account. See Figure 2 for an overall picture for 4 stimuli, also with the episode states. Table 2 shows a ..... Building blocks to create such internal simulations are memory elements in the form of sensory .... IOS Press, 2011, pp. 389-399. Treur ...
In this chapter a Network-Oriented Modelling approach based on temporal-causal network models is presented that addresses the type of internal simulation that is assumed to take place in dreaming. For the different episodes, the internal simulation incorporates interrelated processes of activation of sensory representation states (from memory) providing mental images, and activation of associated feelings. Moreover, the model uses a mechanism for emotion regulation to suppress the feeling levels and the sensory representation states. The structure of the chapter is as follows. In Section 4.2 the basic concepts used are briefly introduced. In Section 4.3 the temporal-causal network model is described in more detail. Section 4.4 discusses simulation results providing dream scenarios. In Section 4.5 the relation of the model with neurological theories and findings is addressed. Finally, Section 4.6 is a discussion.
4.2 Memory Elements, Emotions and Internal Simulation in Dreaming In this section it is discussed how in dreaming memory elements with their associated emotions are used as building blocks for an internal simulation of real life. Using memory elements and their emotional associations Within the literature the role of memory elements providing content for dreams is well-recognized; e.g.: ‘… dreaming tends to express memory elements as though original memories had been reduced to more basic units (..). Often, these appear as isolated features, such as an attribute of a familiar place or character (e.g., “there was a stranger who had my mother’s style of hair”).’ (Levin and Nielsen, 2007, p. 499)
The role of emotional aspects in activating such memory elements is emphasized; e.g.: ‘…elements may be activated as a function of emotional concerns (..) but with the possible introduction of some pseudorandom and incompatible associations.’ (Levin and Nielsen, 2007, p. 500)
In particular, it is recognized that the choice for memory elements with some emotional association and (re)combining them into a dream facilitates fear generation: ‘During dreaming, conjunctive representations are rendered into virtual simulations or “here-andnow” illusions (Nielsen and Stenstrom, 2005) to maximize their impact upon the amygdala, which tends to respond to perceptual, rather than imaginal, stimuli’ (Levin and Nielsen, 2007, p. 500)
The emotional associations of the sensory memory elements may make that a person has to cope with high levels of emotions (e.g., fear) felt in the dream. Emotion regulation mechanisms are used to control emotions that are felt as too strong; e.g., (Goldin, McRae, Ramel, Gross, 2008; Gross, 1998; Gross, 2007). Such mechanisms cover antecedent-focused regulation (e.g., selection and modification of the situation, attentional deployment, and reappraisal) and response-focused regulation (suppression of a response). Dreaming as internal simulation Dreams can be considered as flows of activated sequences of images based on (re)combined memory elements:
‘Recombinations of memory elements give dreams at once their alien and their familiar quality. (…) the new image sequences consist, for the most part, of lifelike simulations of first-person reality. Memory elements are recombined (..) to produce coherent, continuous simulations of waking life experience.’ (Levin and Nielsen, 2007, p. 500)
Such flows can be related to the notion of internal simulation put forward, among others, by (Hesslow, 1994, 2002, 2012; Damasio, 1994, 1999; Goldman, 2006; Barsalou, 2009; Marques and Holland, 2009; Pezzulo, Candidi, Dindo, and Barca, 2013). The idea of internal simulation is that sensory representation states are activated (e.g., mental images), which in response trigger associated preparation states for actions or bodily changes, which, by prediction links, in turn activate other sensory representation states. sensory representation states preparation states sensory representation states
