Multiple synergistic effects of emotion and memory on proactive processes leading to scene recognition

Abstract

21Visual scene recognition is a proactive process through which contextual cues and top-down 22 expectations facilitate the extraction of invariant features. Whether the emotional content of the 23 scenes exerts a reliable influence on these processes or not, however, remains an open question. 24Here, topographic ERP mapping analysis and a distributed source localization method were used 25 to characterize the electrophysiological correlates of proactive processes leading to scene 26 recognition, as well as the potential modulation of these processes by memory and emotion. On 27 each trial, the content of a complex neutral or emotional scene was progressively revealed, and 28 participants were asked to decide whether this scene had previously been encountered or not 29(delayed match-to-sample task). Behavioral results showed earlier recognition for old compared 30to new scenes, as well as delayed recognition for emotional vs. neutral scenes. 31Electrophysiological results revealed that, ~400 ms following stimulus onset, activity in ventral 32 object-selective regions increased linearly as a function of accumulation of perceptual evidence 33 prior to recognition of old scenes. The emotional content of the scenes had an early influence in 34 these areas. By comparison, at the same latency, the processing of new scenes was mostly 35 achieved by dorsal and medial frontal brain areas, including the anterior cingulate cortex and the 36 insula. In the latter region, emotion biased recognition at later stages, likely corresponding to 37 decision making processes. These findings suggest that emotion can operate at distinct and 38 multiple levels during proactive processes leading to scene recognition, depending on the extent 39 of prior encounter with these scenes. 40

Rapid Extraction of Emotion Regularities from Complex Scenes in the Human Brain

“…Research has shown that positivity offset (as well as negativity bias) can be generalized across different kinds of stimuli (Norris, Larsen, Crawford, & Cacioppo, 2011), is temporally stable and trait-like consistent (Ito & Cacioppo, 2005), and can be used as a theoretical construct to interpret serotonergic function in healthy and clinical populations (Ashare, Norris, Wileyto, Cacioppo, & Strasser, 2013;Carver, Johnson, & Joormann, 2009;Gollan et al, 2016). During our task, positivity offset may have outweighed negativity bias because of the absence of proximal environmental danger which, in turn, promoted exploration (Ito & Cacioppo, 2005;Schettino, Loeys, Bossi, & Pourtois, 2012;Schettino, Loeys, Delplanque, & Pourtois, 2011;Schettino, Loeys, & Pourtois, 2013). Furthermore, participants were probably inclined to thoroughly examine images carrying intrinsically rewarding information also because these pictures likely matched their motivational dispositions (Byrne & Eysenck, 1993;Niedenthal, Halberstadt, & Setterlund, 1997;Niedenthal & Setterlund, 1994;Raila, Scholl, & Gruber, 2015).…”

Rapid extraction of emotion regularities from complex scenes in the human brain

“…Research has shown that positivity offset (as well as negativity bias) can be generalized across different kinds of stimuli (Norris, Larsen, Crawford, & Cacioppo, 2011), is temporally stable and trait-like consistent (Ito & Cacioppo, 2005), and can be used as a theoretical construct to interpret serotonergic function in healthy and clinical populations (Ashare, Norris, Wileyto, Cacioppo, & Strasser, 2013;Carver, Johnson, & Joormann, 2009;Gollan et al, 2016). During our task, positivity offset may have outweighed negativity bias because of the absence of proximal environmental danger which, in turn, promoted exploration (Ito & Cacioppo, 2005;Schettino, Loeys, Bossi, & Pourtois, 2012;Schettino, Loeys, Delplanque, & Pourtois, 2011;Schettino, Loeys, & Pourtois, 2013). Furthermore, participants were probably inclined to thoroughly examine images carrying intrinsically rewarding information also because these pictures likely matched their motivational dispositions (Byrne & Eysenck, 1993;Niedenthal, Halberstadt, & Setterlund, 1997;Niedenthal & Setterlund, 1994;Raila, Scholl, & Gruber, 2015).…”

When the outcome is different than expected: Subjective expectancy shapes reward prediction error at the FRN level

“…Separately for each ERP component, positive and negative feedback were compared during the exact same interval (with a mean activity) as used for the standard ERP data analysis (see above). We used paired samples t tests performed on the log-transformed data, and, as in our previous studies, we set the level of significance for all source localization analyses at p < .01 (see also Schettino, Loeys, Delplanque, & Pourtois, 2011;Schettino, Loeys, & Pourtois, 2013).…”