Download Effects of Emotional Valence and Arousal on Recollective and

Journal of Experimental Psychology:
Learning, Memory, and Cognition
2013, Vol. 39, No. 3, 663– 677
© 2012 American Psychological Association
0278-7393/13/$12.00 DOI: 10.1037/a0028578
Effects of Emotional Valence and Arousal on Recollective and
Nonrecollective Recall
Carlos F. A. Gomes and Charles J. Brainerd
Lilian M. Stein
Cornell University
Pontifical Catholic University of Rio Grande do Sul
The authors investigated the effects of valence and arousal on memory using a dual-process model that
quantifies recollective and nonrecollective components of recall without relying on metacognitive
judgments to separate them. The results showed that valenced words increased reconstruction (a
component of nonrecollective retrieval) relative to neutral words. In addition, the authors found that
positive valence increased recollective retrieval in comparison to negative valence, whereas negative
valence increased nonrecollective retrieval relative to positive valence. The latter effect, however,
depended on arousal: It was reliable only when arousal was high. The present findings supported the
notion that emotional valence is a conceptual gist because it affected nonrecollective retrieval and
because subjects’ recall protocols were clustered by valence. The results challenge the hypothesis that
valence affects only recollection, and they clarify previous inconsistent findings about the effects of
emotion on memory accuracy and brain activity.
Keywords: emotion, memory, valence, recall, dual-retrieval models
iment by Bradley, Greenwald, Petry, and Lang (1992) were asked
to recall 60 pictures that they had rated according to valence and
arousal. Recall of pictures rated as either negative or positive was
better than recall of pictures rated as neutral. Similarly, Xu, Zhao,
Zhao, and Yang (2011) found that recognition memory for negative and positive words was better than for neutral words. Therefore, arousal and valence both seem to improve memory for words.
The relative effects of positive and negative valence on memory
have been the focus of research since the 1920s (Flügel, 1925;
Wohlgemuth, 1923).1 Much of this early work was centered on the
Freudian prediction that people tend to forget more unpleasant
than pleasant experiences, owing to the defense mechanism of
repression. More simply, the Freudian hedonic memory hypothesis
anticipated that negatively valenced information would be more
poorly remembered than positively valenced information, a prediction that is also made by more contemporary hypotheses such as
the Pollyanna principle (Matlin & Stang, 1978) and the fading
affect bias (Walker, Skowronski, & Thompson, 2003). In that
connection, Thomson (1930; see also Sharp, 1938; Stagner, 1933)
investigated long-term retention of self-generated negative and
positive words. During the first phase of the study, subjects were
required to write 20 positive words and 20 negative words. During
the second phase, 1 month later, they recalled as many of the
words generated in the first phase as possible. Recall of positive
words (38%) was higher than recall of negative words (24%). Of
How does emotion affect episodic memory? In order to answer
this question, memory researchers have often studied the effects of
two key components of emotion: arousal and valence (Kensinger,
2009). Arousal is defined as the level of activation that is generated
when a stimulus is encoded, which varies from calm (low) to
excited (high). Research on the effects of arousal have suggested
that memory performance for items high in arousal is better than
for items low in arousal (Buchanan, Etzel, Adolphs, & Tranel,
2006; Cahill, Babinsky, Markowitsch, & McGaugh, 1995; Cahill
& McGaugh, 1995). In an experiment by Cahill et al. (1996), for
example, subjects watched 24 film clips in which half were high in
arousal (e.g., animal mutilation and violent crimes) and half were
low in arousal (e.g., court proceedings and travelogues). Three
weeks later, subjects recalled more from the former. Valence,
however, is defined as the level of pleasantness that is generated
when a stimulus is encoded, which varies from strongly negative
to neutral to strongly positive. Studies of the effects of valence
have indicated that memory performance for both negative and
positive items is usually better than for neutral items (Kensinger &
Corkin, 2003; Ochsner, 2000). For instance, subjects in an exper-
This article was published Online First May 21, 2012.
Carlos F. A. Gomes and Charles J. Brainerd, Department of Human
Development, Cornell University; Lilian M. Stein, Postgraduate Program
in Psychology, Pontifical Catholic University of Rio Grande do Sul, Brazil.
Portions of the research were supported by National Institutes of Health
Grant 1RC1AG036915-02 and by Brazilian National Council for Scientific
and Technological Development Grants CNPq; 107804/2007-7, 500486/
2007-7. The software that was used to conduct the analyses of the dualretrieval model that are reported in this article (goodness-of-fit tests,
parameter estimation, and significance tests of parameter values) is available from the first and second authors upon request.
Correspondence concerning this article should be addressed to Carlos
F. A. Gomes, Department of Human Development, Cornell University,
Ithaca, NY 14853. E-mail: [email protected]
1
In fact, the earliest study we could find on the relationship between
valence and memory was reported by Colegrove (1899). This study was a
survey of 1,658 individuals in which one of the questions asked: “Do you
remember pleasant or unpleasant experiences better?” (p. 243). The results
revealed that pleasant experiences were reported as being better remembered than unpleasant experiences. Although this result was observed for
children, young adults, and older adults, it peaked during adolescence and
young adulthood.
663
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GOMES, BRAINERD, AND STEIN
course, such data do not provide evidence for or against a repression mechanism, nor any other psychological process, and the
reason why positive events might be better remembered than
negative ones remained unclear.
Of late, dual-process conceptions have been invoked to explain
emotion’s effects on episodic memory. According to such conceptions, episodic memories can be accompanied by mental reinstatement of contextual features of prior experience (recollective retrieval) or not (nonrecollective retrieval). Studies that have
investigated the role of dual processes in the effects of emotion on
memory have followed the traditional procedure of using subjects’
metacognitive judgments about their memories, especially remember/know judgments, to separate recollective from nonrecollective
retrieval. Most experiments in which metacognitive judgments
were used to quantify recollective and nonrecollective retrieval
have focused on valence rather than arousal (though Ochsner,
2000, reported that high-arousal words elevated rates of remember
judgments relative to low-arousal words). In Kensinger and Corkin’s (2003) research, for instance, subjects studied negative and
neutral words and then performed a recognition test on which they
made remember/know judgments about words that were recognized as old. Remember rates were higher for negative than for
neutral words, but the two know rates did not differ, suggesting
that negative valence stimulates increased recollective retrieval. In
the same vein, Ochsner found higher remember rates for negative
and positive words relative to neutral words, and vivid recollection
of negative information is documented in other experiments (Dewhurst & Parry, 2000; Rimmele, Davachi, Petrov, Dougal, &
Phelps, 2011; Sharot & Yonelinas, 2008). One explanation advanced in the literature (e.g., Christianson, 1992; Ochsner, 2000) is
that items that generate an emotional reaction possess unique
features (e.g., physiological reactions, associations with autobiographical memories) that render them more salient or distinctive
than neutral items. The presence of such distinctive features boosts
recollective retrieval and, consequently, remember judgments.
However, the idea that emotion only enhances recollective retrieval is inconsistent with two other lines of investigation, namely
studies on the effects of emotion on brain activity and on memory
accuracy. If emotion only enhances recollective retrieval, memory
performance for emotional items should be particularly associated
with activity in regions linked to recollection or visual and sensory
processing (e.g., hippocampal, parahippocampal, and temporooccipital regions; Macaluso, Frith, & Driver, 2000; Yonelinas,
Hopfinger, Buonocore, Kroll, & Baynes, 2001) but not with regions linked to familiarity or semantic processing (e.g., prefrontal
cortex [PFC]; Cabeza & Nyberg, 2000; Goldberg, Perfetti, Fiez, &
Schneider, 2007; Yonelinas, Otten, Shaw, & Rugg, 2005). However, functional magnetic resonance imaging data suggest that
memory enhancements due to valence are associated with increased activity in regions within the PFC, and memory enhancements due to arousal are associated with increased activity in the
amygdala (Dolcos, LaBar, & Cabeza, 2004; Kensinger & Corkin,
2004). In addition, if emotion enhances recollective but not nonrecollective retrieval, memory accuracy for emotional items should
be better in comparison to nonemotional items. However, several
experiments have implicated negative valence in reduced memory
accuracy. For instance, it has been reported that negative valence
leads to more false memories of gist-consistent material than
neutral or positive valence (Brainerd, Holliday, Reyna, Yang, &
Toglia, 2010; Brainerd, Stein, Silveira, Rohenkohl, & Reyna,
2008; Dehon, Laroi, & Van der Linden, 2010; Goodman et al.,
2011; Otgaar, Candel, & Merckelbach, 2008; but see Palmer &
Dodson, 2009). Moreover, negative valence has been found to
increase subjective reports of confidence and vividness without
increasing memory accuracy. Talarico and Rubin (2003) found
that when college students’ recollections of the events of September 11 were compared with their recollections of everyday events,
confidence and vividness ratings were higher for the September 11
events than for the everyday events, but there was no difference in
accuracy between them. To explain the contradictory effects of
emotion on memory accuracy and metacognitive judgments,
Phelps and Sharot (2008) concluded that emotion’s impact on
metacognitive judgments is more robust and consistent than its
impact on accuracy for objective details (see also Zimmerman &
Kelley, 2010). The latter explanation, however, does not elucidate
how emotion affects the mechanisms underlying memory performance.
