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Harris, C. B., & Berntsen, D. (2019). Direct and generative autobiographical memory retrieval: How
different are they? Consciousness and Cognition, 74, 102793.
https://doi.org/10.1016/j.concog.2019.102793
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Direct and generative autobiographical memory retrieval
Harris, Celia B.; Berntsen, Dorthe
Consciousness and Cognition
https://doi.org/10.1016/j.concog.2019.102793
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Notice: This is the author’s version of a work that was accepted for publication Consciousness and Cognition A definitiv
e version was subsequently published in Consciousness and Cognition.
DOI:10.1016/j.concog.2019.102793.
Direct and Generative Autobiographical Memory Retrieval: How Different Are They?
Celia B. Harris
Department of Cognitive Science
Macquarie University, Australia
Dorthe Berntsen
Center on Autobiographical Memory Research
Aarhus University, Denmark
Corresponding author:
Dr. Celia B. Harris
Department of Cognitive Science
Macquarie University
Sydney, NSW 2109
Australia
Phone: + 61 2 9850 4066
Email: celia.harris@mq.edu.au
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DOI:10.1016/j.concog.2019.102793.
Abstract
Theories of autobiographical memory have emphasised effortful generative retrieval, but
recent research indicates that subjectively effortless direct retrieval is common. We compared the
processes of direct and generative retrieval. Sixty-five participants retrieved 24 autobiographical
memories across three cue types: concrete, emotional, and personal. We recorded retrieval latency,
and participants judged direct versus generative retrieval and rated memory specificity, vividness,
significance, rehearsal, and emotionality. Overall, direct retrieval was common, especially for
personal cues. Directly retrieved memories were recalled faster, were less likely to be specific, and
were rated more significant, rehearsed, and emotional than generatively retrieved memories. The
speed of both direct and generative retrieval varied similarly according to cue type, suggesting they
did not involve fundamentally different cognitive processes. These findings challenge theories that
assume direct retrieval bypasses constructive processes. Instead we suggest that both direct and
generative retrieval involve construction that is similarly affected by cue concreteness and
relevance.
Keywords: autobiographical memory; retrieval processes; direct retrieval; memory qualities;
involuntary autobiographical memories
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DOI:10.1016/j.concog.2019.102793.
Direct and Generative Autobiographical Memory Retrieval: How Different Are They?
Theoretical accounts of autobiographical memory retrieval often distinguish between
“generative” versus “direct” retrieval (Addis, Knapp, Roberts, & Schacter, 2012; Conway &
Pleydell-Pearce, 2000; Haque & Conway, 2001; Harris, O’Connor, & Sutton, 2015; Uzer, Lee, &
Brown, 2012, Williams et al., 2006). Definitions and criteria for distinguishing between direct and
generative retrieval vary. Generative retrieval is typically defined as involving a “top-down”, “goaldirected” (Conway & Pleydell-Pearce, 2000) “controlled and effortful” (Anderson, Dewhurst, &
Dean, 2017, p. 163) search process, in which individuals search through increasingly specific
autobiographical information in order to bring an event to mind. In contrast, direct retrieval is
variously described as “automatic and effortless” (Uzer, Lee, & Brown, 2012, p. 1296),
“immediate” (Ros, Latorre, & Serrano, 2009, p. 91), or “spontaneous and unexpected” (Conway &
Pleydell-Pearce, 2000, p. 275; Williams et al., 2006, p. 363). Traditionally, theories of
autobiographical remembering, while acknowledging the possibility of direct retrieval, emphasise
and prioritise generative retrieval as the default process by which memories are retrieved and
suggest that direct retrieval is comparatively rare; e.g. “the retrieval of autobiographical memories,
although occasionally spontaneous and apparently effortless, is more usually an effortful and
protracted process” (Haque & Conway, 2001, p. 529).
However, there is recent evidence to suggest that subjectively effortless direct retrieval can
be as common as effortful generative retrieval, at least in certain circumstances. Recent research by
Uzer et al. (2012) was specifically designed to assess the prevalence of direct versus generative
retrieval in laboratory-based intentional memorycuing paradigms. They asked participants to elicit
memories in response to concrete nouns and emotional cue words presented on a computer screen,
and to report for each memory whether it was retrieved directly or generatively. Uzer and
colleagues found surprisingly high rates of direct retrieval: about half of the memories were
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reported to have been retrieved directly. They found that this was the case whether they asked
participants to make this distinction in terms of retrieval time (“this memory came immediately to
mind”), retrieval effort (“I did not have to actively search for this memory”), or the degree to which
they brought additional information to mind (“I did not have to think of additional information from
my life to help me recall this memory”). This study indicated that direct retrieval may be more
common than previously assumed, and may be occurring in relatively high rates even in laboratory
paradigms designed to assess generative retrieval (see also Barzykowski & Staugaard, 2016; Harris
et al., 2015; Uzer & Brown, 2017).
