Individuals with depressive tendencies may categorize ambiguous facial expressions toward negative rather than happy endpoints, but the associated decision-process components remained unclear. The study examined whether the categorization shift was associated with differences in perceptual criterion, discrimination sensitivity, response dynamics, or diffusion-model parameters. Fifty-nine participants, comprising a depressive tendency group and a comparison group, categorized morphed Sad–Happy and Angry–Happy expressions presented in a 750-ms face/mask sequence or a 1500-ms face/no-mask sequence. Psychometric functions, area-under-the-curve (AUC) analyses, quadratic mixed-effects models of response time, and a hierarchical drift–diffusion model were applied. Across conditions, the depressive tendency group showed lower points of subjective equality than the comparison group, indicating a relative shift in the categorization boundary toward the negative versus happy endpoint, without a corresponding group difference in discrimination sensitivity. AUC analyses similarly indicated higher negative-response probabilities in the depressive tendency group, particularly within the low-negativity portion of the continuum. Condition-specific patterns were observed, but direct tests did not support a larger Sad–Happy group difference in the 1500-ms than in the 750-ms presentation sequence. In the focal Sad–Happy 1500-ms face/no-mask sequence, the selected drift–diffusion model indicated a group-related drift-criterion shift toward the Sad boundary and modestly longer non-decision time in the depressive tendency group, whereas starting-point bias and boundary separation did not differ between groups. These findings are consistent with a relative categorization shift toward negative versus Happy facial-expression endpoints among participants with depressive tendencies. The diffusion-model results provided a model-dependent account of the focal choice–response-time pattern under the present presentation-sequence structure.
Neuroimaging has identified cue-elicited activity in ventral striatum and orbitofrontal cortex (OFC) as a reliable signature of craving, yet activation magnitude in either region is an inconsistent predictor of clinical outcome. This theoretical review develops a model on which the clinically consequential variable is not activation but effective coupling: the cue-evoked gain—the factor by which an input is amplified—with which OFC valuation signals modulate ventral striatal action-selection activity. I formalize this gain as valuation–policy coupling (λ), which governs the probability that a drug-related appraisal recruits approach and consumption policies; it is a latent construct indexed by effective OFC–accumbens connectivity, approach bias under load, and pharmacological challenge. Two moderators—meta-awareness and elaborative versus nonelaborative engagement—determine whether the urge is re-entered or exited, and jointly specify a neurocognitive space in which craving occupies a high-λ, high-meta-awareness, elaborative vertex. The framework predicts an awareness-amplification effect—noticing an urge intensifies it under high λ and weakens it under low λ—specifies when mindfulness-based relapse prevention should attenuate craving and when it should not, and yields a discriminative prediction: λ-reducing interventions should reduce cue-modulated OFC–ventral-striatum coupling while leaving regional activation comparatively preserved. λ is a parameter of craving rather than of relapse—which also requires cognitive-control parameters—and a candidate transdiagnostic mechanism. Claims are labeled by evidential status; the λ–meta-awareness interaction is the model’s principal hypothesis, and failure conditions are stated explicitly.
In addition to motor symptoms, Huntington's disease (HD) is associated with cognitive deficits, including the ability to recognize emotions (fear, disgust, and anger) from faces and voices, while happiness and neutral expressions are relatively preserved. Yet the underlying mechanisms remain unclear. We investigated both explicit emotion recognition and implicit attentional biases in manifest HD patients to assess whether altered attentional orienting may explain the selective deficit in negative emotions. To our knowledge, this represents the first study of its kind in HD. Twenty patients with manifest HD and 25 controls completed an explicit facial emotion recognition task (neutral, disgust, fear, and happiness) and two dot-probe tasks (100 ms and 500 ms SOAs) to dissociate automatic and controlled attentional processes. Attentional Facilitation Index (AFI) and Disengagement Index (DI) were calculated. Huntington's disease patients showed reduced accuracy and slower reaction times in recognizing negative emotions (fear and disgust). At the 100-ms SOA in the dot-probe task, they exhibited reduced attentional facilitation for fearful faces and greater facilitation for happy faces compared with controls, with a similar pattern observed for attentional disengagement. No significant differences emerged at 500 ms SOA. These findings reveal a “hyperpositivity effect” in HD: an early automatic prioritization of positive stimuli alongside reduced attentional capture by negative ones. This pattern, which parallels the positivity bias in normal aging, may contribute to the selective impairment in negative emotion recognition. A plausible underlying mechanism involves striatal dopaminergic dysregulation. Longitudinal studies in premanifest gene carriers are needed to clarify this bias’s development.
Irritability is characterized by a propensity to experience frustration and has been associated with reduced self-reported willingness to expend effort for rewards. Frustration may be relevant to effortful reward pursuit, but its effects on effort expenditure have rarely been experimentally tested in humans. To address this gap, this study introduced a frustration induction in the Effort-Doors task to elicit frustrative nonreward (FNR), a negative emotional state triggered by unexpected reward loss, and examined its effects on physical effort expenditure and feedback-related electroencephalographic (EEG) activity. We also tested whether these effects vary as a function of state and trait measures of motivation and affect. Linear mixed-effects models tested the effects of FNR induction on self-reported, behavioral, and EEG measures in young adults (N = 83) completing the Frustrative Effort-Doors task. Models tested manipulation effects, trial-level post-feedback adjustment, and moderation by state and trait measures. Midfrontal theta power (MFθ) extracted using multivariate spatial filtering was examined as a function of feedback (win vs. FNR), effort (low vs. high), and subsequent behavioral adjustments. The frustration condition was associated with decreased self-reported state motivation and increased frustration, arousal, and response speed. Midfrontal theta power was larger following FNR feedback and increased with state frustration during high-effort trials. Higher state frustration was associated with reduced response vigor during effort execution following FNR feedback. MFθ did not predict trial-to-trial behavioral adjustments. These findings indicate that FNR recruits MFθ, a neural marker of cognitive control, while state frustration modulates physical effort execution following unexpected reward loss.
Our interests, attentional focus and their integration, are formed by complex interactions across large-scale neural networks. To date, however, few studies have investigated the temporal dynamics of brain activity related to trait boredom proneness, which itself is related to attentional dysfunction and higher rates of depression and anxiety. In a large consortium dataset, we investigated the resting-state network characteristics of boredom proneness. We found that those reporting to be frequently bored had overall increased cortical connectivity compared with those who said they were never bored. In addition, we found that the frequently bored had increased integrated network topology and greater network flexibility compared with those who were never bored. Further analyses indicated that the pattern of functional connectivity was distinct from that observed for the related behavioural challenges of attention deficit hyperactivity disorder. Through this approach, we provide a potential neural underpinning for trait boredom proneness that links network topological changes related to ascending arousal neuromodulatory systems to heightened integration and flexibility in those highly prone to boredom.
