Spatial Encoding Strategy Theory: The Relationship between Spatial Skill and STEM Achievement

Learners’ spatial skill is a reliable and significant predictor of achievement in STEM, including computing, education. Spatial skill is also malleable, meaning it can be improved through training. Most cognitive skill training improves performance on only a narrow set of similar tasks, but researchers have found ample evidence that spatial training can broadly improve STEM achievement. We do not yet know the cognitive mechanisms that make spatial skill training broadly transferable when other cognitive training is not, but understanding these mechanisms is important for developing training and instruction that consistently benefits learners, especially those starting with low spatial skill. This paper proposes the spatial encoding strategy (SpES) theory to explain the cognitive mechanisms connecting spatial skill and STEM achievement. To motivate SpES theory, the paper reviews research from STEM education, learning sciences, and psychology. SpES theory provides compelling post hoc explanations for the findings from this literature and aligns with neuroscience models about the functions of brain structures. The paper concludes with a plan for testing the theory’s validity and using it to inform future research and instruction. The paper focuses on implications for computing education, but the transferability of spatial skill to STEM performance makes the proposed theory relevant to many education communities

How metaphysical commitments shape the study of psychological mechanisms

The study of psychological mechanisms is an interdisciplinary endeavour, requiring insights from many different domains (from electrophysiology, to psychology, to theoretical neuroscience, to computer science). In this article, I argue that philosophy plays an essential role in this interdisciplinary project, and that effective scientific study of psychological mechanisms requires that working scientists be responsible metaphysicians. This means adopting deliberate metaphysical positions when studying mechanisms that go beyond what is empirically justified regarding the nature of the phenomenon being studied, the conditions of its occurrence, and its boundaries. Such metaphysical commitments are necessary in order to set up experimental protocols, determine which variables to manipulate under experimental conditions, and which conclusions to draw from different scientific models and theories. It is important for scientists to be aware of the metaphysical commitments they adopt, since they can easily be led astray if invoked carelessly

Emergence of qualia from brain activity or from an interaction of proto-consciousness with the brain: which one is the weirder? Available evidence and a research agenda

This contribution to the science of consciousness aims at comparing how two different theories can explain the emergence of different qualia experiences, meta-awareness, meta-cognition, the placebo effect, out-of-body experiences, cognitive therapy and meditation-induced brain changes, etc. The first theory postulates that qualia experiences derive from specific neural patterns, the second one, that qualia experiences derive from the interaction of a proto-consciousness with the brain\u2019s neural activity. From this comparison it will be possible to judge which one seems to better explain the different qualia experiences and to offer a more promising research agenda

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

The brain has a one-back memory for visual stimuli. Neural responses to an image contain as much information about the current image as it does about another image presented immediately before

Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution