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Cognitive ProcessingInternational Quarterly of CognitiveScience ISSN 1612-4782Volume 15Number 4 Cogn Process (2014) 15:515-522DOI 10.1007/s10339-014-0613-5

Influence of emotional valence and arousalon the spread of activation in memory

Sandra Jhean-Larose, Nicolas Leveau &Guy Denhière

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RESEARCH REPORT

Influence of emotional valence and arousal on the spreadof activation in memory

Sandra Jhean-Larose • Nicolas Leveau •

Guy Denhiere

Received: 7 August 2013 / Accepted: 20 March 2014 / Published online: 9 April 2014

� Marta Olivetti Belardinelli and Springer-Verlag Berlin Heidelberg 2014

Abstract Controversy still persists on whether emotional

valence and arousal influence cognitive activities. Our

study sought to compare how these two factors foster the

spread of activation within the semantic network. In a

lexical decision task, prime words were varied depending

on the valence (pleasant or unpleasant) or on the level of

emotional arousal (high or low). Target words were care-

fully selected to avoid semantic priming effects, as well as

to avoid arousing specific emotions (neutral). Three SOA

durations (220, 420 and 720 ms) were applied across three

independent groups. Results indicate that at 220 ms, the

effect of arousal is significantly higher than the effect of

valence in facilitating spreading activation while at

420 ms, the effect of valence is significantly higher than

the effect of arousal in facilitating spreading activation.

These findings suggest that affect is a sequential process

involving the successive intervention of arousal and

valence.

Keywords Emotion � Cognition � Lexical decision task �Semantic memory � Associative network

Introduction

Much of the research on the psychology of emotions has

been concerned with the effect of valence, that is, on how

pleasant or unpleasant emotional experiences impact

behavior (Bodenhausen et al. 1994; Abele et al. 1998;

Krauth-Gruber and Ric 2000; Bestgen 2002; Corson

2002a). Recent models suggest that emotions are episodes

of synchronized organismic changes (Scherer 1984, 2005;

Niedenthal et al. 2009) and cannot be strictly characterized

as a hedonic value. There have been numerous attempts at

modeling, which have led to the introduction of other

dimensions in the characterization of emotions such as

physiological activation or ‘‘arousal’’ (Russell 1980; Lar-

sen and Diener 1992; Russell and Feldman Barrett 1999;

Russell 2003), action tendencies (Frijda 1986), or control

and novelty of the stimulus (Fontaine et al. 2007). In their

study, Fontaine et al. (2007) conclude that valence and

arousal can account for 46.7 % of the observed variance

between two distinct emotions. However, these models

focus primarily on steady-state emotions rather than on

dynamic stimuli. For Scherer (1984), affective states are a

response to endogenous and exogenous stimuli that indi-

viduals analyze sequentially and systematically. The ear-

liest mode corresponds to the assessment of the novelty of

the stimulation in relation to previous experiences, and

then comes the assessment of the hedonic orientation of the

stimulation.

Affective priming (Fazio et al. 1986; Bower 1991;

Klauer 1997), an experimental paradigm inspired by

semantic priming (Neely 1977), makes it possible to

manipulate the automatic or strategic character of the

cognitive processes implemented in a requested task. After

presenting an emotionally connoted stimulus (for example,

the word ‘‘aggressive’’), participants are requested to

S. Jhean-Larose (&)

Laboratoire EDA, Education Discours Apprentissage,

Universite d’Orleans, Universite Paris Descartes-45,

Rue des Saints-Peres, 75006, Paris, France

e-mail: sandra.jhean-larose@parisdescartes.fr

N. Leveau � G. Denhiere

Equipe CHArt Cognition Humaine et Artificielle, 41 rue Gay

Lussac, 75005 Paris, France

e-mail: nicolas.leveau@apsyn.fr

G. Denhiere

e-mail: guy.denhiere@ephe.sorbonne.fr

123

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DOI 10.1007/s10339-014-0613-5

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evaluate an emotionally connoted target (for example, the

word ‘‘rose’’). The evaluation of the target could include,

for example, estimating its emotional valence or could be

on whether it belongs to the French language or not. The

evaluation task therefore requires one to be knowledgeable

on the semantic properties of the word presented. The time

interval between the presentation of the target and the

presentation of the source determines the nature of the

cognitive process in play. The main advantage of priming

techniques in investigating emotional processes lies in the

fact that they overcome the main criticisms against emo-

tional evaluation techniques by making it possible to ana-

lyze the automatic and spontaneous character of

participants’ responses, that is, by considering that affec-

tive judgment precedes the cognitive strategic judgment of

a stimulus (Zajonc 1980, 1984).

Olofsson et al. (2008) have summarized ERP studies

within the last 40 years. They highlighted that in these

studies, valence exerts influence predominantly between 100

and 300 ms, whereas arousal exerts influence from 200 ms

and later. However, if simultaneous effect of valence and

arousal controlled stimulus characteristics have been stud-

ied, it is for 400–800 ms latency range only, and it revealed

significant effect of the sole arousal characteristic. Hinojosa

et al. (2009) have used high- and low-arousing congruent,

and high- and low-arousing incongruent positive pairs for a

priming ERP experiment (SOA = 300 ms). Participants had

to press a button as quickly and accurately as possible, to tell

if the target name was arousing or relaxing. No effect of

arousal was observed. However, in the difference of typical

lexical decision task, instructions here involve strategic long

latency process and might be not congruent to arousal

assessment dynamics. Rossell and Nobre (2004) conducted

an affective priming experiment using lexical decision task

with neutral, happy, fearful and sad pairs. 200, 700 and

950 ms SOA were used. For short or medium SOA

(200–700 ms), the authors observed significant difference

between sad and fear (low- vs. high-arousal) when prime

and target pairs were related; no significant difference was

observed for unrelated prime-target pairs. For longer SOA

(950 ms), the opposite pattern appeared: significant differ-

ence between sad and fear was only observed for unrelated

pairs. The variation of the influence sad and fear stimulus

within time could therefore be explained either by the fact

that they refer to different emotion categories (discrete

emotions), or by the fact that they refer to different arousal

degrees (dimensional emotions). However, for longer SOA

(950 ms), the authors did not observe significant result for

semantically unrelated pairs, suggesting the preponderant

role of semantic vs emotional prime characteristics.

From the cognitive processes point of view of emotions,

Forgas (1995) proposes the AIM (‘‘Affect Infusion

Model’’) model. Affect infusion can be defined as a process

whereby affectively loaded information exerts an influence

on the judgmental process, altering deliberations and out-

comes. This model assumes that the nature and extent of

the influence of affect on judgment is largely dependent on

the type of process chosen by a judge. It distinguishes

between judgments using processes sensitive to affective

states from those using processes insensitive to the affec-

tive state. It identifies four types of processes: the first two

are sensitive to affect infusion: direct access which

involves reproducing a stored reaction and motivated pro-

cessing which implements predetermined patterns of

information search and makes little use of innovative and

constructive processes. The last two processes, heuristic

and substantive, are insensitive to affect infusion and

require a high degree of creativity. Consequently, affect

infusion focuses on automated processes rather than on

strategic, analytical or over-learned processes.

Experimental research mainly based on mood induction

has investigated the influence of affect on accessing

information in memory (Bower et al. 1978; Bower 1981;

Corson’s (2002b) observation that positive moods promote

access to general knowledge can be explained by reduced

spreading time between one conceptual node and another,

that is, by the increase in permeability of the associative

network. Hanze and Hesse (1993) highlighted this increase

in semantic network permeability using a lexical decision

task (SOA of 200 ms) with participants induced into

positive or neutral moods and with semantically associated

pairs. Results indicated a significant decrease in response

time for strongly associated pairs when participants were

induced into positive moods, compared with when partic-

ipants were induced into neutral moods. No significant

differences were observed among participants in positive

or neutral moods for weakly associated pairs. However,

Isen and Daubman (1984) suggest that the increase in

associative network permeability resulting from a positive

mood leads to a richer and more complex cognitive con-

text. The large quantity of conceptual nodes activated at

any given moment decreases the available cognitive

resources. According to these authors, this decrease offers a

cognitive explanation of the preferential use of heuristic

rather than strategic strategies when individuals are in a

positive mood. It is for this reason that this rich cognitive

context promotes the implementation of more flexible and

creative processes that seek to reduce the arising cognitive

load. Consequently, positive moods intervene across two

successive periods: First they lead to a decrease in cogni-

tive resources, and then to the compensatory implementa-

tion of categorization processes of the concepts activated.

Moreover, if the associative network is defined by a

semantic pre-activation preceding a positive mood, the

cognitive context is enriched, to a lesser extent, by the

increase in the spreading of semantic memory than if this

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activation had not been implemented. In this case, there

will be no reduction in cognitive resources and a positive

mood will shorten the time taken to access information in

memory. On the contrary, without pre-activation, positive

moods will increase the permeability of the associative

network and therefore lead to a richer cognitive context and

to a decrease in cognitive resources. Subsequently, it will

take longer to access information in memory.

Given that induced states (generally joy and sadness) in

many of the studies comparing the influence of pleasant

and unpleasant affective states differ in both valence and

arousal, Corson (2006) used a lexical decision task to

study how the modification of arousal influences cognitive

processes independently from valence orientation. Joy

(Pleasant/High Arousal), anger (Unpleasant/High Arou-

sal), sadness (Unpleasant/Low Arousal) and relaxation

(Pleasant/Low Arousal) induced moods were considered.

Participants were asked to judge primes and targets; the

next word was presented 100 ms after the previous word

had been judged (McNamara and Altarriba 1988). Corson

did not conclude in terms of discrete but in terms of

dimensional emotion: He came to the conclusion that

facilitated spreading within the highly associated seman-

tic network occurred for high-arousal moods, whereas

facilitated spreading was not observed in low-arousal

moods.

Nevertheless, the dynamic aspect of activation and

emotional valence on observed behavior has not been

addressed. Below 300 ms, semantic priming is attributed to

automatic processes (Posner and Snyder 1975; Neely 1977;

Ratcliff and McKoon 1981). While affective priming

effects are observed below SOA 300 ms with lexical

primes, they become more moderate as from 500 ms (for a

review see Klauer 1997) and disappear as from 1000 ms

(Hermans et al. 1994).

None of the researches mentioned above considered the

effect of the emotional characteristics of representations

in semantic memory. The main objective of our study is

to highlight the temporal dynamics of the components of

emotional valence and arousal on affect, as well as to

determine when they are activated in semantic memory

and for how long. Our main assumptions concern the

effect of (1) Arousal, (2) Valence and (3) their respective

interaction with SOA relative to response times in a

lexical decision task. Our study sought to analyze how

valence and emotional arousal of a lexical stimulus

influence spreading activation in memory. To prevent

from any semantic priming effect, we have chosen unre-

lated prime-target pairs. To prevent for affective priming

effect, we have chosen neutral target (medium valence

and arousal). The SOA durations were fixed at 220, 420

and 720 ms, ranging from automatic to strategic

processes.

As regards emotional valence, Hanze and Hesse (1993)

argue that positive valence leads to faster spreading of

associative memory due to an increase in permeability of

the associative network. Isen and Daubman (1984) high-

light the reduction in available cognitive resource when

participants are in positive mood, resource that should

preferably be allocated to the highly associated semantic

sub-network. For unrelated positive emotional valence of

prime will thus be accompanied by longer response times.

As valence comes into play secondarily in the emotional

process (Scherer 1984), the modulation of the cognitive

function by affect will therefore be, secondly, preferen-

tially due to emotional valence. Positive emotional valence

of the prime will be accompanied by an increase in lexical

decision reaction time that will be longer at an SOA of

420 ms than at an SOA of 220 ms. However, as affective

priming effects are more moderate as from SOA 500 ms

(Klauer 1997), the increase in lexical decision time will be

inferior at an SOA of 720 ms than at an SOA of 420 ms.

As regards arousal, Corson’s (2006) findings reveal that

high arousal leads to faster spreading of associative

memory activation due to an increase in associative net-

work permeability. For similar reasons as for emotional

valence, an increase in arousal of the prime will therefore

be accompanied by longer response times. However,

according to Scherer (1984), affective states are a result of

a sequential process in which the first two steps are

assessing stimulus novelty, and then evaluating the

unpleasantness or the pleasantness of the situation. The

modulation of the cognitive function by the affective state

will be therefore, initially, preferentially due to arousal.

High arousal of the prime will be accompanied by a more

important increase in the lexical decision reaction time for

a SOA of 220 ms than for longer SOA.

Method

Participants

Eighty-four native French adults voluntarily participated in

the study.

The datasets of two participants were excluded as

average of their response times was more than three stan-

dard deviations from the mean (305 ms for the

SOA = 220 ms group, 284 ms for the SOA = 420 ms

group, 115 ms for the SOA = 720 ms group). Participants

with an error rate superior to 25 % were also excluded.

There were 27 participants per group.

The average education level of the participants was

3.4 years after the high school diploma for the 220 ms

group, 3.7 years for the 420 ms group and 3.4 years for the

720 ms group. There were 41 % women and 59 % men for

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the 220 ms group and the 420 ms group, and 70 % women

and 30 % men for the 720 ms group).

Material

The experimental material was established based on Le-

leu’s semantic atlas of emotional concepts (Leleu 1987;

Hogenraad and Bestgen 1989) and on the LEXIQUE

database (New et al. 2001); Leleu’s (1987) atlas includes

3,000 words; the words arousal and emotional valence have

been evaluated by 39 judges on average on a scale ranging

from 1 to 7. For each word, an integer value of valence and

arousal ranging from 10 to 70 is proposed. As this is an

unpublished norm, we verified its relevance by comparing

the data of similar words with Valemo’s norm (Syssau and

Font 2005). Results indicated a correlation of valence of

.93 (p \ .01) on compared pairs.

Common names and nouns were retained from the Leleu

(1987) Four lists of 20 prime words and one list of 80 target

words were established in order to represent the four axes

of Russell’s (1980) circumplex model: Pleasant/High

arousal, Pleasant/Low arousal, Unpleasant/High arousal,

Unpleasant/Low arousal. Neutral target words were of

average arousal and neutral valence. Regarding the Leleu’s

norm, word valence value range from 51 to 69 for pleasant

words, from 13 to 29 for unpleasant words, and from 34 to

48 for neutral target words. Word arousal value range from

50 to 65 for high-arousal words, from 13 to 32 for low-

arousal words, and from 36 to 49 for neutral target words.

Valence value did not significantly differ between low

(m = 38.17, SD = 16.65) and high (m = 41.87,

SD = 20.42) arousal word categories (t = .88; p \ .38).

Arousal value did not significantly differ between pleasant

(m = 41.78, SD = 17.73) and unpleasant (m = 39.20,

SD = 16.50) word categories (t = .67; p \ .51). Word

characteristics were controlled in word length, word fre-

quency, number of syllables and number of orthographic

neighbors using LEXIQUE database (New et al. 2001) (see

Table 1).

For every «neutral» target word, an emotional prime

was associated from one of the four lists of twenty words.

Each prime and each target word were used only once. The

semantic distance between the prime and the target was

controlled using Latent Semantic Analysis (Landauer and

Dumais 1997) applied to the ‘‘Francais-Total’’ reference

corpus (Denhiere et al. 2007). In LSA, the association

strength between words is calculated by the cosine of the

two word vectors. A cosine of 1.0 stands for very similar

words, a cosine of .0 stands for un-similar words. We have

constructed prime-target pairs so that the cosine is inferior

to .20 (Tables 2, 3).

In addition, 80 pairs of fillers word/pseudo-word (the

pseudo word represents an anagram of a neutral word), and

80 pairs of fillers word/pseudo-word (any pseudo word)

were established. Finally, 40 training pairs comprising 20

word–word pairs and 20 word/pseudo-word pairs were

established.

Procedure

The experiment was administered individually. Pairs were

presented using the Frida software (Poitrenaud 1991). To

respond with a YES, participants pressed on the L key of

the keyboard if they were right-handed (S if they were left

handed) and to respond with a NO, they pressed on the S

key if they were right-handed (L if they were left handed).

They pressed the SPACEBAR to go to the following test.

After a learning phase, the 240 pairs were presented to the

participants; there was a half-way break.

Data analyses

Primes were varied on the emotional valence axis

depending on two modalities (Pleasant/Unpleasant: intra-

group factor V), and on the arousal axis depending on two

modalities (High/Low: intragroup factor A). Participants

were divided into three groups depending on SOA dura-

tion, and one-third was subjected to an SOA of 220 ms,

Table 1 Word length and frequency for prime and target words of each group

Group Prime Target

Word

frequency

Word

length

Syllables

number

Orth.

neighb.

Word

frequency

Word

length

Syllables

number

Orth.

neighb.

Pleasant words 47.0 (31.0) 7.13 (2.29) 2.16 (1.03) 2.29 (3.40) 35.9 (40.5) 7.13 (1.96) 2.03 (.91) 3.26 (3.76)

Unpleasant

words

45.3 (29.6) 6.85 (1.69) 1.95 (.75) 2.38 (3.40) 42.5 (32.2) 6.43 (1.78) 1.90 (.87) 2.35 (2.48)

Low-arousal

words

44.8 (33.7) 6.72 (1.97) 1.82 (.85) 2.41 (3.54) 33.2 (40.1) 7.18 (1.92) 2.13 (.83) 2.03 (2.69)

High-arousal

words

47.0 (31.0) 7.13 (2.29) 2.28 (.89) 2.26 (3.26) 35.9 (40.5) 7.13 (1.96) 1.79 (.92) 3.56 (3.47)

Mean and standard deviation in parenthesis

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another third to an SOA of 420 ms and the last third to an

SOA of 720 ms (Three modality SOA intergroup factor).

A double analysis was carried out on the dependent

variable response time using the following formula:

S27 \ SOA3 [ A2*V2 and P20 \ A2*V2 [ SOA3. S, P,

SOA, A, and V, refer, respectively, to subject factors

(Participant: random factor), Experimental pair (random

factor), SOA (220, 420 or 720 ms), Arousal (high or low)

and emotional Valence (pleasant or unpleasant). Analyses

were conducted using the LE PAC software distributed by

the Modulad1 journal (Lecoutre, 2001). ‘‘F1’’ refers to

analyses that consider the Participant factor as a source of

random variation, and ‘‘F2’’ to those that consider the

Experimental pairs factor as a source of random variation.

The null hypothesis was rejected at the 5 % level of sig-

nificance. The averages presented below were calculated

from each participant’s analysis.

As ANOVA did not enable us to estimate a significant

main effect, we carried out a Bayes-fiducial analysis on

each of the experimental designs above. For each analysis,

the significance of the effect was evaluated based on the

sample. On the basis of this evaluation, we proposed a

confidence level of 95 % (threshold of 5 % error) for

possible values relative to the observed difference. There

are two possible interpretations of these results: The

frequentist interpretation is as follows: «if we repeat an

experiment a number of times within the same conditions,

95 % of the intervals will contain the true value». The

Bayesian interpretation is as follows: «there is a 95 %

chance that the true value is superior to the calculated value

for observed data (Lecoutre and Poitevineau 2000). As

with ANOVA, d1 refers to participant analysis while d2

refers to item analysis.

Results

Periods corresponding to lexical decision errors were

eliminated (4.26 % of relevant pairs as were response times

with a deviation of more that two standard deviations from

the mean (3.98 % of relevant pairs). As a result, 8.24 % of

relevant pairs were rejected. The average response times as

well as the average error rates relative to the lexical deci-

sion task are presented in the table.

In the participant analysis, the arousal main effect was

significant F1(1,78) = 4.06, p \ .05; F2(1,57) = 2.06,

1 The software can be downloaded from: http://www.univ-rouen.fr/

LMRS/Persopage/Lecoutre/Eris.html.

Table 2 Example of

experimental pairs with

emotional characteristics of the

prime and the semantic distance

between the prime and the target

calculated using LSA

(Translated from French)

Noun Emotional valence Emotional arousal LSA cosine

Pleasant/Low Novel 51 32 Attention .13

Clarity 54 28 Religion .10

Simplicity 53 28 Exterior .18

Unpleasant/Low Waiting 28 21 Fish .02

Deaths 16 13 Business .04

Fog 25 23 Dog .18

Pleasant/High Friendship 65 56 Atmosphere .15

Adventure 61 65 Speech .19

Kisses 64 58 Alcohol .00

Unpleasant/High Accident 13 51 Shop .11

Army 22 50 Scene .09

Battle 21 55 Text .18

Table 3 Average response time (in ms), standard deviation (in ms–in

brackets) and percentage error as a function of SOA, valence and

emotional arousal of the prime

SOA (ms) Arousal Valence

Pleasant Unpleasant

220 High 675.42

(35.50)

7.78 %

677.67

(21.26)

9.63 %

Low 651.45

(18.23)

8.33 %

654.69

(20.71)

7.41 %

420 High 663.87

(31.89)

6.67 %

652.70

(28.90)

8.15 %

Low 676.87

(35.17)

11.39 %

659.29

(32.66)

8.33 %

720 High 634.26

(24.32)

8.70 %

636.09

(30.61)

8.52 %

Low 634.83

(34.55)

9.26 %

629.67

(21.80)

8.33 %

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p \ .16.; d1 [ 0.99 ms; d2 [ -0.91 ms. Response time

was significantly higher when the arousal was high

(656 ms) than when it was low (650 ms). The interaction

between Arousal and SOA was significant

[F1(2,78) = 10.68, p \ .001; F2(2,57) = 10.13, p \ .001]

(see Fig. 1). Planned contrasts between the lowest SOA

group (SOA = 220 ms) and the two highest SOA groups

(SOA = 420 ms and 720 ms) indicated a significant dif-

ference between the response time to a high-arousal stim-

ulus and the response time to a low-arousal stimulus at an

SOA of 220 ms (d = 23 ms) compared with an SOA of

720 ms and 420 ms (d = 4 ms) (F1(1,79) = 10.59;

p \ .002; F2(1,58) = 5.90; p \ .02; d1 [ 15.22 ms;

d2 [ 13.06 ms).

There was no significant main effect of valence:

F1(1,78) = 3.78, p \ .06; F2(1,57) = 1.71, p \ .20.;

d1 [ 0.71 ms; d2 [ -1.23 ms. The interaction between

Valence and SOA was significant in the participant ana-

lysis F1(2,78) = 3.11, p \ .05; F2(2,57) = 2.31,p \ .11

(see Fig. 2). For SOA = 220 ms, the valence effect is not

significant F1(1,26) = .10, p \ .76; F2(1,76) = .25,

p \ .62; d1 \ 5.09 ms; d2 [ -6.44 ms. For SOA =

420 ms, the valence effect is significant F1(1,26) = 6.62,

p \ .02; F2(1,76) = 3.94, p \ .05; d1 [ 4.60 ms; d1 \-2.30 ms. For SOA = 720 ms, the valence effect is not

significant F1(1,26) = .32, p \ .58; F2(1,76) = .07,

p \ .80; d1 [ -4.60 ms; d2 \ 8.78 ms. Planned contrast

between the middle range SOA (420 ms) and the two other

groups (SOA = 220 ms and SOA = 720 ms) indicates a

significant difference between the response time to pleasant

stimulus and the response time unpleasant stimulus at an

SOA of 420 ms (d = 14 ms) compared to at an SOA of

220 ms and 720 ms (d = 1 ms): F1(1,79) = 5.95; p \ .02;

F2(1,58) = 4.35; p \ .05; d1 [ 4.16 ms; d2 [ 2.96 ms.

