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This and other subsequent studies tended to embed the emotions using a similar model, i. Tsankova, for example, recently investigated the impact of simulated emotional and immune system on the behavior of artificial agents equipped with neural architecture inspired on the amygdala structure in a goal-following task [ 12 ]. Another recent study by Jha and colleagues described the design of a situated robot with basic decision-making abilities and tested its behavior in an artificial environment.

The robot was asked to select one among different tasks in order to increase its Comfort level. The simulations were conducted using the multiagent emotion generation engine proposed by Godfrey [ 13 , 14 ]. This casual appearance is exactly how evolution, thanks to random mutations, shaped simple and complex adaptive mechanisms in natural organisms throughout generations. So, if we wish to dig more into this genetic aspect, and simulate how these adaptive strategies have evolved, we need to build a computational model which can be, in its simplest features, compared to the process of natural selection, and this can be achieved with the use of genetic algorithms and by leaving the agents free to explore and adapt, rather than to choose a behavior over a predefined set.

In this study we will present a bio-inspired computing methodology to investigate the emergence of emotion-driven behavior in agents equipped with artificial neural networks ANNs and trained with genetic algorithms. The setting we use for our simulations is a fear-based context discrimination task, which is also suitable for the reproduction of the well-known Pavlovian threat conditioning PTC [ 17 ].

The percentage of stimuli to keep or to discard depends on the condition. In case the robot decides to go for the wrong action, an electric shock is administered which determines a loss in terms of life duration. Robots can choose between keeping, discarding or ignoring the stimuli but also need to learn to balance their need of exploration, and the resulting new, unpredictable behavior relies on the internal sensation of danger and safety acquired throughout past experiences of the individual and of its ancestors.

In a preliminary study, we introduced the experimental paradigm in its basic features, analyzing the performance of four different fully-connected neural network architectures and testing two fitness functions for the genetic algorithm on this task. Results showed significant advantage of the processing speed in feedforward networks that led to better performance [ 18 ].

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Here we propose an architecture that allows the robot to gather information from the environment and learn how to use these cues to discriminate different contexts in the presence of the same visual stimuli, triggering an appropriate fearful or approaching behavior. We aim to investigate the emergence of a self-organized phylogenetic system and evolve multiple populations equipped with different architectures, tuning the fitness function and the parameters for the training and the evolution.

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The definition of emotion is not unique in the psychological and neuroscientific literature. For this reason, it is necessary to clearly define the theoretical framework we referred to in order to operationalize the concept of "emotion" and "fear". The first categorization of emotions distinguishes basic emotions, as happiness, anger or disgust, from social emotions, that are more complex in that, emerge later in ontogeny [ 19 ] and depend on interpersonal relationships, like thoughtfulness, boredom, compassion, guilt or admiration [ 20 — 23 ].

Another distinction to be made is between the cognitive aspect of the emotion, i.

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There is a debate about what actually constitutes this group of responses and the level of complexity of these reactions, in particular their modularity and their interpretation. Specifically, there are two different approaches to this problem. One defines the emotion as permeated necessarily by both psychological and physiological aspects, whose combination allows the identification of emotions as universally similar in their components in animals, humans or robots, with different underlying neural structures but achieving the same manifestation [ 3 ].

This view, recently brought forward by Adolphs, embraces also a dimensional perspective of emotions, that are categorized according to their position on theoretical continuums of diverse dimensions, each comprising two sub-categories: similarity intensity and valence , flexibility persistence and learning , coordination hierarchical behavioral control and multi component effects and automaticity priority over behavioral control and poised for social communication [ 24 ].

The second approach, which only partly contrasts with the former, considers emotions as arising from the co-occurrence of spatio-temporal events, in that the underlying brain structure and activation is necessary but not determinant to the identification of an emotion. This prediction, computed using previously constructed internal models, categorizes the sensations providing a meaning and allows the implementation of a specific action plan. Then, the error between the prediction and the actual input is propagated and used as feedback to update the internal model.

Actually, the theory of constructed emotions is based on the idea of the brain as a concept generator, that collects representations; therefore, for any perception, affect or emotion, a cortical mediation is needed [ 4 ]. However, both views are based on the evidence that a single organ in the brain does not control the expression of only one emotion. Considering that in the brain there is not a one-to-one mapping between regions and behaviors, every emotion, considered as a process, consists of the emergence of a pattern from the interaction of multiple interconnected areas.

What determinates the outcome during a specific event will depend on situational variables, involving, for example, the organism's arousal or the temporal pattern of the danger. Cacioppo and Tassinary [ 28 ], among others, proposed a mapping between psychological processes and physiological ones taking into account a spatiotemporal pattern of the co-occurrence of physiological indices. The involvement of the amygdala in several affective processes has been also extensively proved [ 29 — 31 ].

Both views also agree on the concept that emotions emerged during evolution [ 32 ] and on the concept of degeneracy, that defines each emotion as created by multiple spatiotemporal patterns in populations of neurons that can vary among individuals and time and type of the specific trigger event [ 4 , 24 ].

Three components of emotion and the universal emotions - MCAT - Khan Academy

It is indeed the network structure of the brain, from a molecular and molar point of view, that allows the emotional response, but also its temporal occurrence and the surrounding environment. In this paper, we embrace the definition of emotion state shared by Adolphs, LeDoux and other researchers, which consider emotions as adaptive events or states that are likely to occur in humans and animals, but that may or may not have a subjective component, depending on the species and situation involved [ 33 , 34 ], and that therefore do not require awareness nor a cognitive experience.

