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1. Segregation of Three Resting-State Brain Networks Predicts Reappraisal Success Across The Lifespan

   https://doi.org/10.1093/scan/nsaf055  

   Pierce, Jordan E; Neta, Maital (May 2025, Social Cognitive And Affective Neuroscience)

   Abstract Cognitive reappraisal is a form of emotion regulation that involves the reinterpretation of stimuli to change one’s emotional state, often to reduce negative affect. Emotion regulation fMRI tasks generally yield increased activation in prefrontal cortex and, less consistently, decreased amygdala activation. Only a few studies, however, have examined how intrinsic brain organization, characterized via resting-state fMRI, relates to reappraisal, typically focusing on the same task-derived brain regions. Here, we administered an emotion regulation task where participants (n = 227, 6-80 years) viewed or downregulated responses to negative images, then completed a resting-state fMRI scan. We examined the functional connectivity in 300 whole-brain regions of interest comprising 13 functional networks. We found that the network segregation, or relative balance of within- and between-network connectivity, in the default mode (DMN), dorsal attention (DAN), and dorsal somatomotor (SMd) networks was associated with reappraisal success (controlling for age and movement). Specifically, greater connectivity within the DMN and DAN, lower connectivity within the SMd, and greater connectivity between the SMd and lateral SM networks predicted better reappraisal ability. These networks also partially overlapped with brain areas supporting emotion regulation and reactivity, suggesting that functional brain organization is a key factor in shaping emotion regulation across the lifespan. 

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2. Emotion Regulation and Motivation Modulate the Associations Between Math Anxiety, Attention, and Math Performance

   Wang, Z; Li, T; Lyons, I; Spangler, D; Barroso, C; Brockmole, JR; Quintero, M; Wang, M; Chaudhary, A; Mazzocco, MM; et al (December 2025, Journal of educational psychology)

   Although math anxiety (MA) and math performance are generally negatively correlated (Barroso et al., 2021), several studies reported variability in the strength of this association (Lyons & Beilock, 2012a; Tsui & Mazzocco, 2006; Wang et al., 2015). The present study investigated emotion regulation and motivation as potential mechanisms underlying this heterogeneity, particularly with regard to the attention patterns underlying successful math performance. A sample of 207 elementary and middle school students completed a math problem-solving task, during which their attention was measured using eye-tracking. Students’ trait level and state level emotion regulation and motivation were assessed using self-reports and physiological measures, respectively. Our findings revealed that the use of reappraisal as an emotion regulation strategy mitigated attentional interference during math problem-solving, which in turn attenuated performance deficits among students with high MA. In addition, students with high MA exhibited more avoidance of the math problems only if their physiological pattern indicated low state motivation. These findings highlight the importance of enhancing both reappraisal and motivation as potential intervention targets to combat math deficits among students with high MA. 

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3. Resting-state functional network segregation of the default mode network predicts valence bias across the lifespan

   https://doi.org/10.1162/imag_a_00403  

   Pierce, Jordan E; Wig, Gagan S; Harp, Nicholas R; Neta, Maital (January 2024, Imaging Neuroscience)

   Abstract The brain is organized into intrinsically connected functional networks that can be reliably identified during resting-state functional magnetic resonance imaging (fMRI). Healthy aging is marked by decreased network segregation, which is linked to worse cognitive functioning, but aging-related changes in emotion are less well characterized. Valence bias, which represents the tendency to interpret emotionally ambiguous information as positive or negative, is more positive in older than younger adults and is associated with differences in task-based fMRI activation in the amygdala, prefrontal cortex, and a cingulo-opercular (CO) network. Here, we examined valence bias, age, and resting-state network segregation of 12 brain networks in a sample of 221 healthy individuals from 6 to 80 years old. Resting-state network segregation decreased linearly with increasing age, extending prior reports of de-differentiation across the lifespan. Critically, a more positive valence bias was related to lower segregation of the default mode network (DMN), due to stronger functional connectivity of the DMN with CO and, to a lesser extent, the ventral attention network (VAN) in all participants. In contrast to this overall segregation effect, in participants over 39 years old (who tend to show a positive valence bias), bias was also related to weaker connectivity between the DMN and Reward networks. The present findings indicate that specific interactions between the DMN, a task control network (CO), an emotion processing network (Reward), and, to a weaker extent, an attention network (VAN), support a more positive valence bias, perhaps through regulatory control of self-referential processing and reduced emotional reactivity in aging. The current work offers further insight into the functional brain network alterations that may contribute to affective well-being and dysfunction across the lifespan. 

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4. Facing Ambiguity: What We Do in the Space Between Stimulus and Response

   https://doi.org/10.1177/09637214261429347  

   Neta, Maital (April 2026, Current Directions in Psychological Science)

   An extensive body of research on human and nonhuman animals has examined responses to clear valence, including stimuli that either represent a relatively clear threat (e.g., electric shock) or a clear reward (e.g., money). But daily life is replete with events or situations that are ambiguous—they could be threatening and/or rewarding. This article describes work that examines the wide interindividual variability with which humans respond to this dual-valence ambiguity. Although some individuals more readily categorize these events as negative, others are prone to arrive at more positive categorizations. This predilection to lean in a more negative versus positive direction represents a stable, trait-like difference and is referred to as “valence bias.” Valence bias is generalizable across categories of dual-valence ambiguity and has important implications for health and well-being. Here I focus on extensive findings that lend support for the initial negativity hypothesis, which posits that the initial or default response is negative across people and that positivity relies on an additional regulatory mechanism that helps to overcome the initial negativity. I also describe the cognitive and brain mechanisms underlying the valence bias, including emphasizing the importance of a broad set of systems that support human responses to ambiguity. 

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5. Spring Break or Heart Break? Extending Valence Bias to Emotional Words

   https://doi.org/10.1177/1948550620972296  

   Harp, Nicholas R.; Brown, Catherine C.; Neta, Maital (November 2020, Social Psychological and Personality Science)

   null (Ed.)

   Ambiguous stimuli are useful for assessing emotional bias. For example, surprised faces could convey a positive or negative meaning, and the degree to which an individual interprets these expressions as positive or negative represents their “valence bias.” Currently, the most well-validated ambiguous stimuli for assessing valence bias include nonverbal signals (faces and scenes), overlooking an inherent ambiguity in verbal signals. This study identified 32 words with dual-valence ambiguity (i.e., relatively high intersubject variability in valence ratings and relatively slow response times) and length-matched clearly valenced words (16 positive, 16 negative). Preregistered analyses demonstrated that the words-based valence bias correlated with the bias for faces, r s (213) = .27, p < .001, and scenes, r s (204) = .46, p < .001. That is, the same people who interpret ambiguous faces/scenes as positive also interpret ambiguous words as positive. These findings provide a novel tool for measuring valence bias and greater generalizability, resulting in a more robust measure of this bias. 

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