Mecanismos de control emocionalíndices comportamentales y neurales

Resumen

Abstract Humans posses a remarkable ability to quickly and flexibly adapt their behavior to current circumstances. Environmental changes imply alterations in the state of affairs, and these include social interactions. In many situations, the information available to the senses lead to multifaceted representations of the same element. Such multiplicity may generate conflict when the representations result in opposite action schemes (e.g. Botvinick, Braver, Barch, Carter, & Cohen, 2001; Botvinick, Cohen, & Carter, 2004; Kornblum, Hasbroucq, & Osman, 1990). This issue has been long studied on the field of cognitive control. However, most of research to date has employed neutral non-social stimuli (although see Bush, Luu, & Posner, 2000). Furthermore, the vast majority of studies have not offered information about how conflict with affective material affects different processing stages. Experimental series I of the present thesis was aimed at comparing affective and neutral stimuli in a conflict paradigm. For this, we used a flanker word task, modified from the one by Ochsner and cols. (Ochsner, Hughes, Robertson, Cooper, & Gabrieli, 2009). We recorded participant’s brain activity by means of a high-density electroencephalography (HDEEG) system to explore how affective and cognitive conflict influenced perceptual and decision-related stages of information processing. Results from this first experiment revealed that both conflicts were equivalent in terms of their behavioral effects. However, as shown by electrophysiological data, they were mediated by common and dissociable neural mechanisms. Early processing stages reflected on the P1 and N170 potentials, showed parallel sequential conflict effects. Cognitive conflict heightened the amplitude of the N2 and the potentials, whereas affective conflict only modulated the amplitude of the last portion of the P3. The following series in the thesis transferred the experimental situation to a context closer to social interrelations. We are social animals and thus most of our routines, thoughts and behaviors depend on our interactions with other people (Frith & Frith, 2007; Wyer, 2014). This is especially true for emotional phenomena, as they are strongly related to the social realm (Parkinson, 1996). Personal information provided by facial identity and emotional expressions are two useful clues for communication in social contexts (e.g. Keltner & Haidt, 1999). Both facial features offer information about the intentions, thoughts, beliefs, emotions and internal states of others (Frijda, 1988; Frijda & Mesquita, 1994). Sometimes, expectations about future consequences based on identity and emotional expression may generate incongruent inferences about what the other people are going to do the next. In these cases, a conflict between both facial elements may arise, which calls for control of the automatic but inappropriate responses for the situation. How we adjust our behavior to deal with conflict stemming from competitive social clues is an issue still far from being understood. Thus, along three different experimental series we explored emotional conflict in social contexts. In Experimental Series II, III and IV, we employed an adapted Trust Game (Tortosa, Lupiáñez, & Ruz, 2013; Tortosa, Strizhko, Capizzi, & Ruz, 2013). In this paradigm participants have to make economic decisions based on personal information linked to the facial identity of the people with whom they interact. The game partner displayed non-predictive emotional expressions (i.e. happy vs. angry) that had to be ignored. In Experimental Series II, we evaluated emotional conflict at the behavioral level generated by the incongruence between the expectations linked to personal identity and emotional expressions (i.e. a cooperative person looking angry or a non-cooperative partner displaying an expression of happiness). As expected, when both facial clues led to opposite expectations about the most probable behavior of the partners, we observed an effect of conflict on the time needed to make the choices. Next, we aimed to explore the neural mechanisms underlying the detection and resolution of the emotional conflict during interpersonal interactions. For this purpose, in the Experimental Series III functional magnetic resonance images (fMRI) were obtained during the performance of the task described above. As shown by fMRI results, when participants were facing a cooperative partner versus when they interacted with a non-cooperative one, the putamen and the body of the caudate were recruited presumably because of their role in computing reward values linked to positive predicted consequences. When the decision about identity did not entail any conflict, the most significant neural response was localized at the fusiform gyrus, a face-sensitive region involved in the extraction of facial invariant features such as identity. Neuroimaging results also revealed a set of neural areas engaged during conflictive situations, including the anterior cingulate cortex and the superior frontal gyrus. The anterior cingulate cortex was functionally coupled with other areas such as the inferior frontal gyrus and the posterior cingulate cortex/precuneus during conflict. This functional communication may be related to the inhibition of automatic inadequate responses led by emotion intrusion. Finally, at Experimental Series IV, we aimed at exploring the processing levels that reflected the strategic employment of either the identity or the emotion to predict interpersonal outcomes. In addition, we analyzed the stages at which the irrelevant emotional