Although valence and arousal have been factorially manipulated
in some experiments (for a review, see Kensinger, 2004), the
confounding of these variables has been a persistent problem that
makes theoretical interpretation uncertain. For example, in Cahill
et al.’s (1996) study, high-arousal film clips were more negative
than low-arousal film clips. In Cahill and McGaugh’s (1995)
procedure, memory for emotionally arousing versus nonarousing
pictures was assessed by comparing memory for pictures of a
negative arousing event (a car accident, with accompanying brain
damage and limb amputation) with pictures of neutral events. In
Pierce and Kensinger (2011), negative and positive words had
higher arousal values on the affective norms for English words
(ANEW; Bradley & Lang, 1999) than neutral words. Similarly, in
Xu et al.’s (2011) study, positive words were lower in arousal
relative to negative ones. Finally, in Thomson’s (1930) study, it is
unclear whether the positive words generated by the subjects also
differed in arousal relative to the negative words that they generated. Other studies in which materials that differ in valence also
differ in arousal include Sharot, Delgado, and Phelps (2004);
Sharot, Varfaellie, and Yonelinas, (2007), and Dewhurst and Parry
(2000). Importantly, in studies in which valence and arousal have
been factorially manipulated, they have often been found to interact (e.g., Brainerd et al., 2010; Libkuman, Stabler, & Otani, 2004;
Steinmetz, Addis, & Kensinger, 2010; Waring & Kensinger,
2011). For instance, Brainerd et al. (2010) found that the tendency
of negative valence to elevate false memories was greater for highthan for low-arousal items.
The Present Experiments
In the research that we report, we investigated the effects of
emotional arousal and valence on recollective and nonrecollective
retrieval, using procedures that had two salient features. The first
is that the effects of valence were assessed without the confounding influence of arousal, using lists that had been constructed from
emotional word norms. In Experiment 1, valence levels were
manipulated from negative to neutral to positive, with arousal level
controlled. In Experiment 2, valance and arousal levels were
manipulated factorially. The second feature, which requires further
explanation, is that we used a new model-based procedure for
measuring recollective and nonrecollective retrieval (Brainerd,
EMOTIONAL RECALL
Reyna, & Howe, 2009) to investigate how these processes are
affected by valence and arousal.
As mentioned, the traditional approach to measuring dualretrieval processes involves requesting supplementary metacognitive judgments following old/new recognition decisions about previously encoded material, typically remember/know judgments
and occasionally confidence judgments. The use of such supplementary judgments is predicated on the notion that they separate
the contributions of recollective and nonrecollective retrieval (e.g.,
Yonelinas, 2002). Recently, however, this assumption has stimulated a lively debate. Critiques of all of the standard supplementary
judgment techniques have been published, with critiques of remember/know judgments having a particularly long history (e.g.,
Donaldson, 1996; Dunn, 2004, 2008; Rotello, Macmillan, &
Reeder, 2004). As matters now stand, there are multiple reasons
for doubting that the conventional strategy of supplementing old/
new recognition decisions with metacognitive judgments is a valid
method of separating recollective from nonrecollective retrieval
(Brainerd & Reyna, 2010; Malmberg, 2008). This, in turn, raises
doubts about the meaning of the aforementioned findings on how
emotional content affects recollective and nonrecollective retrieval.
Brainerd et al. (2009) proposed an approach to separating recollective and nonrecollective processes that avoids extant criticisms of the metacognitive judgment strategy because (a) it uses
recall rather than recognition as the focal memory task and (b) it
does not require that subjects make metacognitive judgments. This
recall-based procedure has recently been used to determine how
several variables affect recollective and nonrecollective retrieval,
including repetition, semantic organization of lists, list length,
exemplar typicality, chronological age, explicitness of retrieval
cues, and immediate versus delayed testing (e.g., Brainerd &
Reyna, 2010; Brainerd, Aydin, & Reyna, in press). The procedure
works as follows.
Subjects receive a small number of study-test trials (a minimum
of three) on a focal list, using any standard method of recall (e.g.,
associative, cued, free, serial). The resulting sequences of errors
and successes are then analyzed with a simple two-stage Markov
665
chain, which is shown in Appendix A (Equation A1). This model
posits that learning to recall an item consists of three performance
states: an initial zero-recall state U; an intermediate state P, in
which the probability of recalling the item is some value 0 ⬍ p ⬍
1; and an absorbing errorless-recall state L. Items are assumed to
be in state U before learning begins. After the first trial or after any
subsequent trial, an item that is in U may have moved to P, to L,
or remained in U. Items that move to L remain there on all
subsequent trials, items that move to P can move to L on some
subsequent trial, and items that remain in U can move to P or L on
some subsequent trial.
In this model, recollective retrieval is identified with state L, and
nonrecollective retrieval is identified with state P (for a review of
relevant data, see Brainerd et al., 2009). The model parameters that
measure these processes are defined in Table 1. Recollective
retrieval is measured with parameters that give the probability of
moving to state L from state P or state U, which are called direct
access parameters because this form of retrieval is assumed to
activate items’ verbatim traces without searching through and
comparing traces of other items. This form of retrieval generates
recollective phenomenology. Nonrecollective retrieval, however,
involves two component processes: reconstruction and familiarity.
Reconstruction is measured via model parameters that give the
probability of moving to state P from state U, and familiarity is
measured by parameters that give the probability of successful
recall of items that are in state P. Reconstructive retrieval is a
delimited search operation that uses partial information about list
items (e.g., “seasoning”) to construct sets of recall candidates (e.g.,
basil, oregano, pepper, sage). Because such information does not
identify specific items, a further operation, familiarity, is required
to decide which reconstructions ought to be output and which
should be withheld. Reconstructed items are assumed to generate
familiarity signals. Reconstructions that deliver stronger signals
are more likely to be output than those that deliver weaker ones.
Thus, nonrecollective retrieval consists of reconstruction ⫹ familiarity judgment. These operations support the error-prone recall of
state P because reconstruction may generate candidate sets that do
Table 1
Retrieval Processes That are Measured by the Markov Chain
Process/parameter
Definition
Recollective retrieval
Direct access
D1
D2
The probability that an item’s verbatim trace can be accessed after the first study trial.
For an item whose verbatim trace could not be accessed following prior study trials, the probability that its verbatim
trace can be accessed after the current study trial.
Nonrecollective retrieval
Reconstruction
R1
R2
Familiarity judgment
J1
J2
For an item whose verbatim trace cannot be accessed after the first study trial, the probability that the item can be
reconstructed after the first study trial.
For an item whose verbatim trace cannot be accessed after the current study trial and that could not be
reconstructed after prior study trials, the probability that the item can be reconstructed after the current study trial.
For an item that is reconstructed following the first study trial, the probability that the reconstruction is judged to be
familiar enough to output.
For an item that is reconstructed following any study trial after the first one, the probability that the reconstruction
is judged to be familiar enough to output.
GOMES, BRAINERD, AND STEIN
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not contain correct list items (e.g., garlic) and because familiarity
judgment may screen out candidates that are actually list items.
Experiment 1
In this experiment, we manipulated valence (negative, neutral,
positive) and controlled arousal, the aim being to investigate how
valence affects the retrieval processes that underlie recall. From
previous studies of valence effects, we hypothesized that overall
recall performance for valenced words (negative and positive)
would be better than for nonvalenced words. However, our main
objective was to measure how the two types of valence affect
recollective and nonrecollective retrieval, using the parameters in
Table 1.
Method
Subjects. Forty undergraduates (22 females) from a Brazilian
university volunteered to participate in the experiment. Subjects
were aged between 17 and 26 years (M ⫽ 19 years; SD ⫽ 2 years)
and were required to give written informed consent to participate
in the experiment.
