Pulling the lever in a hurry: the influence of impulsivity and sensitivity to reward on moral decision-making under time pressure

BACKGROUND

Making timely moral decisions can save a life. However, literature on how moral decisions are made under time pressure reports conflicting results. Moreover, it is unclear whether and how moral choices under time pressure may be influenced by personality traits like impulsivity and sensitivity to reward and punishment.

METHODS

To address these gaps, in this study we employed a moral dilemma task, manipulating decision time between participants: one group (N = 25) was subjected to time pressure (TP), with 8 s maximum time for response (including the reading time), the other (N = 28) was left free to take all the time to respond (noTP). We measured type of choice (utilitarian vs. non-utilitarian), decision times, self-reported unpleasantness and arousal during decision-making, and participants' impulsivity and BIS-BAS sensitivity.

RESULTS

We found no group effect on the type of choice, suggesting that time pressure per se did not influence moral decisions. However, impulsivity affected the impact of time pressure, in that individuals with higher cognitive instability showed slower response times under no time constraint. In addition, higher sensitivity to reward predicted a higher proportion of utilitarian choices regardless of the time available for decision.

CONCLUSIONS

Results are discussed within the dual-process theory of moral judgement, revealing that the impact of time pressure on moral decision-making might be more complex and multifaceted than expected, potentially interacting with a specific facet of attentional impulsivity.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

How previous experience shapes future affective subjective ratings: A follow-up study investigating implicit learning and cue ambiguity

People use their previous experience to predict future affective events. Since we live in ever-changing environments, affective predictions must generalize from past contexts (from which they may be implicitly learned) to new, potentially ambiguous contexts. This study investigated how past (un)certain relationships influence subjective experience following new ambiguous cues, and whether past relationships can be learned implicitly. Two S1-S2 paradigms were employed as learning and test phases in two experiments. S1s were colored circles, S2s negative or neutral affective pictures. Participants (Experiment 1 N = 121, Experiment 2 N = 116) were assigned to the certain (CG) or uncertain group (UG), and they were presented with 100% (CG) or 50% (UG) S1-S2 congruency during an uninstructed (Experiment 1) or implicit (Experiment 2) learning phase. During the test phase both groups were presented with a new 75% S1-S2 paradigm, and ambiguous (Experiment 1) or unambiguous (Experiment 2) S1s. Participants were asked to rate the expected valence of upcoming S2s (expectancy ratings), or their experienced valence and arousal (valence and arousal ratings). In Experiment 1 ambiguous cues elicited less negative expectancy ratings, and less unpleasant valence ratings, independently of prior experience. In Experiment 2, both groups showed similar expectancies, predicting upcoming pictures' valence according to the 75% contingencies of the test phase. Overall, we found that in the presence of ambiguous cues subjective affective experience is dampened, and that implicit previous experience does not emerge at the subjective level by significantly shaping reported affective experience.

Does your past define you? The role of previous visual experience in subjective reactions to new affective pictures and sounds

According to predictive models of emotion, people use previous experience to construct affective predictions, represented multimodally in the brain. We do not live in a stable world, however. Some environments are uncertain, whereas others are not. In two experiments we investigated how experiencing previous certain versus uncertain contingencies shaped subjective reactions to future affective stimuli, within and across sensory modalities. Two S1-S2 paradigms were used as learning and test phases. S1s were colored circles, S2s negative/neutral affective pictures or sounds. During the learning phase, participants (N = 192, 179) were assigned to the certain (CG) or uncertain group (UG) and presented with 100% (CG) or 50% (UG) S1-S2 congruency between visual stimuli. During the test phase, participants were presented with a new 75% S1-S2 paradigm and visual (Experiment 1) or auditory (Experiment 2) S2s. Participants were asked to rate the expected valence of upcoming S2s (expectancy ratings) or valence and arousal to S2s. In both experiments, the CG reported more extreme expectancy ratings than the UG, suggesting that experiencing previous reliable S1-S2 associations led CG participants to subsequently predict similar associations. No group differences emerged on valence and arousal ratings, which were more prominently influenced by the new 75% contingencies of the test phase rather than by previous learned contingencies. Last, comparing the two experiments, no significant group by experiment interaction was found, supporting the hypothesis of cross-modality generalization at the subjective level. Overall, our results advance knowledge about the mechanisms by which previous learned contingencies shape subjective affective experience. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Adaptive Cognitive Control in Prematurely Born Children: An HD-EEG Investigation