Although Uzer et al. (2012) developed a new method of relying on participant self-report to
distinguish direct and generative retrieval, other research has used different kinds of cues to vary
retrieval type. In this methodology, high-imageability, concrete, or personalised cues are used to
index direct retrieval and abstract, emotional, or more generic cues are used to index generative
retrieval instead of participant self-report (e.g. Addis et al., 2012; Anderson et al., 2017; Williams et
al., 2006). This cue-based approach has some consistency with self-report: both Uzer et al. (2012)
and Harris et al. (2015) found that concrete nouns led to higher rates of self-reported direct retrieval
than emotion cues. However, neither cue type nor retrieval latency perfectly overlapped with selfreported retrieval type. Concrete nouns, assumed to index direct retrieval, resulted in self-reported
direct retrieval for about half of trials, and emotion words, assumed to index generative retrieval,
resulted in self-reported direct retrieval for about a third of trials (Uzer et al., 2012; Harris et al,
2015). Uzer and Brown (2017) found particularly high rates of self-reported direct retrieval –
around 80% – with personally relevant cues that participants generated for themselves. Overall,
while cue type does influence rates of direct retrieval, it far from accurately predicts self-reported
retrieval type.
So far, relatively few studies have been conducted to compare self-reported direct versus
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generative retrieval, and the nature of the distinction – including what information participants rely
on to judge the different retrieval types – remains poorly understood. As noted above, different
operational definitions of direct retrieval have appeared in both theory and research, sometimes
based on effort, sometimes based on retrieval time, and sometimes based on search. Thus, the
distinction between direct and generative retrieval is not clearly defined, with debates about the
relationship between multiple processes involved in memory retrieval such as cue generation,
hierarchical memory search, and memory reconstruction in autobiographical memory retrieval
(Harris et al., 2015; Uzer et al., 2012).
Uzer et al. (2012) argued that the prevalence of direct retrieval supports a “dual strategies”
(p. 1297) interpretation of memory retrieval, according to which direct and generative retrieval
represent distinct cognitive processes. Direct retrieval happens when a memory of a past event is
immediately and effortlessly specified by the presence of a memory cue. Generative retrieval, on
the other hand, involves a protracted search for, and/or generation of, relevant memory cues.
According to Uzer et al. (2012), both processes arrive at the same database of “prestored event
units”. Following this view, we should expect direct retrieval to be uniformly fast regardless of the
nature of retrieval cue, because direct retrieval only happens when the cue is sufficiently strong to
specify a memory with no further search or cuing. In contrast, we should expect the retrieval time of
generative retrieval to be influenced by characteristics of the memory cues, for example, whether
they are concrete or abstract, as chains of cues are generated to yield a cue that can trigger a
memory. According to Uzer et al.’s (2012) account, once the two types of retrieval have arrived at a
memory, there should be few, if any, differences regarding the subjective qualities of the memories
retrieved, and memory qualities should not be influenced by the nature of the cues. This is because
both types of retrieval are proposed to access the same database of prestored event representations
and to differ only in the cue generation processes that precede retrieval of a memory.
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In contrast, Haque and Conway (2001) distinguished between direct and generative retrieval
by proposing that direct retrieval bypasses the top-down memory reconstruction that is involved in
generative retrieval (also see Conway & Pleydell-Pearce, 2000). According to this view, we would
expect both the retrieval time and the qualities of the memories to interact with the characteristics of
the cues. Directly retrieved memories should show uniformly short retrieval times, and their
subjective qualities should be unaffected by cue type, since there is no construction involved in their
recall. Conversely, we would expect the retrieval time and the characteristics of the generatively
retrieved memories to depend on cue type, since the nature of the cue should influence the topdown search and reconstruction processes involved in generative retrieval.
In short, both Uzer et al.’s (2012) account and Haque and Conway’s (2001) accounts of
memory retrieval processes predict that direct retrieval is always fast, regardless of the cue, albeit
for different reasons. Both accounts imply that the nature of the retrieval cue might influence the
frequency of direct retrieval, but in cases where direct retrieval is reported, retrieval time should not
depend on cue characteristics. Conversely, both accounts imply that the speed of generative
retrieval should be cue dependent, as the cue qualities may determine how much additional
information needs to be generated, or how much hierarchical top-down search and generation is
required, prior to successful retrieval of a specific event. The accounts differ with regards to the
qualities of retrieved memories. Uzer et al.’s (2012) account suggests that direct and generatively
retrieved memories should be similar in terms of their qualities. Haque and Conway’s (2001) theory
is more consistent with differences in the qualities of direct and generatively retrieved memories,
and implies that the qualities of directly retrieved memories should be cue-independent, while the
qualities of generatively retrieved memories may be cue-dependent, since only generative retrieval
involves top-down constructive processes.
To examine these two different positions, we examined the retrieval latency for memories
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that were directly retrieved versus generatively retrieved in response to three kinds of cues. We also
examined the qualities of memories produced by these two retrieval types and the extent to which
their retrieval latencies and phenomenology were cue-dependent. We adopted Uzer et al.’s (2012)
methodology to distinguish between direct and generative retrieval; a self-report technique in which
participants were asked to rate whether they had “searched” and used additional information to
retrieve the memory. That is, the degree of search was given as the defining feature of direct
retrieval. We aimed to compare directly retrieved memories and generatively retrieved memories,
and to examine the extent to which prevalence, retrieval speed, and qualities of direct and
generative retrieval varied depending on the nature of the retrieval cue.
