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High‐resolution bathymetry and three‐dimensional seismic data along the Cocos Ridge reveal a 245 km²field of ∼1–4 km in diameter seafloor depressions. The seafloor depressions are part of a two‐tiered honeycomb pattern. The lower‐tier depressions have steep faults that truncate strata with chaotic internal reflections consistent with sediment collapse into the depression. These extend into a lens shaped interval just above igneous basement. Overlying these depressions is a second broader set with rough seafloor morphology with gently dipping boundaries defined by pinch‐out stratigraphic patterns. Drilling results indicate that the lens‐shaped zones that host the deeper depressions represent anomalous regions of high porosity, low velocity, and low density within calcareous rich sediment. Analysis of nannofossils from IODP Site U1414 suggests the collapse structures formed during the late Miocene, whereas the younger shallower depressions likely formed between the early Pliocene and the Pliocene‐Pleistocene boundary. Geochemical and petrological analysis at Site U1414 suggests that hydrothermal circulation during the late Miocene led to carbonate dissolution and collapse. Following collapse, focused fluid‐flow and bottom current scouring resulted in formation of the overlying set of depressions and a honeycomb seafloor morphology. Similar sets of depressions along the Carnegie Ridge to the south support the hypothesis that two‐tiered depressions formed in response to processes that occurred broadly across the Panama Basin between the late Miocene and the Pliocene‐Pleistocene transition. Geochemical results at Site U1414, combined with geophysical data, suggest this two‐tiered system of depressions currently guides ongoing fluid outflow.

When continental rifting is accompanied by localized magmatism under extensional stress, the breakup duration can be short and the continent/ocean transition sharp, as mantle melts are thought to be efficient at heating and weakening the lithosphere. This mode of rifting has been invoked for the Eastern North American Margin (ENAM) based on the existing geophysical data. Here, we present results from multichannel seismic profiles from the ENAM Community Seismic Experiment offshore North Carolina, U.S. Our survey area encompasses both the East Coast Magnetic Anomaly (ECMA) and the Blake Spur Magnetic Anomaly (BSMA), which lies ~200‐km farther seaward. Our prestack depth‐migrated seismic images reveal major changes in the structure of the igneous crust across the BSMA. Between the ECMA and BSMA, we image a proto‐oceanic domain of rough, faulted, and thin igneous crust. The roughness of this oceanic crust is similar to modern ultraslow spreading environments which involve the continued presence of a pre‐existing lithospheric lid. Seaward of the BSMA the basement is smooth, and the crust is relatively thick, which is typical for Jurassic oceanic crust. Across the BSMA, we image a step up in basement and crustal root, which we interpret to represent complete lithospheric breakup and a transition to steady‐state seafloor spreading in agreement with coincident refraction results. Our results would also indicate low extension rates in the final stages of rifting that may have influenced the thermal structure of the lithosphere and could explain the delay for continental breakup. All of these observations show that although continental rifting between eastern North America and northwest Africa was assisted by magmatic activity, it did not lead to rapid localization of extensional strain as previously thought.

We describe and experimentally validate a question-asking framework for machine-learned linguistic knowledge about human emotions. Using the Socratic method as a theoretical inspiration, we develop an experimental method and computational model for computers to learn subjective information about emotions by playing emotion twenty questions (EMO20Q), a game of twenty questions limited to words denoting emotions. Using human–human EMO20Q data we bootstrap a sequential Bayesian model that drives a generalized pushdown automaton-based dialog agent that further learns from 300 human–computer dialogs collected on Amazon Mechanical Turk. The human–human EMO20Q dialogs show the capability of humans to use a large, rich, subjective vocabulary of emotion words. Training on successive batches of human–computer EMO20Q dialogs shows that the automated agent is able to learn from subsequent human–computer interactions. Our results show that the training procedure enables the agent to learn a large set of emotion words. The fully trained agent successfully completes EMO20Q at 67% of human performance and 30% better than the bootstrapped agent. Even when the agent fails to guess the human opponent’s emotion word in the EMO20Q game, the agent’s behavior of searching for knowledge makes it appear human-like, which enables the agent to maintain user engagement and learn new, out-of-vocabulary words. These results lead us to conclude that the question-asking methodology and its implementation as a sequential Bayes pushdown automaton are a successful model for the cognitive abilities involved in learning, retrieving, and using emotion words by an automated agent in a dialog setting.