories. Likewise, participants cued to consider Shirt 

expenses more often used the shopping episode to retrieve additional expenses, compared to participants in other cued 

categories. Surprisingly, participants cued to consider Rug expenses were more likely to use visualization strategies 

(e.g., looking around their living room) compared to other participants. These participants were also less likely than 

others to use associations based on goals and activities. Comparison of the distribution of reasons between the 

participants cued to consider Health Clubs and those cued to consider Sports does not reveal much insight; both groups 

show heavy use of activities to retrieve expenses. 
Table 3. Distribution of reason codes by cued category 

 

Cued Category 

Reason Code Shirts Hospitals Rugs Health Clubs Sports Overall 

Similarity 70.9% 55.4% 68.0% 57.1% 50.9% 60.6% 

Shared Activity 8.5% 13.1% 11.7% 20.4% 28.9% 16.5% 

Shared Goal 5.8% 11.9% 5.6% 9.0% 4.3% 7.2% 

Same Shopping Episode 9.1% 7.2% 4.3% 5.0% 5.7% 6.3% 

Narrative Sequence 1.6% 5.3% 2.6% 4.8% 4.1% 3.7% 

By or For the Same Person 1.5% 2.8% 1.3% 1.2% 2.3% 2.2% 

Visualization 1.1% 1.9% 5.7% 0.8% 1.4% 1.8% 

Other, Not codeable 1.4% 2.3% 0.5% 1.7% 2.2% 1.7% 

These data show that participants used a range of retrieval strategies to recall expenditures, of which semantic 

similarity was only one. Approximately 40% of retrievals were described as being retrieved based on an association 

other than semantic similarity. We believe that the remaining 60% could be further analyzed to show that many of 

those retrievals are also not based on semantic similarity in the way that the CE interview is structured. 

Discussion 

In this study, participants were asked to recall a sequence of five expenses and then describe the reasons why the recall 

of each expense led to the recall of the next expense. Recall was free and unstructured, allowing the participant to 

retrieve expenses in any order and for any reason. Participants were asked to recall expenses related to a starting cue 

category for a designated reference period, but they were also told to continue recalling expenses even if they could 

no longer meet those specifications. Participants completed this task only once, ensuring that they did not “learn” 

through experience to accommodate the exhaustive list paradigm from which the task originates. This recall task 

allows for an exploration of undirected retrieval strategies, which can provide evidence as to which retrieval strategies 

come naturally to respondents retrieving expenditure information. 

Analyses showed that the recalled expenses were often from the expenditure category that the participants were cued 

to consider but that a non-trivial proportion of expenses might have not been captured by the conventional semantic 

category list method of data collection because they were recalled only after “exit”-ing the list category and reporting 

unrelated expenses. Analyses of the open-ended reasons provided by the participants as to the associations between 

recalled expenses showed that participants used a range of retrieval strategies that relied on personal details such as 

the goals, activities, people, and events associated with the expense. These data suggest that the processes of recall of 

autobiographical information from memory may naturally lead respondents to follow associations related to personal 

experiences rather than associations based on semantic meaning alone. 

Acknowledging the evidence that memory is structured in associative networks rich with context and experiential 

details is a necessary step toward understanding a respondent’s response process. The reporting of autobiographical 

information in a survey is a complex process of retrieval that varies between individuals. Many factors affect what 

items are retrieved and how efficiently they are retrieved, and, unfortunately, these factors are likely based on personal 

experiences that are not predictable or knowable. Designing a questionnaire based on rigid structure may increase 

respondent burden through re-retrievals and frustration and lower data quality through underreporting. Flexibility in 

the design of the questionnaire may warrant further consideration. Research is necessary to evaluate the effectiveness 

of such an approach. 

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