Method
Participants
Sixty-five (54 females and 11 males, mean age 22.95 years, SD = 3.65, range 20-46 years)
psychology undergraduates from Aarhus University, Denmark, participated in this study in return
for cinema tickets. All participants were informed that their responses were anonymous, that they
should not elicit anything that was uncomfortable for them to disclose, and that they were free to
withdraw at any point during the procedure. The design was fully within-subjects.
Materials
Participants elicited memories in response to 24 cues. Eight of the cues were Danish
translations of the concrete noun cues used by Uzer et al. (2012): book, pill, bag, dog, river, bread,
pencil, chair, radio. Eight of the cues were Danish translations of the emotion word cues used by
Uzer et al. (2012): surprised, bored, sad, afraid, frustrated, happy, amused, daring, satisfied. The
final eight cues were “personal cues”, the names of four people and four places that were
idiosyncratic to each participant, and provided by participants via email prior to the experimental
session as described below.
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Procedure
Personal cue elicitation. After participants signed up for the study, one week before the
scheduled experimental session the experimenter emailed them and asked them to provide some
information about themselves. Specifically, they were asked to list the first and last name of four
people and four specific places that had impacted their lives regularly in the last three years. They
were also given further instructions on how to give specific responses, and told to exclude direct
family members, their home, and the university campus. These instructions were based on Uzer and
Brown (2017) and were designed to generate unique, specific, distinctive memory cues. Participants
emailed their responses to the experimenter.
Memory elicitation and rating. Participants were tested in groups of between 2 and 8.
They were seated at individual computers. The experimenter told them that they would be presented
with a series of cue words on the computer, and for each one, they should report the first memory
that came into their mind in response to this cue. Participants were encouraged to think of specific
events, to sample widely from their life, and to try not to repeat events if possible. The experimenter
told participants that they should press the space bar as soon as they had the event in mind. They
would then be prompted to type a description of the event and to answer some questions about it.
The experimenter then gave participants detailed instructions about how to report the
retrieval type for each event that they recalled. She explained that there are two ways that people
can retrieve memories, when asked to recall personal events in response to cues: the first is when
the cue directly triggers a memory and no additional information needs to be thought about; the
second is when the cue does not directly trigger a memory so additional information from one’s life
is thought about in order to arrive at a specific memory. These instructions were a direct Danish
translation from Uzer et al.’s (2012) Experiment 3, where they developed instructions for
distinguishing direct and generative retrieval without defining them in terms of effort or time for
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participants, to avoid confounding reaction time or retrieval difficulty with self-reported retrieval
process.
Each item was presented in a random order on the computer screen. At the bottom of the
screen, an instruction appeared reminding participants to “Press the spacebar as soon as you have a
memory in mind”. If participants pressed the spacebar, they were first prompted to report the
retrieval type. To avoid biasing responses in either direction, we used two versions of this question
and counterbalanced which version participants saw. Half the participants reported retrieval type by
pressing “1” for direct and “2” for generative retrieval; the other half reported retrieval type by
pressing “1” for generative and “2” for direct retrieval. Participants then typed in a description of
the memory. They also reported whether it referred to a general or specific event, and again we used
two versions of this question and counterbalanced versions across participants to avoid response
bias. Half the participants reported specificity by pressing “1” for specific and “2” for general event;
the other half reported specificity by pressing “1” for general and “2” for specific event. Finally,
participants reported how old they were in years at the time of the event, and rated on 7-point Likert
scales the memory's clarity (1 = not at all clear, 4 = somewhat clear, 7 = extremely clear), personal
significance (1 = not at all significant, 4 = somewhat significant, 7 = extremely significant),
rehearsal (1 = not rehearsed at all, 4 = somewhat rehearsed, 7 = rehearsed a great deal), and
valence (1 = highly negative, 4 = neutral, 7 = highly positive). If participants did not press the
spacebar, the item timed out after 90 seconds, and the computer automatically presented the next
item. The procedure was repeated until participants had completed all 24 items. This procedure took
approximately 1 hour and 20 minutes, although it varied between participants.
Memory coding. Two independent raters (native Danish speakers) coded two aspects of the
elicited autobiographical memories. First, they coded the specificity of the memory according to a
5-category system, the “SCEPT Decision Tree” (Raes, Hermans, Williams, & Eelen, 2007). Using
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this system, they scored whether each memory was: (1) a specific event; (2) specific extended i.e.
lasting more than a day; (3) a lifetime period (e.g. “when I was in high school”); (4) a categoric or
repeated event (e.g. “going to a restaurant”); or (5) a semantic associate (e.g. “my dog is friendly”).
Both coders scored all of the elicited memories, and their agreement was 84.38%. Disagreements
were resolved by discussion between the two coders.
Results
Analysis Strategy and Manipulation Checks.
Initial tests indicated that we could collapse across participants in the different
counterbalancing conditions. For each participant, we scored the proportion of elicited memories
that they rated as directly retrieved, and the proportion of memories they rated as specific. An
independent samples t-test comparing rates of direct retrieval across counterbalancing conditions
showed no significant difference, t(63) = 0.63, p = .532. Similarly, an independent samples t-test
comparing rates of specific recall across counterbalancing conditions indicated no significant
difference, t(63) = 1.22, p = .229. That is, the way in which we asked participants to report the
retrieval type and specificity for each memory did not appear to bias their overall responding rate in
either direction.