The latter states represent the effects of the prepared actions or bodily changes, without actually having executed them. Being inherently cyclic, the simulation process can go on indefinitely. Internal simulation has been used, for example, to describe (imagined) processes in the external world, e.g., prediction of effects of own actions (Becker and Fuchs, 1985), or processes in another person’s mind, e.g., emotion recognition or mindreading (Goldman, 2006) or processes in a person’s own body (Damasio, 1994). Although usually internal simulation as briefly described above concerns mental processes for awake persons, it is easy to imagine that it may be applicable as well to describe dreaming. Feeling emotions by internal simulation of body states The idea of internal simulation has been exploited in particular by applying it to bodily changes expressing emotions, using the notion of as-if body loop (Damasio, 1994). For more details on the role of body loops and as-if body loops in emotions, see Chapter 3. On purposes of dreaming as internal simulation One theory explicitly referring to a purpose of dreaming as internal simulation is the threat simulation theory of the evolutionary function of dreaming (e.g. Revonsuo, 2000; Valli, Revonsuo, Palkas, Ismail, Ali, Punamaki, 2005; Valli and Revonsuo, 2009). This theory assumes that dreaming is an evolutionary adaptation to be able to rehearse coping with threatening situations in a safe manner. Others consider the function of dreaming in strengthening the emotion regulation capabilities for fear; e.g., (Levin and Nielsen, 2007, 2009; Franzen, Buysse, Dahl, Thompson, Siegle, 2009; Gujar, McDonald, Nishida, Walker, 2011; Walker, 2009; Walker and van der Helm, 2009; Yoo, Gujar, Hu, Jolesz, Walker, 2007; van der Helm, Yao, Dutt, Rao, Saletin, & Walker, 2011; Rosales-Lagarde, Armony, del Río-Portilla, Trejo-Martínez, Conde, Corsi-Cabrera, 2012 Mauss, Troy, LeBourgeois, 2013; Goldstein and Walker, 2014; Pace-Schott, Germain, Milad, 2015). For this perspective, the purpose of dreaming is to improve the coping with the own fear emotions in real life. For both purposes adequate exercising material is needed for the dreams: fearful situations have to be imagined, built on memory elements suitable for fear arousal. The temporal-causal network model presented in Section 4.3 provides
this, but it abstracts from the purpose; it does not commit to any of the purposes mentioned.
4.3 A Temporal-Causal Network Model Generating Dream Episodes The temporal-causal network model presented here models the mechanisms discussed in Section 4.2. It is meant to address scenarios of the following type: Fearful stimulus A (maybe traumatic) stimulus s1 is given for which previously a high extent of fear has been developed, and for which from time to time a sensory representation state is triggered by memory (for the model this is considered an external trigger) Emotional response The activation of the sensory representation of s1 leads to preparation for a bodily fear response b, and by an as-if body loop to an enhanced feeling level based on b Emotion regulation By emotion regulation the sensory representation of s1 and the feeling state are suppressed: both the experience of fear, and the activation level of the sensory representation of s1 become low; also no episode state for s1 occurs, as this is blocked due to the traumatic event Visualisation of the fear Other fear-associated stimuli sk for k ≥ 2 are available for which the person has less strong previous experiences; the sensory representation states for these sk are activated by links from the preparation state for b, depending on the strength of these links; this can be viewed as visualization of the fear Emotional response to visualisation When the sensory representation state of a stimulus sk is activated, this leads to an enhanced activation level of the preparation state for b Experiencing more fear Due to the higher activation level of preparation for b, via the as-if body loop also the feeling level for b becomes higher: the person experiences more fear Stronger emotion regulation By the control states for emotion regulation for an active sensory representation for sk both the fear feeling level and the sensory activation level of sk are suppressed
Competition for dream episodes The active sensory representations for sk lead to corresponding dream episode states, which are in competion with each other by mutual inhibition to get dominance as a dream episode In Figure 1 the basic model for a given sensory representation state srs sk is shown. It shows emotion generation via emotional response preparation state psb and feeling state fsb (as-if body loop) and emotion regulation through control state cs sk,b suppressing the feeling state fsb and the given sensory representation state srssk; a summary of the states used is shown in Table 1. The inhibiting links are indicated by dotted arrows (in red). The two links between srssk and psb indicate the association between stimulus sk and emotional response b; the link from psb to srssk indicates the (predictive link) which is a basis for the internal simulation: e.g., the emotion triggers a certain (expected) mental image. The links between psb and fsb indicate an as-if body loop.