Preterm birth is a neurodevelopmental risk condition often associated with cognitive control (CC) impairment. Recent evidence showed that CC can be implicitly adapted through associative learning. In the present study we investigated the ability to flexibly adjust CC as a function of implicit stimulus-response temporal regularities in preterm (PT; N = 21; mean age 8 ± 1.3 years; gestational age 30 ± 18.5 weeks) and full-term (FT; N = 20; mean age 8 ± 1.3 years) school-age children. All children underwent an HD-EEG recording while undergoing the Dynamic Temporal Prediction (DTP) task, a simple S1–S2 detection task purposely designed to generate local-global temporal predictability of imperative stimuli. The Wisconsin card sorting test (WCST) was administered to measure explicit CC. The PT group showed more premature and slower (DTP) as well as perseverative (WCST) responses than the FT group. Moreover, pre-terms showed poor adaptive CC as revealed by less efficient global response-speed adjustment. This behavioral pattern was mirrored by a reduced and less sensitive to global manipulation anticipatory Contingent Negative Variation (CNV) and by different cortical source recruitment. These findings suggest that implicit CC may be a reliable endophenotypic marker of atypical cognitive development associated with preterm birth.

Doing Experimental Psychological Research from Remote: How Alerting Differently Impacts Online vs. Lab Setting

Due to pandemic-imposed restrictions on lab-based research, we have recently witnessed a flourishing of online studies in experimental psychology, based on the collection of fine behavioral measures such as reaction times (RTs) and accuracy. However, it remains unclear whether participants’ alerting levels may have a different impact on behavioral performance in the online vs. lab setting. In this work we administered online and in-lab the dynamic temporal prediction (DTP) task, which requires an implicit modulation of participants’ alerting by alternating experimental conditions implying either slower or faster response rates. We then compared data distribution, RTs, accuracy, and time-on-task effects across the adult lifespan between the settings. We replicated online and across the whole age range considered (19–69 y) all the task-specific effects already found in-lab (both in terms of RTs and accuracy) beyond the overall RTs delay typical of the online setting. Moreover, we found an interaction between the setting and task-specific features so that participants showed slower RTs only in experimental conditions implying a less urgent response rate, while no RTs delay and a slight accuracy increase emerged in faster conditions. Thus, the online setting has been shown to be methodologically sound in eliciting comparable effects to those found in-lab. Moreover, behavioral performance seems to be more sensitive to task-induced alerting shifts in the online as compared to the lab setting, leading to either a heightened or reduced efficiency depending on a faster or slower response rate of experimental conditions, respectively.

Emotion regulation strategies differentially modulate neural activity across affective prediction stages: An HD-EEG investigation

Emotion regulation (ER) strategies can influence how affective predictions are constructed by the brain (generation stage) to prearrange action (implementation stage) and update internal models according to incoming stimuli (updating stage). However, neurocomputational mechanisms by which this is achieved are unclear. We investigated through high-density EEG if different ER strategies (expressive suppression vs. cognitive reappraisal) predicted event-related potentials (ERPs) and brain source activity across affective prediction stages, as a function of contextual uncertainty. An S1-S2 paradigm with emotional faces and pictures as S1s and S2s was presented to 36 undergraduates. Contextual uncertainty was manipulated across three blocks with 100, 75, or 50% S1-S2 affective congruency. The effects of ER strategies, as assessed through the Emotion Regulation Questionnaire, on ERP and brain source activity were tested for each prediction stage through linear mixed-effects models. No ER strategy affected prediction generation. During implementation, in the 75% block, a higher tendency to suppress emotions predicted higher activity in the left supplementary motor area at 1,500–2,000 ms post-stimulus, and smaller amplitude of the Contingent Negative Variation at 2,000–2,500 ms. During updating, in the 75% block, a higher tendency to cognitively reappraise emotions predicted larger P2, Late Positive Potential, and right orbitofrontal cortex activity. These results suggest that both ER strategies interact with the levels of contextual uncertainty by differently modulating ERPs and source activity, and that different strategies are deployed in a moderately predictive context, supporting the efficient updating of affective predictive models only in the context in which model updating occurs.