Materials. Each subject learned to recall a list of 34 words
drawn from the Brazilian version of the ANEW (Kristensen,
Gomes, Vieira, & Justo, 2011). The original words in Portuguese
and their English translations are presented in Appendix B. The
study list was composed of 10 negative words (Mvalence ⫽ 2.02;
Marousal ⫽ 5.36), 10 neutral words2 (Mvalence ⫽ 5.04; Marousal ⫽
5.28), 10 positive words (Mvalence ⫽ 7.98; Marousal ⫽ 5.36), and
four filler words that were always presented in the first, second,
33rd, and 34th position in order to minimize primacy and recency
effects. Analyses of variance (ANOVAs) indicated that the negative, neutral, and positive words differed with respect to valence
but not arousal. In addition, an investigation of whether valence
conditions differed with respect to five other dimensions was
undertaken, namely semantic relatedness, word length, frequency,
concreteness, and imagery. Semantic relatedness was measured by
computing the frequency of six types of conceptual relations
(antonymy, synonymy, taxonomic properties, entity properties,
situation properties, and introspective properties; Wu & Barsalou,
2009) between all 55 possible pairs of words within each list. The
scoring showed that the frequency of conceptual relations was low
and similar across word lists. Similarly, ANOVAs indicated that
there were no reliable differences among the positive, negative,
and neutral words regarding word length, printed and spoken
frequency in Portuguese (Sardinha, 2003), concreteness (Toglia &
Battig, 1978), and imagery (Clark & Paivio, 2004).
Procedure. Subjects were tested in groups and underwent a
standard three-trial study-test procedure that has been used in
recent experiments in which recollective and nonrecollective retrieval have been measured with the present procedure (e.g., Brainerd et al., in press; Brainerd & Reyna, 2010; Brainerd et al., 2009).
The procedure consists of three pairs of study-test cycles, with a
short buffer activity interpolated between each study and test cycle
to eliminate short-term memory effects. The overall procedure was
S1B1aT1a B1bT1b, S2B2T2, S3B3T3, where S denotes a study cycle
on the word list, B denotes a buffer activity (30 s of letter
shadowing), and T denotes a free-recall test. This design has important advantages over a canonical design (S1B1T1S2B2T2S3B3T3).
Specifically, it generates additional data (T1b) that allows the
Markov chain to be fit twice across study-test cycles (i.e., across
T1aT2T3 and T1bT2T3), and, therefore, it generates two sets of
parameter estimates that will differ if subjects tend to forget items
when additional recall tests are imposed between study cycles.
During study cycles, subjects viewed words one at a time at a
3-s rate (with a 1-s interword interval) on a computer screen and
were told to pay close attention because their memory for the
words would be tested later. With the exception of filler words, the
presentation order of 30 focal words was random. During test
cycles, subjects performed written free recall. They were told to
write down as many studied words as possible and not to worry
about spelling. Written recall continued until 30 s had elapsed
without the emission of a new word.
Results
The results are presented in three parts. First, we report standard
descriptive statistics and ANOVAs that assessed the qualitative
effects of emotional valence on recall. Second, we report the
model-based results, which assessed the effects of valence on
recollective and nonrecollective retrieval. The latter analysis is
divided into model fit and process-level analyses. Third, we report
an additional analysis that evaluated the effects of valence on
clustering in free recall, which is an index of semantic processing
in recall. For all statistical tests, we adopted a .05 level of significance.
Descriptive statistics and ANOVAs. We measured recall
performance with three descriptive statistics: the proportion of
words recalled on each test, the mean trial of last error (MTLE),
and the mean total errors per item (MTEI). We computed all three
measures separately for T1aT2T3 and T1bT2T3 and then averaged
(T1T2T3). The average proportion of negative, neutral, and positive
words recalled as a function of test trial are shown in Table 2.
Inspection of Table 2 indicates better recall for positive than for
negative valence and better recall for negative than for neutral
valence. We submitted the recall proportion data to a 3 (valence:
negative, neutral, positive) ⫻ 3 (test trial: T1, T2, T3) repeated
measures ANOVA. There was a main effect of valence, F(2, 78) ⫽
52.59, MSE ⫽ .02, ␩2p ⫽ .57, and a main effect of test trial, F(2,
78) ⫽ 251.60, MSE ⫽ .02, ␩2p ⫽ .87. Regarding the main effect of
valence, paired comparisons showed that subjects recalled more
positive (M ⫽ 56%) than negative words (M ⫽ 47%), and recalled
more negative than neutral words (M ⫽ 35%). Naturally, the
proportion of words recalled by subjects increased from Trial 1
(M ⫽ 26%) to Trial 2 (M ⫽ 51%), and from Trial 2 to Trial 3 (M ⫽
62%).
2
In prior studies in which valenced words have been compared with
arousal controlled (e.g., Brainerd et al., 2008), the procedure has been the
same as the one used in Experiment 1. Specifically, sets of negative,
“neutral,” and positive words have been compared in which all three sets
have intermediate arousal scores (close to 5 on the 9-point ANEW scale).
Thus, the term neutral refers to valence rather than arousal. However,
“neutral” can have an arousal meaning, too, and words that are neutral in
that sense are low-arousal words, whereas words of intermediate arousal
levels are better conceptualized as “ambivalent.” In summary, the neutral
words in Experiment 1 were neutral with respect to valence, not arousal,
and if the arousal meaning is added, then the present set of neutral words
could be termed neutral-ambivalent.
EMOTIONAL RECALL
667
Table 2
Recall Performance Statistics as a Function of Experimental Condition
Proportion recalled
Condition
Negative
Neutral
Positive
T1
.26 (.13)
.18 (.14)
.33 (.15)
Overall recall
T2
T3
Experiment 1
.52 (.18)
.65 (.19)
.39 (.22)
.49 (.21)
.63 (.16)
.71 (.18)
MTLE
MTEI
1.75 (.42)
2.10 (.52)
1.53 (.43)
1.58 (.41)
1.94 (.50)
1.34 (.37)
Experiment 2
Negative
Low arousal
High arousal
Positive
Low arousal
High arousal
.23 (.13)
.16 (.13)
.38 (.16)
.35 (.17)
.53 (.16)
.51 (.20)
1.99 (.37)
2.12 (.41)
1.86 (.36)
1.99 (.42)
.22 (.12)
.23 (.14)
.42 (.18)
.45 (.16)
.53 (.19)
.58 (.18)
1.96 (.41)
1.87 (.39)
1.83 (.40)
1.75 (.39)
Note. MTLE ⫽ mean trial of last error; MTEI ⫽ mean total errors per item. Standard deviation appears in
parentheses.
We also found a main effect of valence using the MTLE and the
MTEI measures. Summary statistics for MTLE and MTEI are
presented in Table 2. Inspection of Table 2 suggests that learning
to recall words was easier for positive than for both negative and
neutral valence, and easier for negative than for neutral valence.
We conducted two repeated measures ANOVAs with valence
(negative, neutral, positive) as the independent variable and either
MTLE or MTEI as the dependent variable. The analyses revealed
a main effect of valence for MTLE, F(2, 78) ⫽ 30.98, MSE ⫽ .11,
␩2p ⫽ .44, and for MTEI, F(2, 78) ⫽ 52.59, MSE ⫽ .07, ␩2p ⫽ .57.
Paired comparisons indicated that, independent of the type of
performance measure used, positive recall was better than negative
or neutral recall, and negative recall was better than neutral recall.
Summing up, recall of valenced words (positive or negative)
was better than recall of neutral words, regardless of the performance measure. Furthermore, recall of positive words was better
than recall of negative words, regardless of the performance measure.
Model analyses. In this section, we used the Markov chains
in Appendix A (see also Brainerd et al., in press) to measure the
effects of valence on recollective and nonrecollective retrieval.
The Markov chains were applied to the outcome space implied by
T1aT2T3 and T1bT2T3 free-recall tests, separately. Because parameter estimates were very similar between the two, which happens
when forgetting between T1a and T1b is roughly zero (a common
finding with healthy young adults; e.g., Brainerd et al., 2009), we
pooled the data of both spaces and applied the Markov chains to
the pooled data to generate the final set of parameter estimates.
The intrusion rate across trials was very low (less than 1% of the
words recalled) and, therefore, did not affect model-based results.
As is customary, the model analyses proceed in two steps: (a) fit
followed by (b) parameter estimation and significance testing.