suppressing srs
cssk,b
monitoring feeling
monitoring srs
srssk
responding
visualising
suppressing feeling
psb
feeling
fsb
amplifying
Figure 1. Graphical conceptual representation of a temporal-causal network model for generation and regulation of felt emotions
state psb fsb srssk cssk, b dessk mtsk
Table 1. Overview of the states used explanation Preparation state for bodily response b Feeling state for b Sensory representation state for sk Control state for regulation of sensory representation of sk and feeling b Dream episode state for sk Memory trigger for sk
As shown in Table 1 a dream episode state for sk is indicated by dessk. moreover the trigger for srssk from memory is indicated by mtsk; this will be applied for s1. Note that in Figure 1 a sensory representation state for only one stimulus sk is depicted. In the specification of the model below an arbitrary number n of such states are taken into account. See Figure 2 for an overall picture for 4 stimuli, also with the episode states. Table 2 shows a conceptual matrix representation of the model depicted in conceptual
graphical representation in Figures 1 and 2. In Table 3 the connection weights are described. The numerical representation of the model by a set of local dynamic properties involving differential equations is presented below and is summarized in Box 1. During processing, each state property has a strength represented by a real number between 0 and 1. Parameter is a speed factor, indicating the speed by which an activation level is updated upon received input from other states. Below, the dynamics are described in more detail subsequently for each state by a dynamic (temporally) Local Property (LP) specifying how the activation value for this state is updated (after a time step of t) based on the activation values of the states connected to it (the incoming arrows in Figures 1 and 2). fsb css1,b
css4,b
css2,b
psb
css3,b
srss1
srss4
srss2
srss3
dess1
dess4
dess2
dess3
Figure 2. Conceptual representation of the overall temporal-causal network model with four episode states and one feeling state (m = 1, n = 4) Table 2 Conceptual matrix representation for the temporal-causal network model depicted in Figs. 1 and 2 to from
mtsk
srssk
psb cssk,b dessk
dessk
responding_b
monitoring_sk
manifesting_ sk
fsb
visualising_ sk
feeling_b
suppressing_representation
suppressing_feeling mutual_suppressing amplifying_b
fsb cY(…)
cssk,b
memory_triggering_ sk
mtsk srssk
Y
psb
monitoring_b
-
srssk
psb
cssk,b
dessk
fsb
-
csrssk(V)
cpsb(V1, V2)
ccssk,b(V)
cdessk(V1, V2)
cfsb(V)
from states srss1, …, srssn fsb psb css1,b, …, cssn,b
Table 3. Overview of connections and weight names to weight names connection types state 1,1, ... 1,n responding psb amplifying 2
psb cssk,b mtsk srssk fsb srssk dess1, …, dessn cssk,b
3 4,1, …4,n 5,k 6,k 0,k 7,k 8,k 9,k 10,1,k, …, 10,n,k 11,k
fsb
srssk cssk,b
dessk
feeling suppressing feeling visualising suppressing srs memory triggering monitoring srs monitoring feeling episode manifesting mutual suppressing traumatic suppressing
The numerical representation of the temporal-causal network model is discussed in the form of local dynamic properties LP1, …. for each of the states. In these dynamic local properties logistic sum combination functions are used. In the simulation experiments for the combination function in LP1 to LP4 the following advanced logistic sum function is used: alogistic,(V1, …, Vk) = (
1 1+ 𝑒 −( 𝑉1+...+𝑉𝑘 − )
-
1 1+ 𝑒
) (1 + 𝑒− )
Here is a steepness and a threshold parameter. In LP5 the simple logistic sum function is used: slogistic,(V1, …, Vk) =
1 1+ 𝑒 −( 𝑉1+...+𝑉𝑘 − )
The first property LP1 describes how preparation for response b is affected by the sensory representations of stimuli sk (triggering the response), and by the feeling state for b: LP1 Preparation state for response b dpsb(t)/dt = [ alogistic(1,1srss1(t), …, 1,n srssn(t), 2fsb(t)) - psb(t) ] psb(t+t) = psb(t) + [ alogistic(1,1srss1(t), …, 1,n srssn(t), 2fsb(t)) - psb(t) ] t The feeling state for b is not only affected by a corresponding preparation state for b, but also by the (inhibiting) control states for sk and b. This is expressed in dynamic property LP2. Note that for this suppressing effect the connection weight 4,k from the control state for sk and b to feeling state for b is taken negative, for example 4,k = -1.