information influenced the decoding of facial identity. Electrophysiological results revealed that when emotion was a relevant source of predictive information early perceptive stages were enhanced, as reflected on the P1, N1 and VPP potentials. Using identity for the same predictions enhanced the amplitude of the N2 and the P3b potentials. In addition, the influence of the emotional irrelevant content from the faces was reflected on the N170 and P3b potentials. In sum, although emotions were fast decoded, they did not exert their influence on the processing of identity until both features had been decoded, at the level of the N170 potential. Conclusions Results from the Experimental Series I suggest that the first levels of information processing, mainly perceptual analyses, deal with cognitive and affective conflicts through the same mechanisms. By contrast, later stages involved in cognitive control and response planning diverge according to the nature of the task requirements generating the conflict. As the results from Experimental Series II-III and IV show, when emotional conflict is studied in social contexts, social constructs and emotions lead to different expectations about the proximal behavior of others (Fischer & Manstead, 2008; Haidt, 2001; Keltner & Haidt, 1999; Oosterhof & Todorov, 2008). The inconsistency between personal predispositions of cooperation and irrelevant emotional facial information leads to opposite expectations and increases demands on decision-making, which are reflected in slower response times. As neuroimaging data revealed, decisions guided by facial identity are biased by the reward value (i.e. gain or loss) of action-outcomes linked to each identity. Facial identities predicting gain strongly activated relevant areas from the dorsal striatum involved in the formation of stimulus-action-reward associations. When their decoding of was not hindered by irrelevant and non-predictive emotional information, then occipital and temporal areas from the distributed neural system for face processing, relevant for the configuration of the identity, implemented the most adequate set of actions (Gobbini & Haxby, 2007; Haxby & Gobbini, 2011; Haxby et al., 2000, 2002). However, incongruent situations, call for additional conflict detection and control implementation mechanisms operated by a network including middle-frontal, frontolateral and a posteromedial areas. Electrophysiological results support an earlier capture of attentional resources by emotional relevant information than by facial identities when their strategic use for outcome predictions is contrasted in a decision-making paradigm. Shortly after, both facial aspects are processed in detail and interact by generating an emotional conflict effect when the expectations arising from expression and identity contradicted each other (in parallel with behavioral data). After this, expectations from facial identity exerted influence on processing stages linked to the evaluation of the value of actions to guide future decisions. Finally, the emotional conflict between both facial aspects was reflected at the level of decision-making process. At this level, conflictive items were inhibited in favor of the selection of those with the highest motivational value for predicting outcomes. In sum, our results show that explicitly ignored emotions influence responses in a mandatory manner during interpersonal interactions. Our study, for very first time, shows how control mechanisms operate when emotional conflict emerges from incongruent expectations and non-overlapping action tendencies associated to invariant and changeable facial aspects during personal interactions.   References Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624-652. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: an update. Trends in Cognitive Sciences, 8(12), 539-546. http://doi.org/10.1016/j.tics.2004.10.003 Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4(6), 215-222. Frijda, N. H. (1988). The laws of emotion. American Psychologist, 43(5), 349-358. http://doi.org/10.1037/0003-066X.43.5.349 Frijda, N. H., & Mesquita, B. (1994). The social roles and functions of emotions. En S. Kitayama & H. R. Markus (Eds.), Emotion and culture: Empirical studies of mutual influence (pp. 51-87). Washington, DC, US: American Psychological Association. Frith, C. D., & Frith, U. (2007). Social cognition in humans. Current Biology: CB, 17(16), R724-732. http://doi.org/10.1016/j.cub.2007.05.068 Keltner, D., & Haidt, J. (1999). Social Functions of Emotions at Four Levels of Analysis. Cognition and Emotion, 13(5), 505-521. http://doi.org/10.1080/026999399379168 Kornblum, S., Hasbroucq, T., & Osman, A. (1990). Dimensional overlap: cognitive basis for stimulus-response compatibility--a model and taxonomy. Psychological Review, 97(2), 253-270. Ochsner, K. N., Hughes, B., Robertson, E. R., Cooper, J. C., & Gabrieli, J. D. E. (2009). Neural systems supporting the control of affective and cognitive conflicts. Journal of Cognitive Neuroscience, 21(9), 1842-1855. http://doi.org/10.1162/jocn.2009.21129 Parkinson, B. (1996). Emotions are social. British Journal of Psychology (London, England: 1953), 87 ( Pt 4), 663-683. Tortosa, M. I., Lupiáñez, J., & Ruz, M. (2013). Race, emotion and trust: an ERP study. Brain Research, 1494, 44-55. http://doi.org/10.1016/j.brainres.2012.11.037 Tortosa, M. I., Strizhko, T., Capizzi, M., & Ruz, M. (2013). Interpersonal effects of emotion in a multi-round Trust Game. Recuperado 22 de diciembre de 2015, a partir de http://www.redalyc.org/articulo.oa?id=16929535003 Wyer, R. S. (2014). The Automaticity of Everyday Life: Advances in Social Cognition. Psychology Press.