Fit. As is also customary (see Brainerd & Reyna, 2010), there
were two types of fit analyses: necessarity tests and sufficiency
tests. Necessity tests ask whether simpler models, which assume
that recall involves only a single retrieval process, can account for
the present data. If so, the dual-retrieval model fails on grounds of
parsimony. As shown in Appendix A (Equations A12 and A14),
there are two such one-process models that can be fit to the present
data—namely, a model that posits a single recollective retrieval
operation and a model that posits a single nonrecollective retrieval
operation. The recollection-only model produces a G2 test statistic
with 5 degrees of freedom (see Equation A15), and the
nonrecollection-only model produces a G2 test statistic with 3
degrees of freedom (see Equation A13). We computed those tests
for all three valence conditions. Because the G2 statistic is asymptotically distributed as ␹2 (Riefer & Batchelder, 1988), the critical
value for rejection of the null hypothesis of fit at the .05 level for
any of the conditions of this experiment is 11.07 for the oneprocess recollective model and 7.82 for the one-process nonrecollective model. As there were three conditions, the critical value of
G2 to reject the recollective model for the experiment as a whole
is 25.00, and the critical value to reject the nonrecollective model
for the experiment as whole is 16.92. The null hypothesis of fit was
rejected for the experiment as a whole, with the values of the G2
statistics exceeding their critical values by wide margins, G2(15) ⫽
1655.38, and G2(9) ⫽ 129.17. We also computed G2 statistics for
the one-process models for each experimental condition. The values for the negative, neutral, and positive valence condition were,
respectively, 614.19, 386.18, and 655.01 for the recollective
model, and 73.88, 34.00, and 21.29 for the nonrecollective model.
Therefore, the null hypothesis of fit was rejected for the oneprocess models in each and every experimental condition, and the
dual-retrieval model consequently passed the necessity tests.
Turning to sufficiency tests, these tests ask whether the dualretrieval model is adequate to account for the data, or whether a
more complex model (i.e., a model with additional retrieval processes) is needed. This test (see Equation A11) produces a G2
statistic with one degree of freedom, which is therefore distributed
as ␹2(1), with a critical value of 3.84 to reject the dual-retrieval
model for any of the conditions of this experiment. Because there
are three conditions, a G2 value of 7.82 rejects the model for the
experiment as whole. The G2 value for the experiment as a whole
was less than half of the critical value, 3.11. The G2 values for the
negative, neutral, and positive valence condition were .58, .04, and
GOMES, BRAINERD, AND STEIN
668
2.49, respectively. Therefore, the dual-retrieval model passed the
sufficiency tests as well as the necessity tests.
Parameter estimates and significance tests. We now turn to
the process-level analysis of the effects of valence on recall. Table
3 shows maximum likelihood estimates for the parameters of the
dual-retrieval model for each experimental condition, which will
be used to determine how valence affects recollective and nonrecollective retrieval. The grand average of the standard deviation of
estimates across conditions was .11. There are two important
comparisons in Table 3. The first is between valenced (positive,
negative) and nonvalenced words (neutral), and the second is
between positively and negatively valenced words. Results for the
former comparison explain why valence improves recall, whereas
results for the latter explain why positive valence improves recall
more than negative valence. It turned out that the general superiority of valenced words was due to advantages in nonrecollective
retrieval, whereas the superiority of positive over negative valence
was due to advantages in recollective retrieval. In what follows, we
present two sources of evidence that demonstrate this pattern:
likelihood ratio tests of parameter differences among experimental
conditions and recall performance decomposition in terms of recollective and nonrecollective recall.
Statistical reliability of differences in parameter estimates was
determined with likelihood ratio tests. In brief, between-condition
likelihood ratio tests compare the goodness of fit of a joint model
in which one parameter is set equal between any two conditions
(e.g., D1 positive ⫽ D1 negative) against the fit of a joint model in
which all parameters are free to vary between the same two
conditions, which produces a G2 test statistic that is asymptotically
distributed as ␹2(1). Inspection of the parameter values in Table 3
reveals three main results that were investigated with likelihood
ratio tests. First, direct access parameters (recollective retrieval)
were higher for positive valence relative to neutral (⌬M ⫽ .20) and
negative valence (⌬M ⫽ .25). There was a reliable difference
between positive and neutral valence for D1, G2(1) ⫽ 5.42, and D2,
G2(1) ⫽ 4.44, and a reliable difference between positive and
negative valence for D1, G2(1) ⫽ 7.38, and D2, G2(1) ⫽ 3.93.
Second, reconstruction (nonrecollective retrieval) was lower for
neutral relative to positive valence (⌬M ⫽ .09). There was a
reliable difference in R1 for positive versus neutral valence,
G2(1) ⫽ 4.68. Third, familiarity judgment (nonrecollective retrieval) was higher for negative relative to positive (⌬M ⫽ .40) and
neutral valence (⌬M ⫽ .27). There was a reliable difference
between negative and positive valence for J1, G2(1) ⫽ 6.45, and
J2, G2(1) ⫽ 5.04. In addition, there was a reliable difference in J2
for negative versus neutral valence, G2(1) ⫽ 3.93.
The parameter estimates in Table 3 can be used to decompose
recall performance on each test trial into its recollective and
nonrecollective components (see Brainerd & Reyna, 2010). When
the parameter estimates are inserted in the dual-retrieval model’s
starting vector W and the transition matrix M (see Appendix A
Equation A1) and the two are multiplied, a new vector Z is
generated that has four elements: (a) the proportion of words that
were recalled via recollective retrieval, P(a); (b) the proportion of
words that were reconstructed but not recalled, P(b); (c) the
proportion of words that were recalled via nonrecollective retrieval, that is, those that were reconstructed and recalled, P(c); and
(d) the proportion of words that were neither recollected nor
reconstructed, P(d). For the ith test trial (for i ⱖ 1), this vector is
Zi ⫽ [P(a), P(b), P(c), P(d)]i ⫽ W ⫻ Mi⫺1. The proportion of
words recalled on the ith test trial is simply the sum of P(a)i and
P(c)i. Thus, total recall on each test is expressed in terms of its
recollective and nonrecollective components.
When this was done (see Figure 1), we found that recall of
positive words was primarily recollective, whereas recall of negative words was exclusively nonrecollective. This pattern held
across all test trials. Moreover, analysis of learning over trials (i.e.,
the difference between performance on consecutive tests) indicated that learning to recall neutral and negative words was mainly
due to increases in nonrecollective retrieval (for neutral words:
⌬T1,T2 ⫽ 19%, ⌬T2,T3 ⫽ 10%; for negative words: ⌬T1,T2 ⫽ 25%,
⌬T2,T3 ⫽ 14%) rather than to increases in recollective retrieval (for
neutral words: ⌬T1,T2 ⫽ 2%, ⌬T2,T3 ⫽ 1%; for negative words:
⌬T1,T2 ⫽ 0%, ⌬T2,T3 ⫽ 0%). Conversely, learning to recall positive words was mainly due to increases in recollective retrieval
(⌬T1,T2 ⫽ 20%, ⌬T2,T3 ⫽ 10%) rather than to increases in nonrecollective retrieval (⌬T1,T2 ⫽ 10%, ⌬T2,T3 ⫽ ⫺2%).
Summary. In summary, goodness-of-fit tests showed that the
dual-retrieval model was both necessary and sufficient to account
for the data. Parameter estimates indicated that positive and neg-
Table 3
Parameter Estimates of the Dual-Retrieval Model for Each Emotional Condition
Direct access
Condition
Negative
Neutral
Positive
Reconstruction
D1
D2
Mean D
.00
.08
.20
.00
.02
.29
.00
.05
.25
R1
R2
Experiment 1
.32
.43
.25
.33
.59
.17
Familiarity judgment
Mean R
J1
J2
Mean J
.38
.29
.38
.81
.44
.27
.83
.65
.56
.82
.55
.42
.56
.72
.78
.86
.68
.56
.53
.49
Experiment 2
Negative
Low arousal
High arousal
Positive
Low arousal
High arousal
.12
.03
.15
.08
.14
.06
.13
.13
.24
.28
.19
.21
.11
.15
.11
.18
.11
.17
.31
.22
.16
.21
.24
.22
1.0
1.0
.38
.41
EMOTIONAL RECALL
669
80%
Trial 1
Recall Performance
70%
60%
Nonrecollective
50%
Recollective
Trial 2
Trial 3
21%
23%
40%
65%
30%
13%
51%
39%
50%
29%
40%
20%
26%
10%
10%
20%
11%
10%
8%
0%
Negative
Neutral
Positive
Negative
Neutral
Positive
Negative
Neutral
Positive
Figure 1. Recall decomposition in terms of recollective and nonrecollective retrieval as a function of valence
and test trial in Experiment 1.
ative valence affected recollective and nonrecollective retrieval in
different ways. Specifically, positive valence (a) increased direct
access relative to both negative and neutral valence, and (b) it also
increased reconstruction relative to neutral valence. Negative valence increased familiarity judgment relative to positive and neutral valence. Thus, the reason that valenced items are remembered
better than neutral ones is different for positive valence (direct
access and reconstruction advantages) than for negative valence
(familiarity advantage), although both positive and negative valence increased nonrecollective retrieval relative to neutral (on the
positive valence side, reconstruction; on the negative valence side,
familiarity judgment). Finally, decomposition of recall performance into its recollective and nonrecollective components
showed that learning to recall positive words was largely a matter
of learning how to retrieve recollectively, whereas learning how to
recall negative or neutral words was essentially a matter of learning how to retrieve nonrecollectively.