LP2 Feeling state for b d fsb(t)/dt = [alogistic(3 psb(t), 4,1 css1, b(t), .., 4,n cssn, b(t)) – fsb(t) ] fsb(t+t) = fsb(t) + [alogistic(3 psb(t), 4,1 css1, b(t), .., 4,n cssn, b(t)) – fsb(t) ] t The sensory representation state for sk is triggered by memory state mtsk and further affected by the preparation state for b, and by the suppressing control state for sk and b. For this suppressing effect the connection weight 6,k from the control state for sk and b is taken negative. This is expressed in dynamic property LP3. LP3 Sensory representation state for sk d srssk(t)/dt = [alogistic(5k psb(t), 6,k cssk,b(t), 6,k mtsk(t)) – srssk(t) ] srssk(t+t) = srssk(t) + [alogistic(5k psb(t), 6,k cssk,b(t), 6,k mtsk(t)) – srssk(t) ] t Note that property LP3 can be used to describe how the sensory representation of any traumatic sk is triggered from memory, as a starting point for a dream: in a scenario the memory trigger values are taken 1. For non-traumatic sk such triggering does not take place: the values are set to 0. Activation of a control state for a specific sensory representation for sk and b is based on the level of feeling b and the level of the sensory representation of sk: LP4 Control state for sk and b d cssk,b(t)/dt = [alogistic(7,k srssk(t), 8,k fsb(t)) – cssk,b(t)] cssk,b(t+t) = cssk,b(t) + [alogistic(7,k srssk(t), 8,k fsb(t)) – cssk,b(t)] t Due to the inherent parallellism in neural processes, at each point in time multiple sensory representation states can be active simultaneously. For cases of awake functioning the Global Workspace Theory (Baars, 1997, 2002) was developed to describe how a single flow of conscious experience can come out of such a large multiplicity of (unconscious) processes; see also (Shanahan, 2006) for an approach combining internal simulation and Global Workspace Theory, and (Dennett, 1991, 2005) for a comparable perspective; see also (Windt and Noreika, 2011). The basic idea is that based on the various unconscious processes a winner-takes-it-all competition takes place to determine which one will get dominance and be included in the single flow of consciousness (after which it is accessible to all processes). This idea was applied in the dreaming context to determine which sensory representation elements will be included as an episode state dessk in a dream episode during the flow of dreams. This competition process is decribed in LP5, using inhibiting connections from the episode states dessi with i ≠ k to dessk. For the suppressing effects the connection weights from the dessi with i ≠ k to dessk are taken negative. Note that for the sake of notational simplicity 10,k,k = 0 is used. For traumatic stimuli sk an additional and strong way of inhibition of the corresponding episode state takes place,
blocking the generation of an episode state for this stimulus. It is based on the control state for sk and b and is assumed to have a strong negative connection strength 11,k. For non-traumatic stimuli this connection is given strength 0; note that for the sake of simplicity this connection was not depicted in Fig. 2. LP5 Episode state for sk
d dessk(t)/dt = [slogistic(9k srssk(t), 11,k cssk,b(t), 10,1,k dess1(t), …, 10,n,k dessn(t)) – dessk(t)] dessk(t+t) = dessk(t) + [slogistic(9k srssk(t), 11,k cssk,b(t), 10,1,k dess1(t), …, 10,n,k dessn(t)) – dessk(t)] t
LP1 Generating and amplifying a preparation state for response b d psb(t)/dt = [ alogistic(11srss1(t), …, 1n srssn(t), 2fsb(t)) - psb(t) ] psb(t+t) = psb(t) + [ alogistic(11srss1(t), …, 1n srssn(t), 2fsb(t)) - psb(t) ] t LP2 Generating and regulating a feeling state for b d fsb(t)/dt = [alogistic(3 psb(t), 41 css1, b(t), .., 4n cssn, b(t)) – fsb(t) ] fsb(t+t) = fsb(t) + [alogistic(3 psb(t), 41 css1, b(t), .., 4n cssn, b(t)) – fsb(t) ] t LP3 Generating and regulating a sensory representation state for sk d srssk(t)/dt = [alogistic(5k psb(t), 6k cssk,b(t), 6k mtsk(t)) – srssk(t) ] srssk(t+t) = srssk(t) + [alogistic(5k psb(t), 6k cssk,b(t), 6k mtsk(t)) – srssk(t) ] t LP4 Generating a control state for sk and b d cssk,b(t)/dt = [alogistic(7k srssk(t), 8k fsb(t)) – cssk,b(t)] cssk,b(t+t) = cssk,b(t) + [alogistic(7k srssk(t), 8k fsb(t)) – cssk,b(t)] t LP5 Generating and suppressing an episode state for sk d dessk(t)/dt = [slogistic(9k srssk(t), 11,k cssk,b(t), 10,1k dess1(t), …, 10,nk dessn(t)) – dessk(t)] dessk(t+t) = dessk(t) + [slogistic(9k srssk(t), 11,k cssk,b(t), 10,1k dess1(t), …, 10,nk dessn(t)) – dessk(t)] t Box 1. The temporal-causal network model in numerical representation