Unbalanced functional connectivity at rest affects the ERP correlates of affective prediction in high intolerance of uncertainty individuals: A high density EEG investigation

In a recent study we outlined the link between Intolerance of Uncertainty (IU) and the neural correlates of affective predictions, as constructed by the brain (generation stage) to prepare to relevant stimuli (implementation stage), and update predictive models according to incoming stimuli (updating stage). In this study we further explored whether the brain's functional organization at rest can modulate neural activity elicited within an emotional S1-S2 paradigm as a function of IU and uncertainty of S1-S2 contingencies. We computed resting state functional connectivity (RS-FC) from a 3-min resting period recorded with high density EEG, and we tested whether RS graph theory nodal measures (i.e., strength, clustering coefficient, betweenness centrality) predicted in-task ERP modulation as a function of IU. We found that RS-FC differently predicted in-task ERPs within the generation and updating stages. Higher IU levels were associated to altered RS-FC patterns within both domain-specific (i.e., right superior temporal sulcus) and domain-general regions (i.e., right orbitofrontal cortex), predictive of a reduced modulation of in-task ERPs in the generation and updating stages. This is presumably ascribable to an unbalancing between synchronization and integration within these regions, which may disrupt the exchange of information between top-down and bottom-up pathways. This altered RS-FC pattern may in turn result in the construction of less efficient affective predictions and a reduced ability to deal with contextual uncertainty in individuals high in IU.

Dealing with uncertainty: A high-density EEG investigation on how intolerance of uncertainty affects emotional predictions

Intolerance of uncertainty (IU) can influence emotional predictions, constructed by the brain (generation stage) to prearrange action (implementation stage), and update internal models according to incoming stimuli (updating stage). However, neurocomputational mechanisms by which IU affects emotional predictions are unclear. This high-density EEG study investigated if IU predicted event-related potentials (ERPs) and brain sources activity developing along the stages of emotional predictions, as a function of contextual uncertainty. Thirty-six undergraduates underwent a S1-S2 paradigm, with emotional faces and pictures as S1s and S2s, respectively. Contextual uncertainty was manipulated across three blocks, each with 100%, 75%, or 50% S1-S2 emotional congruency. ERPs, brain sources and their relationship with IU scores were analyzed for each stage. IU did not affect prediction generation. During prediction implementation, higher IU predicted larger Contingent Negative Variation in the 75% block, and lower left anterior cingulate cortex and supplementary motor area activations. During prediction updating, as IU increased P2 to positive S2s decreased, along with P2 and Late Positive Potential in the 75% block, and right orbito-frontal cortex activity to emotional S2s. IU was therefore associated with altered uncertainty assessment and heightened attention deployment during implementation, and to uncertainty avoidance, reduced attention to safety cues and disrupted access to emotion regulation strategies during prediction updating.

What's next? Neural correlates of emotional predictions: A high-density EEG investigation

Emotions were recently reconsidered as predictions, constructed by the brain (generation stage) to prearrange action (implementation stage), and update internal models according to incoming stimuli (updating stage). However, it is unclear how emotional predictions are shaped by stimuli predictability. This study investigated the role of stimuli predictability on emotional predictions through high-density EEG. Twenty-six undergraduates underwent a S1-S2 paradigm, with emotional faces as S1s and emotional pictures as S2s. Stimuli predictability was manipulated across three blocks, in which S1 valence was predictive of S2 in the 100%, 75%, or 50% of trials. ERPs and brain sources were analyzed for each stage. During prediction generation, a larger N170/superior temporal sulcus activity emerged to fearful faces in blocks with full (100%) and medium (75%) predictive ratios. During implementation, the random block (50%) elicited a valence-independent pre-allocation of resources, reflected by a larger CNV and activation of a wide left network. In the updating stage, emotional pictures always elicited a larger LPP, while a larger P2 to neutral stimuli and a higher activity of the orbitofrontal cortex signaled early valence-dependent and late block-dependent prediction errors. These findings provide the first evidence of how stimuli predictability shape each neurocomputational stage of emotional predictions construction.