Clustering. To investigate the effects of valence on clustering
in free recall (i.e., when two or more words of the same valence are
recalled next to each other), we computed Bousfield’s (1953) ratio
of repetition (RR) statistic separately for negative, neutral, and
positive words. Averaged across all trials, the mean RR was .47,
.33, and .48 for negative, neutral, and positive valence, respectively. We submitted the data to a repeated measures ANOVA, and
the results showed a main effect of valence, F(2, 78) ⫽ 8.73,
MSE ⫽ .03, ␩2p ⫽ .18. Paired comparisons showed that the RR was
lower for neutral words than for both positive and negative words.
Furthermore, although there was a reliable correlation between RR
for negative and positive words, r(40) ⫽ .35, there was not a
reliable correlation between RR for neutral words and either negative or positive words.
Discussion
The aim of the first experiment was to investigate how valence
affects recall and its underlying retrieval processes. On the basis of
the previous literature (Buchanan, 2007; Kensinger & Corkin,
2003; Ochsner, 2000), we hypothesized that recall of valenced
words (negative, positive) would be better than recall of nonvalenced words (neutral). This hypothesis was supported. At the
process level, this effect was due to increased nonrecollective
retrieval of negative words and increased recollective and nonrecollective retrieval of positive words. On average, reconstruction
was higher for valenced relative to nonvalenced words. The latter
result was further supported by analysis of clustering in free recall,
which showed that clustering was higher for valenced relative to
nonvalenced words. In that connection, prior research with the
present model has shown that nonrecollective retrieval increases as
traditional measures of semantic relatedness of lists increase but
recollective retrieval does not (Brainerd & Reyna, 2010). In addition, process-level analyses indicated that better recall of positive
than negative words was due to increased recollective retrieval of
positive words.
One limitation of Experiment 1 is that holding arousal constant
did not allow us to investigate whether valence effects on recall
depend on arousal. This is an important limitation because emotional arousal alone has been implicated in changes in memory
performance (Cahill & McGaugh, 1998). Specifically, arousing
events are usually more likely to be recalled than nonarousing ones
(Bradley et al., 1992), they can lead to focal memory enhancements (Christianson & Loftus, 1991), and they can render subsequent events less likely to be recalled (Hurlemann et al., 2005).
Furthermore, arousal has been linked to biological mechanisms
that are distinct from those associated with valence, such as
changes in skin conductance (Lang, Greenwald, Bradley, &
Hamm, 1993) and increased release of glucocorticoids from the
adrenal cortex (McGaugh, 2004). Moreover, a few studies suggest
that the effects of valence and arousal interact (e.g., Brainerd et al.,
2010; Libkuman et al., 2004; Steinmetz et al., 2010). To illustrate,
Waring and Kensinger (2011) showed subjects a sequence of
scenes composed of negative, neutral, or positive items, such that
half the negative and positive items were high in arousal and half
were low in arousal. Later, subjects performed a recognition test.
GOMES, BRAINERD, AND STEIN
670
Recognition was better for positive items high in arousal relative to
positive items low in arousal. However, recognition was worse for
negative items high in arousal relative to negative items low in
arousal. This pattern is instructive because it suggests that how
valence effects are expressed may depend on arousal, so that the
findings of Experiment 1 may be limited to designs with constant
levels of arousal. To overcome this limitation, we manipulated
valence (negative, positive) and arousal (low, high) in a factorial
design.
Experiment 2
The aim of Experiment 2 was to investigate how the effects of
valence on recall and its underlying retrieval processes are affected
by changes in arousal level.
Method
Subjects.
Sixty-three undergraduates (45 females) from a
Brazilian university participated in the experiment. Subjects were
aged between 17 and 26 years (M ⫽ 21 years, SD ⫽ 3 years) and
were required to give written informed consent to participate in the
experiment.
Design and materials. A 2 (valence: negative, positive) ⫻ 2
(arousal: low, high) within-subjects design was used. Each subject
learned to recall a list of 36 words drawn from the Brazilian
version of the ANEW (Kristensen et al., 2011; see Appendix B).
The study list was composed of nine negative and high-arousal
words (negative-high; Mvalence ⫽ 3.16; Marousal ⫽ 5.63), nine
negative and low-arousal words (negative-low; Mvalence ⫽ 3.31;
Marousal ⫽ 4.11), nine positive and high-arousal words (positivehigh; Mvalence ⫽ 7.61; Marousal ⫽ 5.50), and nine positive and
low-arousal words (positive-low; Mvalence ⫽ 7.71; Marousal ⫽
3.93). Statistical tests (ANOVAs) confirmed four results that are
apparent from the average valence and arousal scores for each
condition: (a) The average valence score for positive words (M ⫽
7.66) was statistically different from the average valence score for
negative words (M ⫽ 3.24); (b) the average arousal score for
high-arousal words (M ⫽ 5.57) was statistically different from the
average arousal score for low-arousal words (M ⫽ 4.02); (c) there
were no reliable differences regarding valence scores between
positive-high and positive-low words, or between negative-high
and negative-low words; and (d) there were no reliable differences
regarding arousal scores between positive-high and negative-high
words, or between positive-low and negative-low words. However,
between-condition differences in arousal (⌬M ⫽ 1.55) were
smaller than between-condition differences in valence (⌬M ⫽
4.42). Such differences are due to the absence of either very highor very low-arousal words that are also strongly valenced in
affective word pools. For this reason, the only other choices would
be either not to manipulate arousal at all or to confound valence
and arousal. The latter alternatives are obviously less desirable
than factorial manipulation with a weaker arousal than valence
manipulation. Moreover, our conclusions concerning the effects of
valence and arousal on memory are qualitative rather than quantitative. More specifically, our conclusions are about whether
valence and arousal affect retrieval processes rather than about
whether these effects vary as a function of the strength of these
manipulations. We acknowledge, however, that differences in sta-
tistical power could lead to situations in which valence has reliable
effects but arousal does not.
In addition, the frequency of conceptual relations was low and
similar across all four Valence ⫻ Arousal conditions. Finally,
there were no reliable differences among the word lists regarding
word length, printed and spoken frequency in Portuguese, concreteness, and imagery.
Procedure. The experimental procedure was identical to the
one used in Experiment 1 except for the following differences: In
Experiment 2, subjects saw a list of 36 words and no filler words
were used.
Results
Descriptive statistics and ANOVAs. As before, we measured recall performance by the proportion of words recalled on
each test trial, the MTLE, and the MTEI. Regarding the first
measure, Table 2 shows the average proportion of negative-low,
negative-high, positive-low, and positive-high words recalled as a
function of test trial. Visual inspection of Table 2 suggests that
recall performance for positive-high words was better than for
negative-high words. However, recall performance for positivelow words was similar to recall performance for negative-low
words, showing that valence interacted with arousal. To investigate whether the numerical differences in the proportion of words
recalled among conditions were statistically reliable, we conducted
a 2 (valence: negative, positive) ⫻ 2 (arousal: low, high) ⫻ 3 (test
trial: T1, T2, T3) repeated measures ANOVA. The results showed
a main effect of valence, F(1, 62) ⫽ 13.10, MSE ⫽ .03, ␩2p ⫽ .17;
a main effect of test trial, F(2, 124) ⫽ 508.90, MSE ⫽ .01, ␩2p ⫽
.89; and an interaction between valence and arousal, F(1, 62) ⫽
10.64, MSE ⫽ .02, ␩2p ⫽ .14. Paired comparisons showed that
subjects recalled more positive words on average (M ⫽ 40%) than
negative (M ⫽ 36%). Analysis of the interaction between valence
and arousal, however, showed that valence effects depended on
arousal. On the one hand, the proportion of positive-high words
recalled (M ⫽ 42%) was higher than the proportion of negativehigh words (M ⫽ 34%). On the other hand, there was not a reliable
difference between the proportion of positive-low (M ⫽ 39%) and
negative-low words recalled (M ⫽ 38%). As it is apparent in Table
2, the proportion of words recalled increased from Trial 1 (M ⫽
21%) to Trial 2 (M ⫽ 40%), and from Trial 2 to Trial 3 (M ⫽
54%).
We found similar results using the MTLE and the MTEI measures. The average MTLE and MTEI score and standard deviation
are presented in Table 2 for each condition. Inspection of Table 2
indicates that learning to recall positive-high words was easier than
learning to recall negative-high words, and learning to recall
positive-low words was not easier than learning to recall negativelow words. We submitted these data to a 2 (valence: negative,
positive) ⫻ 2 (arousal: low, high) repeated measures ANOVA
using either MTLE or MTEI as the dependent variable. The
analyses revealed a main effect of valence for MTLE, F(1, 62) ⫽
14.01, MSE ⫽ .09, ␩2p ⫽ .18, and for MTEI, F(1, 62) ⫽ 13.10,
MSE ⫽ .09, ␩2p ⫽ .17, and an interaction between valence and
arousal for MTLE, F(1, 62) ⫽ 9.25, MSE ⫽ .08, ␩2p ⫽ .13, and for
MTEI, F(1, 62) ⫽ 10.46, MSE ⫽ .07, ␩2p ⫽ .14. Paired comparisons showed that learning to recall was easier for positive-high
than for negative-high words, independent of the performance
EMOTIONAL RECALL
671
iment 1, the value for the experiment as a whole, 5.80, was below
the critical value for rejection of the null hypothesis of fit. The G2
values for the negative-low, negative-high, positive-low, and
positive-high were 1.87, .22, 3.12, and .59, respectively. Thus, the
dual-retrieval model passed both necessity and sufficiency tests.
Parameter estimates and significance tests. Maximum likelihood estimates of the parameters of the dual-retrieval model are
presented in Table 3 for each Valence ⫻ Arousal condition. The
grand mean of the standard deviation of estimates across conditions was .05. Inspection of these data reveals an interaction
between valence and arousal at the process level. Direct access was
low for negative-high words in comparison to positive-high words
(⌬M ⫽ .11) and negative-low words (⌬M ⫽ .08), but it was similar
for positive-low and negative-low words (⌬M ⫽ .03). Likelihood
ratio tests revealed a reliable difference between positive-high and
negative-high words for D1, G2(1) ⫽ 10.03, and a reliable difference between negative-low and negative-high words for D1,
G2(1) ⫽ 7.04. In addition, reconstruction was slightly higher for
positive-low relative to negative-low words (⌬M ⫽ .05), which
was due to a reliable difference in R1, G2(1) ⫽ 6.14. Finally,
familiarity judgment was higher for negative words relative to
positive words (⌬M ⫽ .31), and this difference was larger for
high-arousal words (⌬M ⫽ .37) than for low-arousal words (⌬M ⫽
.25). However, likelihood ratio tests revealed a reliable difference
only between negative-low and positive-low words for J1, G2(1) ⫽
4.87. Thus, as before, the recall superiority of positive over negative words was due to advantages in recollective retrieval, but this
superiority only held for words high in arousal.
Finally, we decomposed recall performance on Trials 1, 2, and
3 in terms of recollective and nonrecollective retrieval. The results
are presented in Figure 2. As in the first experiment, the decomposition showed that recall of positive-high words was primarily
supported by recollective retrieval, whereas recall of negative-high
words was primarily supported by nonrecollective retrieval. Analysis of the learning over trials indicated that learning to recall
positive-high and negative-low words was due to increases in
measure. However, there was no difference between positive-low
and negative-low words in recall performance.
In summary, valence interacted with arousal regardless of the
performance measure. Specifically, recall performance was higher
for positive than for negative valence only when arousal was high.
Next, we evaluated how valence and arousal affected underling
retrieval operations.
Model analyses. As in Experiment 1, we used the Markov
chain presented in Appendix A to measure the effects of valence
and arousal on recollective and nonrecollective retrieval. Model fit
analyses are reported first, followed by parameter estimation and
significance testing.
Fit. We started with the necessity tests, which ask whether
simpler one-stage models can account for the present data. The
recollection-only model produces a G2 test statistic with 5 degrees
of freedom for each condition, and, therefore, the critical value for
rejecting the null hypothesis of fit is 11.07 in each condition and
31.41 across the four Valence ⫻ Arousal conditions. The
nonrecollection-only model produces a G2 test statistic with 3
degrees of freedom for each condition, and, therefore, the critical
value for rejecting the null hypothesis of fit is 7.82 in each
condition and 21.03 across the four Valence ⫻ Arousal conditions.
The null hypothesis of fit was rejected for both models for the
experiment as a whole, with the values of the G2 statistics exceeding their critical values by wide margins, G2(20) ⫽ 2631.88, and
G2(12) ⫽ 281.33. The values of the G2 statistic for the negativelow, negative-high, positive-low, and positive-high conditions
were, respectively, 827.47, 648.05, 545.97, and 610.39 for the
recollective model, and 74.17, 97.23, 53.00, and 56.93 for the
nonrecollective model.
Next we turn to the sufficiency test, which asks whether the
dual-retrieval model is adequate to account for the data. The
dual-retrieval model produces a G2 test statistic with 1 degree of
freedom for each condition, and, therefore, the critical value for
rejecting the null hypothesis of fit is 3.84 in each condition and
9.49 across the four Valence ⫻ Arousal conditions. As in Exper-
70%
Trial 1
Trial 2
Trial 3
Recall Performance
60%
50%
Nonrecollective
19%
Recollective
20%
40%
15%
30%
20%
27%
16%
35%
23%
25%
11%
11%
12%
29%
24%
13%
10%
8%
11%
15%
39%
33%
26%
20%
15%
10%
3%
0%
Low
High
Negative
Low
High
Positive
Low
High
Negative
Low
High
Positive
Low
High
Negative
Low
High
Positive
Figure 2. Recall decomposition in terms of recollective and nonrecollective retrieval as a function of valence,
arousal, and test trial in Experiment 2.
672
GOMES, BRAINERD, AND STEIN
recollective retrieval (for positive-high: ⌬T1,T2 ⫽ 14%, ⌬T2,T3 ⫽
10%; for negative-low: ⌬T1,T2 ⫽ 12%, ⌬T2,T3 ⫽ 9%) more than to
increases in nonrecollective retrieval (for positive-high: ⌬T1,T2 ⫽
8%, ⌬T2,T3 ⫽ 3%; for negative-low: ⌬T1,T2 ⫽ 4%, ⌬T2,T3 ⫽ 5%).
However, learning to recall negative-high words was due to increases in nonrecollective retrieval (⌬T1,T2 ⫽ 12%, ⌬T2,T3 ⫽ 10%)
more than to increases in recollective retrieval (⌬T1,T2 ⫽ 7%,
⌬T2,T3 ⫽ 5%). Learning to recall positive-low words was due to
similar increases in recollective (⌬T1,T2 ⫽ 9%, ⌬T2,T3 ⫽ 6%) and
nonrecollective retrieval (⌬T1,T2 ⫽ 12%, ⌬T2,T3 ⫽ 4%).
Summary. Summing up, the effect of valence on retrieval
processes interacted with arousal as follows: First, when arousal
was high, recall of positive words involved more recollection and
less nonrecollective recall than recall of negative words (see Figure 2). This is the same pattern as in Experiment 1. Second, when
arousal was low, levels of recollection and reconstruction were
similar for positive and negative words. However, it can be seen in
Figure 2 that these different valence patterns are due to the fact that
process-level effects of arousal are the opposite for the two valences. When valence is positive, high arousal drives recollective
retrieval up and nonrecollective retrieval down. However, when
valence is negative, high arousal drives recollective retrieval down
and nonrecollective retrieval up. As discussed by Brainerd and
Reyna (2010), this pattern points to an important property of our
model-based approach to measuring underlying retrieval processes: When a manipulation causes performance in one condition
to be better than in an another, or produces no between-condition
differences, the model can show that the manipulation has opposite
effects on underlying processes.
Discussion
In Experiment 2, we investigated how the effects of valence on
recall and retrieval processes interact with the effects of arousal.
Our results showed that arousal interacts with valence, as predicted
by other studies that manipulated valence and arousal factorially
(Brainerd et al., 2010; Libkuman et al., 2004; Steinmetz et al.,
2010; Waring & Kensinger, 2011). Specifically, recall was better
for positive than negative words only when both were high in
arousal. At the process level, this effect was due to increased
recollective retrieval (direct access) of positive words. Importantly, for negative valence, high arousal decreased recollective
retrieval relative to low. This result helps to explain, for instance,
the low rate of recollective recall of negative words in Experiment
1, as the arousal values of those words were closer to the negativehigh words in Experiment 2 than to the negative-low words.
Moreover, our results indicate that the low rate of recollective
recall of negative-high words was mainly due to the low level of
the direct access during the first learning cycle (D1) rather than
during later learning cycles (D2). Overall, recollective retrieval
dominated when positive-high words were being recalled, but
nonrecollective retrieval dominated when negative-high words
were being recalled. Also, recall of negative-low words was dominated by recollection, whereas recall of positive-low words exhibited roughly equal contributions of recollective and nonrecollective retrieval. Thus, the relative contribution of the two forms of
retrieval to overall recall of the two valence types depended on
arousal.
There were similarities and differences in the results of Experiments 2 versus Experiment 1. On the one hand, better recall of
positive versus negative words was due to advantages in recollective retrieval in both experiments. On the other hand, there were
differences in the absolute values of parameter estimates between
the two experiments. Those differences, however, are not problematical for several reasons. In addition to measurement error,
such differences could have been caused by differences in subject
samples and methodological differences. With respect to methodological differences, list length and the number of within-subject
conditions varied between experiments.
General Discussion
The aim of this study was to investigate the effects of emotional
valence and arousal on recollective and nonrecollective retrieval,
using a model-based approach that avoids extant criticisms of
traditional metacognitive methods of measuring these processes. In
two experiments, we manipulated valence and either controlled
arousal or manipulated it factorially. Recollective retrieval and two
components of nonrecollective retrieval (reconstruction and familiarity judgment) were measured. The experiments produced two
principal results that clarify previous findings about the effects of
emotion on memory accuracy (Bradley et al., 1992; Brainerd et al.,
2010, 2008; Dehon et al., 2010; Goodman et al., 2011; Kensinger
& Corkin, 2003) and brain activity (Dolcos et al., 2004; Kensinger
& Corkin, 2004) and that reconcile some inconsistencies in these
findings. First, our results suggested that valenced words (negative, positive), as opposed to nonvalenced words (neutral), increase
nonrecollective retrieval. Second, our results suggested that positive valence foments recollective retrieval relative to negative,
whereas negative valence foments nonrecollective retrieval relative to positive. The second experiment produced a further result of
theoretical significance: Valence effects depended on arousal, both
at the performance and process levels. In what follows, we discuss
the theoretical and methodological implications of the findings
reported here as well as the limitations of the present study.
The investigation of the role of dual processes in the effects of
emotion on memory has followed the traditional approach of using
metamnemonic judgments, such as remember/know and confidence judgments, to separate recollective from nonrecollective
retrieval. Studies in which the remember/know procedure has been
used have shown that valenced items increase remember judgments in comparison to nonvalenced items (Dewhurst & Parry,
2000; Kensinger & Corkin, 2003; Ochsner, 2000; Sharot et al.,
2007). However, the idea that emotion enhances recollective retrieval is inconsistent with findings about the effects of emotion on
brain activity and memory accuracy. For instance, successful encoding of valenced material has been associated with activity in
regions that are implicated in semantic processing (Dolcos et al.,
2004; Kensinger & Corkin, 2004). The findings of our experiments
fit well with the neuroimaging data. Specifically, valenced words
increased the more semantically driven form of retrieval, reconstruction, and also increased a classic qualitative index of semantic
processing in recall, clustering. This pattern suggests that retrieval
of valenced memories should correlate with brain activity in regions implicated in semantic processing, the PFC being a case in
point.
EMOTIONAL RECALL
Turning to the findings about the effects of emotion on memory
accuracy, negative valence has been shown to increase false memories of gist-consistent events relative to neutral or positive (Brainerd et al., 2010, 2008; Dehon et al., 2010; Goodman et al., 2011).
Brainerd et al. (2008) found that levels of false memory increase
from positive to neutral to negative because of increasing retrieval
of gist memories and decreasing retrieval of verbatim memories
that allow rejection of false information. Our results connecting
negative words to an error-prone retrieval mechanism (reconstruction from gist ⫹ familiarity) and retrieval of positive words to an
errorless retrieval mechanism (direct access of targets’ verbatim
traces) are consistent with this pattern. Retrieval of gist memories
support both true and false memories (Brainerd & Reyna, 2002;
Schacter, Verfaellie, & Pradere, 1996), which explains why subjects in our experiment recalled more negative than neutral words
and why subjects in Brainerd et al.’s experiment showed more
false memories for negative than for positive or neutral words
(increased gist resemblance between true and false items). Although our findings also suggested that positive valence increases
nonrecollective retrieval relative to neutral, (a) it did not increase
nonrecollective retrieval as much as negative valence and (b) it
greatly increased recollective retrieval, which should reduce false
memories and improve accuracy. Results reported in other studies
support the latter conclusion. In associative recognition, a test that
is slanted toward memory for surface features, accuracy is better
for positive word pairs relative to negative (Pierce & Kensinger,
2011). In addition, associative recall of names paired with happy
faces is better than for names paired with neutral faces (Tsukiura
& Cabeza, 2008, 2011).
One explanation of why valenced words increase nonrecollective retrieval in comparison to nonvalenced words is that valence
is a conceptual gist that people often rely on to reconstruct past
events (Rivers, Reyna, & Mills, 2008). Knowledge about the
valence of emotional states evoked during an episode (e.g., anger,
fear, joy, love) can then provide a base for reconstruction of
missing elements of that episode, whether they are true or false.
This could make reconstruction more efficient by reducing the
number of output candidates that are generated (e.g., only things
that make me happy). Or it might increase the strengths of familiarity signals, too. Our results provided evidence of both effects.
Why has the previous literature indicated that emotion enhances
recollective but not nonrecollective retrieval? Here, it is important
to note Phelps and Sharot’s (2008) conclusion that the influence of
emotion on metacognitive judgments about memory (traditional
methods of separating dual processes) is more consistent and
robust than is its influence on actual memory performance (our
separation method). Zimmerman and Kelley (2010), for instance,
showed that predictions about future associative recall of negative
word pairs were higher than predictions of recall of neutral word
pairs, but actual recall of negative and neutral pairs were similar,
which suggests that something other than actual memory for
emotional items is used in metacognitive judgments. For example,
such judgments appear to be influenced by beliefs about how
emotion (valence and arousal) affects memory (Magnussen et al.,
2006). This does not rule out the possibility that emotion affects
actual memory processes, as suggested by the previous literature.
In fact, our results support that idea. However, the simple hypothesis that valence affects only recollection did not prove successful,
673
and further, how retrieval processes reacted to valence depended
on arousal.
Regarding the influence of arousal, recall of positive words was
superior to recall of negative words only when arousal was high.
Second, recollective retrieval was higher for positive than for
negative words only when arousal was high. Third, recall of
negative words was chiefly supported by nonrecollective retrieval
only when arousal was high, and recall of positive words was
chiefly supported by recollective retrieval only when arousal was
high. As mentioned, the confounding of valence and arousal has
been a persistent problem in studies of the effects of emotion on
memory. When arousal is manipulated, arousing items are often
more negative than nonarousing ones (e.g., Cahill et al., 1996;
Cahill & McGaugh, 1995). When valence is manipulated, negative
and positive items are often more arousing than neutral (e.g.,
Dewhurst & Parry, 2000; Sharot et al., 2004, 2007; Pierce &
Kensinger, 2011), and negative items are often more arousing than
positive (e.g., Xu et al., 2011). Along with other studies in which
valence and arousal have been factorially manipulated (Brainerd et
al., 2010; Libkuman et al., 2004; Steinmetz et al., 2010; Waring &
Kensinger, 2011), our results showed that confounding valence
and arousal has important theoretical consequences. Specifically,
the manner in which valence differences affect underlying retrieval
processes depends on arousal, which surely affects how valence
differences correlate with patterns of brain activity.
Finally, a potentially important limitation of this study concerns
the arousal levels of the words used in both experiments. Arousal
scores vary from 1 (calm) to 9 (excited) on the ANEW. In the first
experiment, arousal was controlled at 5.3. In the second experiment, arousal varied from 4.0 to 5.6. Thus, the arousal values of
the words that were used in our experiments did not, on average,
come from the lower third of the rating scale (very low arousal) or
the upper third (very high arousal). Consequently, although valence and arousal were not confounded in either of our experiments and although they were factorially manipulated in Experiment 2, extreme values of arousal were not used. It is conceivable
that the manner in which arousal affects underlying retrieval
processes and the way valence interacts with arousal could be
different at the extreme ends of the scale.
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(Appendices follow)
GOMES, BRAINERD, AND STEIN
676
Appendix A
Dual-Retrieval Model
Consider a multitrial experiment in which subjects undergo
three learning cycles of the form S1T1 S2T2 S3T3. On each recall
test, a target is either recalled (correct) or not (error). Therefore, a
target’s history of recall across all three tests generates one out of
eight possible patterns of correct (C) and error (E) responses:
C1C2C3, C1C2E3, . . . , E1E2E3. The six parameters presented in
Table 1 can be estimated from the frequency of such error-success
patterns by applying a two-stage Markov chain that contains those
parameters. The states of the two-stage model are U (an initial
L共n ⫹ 1兲
1
D2
0
D2
L共n兲
M ⫽ PE 共n兲
PC 共n兲
U共n兲
W ⫽ 关L共1兲, PE 共1兲, PC 共1兲, U共1兲兴
⫽ 关D1, 共1 ⫺ D1兲R1共1 ⫺ J1兲, 共1 ⫺ D1兲R1J1, 共1 ⫺ D1兲共1 ⫺ R1兲兴.