Participants successfully recalled memories within the time limit to 96.41% of the memory
cues, leading to 1500 elicited memories from 1560 presented cues. A 3-level (cue type) within
subjects ANOVA on proportion of memories successfully recalled yielded a significant main effect,
F(2,63) = 15.15, p < .001, ηp2 = .19. Follow up pairwise comparisons (reported ps throughout
include a Bonferroni adjustment for multiple comparisons where appropriate) indicated that
personal cues (M = 99.23%, SD = 0.03) and concrete cues (M = 97.69%, SD = 5.80) both resulted in
higher rates of retrieval success than emotion cues (M = 91.54%, SD = 13.28), all ps < .002, with no
significant difference between personal and concrete cues, p = .176. Despite these differences in
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success rates between cues, participants successfully recalled memories in response to most of the
cues.
Prevalence and Speed of Direct and Generative Retrieval
For the memory qualities (reported by the participant or scored by the coders), we used IBM
SPSS Statistics 24 to apply multi-level models that analysed memories as nested within the random
factor of “participant” who recalled them, to take into account that each participant contributed
multiple memories to the dataset (see Wright, 1998). We also included the cue order (i.e. a number
between 1 and 24 to indicate when it was presented) and cue content (i.e. a number between 1 and
24 to indicate which specific item the cue was) as random factors in the model. We used the
maximum likelihood method and we specified a random intercept. This approach was adopted due
to the variability in the rates of direct retrieval across participants. Reported direct retrieval ranged
from 21% to 79% of elicited memories for each participant, such that creating mean scores at the
participant level would bias the results and give participants who reported fewer “direct” memories
more weight in the dataset (for a discussion and comparison of both approaches to analysis, see
Harris et al., 2015; Wright, 1998).
Overall, participants reported direct retrieval for 713 (47.5%) elicited memories. To test the
relationship between cue type and retrieval type, we conducted an ANOVA for the effects of cue
type (concrete vs. emotion vs. personal) on retrieval type. Direct retrieval was scored as “1” and
generative retrieval was scored as “0” so that the means represent proportion of memories directly
retrieved. We specified cue type as a fixed factor, and participant, cue order, and cue content as
random factors. This analysis yielded a main effect of cue type, F(2,760.63) = 50.79, p < .001. The
model parameters for the difference between concrete cues and personal cues were β = .211, SE =
.03, t(341.32) = 7.13, p < .001, 95% CI of the difference = .15-.27; the model parameters for the
difference between emotion cues and personal cues were β = .290, SE = .03, t(1308.18) = 9.71, p <
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.001, 95% CI of the difference = .23-.35. Follow up pairwise comparisons indicated that regardless
of retrieval type, personal cues resulted in the highest rates of direct retrieval (M = 0.64, SD = 0.48),
followed by concrete cues (M = 0.43, SD = 0.49), and emotion cues resulted in the lowest rates (M
= 0.35, SD = 0.48), all ps < .026. Thus, consistent with previous research, direct retrieval was
common, and the nature of the cue influenced the rates of direct retrieval (Uzer & Brown, 2017;
Uzer et al., 2012).
To test the relationship between reported retrieval type and retrieval speed, we conducted a 2
(retrieval type: direct vs. generative) × 3 (cue type: concrete vs. emotion vs. personal) ANOVA on
time taken to recall a memory (reported in seconds throughout). We specified retrieval type and cue
type as fixed factors, and participant, cue order, and cue content as random factors. This analysis
yielded a main effect of retrieval type, F(1,1495.10) = 174.02, p < .001, and a main effect of cue
type, F(2,1441.30) = 40.06, p < .001. Overall, regardless of cue type, reported direct retrieval was
significantly faster than reported generative retrieval, as expected (see Table 1). Follow up pairwise
comparisons indicated that regardless of retrieval type, personal cues resulted in the fastest retrieval
(M = 6.56, SD = 8.19), followed by concrete cues (M = 10.16, SD = 12.58), and emotion cues
resulted in the slowest retrieval (M = 15.48, SD = 16.20), all ps < .001. However, these main effects
were moderated by a significant interaction between cue type and retrieval type, F(2,1478.48) =
4.34, p = .013.
Separate one-way ANOVAs tested the effect of cue type for directly retrieved memories and
generatively retrieved memories. For both multi-level ANOVAs, we specified cue type as a fixed
factor and we specified participant as a random factors (we removed cue order and cue item from
these analyses as the model would not converge when they were included). For both directly and
generatively retrieved memories, there were significant main effects of cue, F(2,677.65) = 24.26, p
< .001 and F(2,758.92) = 22.32, p < .001, respectively. Fixed effects comparisons for the different
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levels of cue type indicated that, for directly retrieved memories, concrete and personal cues
resulted in significantly faster retrieval than emotion cues, model parameters β = 4.38, SE = .65,
t(683.29) = 6.71, p < .001, 95% CI of the difference = 3.10-5.66 seconds. However concrete and
personal cues did not differ from each other, β = .44, SE = .60, t(674.68) = 0.75, p = .456, 95% CI
of the difference = -0.72-1.61 (see Table 1 for means). For generatively retrieved memories,
concrete and personal cues resulted in significantly faster retrieval than emotion cues, model
parameters β = 8.72, SE = 1.36, t(768.10) = 6.42, p < .001, 95% CI of the difference = 6.05-11.38
seconds. Personal cues resulted in significantly faster retrieval than concrete cues, model parameters
β = 3.54, SE = 1.37, t(763.83) = 2.59, p = .010, 95% CI of the difference = 0.86-6.27 seconds.
