Prediction From Minimal Experience: How People Predict the Duration of an Ongoing Epidemic.

People are known for good predictions in domains they have rich experience with, such as everyday statistics and intuitive physics. But how well can they predict for problems they lack experience with, such as the duration of an ongoing epidemic caused by a new virus? Amid the first wave of COVID-19 in China, we conducted an online diary study, asking each of over 400 participants to predict the remaining duration of the epidemic, once per day for 14 days. Participants' predictions reflected a reasonable use of publicly available information but were meanwhile biased, subject to the influence of negative affect and future time perspectives. Computational modeling revealed that participants neither relied on prior distributions of epidemic durations as in inferring everyday statistics, nor on mechanistic simulations of epidemic dynamics as in computing intuitive physics. Instead, with minimal experience, participants' predictions were best explained by similarity-based generalization of the temporal pattern of epidemic statistics. In two control experiments, we further confirmed that such cognitive algorithm is not specific to the epidemic scenario and that minimal and rich experience do lead to different prediction behaviors for the same observations. We conclude that people generalize patterns in recent history to predict the future under minimal experience.

Distinct neural-behavioral correspondence within face processing and attention networks for the composite face effect.

The composite face effect (CFE) is recognized as a hallmark for holistic face processing, but our knowledge remains sparse about its cognitive and neural loci. Using functional magnetic resonance imaging with independent localizer and complete composite face task, we here investigated its neural-behavioral correspondence within face processing and attention networks. Complementing classical comparisons, we adopted a dimensional reduction approach to explore the core cognitive constructs of the behavioral CFE measurement. Our univariate analyses found an alignment effect in regions associated with both the extended face processing network and attention networks. Further representational similarity analyses based on Euclidian distances among all experimental conditions were used to identify cortical regions with reliable neural-behavioral correspondences. Multidimensional scaling and hierarchical clustering analyses for neural-behavioral correspondence data revealed two principal components underlying the behavioral CFE effect, which fit best to the neural responses in the bilateral insula and medial frontal gyrus. These findings highlight the distinct neurocognitive contributions of both face processing and attentional networks to the behavioral CFE outcome, which bridge the gaps between face recognition and attentional control models.

Effects of discrepancy between imagined and perceived sounds on the N2 component of the event-related potential.

Two experiments were conducted to examine whether the N2 component of the event-related potential (ERP), typically elicited in a S1-S2 matching task and considered to reflect mismatch process, can still be elicited when the S1 was imagined instead of perceived and to investigate how N2 amplitude varied with the degree of S1-S2 discrepancy. Three levels of discrepancy were defined by the degree of separation between the heard (S2) and imagined (S1) sounds. It was found that the N2 was reliably elicited when the perceived S2 differed from the imagined S1, but whether N2 amplitude increased with the degree of discrepancy depended in part on the S1-S2 discriminability (as evidenced by reaction time). Specifically, the effect of increasing discrepancy was attenuated as discriminability increased from hard to easy. These results, together with the dynamic ERP topography observed within the N2 window, suggest that the N2 effect reflects two sequential but overlapping processes: automatic mismatch and controlled detection.