PE 共n ⫹ 1兲
0
共1 ⫺ D2 兲共1 ⫺ J2 兲
共1 ⫺ J2 兲
共1 ⫺ D2 兲R2 共1 ⫺ J2 兲
The probabilities of the eight individual error-success patterns are
obtained by multiplying the vector and the matrix together. Those
expressions are:
P共C1 C2 C3 兲 ⫽ D1 ⫹ 共1 ⫺ D1兲R1J1共J2兲
no-recall state), P (an intermediate partial-recall state, with a
correct recall hidden state PC and an incorrect recall hidden state
PE), and L (a terminal and an absorbing criterion-recall state). The
two-stage Markov process for these states consists of the following
starting vector W and transition matrix M:
2
(A2)
P共C1 C2 E3 兲 ⫽ 共1 ⫺ D1兲R1J1J2共1 ⫺ J2兲
(A3)
P共C1 E2 C3 兲 ⫽ 共1 ⫺ D1兲R1J1(1⫺J2)关D2 ⫹ 共1 ⫺ D1兲J2兴
(A4)
P共C1 E2 E3 兲 ⫽ 共1 ⫺ D1兲R1J1(1⫺J2) 共1 ⫺ D2兲
(A5)
2
P共E1 C2 C3 兲 ⫽ 共1 ⫺ D1兲共1 ⫺ R1兲D2 ⫹ 共1 ⫺ D1兲R1共1⫺J1兲
⫻ 关D2⫹共1⫺D2兲共J2兲 2 兴⫹共1⫺D1兲共1⫺R1兲共1⫺D2兲R2共J2兲 2
(A6)
PC 共n ⫹ 1兲
0
共1 ⫺ D2 兲J2
J2
共1 ⫺ D2 兲R2 J2
.
L6 ⫽ ⌸共pi兲N共i兲 ,
G 2 ⫽ ⫺2ln关L6 /L7兴,
⫹ R1共1 ⫺ J1兲共1 ⫺ D2兲J2共1 ⫺ J2兲兴
(A7)
P共E1 E2 C3 兲 ⫽ 共1 ⫺ D1兲共1 ⫺ R1兲共1 ⫺ D2兲关共1 ⫺ R2兲D2
⫹ 共1 ⫺ R2兲共1 ⫺ D2兲R2J2 ⫹ R2共1 ⫺ J2兲D2
⫹ R2共1 ⫺ J2兲共1 ⫺ D2兲J2兴 ⫹ 共1 ⫺ D1兲R1共1 ⫺ J1兲共1 ⫺ D2兲共1 ⫺ J2兲
⫻ 关D2 ⫹ 共1 ⫺ D2兲J2兴; (A8)
P共E1 E2 E3 兲 ⫽ 共1 ⫺ D1兲共1 ⫺ R1兲兵关共1 ⫺ D2兲共1 ⫺ R2兲兴2 ⫹ 共1 ⫺ D2兲
(A1)
(A10)
in which the pi are the eight expressions on the right sides of
Equations A2–A9, and the N(i) are empirical data counts of the
corresponding error-success sequences. Because six memory parameters are estimated, the likelihood value in A10 is computed
with 1 degree of freedom. A goodness-of-fit test that evaluates the
null hypothesis that learning to recall involves two processes is
then obtained by computing a likelihood ratio statistic that compares the likelihood in A10 with the likelihood of the same data
when all seven observable probabilities are free to vary. That test
statistic, which is asymptotically distributed as ␹2(1), is
P共E1 C2 E3 兲 ⫽ 共1 ⫺ D1兲关共1 ⫺ R1兲共1 ⫺ D2兲R2J2共1 ⫺ J2兲
(A11)
where L7 is the likelihood of the data when all seven observable
probabilities are free to vary.
For any set of data over which A11 can be defined, it is also
possible to define two one-stage models that incorporate singletrace conceptions. Specifically, a single nonrecollective process is
obtained from the two-process model by eliminating the U state
from it, which results in the following initial probability vector W'
and a transition matrix M':
W' ⫽ 关L共1兲, PE 共1兲, PC 共1兲兴 ⫽ 关D1, 共1 ⫺ D1兲共1 ⫺ J1兲, 共1 ⫺ D1兲J1兴.
⫻ 共1 ⫺ R2兲共1 ⫺ D2兲R2共1 ⫺ J2兲 ⫹ 共1 ⫺ D2兲 R2共1 ⫺ J2兲 其
2
U共n ⫹ 1兲
0
0
0
共1 ⫺ D2 兲共1 ⫺ R2 兲
2
⫹ 共1 ⫺ D1兲R1共1 ⫺ J1兲共1 ⫺ J2兲2 共1 ⫺ D2兲2 ; (A9)
Maximum likelihood estimates of the six parameters in Table 1
are then obtained by maximizing the following likelihood function:
L(n)
M' ⫽ P 共n兲
E
PC 共n兲
(Appendices continue)
L共n ⫹ 1兲
1
D2
0
PE 共n ⫹ 1兲
0
共1 ⫺ D2 兲共1 ⫺ J2 兲
共1 ⫺ J2 兲
PC 共n ⫹ 1兲
0
共1 ⫺ D2 兲J2 .
J2
(A12)
EMOTIONAL RECALL
Probabilities for each error-success pattern are then obtained as in
the two-process model, which in turn can be used to maximize a
simplified version of A11 that contains only the parameters in
A12. The revised fit statistic is then,
G ⫽ ⫺2ln关L4 /L7兴,
2
(A13)
which is asymptotically distributed as ␹2(3) because the model has
four parameters. In addition, a single recollective process model
can be obtained from the two-process model by eliminating the P
state. This generates the following initial probability vector W"
and a transition matrix M":
W" ⫽ 关L共1兲, U共1兲兴 ⫽ 关D1, 共1 ⫺ D1兲兴.
677
M" ⫽ L(n)
U(n)
L共n ⫹ 1兲 U共n ⫹ 1兲
1
0
共1 ⫺ D2 兲
D2
(A14)
The likelihood of any set of data over which A11 can be defined
can also be estimated for this one-process model maximizing a
simplified version of A11 that contains only the parameters in
A14. The revised fit statistic is then,
G2 ⫽ ⫺2ln关L2 /L7],
(A15)
which is asymptotically distributed as ␹2(5) because the model has
two parameters. In particular, note that the two one-stage models are
hierarchically nested models. The implication is that if the null hypothesis of fit is rejected for A13, then it is also rejected for A15.
Appendix B
Original Word Lists Used in Experiments 1 and 2
(and their English translations in parentheses)
Experiment 1
Fillers: Chaleira (kettle), martelo (hammer), metal (metal), and
quadrado (square); Negative targets: Abandonado (abandoned),
egoı́sta (selfish), horrı́vel (horrible), idiota (idiot), infecção (infection), lama (mud), lixo (garbage), luto (mourning), podre (rotten),
and trevas (darkness); Neutral targets: Controle (control), fogo
(fire), interesse (interest), juventude (youth), processo (process),
reunião (meeting), sociedade (society), tempo (time), tobogã (toboggan), and falso (false); Positive targets: Beijo (kiss), conhecimento (knowledge), dinheiro (money), encontro (date), graduado
(graduate), noiva (bride), pai (father), sexo (sex), sucesso (success), and vida (life).
Experiment 2
Negative-low targets: Cicatriz (scar), débil (feeble), desajeitado
(clumsy), desertor (deserter), detestar (detest), pecado (sin), porão
(basement), preguiçoso (lazy), and ralé (rabble); Negative-high
targets: Faminto (starving), fuga (flight), hospital (hospital), incomodar (annoy), infante (infant), perseguir (chase), pressão (pressure), suspeito (suspicious), and tubarão (shark); Positive-low targets: Adorável (adorable), favor (favor), gramado (lawn), inspirar
(inspire), lago (lake), papel (paper), sábio (wise), satisfeito (satisfied), and terra (earth); Positive-high targets: Aventura (adventure), curioso (curious), dinheiro (money), encontro (date), graduado (graduate), liberdade (freedom), noiva (bride), paixão
(passion), and vida (life).
Received January 2, 2012
Revision received March 12, 2012
Accepted March 19, 2012 䡲