Overall, these results suggest that regardless of whether direct or generative retrieval was reported,
retrieval latency was cue-dependent, with retrieval to abstract emotion cues being particularly slow
(see means in Table 1).
Despite the average differences between direct and generative retrieval in terms of time
taken to recall, it is important to note that there was a large range in elicitation latencies, and latency
was not necessarily a proxy for retrieval type (see Figure 1 for the distribution). There was a
considerable amount of overlap in elicitation latencies, rather than a clearly bimodal distribution
indicating distinct “fast” and “slow” retrieval trials (see Figure 1). We return to this point in the
Discussion.
Retrieval type and Memory Specificity
For each elicited memory, participants rated whether it referred to a specific event or not. We
calculated the proportion of events elicited to each cue that were specific. Overall, rates of specific
retrieval were very high (76.53% of the memories), consistent with experimental instructions to
recall specific events. To test the relationship between retrieval type and specificity, we conducted a
multi-level ANOVA for the effects of retrieval type (direct vs. generative) on participant-rated
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specificity. Specific event retrieval was scored as “1” and non-specific retrieval was scored as “0”
so that the means represent proportion of memories that were specific events. We specified retrieval
type as a fixed factor, and participant as a random factor (we removed cue order and cue content as
the model could not achieve convergence with these random factors included). This analysis yielded
a main effect of retrieval type, F(1,1480.80) = 4.83, p = .028 (see Table 2 for model parameters).
Generative retrieval resulted in higher rates of specific memories (M = 0.80, SD = .40) than direct
retrieval did (M = .73, SD = .44).
These results were confirmed by more detailed coding conducted by trained, independent
coders. These coders assigned participants’ event descriptions as belonging to one of 5 categories:
specific event, repeated event, categoric event, lifetime period, or semantic information. There were
1492 memory descriptions, with 8 missing (from participants pressing “enter” to continue without
typing a memory description). We calculated the proportion of events elicited to each cue that were
specific vs. not specific (collapsed across the other 4 categories), and conducted the same multilevel ANOVA reported above. The results of this analysis mirrored participants’ self ratings,
yielding a main effect of retrieval type, F(1,1468.80) = 6.63, p = .010 (see Table 2 for model
parameters). As previously, this analysis indicated that generative retrieval resulted in higher rates
of specific memories (M = 0.83, SD = .38) than direct retrieval did (M = .76, SD = .43).
For participant-rated and coded specificity, we were interested in whether memory
specificity was cue dependent in similar or different ways for direct vs. generative retrieval. Means
split by cue type are presented in Table 1. We conducted two multi-level ANOVAs for retrieval type
(direct vs. generative) × cue type (concrete vs. emotion vs. personal) on specificity ratings. We
specified retrieval type and cue type as fixed factors, and participant, cue order, cue content, and
intercept as random factors. For both specificity measures, this analysis yielded a significant main
effect of retrieval type, as reported above, and but no main or interaction effects of cue type, all Fs
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< 2.49, all ps > .084. Therefore, memory specificity varied with retrieval type but not with cue type.
Overall, generative retrieval yielded more specific memories than direct retrieval.
Retrieval type and Memory Qualities
Across reported age, and ratings of clarity, rehearsal, significance, emotional valence, and
emotional intensity, we conducted a series of multi-level ANOVAs for retrieval type (direct vs.
generative) on participant responses, to examine whether there were differences between directly
and generatively retrieved memories. As above, we specified retrieval type as a fixed factor, and
participant, cue order, cue content, and intercept as random factors. Table 1 presents means and
Table 2 presents model parameters across quality variables.
For reported age of the memory, there was no effect of retrieval type, F(2,1456.70) = 1.05, p
= .306. Similarly, for the emotional valence of the memory, there was also no effect of retrieval
type, F(2,1467.55) = 1.89, p = .170. However, for the remaining measures, retrieval type did
influence memory quality. For reported clarity, the main effect of retrieval type, F(1,1468.61) =
12.90, p < .001, indicated that directly retrieved memories were rated as more clear and vivid than
generatively retrieved memories. For reported personal significance, the main effect of retrieval
type, F(1,1460.77) = 27.68, p < .001, indicated that directly retrieved memories were rated as more
personally significant than generatively retrieved memories. For reported rehearsal, the main effect
of retrieval type, F(1,1472.38) = 17.96, p < .001, indicated that directly retrieved memories were
rated as more frequently rehearsed than generatively retrieved memories. For emotional intensity,
we calculated scores by obtaining the absolute distance from the “neutral” midpoint of 4 on the
valence scale for each memory. This score gave an indication of rated emotion regardless of
valence, with a lower score indicating a rating closer to the midpoint. Scores could range from 0 to
3. For reported emotional intensity, the main effect of retrieval type, F(1,1483.08) = 24.60, p < .001,
indicated that directly retrieved memories were rated as more emotionally intense than generatively
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e version was subsequently published in Consciousness and Cognition.
DOI:10.1016/j.concog.2019.102793.
retrieved memories.
For the characteristics that varied with retrieval type, we were interested in whether memory
qualities were cue dependent in similar or different ways for direct vs. generative retrieval. Means
split by cue type are presented in Table 1. We conducted a series of multi-level ANOVAs for
retrieval type (direct vs. generative) × cue type (concrete vs. emotion vs. personal) on clarity,
rehearsal, significance, and emotional intensity ratings. We specified retrieval type and cue type as
fixed factors, and participant, cue order, cue content, and intercept as random factors. For all
characteristics, this analysis yielded a significant main effect of retrieval type, as reported above,
and a significant main effect of cue type, all Fs > 40.34, all ps < .001. Importantly, the interaction
between retrieval type and cue type was not significant for any of these comparisons, all Fs < 1.39,
all ps > .251. These findings suggest that, although directly retrieved memories were on average
rated higher across these phenomenological characteristics, both directly and generatively retrieved
memories showed higher phenomenology ratings for memories retrieved to emotion and personal
cues than for those memories elicited to concrete cues (see Table 1). That is, for both direct and
generatively retrieved memories, associated phenomenology was cue-dependent in similar ways.
We return to this point in the Discussion.
Discussion
We replicated recent findings (Barzykowski & Staugaard, 2016; Harris et al., 2015; Mace,
Clevinger, Delaney, Mendez, & Simpson, 2017; Uzer et al., 2012) indicating a high prevalence of
self-reported direct memory retrieval. Direct retrieval was reported on approximately half of the
trials. Direct retrieval was more common in response to concrete, highly imageable cues than to
abstract, low imageable emotion cues (see also Anderson et a., 2017; Williams et al., 2006), but
most often was reported in response to personally-relevant cues (see also Uzer & Brown, 2017).
The findings for personally-relevant cues suggest that cues that were personally meaningful and
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e version was subsequently published in Consciousness and Cognition.
DOI:10.1016/j.concog.2019.102793.
relevant were more likely to directly activate a memory. Meaningfulness is likely to be
idiosyncratic: some generic cues triggered direct retrieval, but the cues that resulted in direct
retrieval for some participants did not necessarily result in direct retrieval for others. Overall, the
nature of the cue was not a good proxy of self-reported retrieval type, with direct retrieval reported
on less than half the trials in response to concrete nouns. This finding suggests that paradigms that
use concrete and abstract cues as the method for distinguishing direct and generative retrieval (e.g.
Anderson et al., 2017; Williams et al., 2006) do not neatly separate retrieval trials into the same
categories as those derived from self-report.
Directly retrieved memories were elicited faster than generatively retrieved memories on
average, consistent with previous findings (Uzer & Brown, 2017; Uzer et al., 2012). Interestingly, at
the level of individual responses, retrieval latency did not necessarily mirror retrieval type. In fact,
the slowest 30% of the self-reported directly retrieved memories took more than 5 seconds to recall,
and 10% took more than 11 seconds to recall, while 30% of the self-reported generative retrieval
trials took less than 6 seconds. One possible interpretation of the substantial range and overlap of
retrieval latencies associated with both direct and generative retrieval is that participants simply are
not accurate when self-reporting retrieval type. Reliance on self-report assumes that participants can
accurately reflect on their retrieval processes. Alternatively, these surprising latency results may
yield insights into the nuances of the distinction between direct and generative retrieval. We defined
direct retrieval for participants in terms of the amount of information they had needed to search for
in order to recall the target event, consistent with Uzer et al., (2012). Some previous research has
used speed of retrieval as a proxy for indexing whether retrieval was direct or generative (e.g.
Anderson et al., 2017; Haque & Conway, 2001; Williams et al., 2006) , and some definitions of
direct and generative retrieval refer to retrieval speed: direct retrieval is “immediate” (e.g. Ros et
al., 2009, p. 91) while generative retrieval is “protracted” (e.g. Haque & Conway, 2001, p. 529).
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DOI:10.1016/j.concog.2019.102793.
Our findings suggest it is possible for direct retrieval to be slow, and for generative retrieval to be
fast.
Directly retrieved memories had distinct phenomenological characteristics compared to
generatively retrieved memories. First, directly retrieved memories were less likely to refer to
specific events than were generatively retrieved memories. Differences for specificity were small,
but we had explicitly instructed participants to report specific events and the majority of memories
were specific regardless of retrieval type. We also found that directly retrieved memories on average
were rated as clearer, more personally significant, more frequently rehearsed, and more emotionally
intense than generatively retrieved memories. These differences in phenomenology of memories
retrieved via direct versus generative retrieval are consistent with prior research (Barzykowski &
Staugaard, 2016; Harris et al., 2015). Overall these findings suggest that direct retrieval accesses the
most available and personally relevant memories.
Most importantly, we examined two possibilities based on opposing theoretical accounts of
direct and generative retrieval. According to Uzer et al. (2012), both direct and generative retrieval
access “pre-stored event representations” (p. 1306), and differ only in the amount of cue elaboration
that precedes retrieval. This account appears to predict that the retrieval latency of generative
retrieval – but not of direct retrieval – should be cue dependent, and that the qualities of directly and
generatively retrieved memories should be the same as each other. According to Haque and Conway
(2001), generative retrieval involves a top-down memory construction process which direct retrieval
bypasses. Similarly, Barzykowski and Staugaard (2016) suggested that direct retrieval may come
about because less memory reconstruction is required for frequently rehearsed events, and argued
that generative retrieval involves more memory reconstruction. This latter account therefore also
predicts that the retrieval latency of generative retrieval – but not of direct retrieval – should be cue
dependent, but that the qualities of directly and generatively retrieved memories may differ.
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DOI:10.1016/j.concog.2019.102793.
Counter to both these theories, we found that the speed of direct and generative retrieval
were cue dependent in broadly similar ways, with emotion cues resulting in the slowest direct
retrieval as well as the slowest generative retrieval. Uzer et al. (2012) reported no interaction
between retrieval type and cue, but they interpreted that finding as indicating that retrieval latencies
for both retrieval types were similarly cue-independent. Instead, our findings as illustrated in Table
2 show that the retrieval latencies for both retrieval types are similarly cue-dependent. Moreover,
phenomenology of direct and generative retrieval were also similarly cue dependent, with emotion
cues and personal cues leading to richer phenomenology than concrete cues, regardless of retrieval
type. Thus, our results do not support the view that autobiographical memory retrieval accesses prestored event representations, nor that direct retrieval bypasses memory construction, suggesting
instead that direct and generative retrieval do not involve fundamentally different processes.
We propose a third theoretical position: that both direct and generative retrieval involve
construction of autobiographical memories, differing only in the degree of subjective effort required
for retrieval. Further work is needed to understand what pre-retrieval processes participants are
reflecting on when they report a lack of search or effort in recall. There are multiple effortful preretrieval processes including cue elaboration, semantic association, memory construction, control
processes, and the inhibition of irrelevant information, and each of these may be reduced in direct
retrieval. Further research is needed to understand how these multiple pre-retrieval processes can
vary – independently or together – and how these variations influence subjective experiences of
retrieval as well as the resulting memory phenomenology (see also Harris et al., 2015).
Importantly for definitions of direct retrieval which characterize them as “spontaneous” or
“unexpected”, the profile of directly retrieved memories is not similar to the profile of involuntary
autobiographical memories, as observed in diary studies. First, involuntary autobiographical
memories, by definition, occur with no preceding instruction or attempt to retrieve a memory
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e version was subsequently published in Consciousness and Cognition.
DOI:10.1016/j.concog.2019.102793.
(Berntsen, 1996). In contrast, directly retrieved memories, when measured in experiments, are
always preceded by cues and an explicit instruction to retrieve a memory in response to each cue.
Second, the characteristics and phenomenology of the two classes of memories also differ. For
instance, in diary studies, involuntary memories are more likely to be specific than voluntary
memories (Berntsen, 1998; 2010; Mace, 2006; Schlagman & Kvavilashvili, 2008), although diary
studies do not directly instruct participants to report only specific events in the same way that
laboratory methods do. Involuntary memories are associatively cued by features in the ongoing
situation rather than an intention to retrieve (Berntsen, 1996; Mace, 2006; Berntsen & Jacobsen,
2008; Rasmussen & Berntsen, 2011). In diary studies, involuntary retrieval results in memories with
somewhat idiosyncratic or ‘unusual’ content (Berntsen & Hall, 2006). Several studies have found
them to be less relevant to the person’s life story (e.g. Cole, Staugaard, & Berntsen, 2016;
Johannessen & Berntsen, 2010; Rubin et al., 2008) and less rehearsed than voluntary memories
(Berntsen, 1998; Johannessen & Berntsen, 2010; Rubin, Boals & Berntsen, 2008; Rubin, Dennis, &
Beckham, 2011). Overall, our results regarding the specificity of directly retrieved memories, and
their phenomenological characteristics, suggest that direct retrieval involves recalling memories that
are already highly accessible, relevant, and frequently rehearsed. Extensive executive search for
these memories may not be required because they are readily accessible when in “retrieval mode”
with the intention to recall (Tulving, 1983, 2002). Overall, directly retrieved memories do not fit the
profile of involuntary memories and the two phenomena cannot be conceptually equated. In terms
of specificity and phenomenology, the profile of directly retrieved memories (compared to
generatively retrieved) is directly opposite to the profile of involuntary memories (compared to
voluntary) for several important variables. Based on our findings, it appears that a conscious
initiation of the retrieval process, with the general intention to recall a memory, matters for the
characteristics of the memories that are recalled, such that direct retrieval and involuntary retrieval
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DOI:10.1016/j.concog.2019.102793.
are different despite their shared lack of effort. Intentional recall – even when relatively effortless
i.e. direct retrieval – appears to facilitate retrieval of memories that are most personally-relevant and
frequently rehearsed.
Overall, these findings contribute to efforts to understand the nature of the distinction
between direct and generative retrieval, using the relatively recent self-report methodology to
distinguish between these two types of retrieval. There are distinct phenomenological characteristics
associated with memories recalled via direct versus generative retrieval, consistent with previous
research, suggesting that participant self-report of retrieval processes reflects genuine differences in
the nature of memories recalled. However, our findings that the qualities of directly retrieved and
generatively retrieved memories were similar in terms of their cue-dependence suggests that these
retrieval types are not fundamentally different in terms of the cognitive processes involved. Our
findings were counter to the predictions derived from of two existing models of direct versus
generative autobiographical memory retrieval. Counter to these models, the present findings suggest
that both direct and generative retrieval are the result of similar constructive memory processes,
even though there are differences in the content of memories recalled. Direct retrieval is
experienced when memories that are already highly accessible are retrieved, but direct retrieval
does not appear to reflect a fundamental difference in the nature of the pre-retrieval processes
involved in bringing a memory to mind.
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DOI:10.1016/j.concog.2019.102793.
Author note
This work was supported by the Danish National Research Foundation (DNRF89), the Danish
Council for Independent Research: Humanities (FKK) and the MindLab UNIK initiative at Aarhus
University, which is funded by the Danish Ministry of Science and Technology and Innovation.
Celia Harris was supported by an Australian Research Council Discovery Early Career Research
Award (DE150100396). We are grateful for this support.
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DOI:10.1016/j.concog.2019.102793.
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ss and Cognition.
DOI:10.1016/j.concog.2019.102793.
Table 1. Qualities of Elicited Memories, By Retrieval Type and Cue Type
Retrieval
Cue Type
Type
Direct
Latency
(seconds)
Concrete
Emotion
Personal
Overall
Generative Concrete
Emotion
Personal
Overall
Specificity Specificity
(prop)
Age
Clarity
Rehearsal
Significan
Emotion
Emotion
– Coded
(years
(1-7)
(1-7)
ce
Valence
Intensity
(prop)
ago)
(1-7)
(1-7)
(0-3)
5.60
0.70
0.73
5.31
4.75
3.16
3.38
4.59
1.43
(6.20)
(0.46)
(0.44)
(6.39)
(1.55)
(1.65)
(1.74)
(1.65)
(1.00)
9.36
0.69
0.74
2.60
5.50
4.81
4.92
3.69
2.22
(12.51)
(0.46)
(0.44)
(4.97)
(1.49)
(1.81)
(1.87)
(2.37)
(0.85)
4.25
0.77
0.78
3.75
5.45
4.35
5.01
5.29
2.09
(4.16)
(0.42)
(0.42)
(4.66)
(1.37)
(1.72)
(1.68)
(1.86)
(0.86)
5.86
0.73
0.76
3.96
5.25
4.10
4.49
4.70
1.92
(7.76)
(0.44)
(0.43)
(5.40)
(1.49)
(1.84)
(1.90)
(2.03)
(0.96)
13.52
0.80
0.84
4.62
4.45
3.02
2.97
4.50
1.20
(14.85)
(0.40)
(0.37)
(5.35)
(1.53)
(1.63)
(1.70)
(1.48)
(1.01)
18.76
0.79
0.79
2.49
5.16
4.12
4.31
4.16
1.92
(17.01)
(0.41)
(0.41)
(3.96)
(1.63)
(1.65)
(1.86)
(2.17)
(1.02)
10.66
0.80
0.87
4.09
5.13
4.00
4.40
5.26
1.83
(11.38)
(0.40)
(0.34)
(4.86)
(1.34)
(1.58)
(1.72)
(1.62)
(0.93)
14.91
0.80
0.83
3.66
4.89
3.68
3.83
4.54
1.63
(15.38)
(0.40)
(0.38)
(4.82)
(1.56)
(1.70)
(1.89)
(1.86)
(1.05)
Note: values are means across elicited memories, and standard deviations in parentheses.
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ss and Cognition.
DOI:10.1016/j.concog.2019.102793.
Table 2. Parameters for Reported Multilevel Model Comparing Directly vs. Generatively Retrieved Memories on Each Quality
Latency
Specificity
Specificity
Age
Clarity
Rehearsal
Significance
Emotion
Emotion
(seconds)
Rated
Coded
(years ago)
(1-7)
(1-7)
(1-7)
Valence
Intensity
(1-7)
(0-3)
(proportion) (proportion)
Model
Estimate
SE
Model df
5.81
0.04
0.05
0.25
0.28
0.36
0.49
4.57
0.26
1.08
0.02
0.02
0.25
0.08
0.09
0.09
0.09
0.05
1486.76
1480.80
1468.04
1456.70
1468.61
1472.38
1460.77
1467.55
1483.07
Model t
5.38
2.20
2.57
1.02
3.59
4.24
5.26
1.37
4.96
Model p
< .001
.028
.010
.306
< .001
< .001
< .001
.170
< .001
95% CI of
Difference
3.69-7.93
.005-.09
.01-.09
-0.74-0.23
0.13-0.43
0.19-0.53
0.31-0.67
-0.33-0.06
0.16-0.36
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DOI:10.1016/j.concog.2019.102793.
Figure 1. Distribution of retrieval latencies for directly and generatively retrieved memories.
29