The frequency-domain marine controlled source electromagnetic (CSEM) method has recently become a tool in determining subsurface resistivity related to hydrocarbon formations in the deep water environment. In shallow water, this frequency-domain method is subject to airwave saturation that severely limits sensitivity to targets at depth. It has been suggested that time-domain CSEM may offer an improved resolution to these deep targets, as well as increased sensitivity to resistors in the presence of the airwave. In order to examine and test these claims, a modeling code has been developed for computing time-domain responses for layered 1D models with arbitrarily located and oriented transmitters and receivers. The code extends the open-source frequency domain code Dipole1D by efficiently computing the time- domain, step-on, and impulse responses by Fourier transformation of the frequency-domain kernels. Impulse responses are used along with pseudo-random binary sequences (PRBS) to generate synthetic time-domain data. A realistic noise model and waveform scaling effects are then applied to synthetic step-on, PRBS, and the frequency -domain SIO "Waveform D" data generated from this code. Wiener deconvolution is applied to recover impulse responses from the PRBS data, allowing for a systematic examination of the sensitivity and resolution of time- domain and frequency-domain CSEM to representative targets of interest for offshore hydrocarbon exploration. These studies suggest that there is no large ad- vantage to time -domain techniques, as previously suggested, and rather that the frequency-domain Waveform D should give better results in the presence of noise for the shallow marine setting

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between words/concepts. Traditional measures of semantic similarity typically rely on corpus-derived linguistic distributional similarity (e.g. LSA) or distance in taxonomic databases (e.g. WordNet), which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor similarity between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor norms. We demonstrate that, in modelling human similarity and relatedness judgements, sensorimotor similarity has comparable explanatory power to LSA and WordNet distance, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both fully grounded and computationally efficient. We further introduce a web-based tool for easily calculating and visualising sensorimotor similarity between words, featuring coverage of nearly 800 million word pairs.

Category production tasks (aka semantic fluency) typically concentrate on concrete categories, meaning little is known about abstract categories and potential differences in their structure. Using a category production task for 67 concrete (e.g., animals, tools) and 50 abstract (e.g., science, emotion) categories, we investigated differences in produced member concepts. Abstract categories were smaller than concrete categories, their member concepts were generated more slowly, and had longer and more phonologically-distinct names. Abstract category members were grounded in sensorimotor experience (i.e., with high sensorimotor strength), though overall to a lesser extent than concrete category members. Several ostensibly abstract categories appeared amongst the highest-rated in sensorimotor strength (e.g., sport) while some ostensibly concrete categories were amongst the lowest-rated (e.g., chemical element). The data highlight linguistic and semantic differences in concrete and abstract category structure, but also that they share a common sensorimotor grounding.