4.4 Simulations of Example Dream Scenarios A variety of simulation experiments have been performed, using numerical software. In the simulation experiments discussed below the settings were as shown in Table 3 (set by hand). As shown in the left hand side of the table, all non-inhibiting connections to preparation, feeling and control states have strength 1, and all inhibiting connections to feeling and sensory representation states have strengths -0.2, resp. -0.5, with an exception for the sensory representation state for s1, which is inhibited by strength -1 (due to a previous traumatic event involving s1). Small differences in emotional association between the different sk are expressed by different strengths from preparation of emotional response to sensory representation states, varying from 0.5 to 0.45. The sensory representation states are connected to the corresponding episode
states with strength 1.2 and the latter states mutually inhibit each other by strength -0.6. The threshold and steepness values used are shown in the right hand side of Table 4. Relatively low steepness values were used, except for the episode states. The threshold values for preparation and feeling states were taken 0.5; in order to model differences in emotional associations between the sk, different threshold values were taken for their sensory representation and control states. The initial values of all states were set to 0, except for the initial value of srss1 which was set to 1 (a memory activation for a traumatic event). The speed factor was 0.5, and the step size ∆t was 0.1. It may be convenient to read the scenario with a certain interpretation in mind. For example, s1 may refer to a traumatic experience of seeing somebody who was dying (without having possibilities to save the person). Moreover, s2 may refer to a situation where a presentation is due in a few minutes time, and no laptop nor slides are available. Finally, s3 may refer to a situation where an enormous traffic jam stands in the way of reaching an important meeting in time. Table 4. Settings used for connection strength, threshold and steepness parameters: scenarios 1 and 2 from state
connection 1
fsb
1,k 2
psb
3
1
cssk,b
4,k
-0.2
psb
5,1 6,1
0.5
css1,b psb
5,2
0.5
css2,b
6,2
-0.5
psb
5,3
0.45
css3,b
6,3
-0.5
srssk fsb
7,k 8,k
1 1
srssk
9,k 10,j,k
1.2
srssk
essj
1
-1
-0.6
to state
threshold
steepness
psb
0.5
4
fsb
0.5
4
srss1
0.5
4
srss2
0.2
4
srss3
0.22
4
css1,b
0.8
8
css2,b
1.1
8
css3,b
1.4
8
essk
0
200
Scenario 1: no dream episode The first scenario discussed addresses the case where s2 and s3 do not play a role (by putting the connections from preparation to sensory representation 5,k = 0 for k≥2). In Figure 3 is is shown that the control state for s1 becomes active to reach level 0.2, and the activity of the sensory representation (indicated by rep) for s1 drops to a low level. The preparation level stays below 0.5 and the feeling level stays below 0.4. Due to these modest values no dream episode state
develops based on s1. The feeling level can be considered too low to seriously activate an internal simulation. 0.6
feel b
0.4
prep b rep s1 0.2
cs(s1, b)
0.0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Figure 3. Scenario 1: no strong feeling and no dream episode generated
Scenario 2: two dream episodes In Figure 4 a scenario is shown where the episode state dess2 based on srss2 is succeeded (after time point 13) by an episode state dess3 based on srss3 (see upper graph). Here the connection from preparation for emotional response to sensory representation of s3 has been given strength 5,3 = 0.45. As shown in the lower graph in Figure 4, for this case the feeling level goes to 0.7, which is a situation in which regulation facilities become active. For example, due to this high feeling level the suppressing control state for s1 becomes more active. In the lower graph of Figure 4 the comparison between the sensory representations of s2 and s3 is shown; it is shown that first, up to time point 8, the sensory representation of s2 dominates, reaching a level of around 0.6, which leads to a dream episode state dess2 based on it, as shown in the upper graph in Figure 4. But after time point 8 the sensory representation for s2 is suppressed by the triggered regulation, and therefore beaten by the sensory representation for s3. As a consequence, after time point 13 the episode state for s3 has won the competition, and provides the basis for a second dream episode. Note that the competition process took about 5 time units before the episode related to the sensory representation state that became the highest activated one at time 9 was able to beat the previous one.
1.0 0.8 episode s1
0.6
episode s2
0.4
episode s3
0.2 0.0 0
2
4
6
8
10
12
14
16
18
20
22
24
1.0 feel b 0.8
prep b rep s1
0.6
rep s2 0.4
rep s3 cs(s1, b)
0.2 0.0 0
2
4
6
8
10
12
14
16
18
20
22
24
Figure 4. Secenario 2: two subsequent dream episodes
Scenario 3: three dream episodes Similarly, scenarios for three or more dream episodes can be shown; see Fig. 5. Note that which episode states pop up depends on the association strengths to the emotional response. For example, if the emotional association strength 5,3 for s3 is made slightly lower, then the episode state for s3 will never pop up due to the mutual inhibition. Moreover, the strength of the inhibition links affect whether or not two different episode states are considered compatible. If such mutual inhibition links have lower or zero strengths, then in one episode multiple (apparently compatible) episode states can co-occur. In Table 4 the settings for this scenario are shown, where the cells with values that differ from the values in the previous scenarios are shaded in yellow.
Table 4. Settings used for connection strength, threshold and steepness parameters: scenario 3 from state
connection
fsb
1,k 2
1
psb
3
1
cssk,b
4,k
-0.2
psb
5,1
0.5
css1,b
6,1
-2
srssk
1
psb
5,2
0.5
css2,b
6,2
-0.5
psb
5,3
0.43
css3,b
6,3
-0.5
psb
5,4
0.38
css4,b
6,4
-0.5
srssk fsb
7,k 8,k
1 1
srssk dessj
9,k 10,j,k
1 -0.4
to state
threshold
steepness
psb
0.5
4
fsb
0.5
4
srss1
0.5
8
srss2
0.25
8
srss3
0.25
8
srss4
0.25
8
css1,b
0.7
8
css2,b
1.1
8
css3,b
1.4
8
css4,b
1.8
8
dessk
0.25
60
1.2
1.0 feel b 0.8
prep b rep s1
0.6
rep s2 rep s3
0.4
rep s4 cs(s1, b)
0.2
0.0 0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
1.2 1.0 episode s1
0.8
episode s2 episode s3
0.6
episode s4 0.4 0.2 0.0 0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
Figure 5. Secenario 3: Three subsequent dream episodes
4.5 Relations to Neurological Theories and Findings In (Levin and Nielsen, 2007) dreaming is related to four main components Amygdala, Medial PreFrontal Cortex (MPFC), Hippocampus, Anterior Cingulate Cortex (ACC). ‘There is ample evidence of anatomical connections between the regions (..). Amygdala, in particular, is massively connected to the other regions in a reciprocal fashion (..). The four regions are also robustly connected to sensory, motor, and autonomic brain regions and thus are well suited to mediate higher cognitive functions, behaviors, and affective responses.’ (Levin and Nielsen, 2007, p. 505)
The biological counterparts of the preparation and sensory representation states in the model can be found in the sensory and (pre)motor cortices, indicated in (Levin and Nielsen, 2007) to be ‘robustly connected’ to the components as mentioned. The relations between sensory memory elements and their emotional associations are stored in the Hippocampus; in the model these relations are assumed to be fixed and modelled by the (bidirectional) connections between the sensory representations states srssk and preparation states psb of the emotional response b. The feeling state fsb in the model can be related to the Amygdala, in combination with some limbic areas involved in maintaining ‘body maps’. As discussed in Section 4.2, the interaction between preparation state psb and feeling state fsb is in line with the neurological theories of Damasio (1994, 1999, 2003, 2010). About the role of ACC empirical studies show evidence in different directions (e.g., Levin and Nielsen, 2007, pp. 505-512); therefore it is not clear yet how it can be related to the model. The interaction between MPFC and Amygdala has been extensively studied; e.g. (Damasio, 1994, 1999; Davidson, 2002; Sotres-Bayon, Bush, LeDoux, 2004; Salzman and Fusi, 2010; Levin and Nielsen, 2007). In various empirical studies it has been found that lower activity of MPFC correlates to less controlled feeling levels, and, moreover, REM sleep is found to strengthen MPFC activation and reduce feeling levels. This regulating role of MPFC with respect to Amygdala activation makes these two neurological components suitable candidates for biological counterparts of the control state cssk,b and the feeling states fsb in the temporal-causal network model presented in Section 4.3. As before, the connections between the two types of states may be related to the Hippocampus. Note that in the model the control states cssk,b also have a role in suppressing the activation of the corresponding sensory representation state srssk, which can be justified as being a form of emotion regulation by attentional deployment; e.g. (Gross, 1998, 2007); see also Section 4.2. The episode states dessk and their competition, as explained in Section 4.3 can be justified by referring to the Global Workspace Theory of consciousness by Baars (1997, 2002) and the perspective of Dennett (1991, 2005) who assume a similar competition mechanism.
