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The Infuence of Context Representations on Cognitive Control States
Get PDFCognitive control operates via two distinct mechanisms, proactive and reactive control. These control states are engaged differentially, depending on a number of within-subject factors, but also between-group variables. While research has begun to explore if shifts in control can be experimentally modulated, little is known about whether context impacts which control state is utilized. Thus, we test if contextual factors temporarily bias the use of a particular control state long enough to impact performance on a subsequent task. Our methodology involves two parts: first participants are exposed to a context manipulation designed to promote proactive or reactive processing through amount or availability of advanced preparation within a task-switching paradigm. Then, they complete an AX-CPT task, where we assess immediate transfer on preferential adoption of one control mode over another. We present results from a Pilot Study that revealed anecdotal evidence of proactive versus reactive processing for a context manipulation using long and short preparation times. We also present data from a follow-up Registered Experiment that implements a context manipulation using long or no preparation times to assess if a more extreme context leads to pronounced differences on AX-CPT performance. Together, the results suggest that contextual representations do not impact the engagement of a particular control state, but rather, there is a general preference for the engagement of proactive control
Modeling and Simulation of a Hybrid Battery Pack Using Li-Ion Battery and Supercapacitors for Class 2A Light Duty Pick-Up Truck Application
No full textThis paper investigates the battery pack capacity optimization problem for a full-size electric pick-up truck using a hybrid battery pack consisting of supercapacitors and Li-Ion batteries. It also aims to simulate and study the thermal and ageing effects on the battery pack. A simplified mathematical model of an electric pick-up truck was developed using the power consumption-based approach which includes roadway grade, rolling resistance and wind resistance as the vehicle model along with modelling the electric powertrain. The proposed model takes into account the dynamic behavior of the system resulting in real time power demand by the electric motor and energy delivered by the storage system. The validation of the model is done by running the model through different US EPA Drive Cycles which provide the second-by-second velocity and time data inputs. The energy consumed by the vehicle model is then subsequently used to compute the battery ageing effects of the battery pack
