abstract: When a rolling ball exits a spiral tube, it typically maintains its final inertial state and travels along straight line in concordance with Newton's first law of motion. Yet, most people predict that the ball will curve, a "naive physics" misconception called the curvilinear impetus (CI) bias. In the current paper, we explore the ecological hypothesis that the CI bias arises from overgeneralization of correct motion of biological agents. Previous research has established that humans curve when exiting a spiral maze, and college students believe this motion is the same for balls and humans. The current paper consists of two follow up experiments. The first experiment tested the exiting behavior of rodents from a spiral rat maze. Though there were weaknesses in design and procedures of the maze, the findings support that rats do not behave like humans who exhibit the CI bias when exiting a spiral maze. These results are consistent with the CI bias being an overgeneralization of human motion, rather than generic biological motion. The second experiment tested physics teachers on their conception of how a humans and balls behave when exiting a spiral tube. Teachers demonstrated correct knowledge of the straight trajectory of a ball, but generalized the ball's behavior to human motion. Thus physics teachers exhibit the opposite bias from college students and presume that all motion is like inanimate motion. This evidence supports that this type of naive physics inertial bias is at least partly due to participants overgeneralizing both inanimate and animate motion to be the same, perhaps in an effort to minimize cognitive reference memory load. In short, physics training appears not to eliminate the bias, but rather to simply shift it from the presumption of stereotypical animate to stereotypical inanimate behavior. / Dissertation/Thesis / M.A. Psychology 2013

I denna studie undersöks elevers förförståelse inom elektromagnetism innan undervisning om detta i kursen Fysik 2 på gymnasiets Naturvetenskapliga och Tekniska program. Syftet med studien är att öka den kvalitativa kunskapen om denna elevgrupps förförståelse för att därigenom kunna möta den med lämplig undervisning inom det elektromagnetiska området. Datainsamlingen har skett med en skriftlig enkät med flervalsfrågor som undersöker elevers begreppsliga förståelse för elektromagnetiska fenomen. Enkäten har sedan följts upp med elevintervjuer kring frågorna för att ytterligare kartlägga hur elever uppfattar elektromagnetiska fenomen. Enkätfrågorna är baserade på erkända test som används i hela världen. Studien visar att de alternativa uppfattningar om elektromagnetism som finns beskrivna i litteraturen även förekommer i elevgruppen och resultaten tyder på att de alternativa uppfattningarna är vanligare än vetenskapligt grundad förståelse. elevgruppen har ett vardagstänkande kring elektromagnetiska fenomen som utgår från magneters attraktion och repulsion - samt att de förenklat kan beskriva kompassens funktion. elevgruppens förförståelse i detta skede inte bara består av vardagstänkande enligt ovan utan även är tydligt präglad av tidigare fysikundervisning (främst kursen Fysik 1). eleverna i resonemangen om elektromagnetism visar mycket skiftande förmåga att applicera denna förförståelse korrekt på elektromagnetiska fenomen - och denna förmåga skiljer inte bara mellan individer utan även mellan olika sammanhang och de beskrivningssätt och representationer som används för att presentera problem och fenomen i dessa sammanhang. Insamlade data har bearbetats i en fenomenografisk analys av elevintervjuerna. Resultatet av den analysen är en beskrivning av elevernas förförståelse där tre kvalitativa nivåer föreslås. Exempel ur studiens elevintervjuer på de tre nivåerna av förförståelse redovisas. Med hjälp av nivåerna beskrivs hur förförståelsen i ökande grad präglas av ett vetenskapligt tänkande baserat på en förståelse av andra delar av fysiken och hur denna förståelse på olika sätt och med varierande korrekthet appliceras på elektromagnetiska fenomen och problemställningar.

Odour localisation is the problem of finding the source of an odour or other volatile chemical. It promises many valuable practical and humanitarian applications. Most localisation methods require a robot to reactively track an odour plume along its entire length. This approach is time consuming and may be not be possible in a cluttered indoor environment, where airflow tends to form sectors of circulating airflow. Such environments may be encountered in crawl-ways under floors, roof cavities, mines, caves, tree-canopies, air-ducts, sewers or tunnel systems. Operation in these places is important for such applications as search and rescue and locating the sources of toxic chemicals in an industrial setting. This thesis addresses odour localisation in this class of environments. The solution consists of a sense-map-plan-act style control scheme (and low level behaviour based controller) with two main stages. Firstly, the airflow in the environment is modelled using naive physics rules which are encapsulated into an algorithm named a Naive Reasoning Machine. It was used in preference to conventional methods as it is fast, does not require boundary conditions, and most importantly, provides approximate solutions to the degree of accuracy required for the task, with analogical data structures that are readily useful to a reasoning algorithm. Secondly, a reasoning algorithm navigates the robot to specific target locations that are determined with a physical map, the airflow map, and knowledge of odour dispersal. Sensor measurements at the target positions provide information regarding the likelihood that odour was emitted from potential odour source locations. The target positions and their traversal are determined so that all the potential odour source sites are accounted for. The core method provides values corresponding to the confidence that the odour source is located in a given region. A second search stage exploiting vision is then used to locate the specific location of the odour source within the predicted region. This comprises the second part of a bi-modal, two-stage search, with each stage exploiting complementary sensing modalities. Single hypothesis airflow modelling faces limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. A method is presented for dealing with these uncertainties, by generating multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. This method improves the robustness of odour localisation in the presence of uncertainties, making it possible where the single hypothesis method would fail. It also demonstrates the potential for integrating naive physics into a statistical framework. Extensive experimental results are presented to support the methods described above.