4.6 Discussion In this chapter based on a Network-Oriented Modelling approach, a temporal-causal network model was presented that models the generation of dream episodes from an internal simulation perspective, abstracting from a specific purpose. The contents of this chapter are based on (Treur, 2011a). The assumption that dreaming, especially when negative emotions are involved, can be considered as a purposeful form of internal simulation is widely supported; see, for example, for the purpose of improving coping skills to handle threatful situations (Revonsuo, 2000; Valli, Revonsuo, Palkas, Ismail, Ali, Punamaki, 2005; Valli and
Revonsuo, 2009), or for the purpose of strengthening fear emotion regulation capabilities (Levin and Nielsen, 2007, 2009; Franzen, Buysse, Dahl, Thompson, Siegle, 2009; Gujar, McDonald, Nishida, Walker, 2011; Walker, 2009; Walker and van der Helm, 2009; Yoo, Gujar, Hu, Jolesz, Walker, 2007; van der Helm, Yao, Dutt, Rao, Saletin, & Walker, 2011; Rosales-Lagarde, Armony, del Río-Portilla, Trejo-Martínez, Conde, Corsi-Cabrera, 2012; Markarian, Pickett, Deveson, Kanona, 2013; Mauss, Troy, LeBourgeois, 2013; Pace-Schott, Germain, Milad, 2015). Internal simulation takes place when in response to sensory representation states activations associated preparation states for actions or bodily changes are activated, which in turn, by prediction links, activate other sensory representation states; see, for examplee, (Hesslow, 1994, 2002, 2012; Damasio, 1994, 1999; Goldman, 2006; Barsalou, 2009; Marques and Holland, 2009; Pezzulo, Candidi, Dindo, and Barca, 2013). Building blocks to create such internal simulations are memory elements in the form of sensory representations and their associated emotions. The model exploits a mutual (winner-takes-it-all) competition process to determine sensory representation states that dominate in different dream episodes, comparable to one of the central ideas underlying Baars (1997, 2002)’s Global Workspace Theory of consciousness and Dennet (1991, 2005)’s perspective on consciousness. Emotion regulation mechanisms (Goldin, McRae, Ramel, Gross, 2008; Gross, 1998, 2007) were incorporated to regulate the activation levels of the feeling and the sensory representation states. The model was evaluated by a number of simulation experiments for scenarios with different numbers of dream episodes. Note that the presented temporal-causal network model is meant as a plausible model of a human. Mechanisms identified in the neurological and cognitive literature were used in order to obtain a human-like temporal-causal network model, and to support its plausibility. Once such a human-like model is available, potential applications can be explored. A specific class of possible applications may concern virtual agents in the context of serious or non-serious gaming. In this context also some types of validation can be performed, for example, by evaluating how believable they are considered (also in dependence of parameter settings). Such applications and validations are a subject for future research. A number of variations of the current model can be made. One variation is to take into account more than one emotion triggered by certain sensory representations. The model can easily be extended to cover this case. Another variation which is possible is to incorporate dependencies between sensory representations (e.g., resulting from sensory preconditioning; e.g. (Brogden, 1939; Hall, 1996). As an extension of the current model, an adaptive temporal-causal network model has been developed for fear extinction learning during dreaming (Treur, 2011b); this will be discussed in Chapter 5.
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