The Arousal*Valence*SOA analysis revealed no sig-

nificant interaction between these three factors

F1(2,78) = .03, p \ .98.; F2(2 152) = .18, p \ .84.;

d1 \ 4.4 ms; d2 \ 11.8 ms.

Discussion

This study sought to compare the influence of valence and

arousal on spreading activation in semantic memory. To

achieve this, we have chosen a lexical decision priming

task using primes whose valence and emotional arousal had

already been rated by judges ‘Leleu 1987). The semantic

association between the primes and the targets was con-

trolled using Latent Semantic Analysis (Landauer and

Dumais 1997) applied to the ‘‘Francais-Total’’ corpus

(Denhiere et al. 2007) to avoid semantic priming effects on

lexical decision reaction times. Similarly, word frequency,

the number of syllables and the number of orthographic

neighbors were homogenized using the LEXIQUE data-

base (New et al. 2001). The effects observed can therefore

be legitimately attributed to the emotional properties of the

primes or to SOA variation.

First, our results are consistent with those found by Cor-

son (2006). High arousal leads to faster spread of activation

of associative memory as there is increased permeability of

the associative network. Indeed, an increase in the emotional

arousal of the prime is accompanied by a longer response

time at an SOA of 220 ms than at an SOA of 420 ms; this

effect tends to disappear at an SOA of 720 ms.

We observed two important interactions: between SOA

and arousal, and between SOA and emotional valence.

When SOA was at 220 ms, the response time was signifi-

cantly longer for high-arousal primes than for low-arousal

primes when compared to the groups with longer SOA

durations (420 and 720 ms) (a difference of 19 ms). Sim-

ilarly, when SOA was at 420 ms, the response time was

significantly longer for primes with pleasant valence than

for primes with unpleasant valence when compared to

groups with shorter (220 ms) and longer (720 ms) SOA

durations (a difference of 13 ms).

Our results enable us to highlight the role that emotional

valence and arousal play in facilitating the spread of

semantic memory. These two characteristics have similar

consequences on cognitive processes but take place suc-

cessively. Arousal is the first to occur within the first

10 ms. It occurs automatically, in other words, it occurs

subconsciously and no control can moderate its effects.

Fig. 1 Response time (in ms) depending on Arousal and SOA

Fig. 2 Response time (in ms) depending on emotional Valence and

SOA

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Emotional valence occurs in a second phase and is more

strategic and can therefore be moderated by the attentional

system. If we consider the affect infusion model (Forgas,

1995), the cognitive processes implemented on priming

tasks at an SOA inferior to 420 ms are therefore more

sensitive to arousal. The cognitive processes that occur

over longer periods are sensitive to emotional valence.

Moreover, consistent with Klauer’s (1997) observations,

we observed that the influence of the priming effect on

arousal decreased when the SOA was higher than 600 ms.

The sequential approach to affect on the basis of valence

and arousal was suggested by Scherer (1984); however, the

effect of these factors on affect infusion has been contro-

versial until recently (Hanze and Hesse, 1993; Corson 2006).

While Hanze and Hesse (1993) consider that the influ-

ence of valence can only be observed in semantically

related pairs, Corson (2006) argues that the effect of

arousal can only be observed among semantically unrelated

pairs. The semantic pre-activation that occurs when a

prime word is presented therefore facilitates the infusion of

emotional valence on the permeability of the associative

network, while hindering the infusion of arousal on the

permeability of the associative network. Affect therefore

intervenes differently depending on temporal dynamics on

the one hand and on the other hand, depending on the level

of activation of the semantic space in which the concerned

affective state will intervene. Differential analysis on how

pre-activating semantic spaces impacts the dynamics of

affect infusion requires further investigation.

Moreover, it seems necessary to carry out further

research on affect infusion relative to (1) new emotional

factors (action orientation, novelty of the stimulus), (2)

broadening the study to cover the deliberated processes

intervening after the first second, and (3) superior linguistic

units (phrase, text). It could also be interesting to analyze

the role of the emotional dynamics present in the text

during the ‘‘reading-comprehension’’ activity in regards to

the emergence of affective manifestations in the reader and

to his subjective understanding of the text in general.

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