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Considerable delays between causes and effects are commonly found in real life. However, previous studies have only investigated how well people can learn probabilistic relations with delays on the order of seconds. In the current study we tested whether people can learn a cause-effect relation with delays of 0, 3, 9, or 21hours, and the study lasted 16 days. We found that learning was slowed with longer delays, but by the end of 16 days participants had learned the cause-effect relation in all four conditions, and they had learned the relation about equally well in all four conditions. This suggests that in real-world situations people may still be fairly accurate at inferring cause-effect relations with delays if they have enough experience. We also discuss ways that delays may interact with other real-world factors that could complicate learning. 

The memory benefit that arises from distributing learning over time rather than in consecutive sessions is one of the most robust effects in cognitive psychology. While prior work has mainly focused on repeated exposures to the same information, in the real world, mnemonic content is dynamic, with some pieces of information staying stable while others vary. Thus, open questions remain about the efficacy of the spacing effect in the face of variability in the mnemonic content. Here, in two experiments, we investigated the contributions of mnemonic variability and the timescale of spacing intervals, ranging from seconds to days, to long-term memory. For item memory, both mnemonic variability and spacing intervals were beneficial for memory; however, mnemonic variability was greater at shorter spacing intervals. In contrast, for associative memory, repetition rather than mnemonic variability was beneficial for memory, and spacing benefits only emerged in the absence of mnemonic variability. These results highlight a critical role for mnemonic variability and the timescale of spacing intervals in the spacing effect, bringing this classic memory paradigm into more ecologically valid contexts. 

People often test changes to see if the change is producing the desired result (e.g., does taking an antidepressant improve my mood, or does keeping to a consistent schedule reduce a child’s tantrums?). Despite the prevalence of such decisions in everyday life, it is unknown how well people can assess whether the change has influenced the result. According to interrupted time series analysis (ITSA), doing so involves assessing whether there has been a change to the mean (‘level’) or slope of the outcome, after versus before the change. Making this assessment could be hard for multiple reasons. First, people may have difficulty understanding the need to control the slope prior to the change. Additionally, one may need to remember events that occurred prior to the change, which may be a long time ago. In Experiments 1 and 2, we tested how well people can judge causality in 9 ITSA situations across 4 presentation formats in which participants were presented with the data simultaneously or in quick succession. We also explored individual differences. In Experiment 3, we tested how well people can judge causality when the events were spaced out once per day, mimicking a more realistic timeframe of how people make changes in their lives. We found that participants were able to learn accurate causal relations when there is a zero pre-intervention slope in the time series but had difficulty controlling for nonzero pre-intervention slopes. We discuss these results in terms of 2 heuristics that people might use. 

Beyond lithium-ion technologies, lithium−sulfur batteries stand out because of their multielectron redox reactions and high theoretical specific energy (2500 Wh kg−1). However, the intrinsic irreversible transformation of soluble lithium polysulfides to solid short-chain sulfur species (Li2S2 and Li2S) and the associated large volume change of electrode materials significantly impair the long-term stability of the battery. Here we present a liquid sulfur electrode consisting of lithium thiophosphate complexes dissolved in organic solvents that enable the bonding and storage of discharge reaction products without precipitation. Insights garnered from coupled spectroscopic and density functional theory studies guide the complex molecular design, complexation mechanism, and associated electrochemical reaction mechanism. With the novel complexes as cathode materials, high specific capacity (1425 mAh g−1 at 0.2 C) and excellent cycling stability (80% retention after 400 cycles at 0.5 C) are achieved at room temperature. Moreover, the highly reversible all-liquid electrochemical conversion enables excellent low temperature battery operability (>400 mAh g−1 at −40 °C and >200 mAh g−1 at −60 °C). This work opens new avenues to design and tailor the sulfur electrode for enhanced electrochemical performance across a wide operating temperature range. 

Despite the various strategies for achieving metal–nitrogen–carbon (M–N–C) single-atom catalysts (SACs) with different microenvironments for electrochemical carbon dioxide reduction reaction (CO 2 RR), the synthesis–structure–performance correlation remains elusive due to the lack of well-controlled synthetic approaches. Here, we employed Ni nanoparticles as starting materials for the direct synthesis of nickel (Ni) SACs in one spot through harvesting the interaction between metallic Ni and N atoms in the precursor during the chemical vapor deposition growth of hierarchical N-doped graphene fibers. By combining with first-principle calculations, we found that the Ni-N configuration is closely correlated to the N contents in the precursor, in which the acetonitrile with a high N/C ratio favors the formation of Ni-N 3 , while the pyridine with a low N/C ratio is more likely to promote the evolution of Ni-N 2 . Moreover, we revealed that the presence of N favors the formation of H-terminated edge of sp 2 carbon and consequently leads to the formation of graphene fibers consisting of vertically stacked graphene flakes, instead of the traditional growth of carbon nanotubes on Ni nanoparticles. With a high capability in balancing the *COOH formation and *CO desorption, the as-prepared hierarchical N-doped graphene nanofibers with Ni-N 3 sites exhibit a superior CO 2 RR performance compared to that with Ni-N 2 and Ni-N 4 ones. 

Abstract Since the 1950s, observations and climate models show an amplification of sea surface temperature (SST) seasonal cycle in response to global warming over most of the global oceans except for the Southern Ocean (SO), however the cause remains poorly understood. In this study, we analyzed observations, ocean reanalysis, and a set of historical and abruptly quadrupled CO₂simulations from the Coupled Model Intercomparison Project Phase 6 archive and found that the weakened SST seasonal cycle over the SO could be mainly attributed to the intensification of summertime westerly winds. Under the historical warming, the intensification of summertime westerly winds over the SO effectively deepens ocean mixed layer and damps surface warming, but this effect is considerably weaker in winter, thus weakening the SST seasonal cycle. This wind‐driven mechanism is further supported by our targeted coupled model experiments with the wind intensification effects being removed.