In the case of amygdala, for example, recent neuroscientific evidences support this hypothesis [ 35 ], and recently a distinction between attentional unawareness and sensory unawareness was proposed [ 36 , 37 ]. Even though most studies in the field of affective neuroscience rely on brain imaging techniques that do not track the modifications of the brain activation in a specific time period e.


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Anger and fear, in particular, can impact survivability only if they occur promptly [ 36 ]. A recent study by Costa and colleagues supports the idea that fear is processed earlier than other basic emotions [ 38 ]. These emotions, according to the evolutionary theories, were selected through evolution in order to promote the survivability of the species in their specific primitive environment. In the case of fear, an example may be the situation of being alone at night, that triggers the activation of a detection circuit that tries to perceive possible cues of the presence of a threat [ 41 ].

This functionalism extends the possibility that other states such as coma, shock, or confusion could have been evolved as modes of operation triggered when an organism is coping with illness [ 41 ]. Regarding the emotion of fear, however, there is in general more agreement on its evolutionary basis, since it can also be proved by the fact that mammalians exhibit basic fear responses even before having experienced pain or danger themselves [ 46 ].


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The reason why fear is by far the most studied emotion in humans and animals depends on several factors, among which the easiness of eliciting aversive affective states in rats, the similarity of the response pattern of fear among mammalians and the agreement reached upon the role of the amygdala in processing aversive stimuli and conditioned fear responses. Lately, much effort has been made in the field of neuroscience in order to clearly define the circuits involved in the genesis of affective states in animals.

While the boundaries of the "limbic system"—if the concept is not yet abandoned—have been trespassed turning it into a fading region which includes some parts of the mid-brain and neocortex [ 47 ], there is good evidence about the role of the amygdala in the emotion of fear [ 48 — 50 ]. The principal paradigm for the investigation of fear is Pavlovian threat or fear conditioning [ 17 ]. In the case of an auditory input, for example, a CS can be a tone, and the US a footshock. The stimulus is, in the first instance, perceived by the sense receptors and sent to the sensory thalamus and then to the cortex.

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After the pair of CS and US has been presented a few times, the CS elicits the same physiological response which occurs in the presence of the US [ 51 ]. The pairing of the two stimuli, in fact, appears in the lateral nucleus of the amygdala LA thanks to the incoming signals from the cortex and the thalamus. The LA, specifically in its dorsal region, is connected directly with the central nucleus CE , which then sends the information of exposure to the CS to the hypothalamic and brainstem areas which process the autonomic responses [ 1 , 52 — 54 ].

Basal and accessory basal nuclei of amygdala do not appear to be involved in CS conditioning [ 55 ]. This two-pathway connection received by the lateral nucleus of the amygdala is crucial for a rapid encoding and response to the conditioned and unconditioned stimulus, which can be elicited prior to cognitive processing.


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Among the species-typical responses to fear in rats—but that extends similarly to a great variety of mammalians—we cite freezing, variation of heart rate and blood pressure along with ADH and ACTH hormone secretion. Bechara and Damasio proved the centrality of the role of the amygdala nuclei also in humans [ 29 ]. Another consequence of fear conditioning is the association of unconditioned responses also to the environment where the CS-US pairing takes place. As previously mentioned, the activation of the amygdala is necessary but not sufficient to establish the observable affective response.

In order to define the emergency of different processes from similarly interconnected areas, Pessoa proposes the difference between structural and functional connectivity, whereas structural responses in two regions could be correlated, but the effect of intermediate regions and the context in which the stimulus appears will determine the functional response [ 26 ]. Structural interconnectivity itself can only allow the prediction of few features of the emotional behaviors.

Few have been the studies that tried to propose a computational model of the neural connections able to determine the emotional response. Another model, recently developed by Navarro-Guerrero and colleagues, aimed at proposing a possible self-defense system for autonomous robots [ 58 ]. The architecture was composed of an echo state network, which represented their simulated prefrontal cortex, and its connections to a feedforward network, that constituted various units of the amygdala nuclei, the thalamus and the auditory system.

This network was also tested in a physical robot NAO. Even though the theoretical network models discussed above represent a valid simplification of the structures inside the brain involved in the genesis of emotions, what lacks to these approaches is an attempt to explain how each of these modules specialized to their current, assigned role in the genesis of the emotion.

The present study wishes instead to try to fill this gap, proposing a system which aims at modeling the emergence of fearful behavior in its evolutionary aspect, opening up to the future investigation of the spontaneous specialization of each node of the network. Other than that, our model helps investigate the temporal evolution of the activation during the emotional response, an aspect of neglected in neuroscientific studies which do not involve a measurement through time.

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Voice and Emotion Processing in the Human Neonatal Brain

In our preliminary study, we described the methodology used to build the framework in which the artificial agents are tested. In particular, we used simulated robotic agents equipped with a neural network and trained them with genetic algorithms to learn to recognize dangerous from non-dangerous conditions by picking up or avoiding stimuli of different colors. We evolved populations of robots for generations and compared the outcome of four architectures, a shallow feedforward network, a recurrent neural network, a recurrent neural network with the additional recurrence of the motor neurons and a network with the only recurrence of the motor neurons, and evaluated their performance using different fitness functions.

We observed that, in the unsafe conditions, dangerous stimuli were able to trigger an avoidance behavior which resulted in both navigating around them and refraining from picking them up.