Knowledge Representation, Heuristics, and Awareness in Artificial Grammar Learning

People can become sensitive to the general structure of different parts of the environment, often without studying that general structure directly, but through being incidentally exposed to instances that conform to the structure. When such learning proceeds unintentionally and gives rise to knowledge that is difficult to verbalize it is often referred to as implicit learning. One of the most commonly used experimental paradigms in the study of implicit learning is artificial grammar learning, in which participants are exposed to sequences that conform to a set of rules without being informed about the presence of rules. In a subsequent test phase, participants can usually distinguish between sequences that conform to and sequences that violate the rules, without being able to say much about the underlying rules. There are many different theories about the kind of knowledge representations that underlie sensitivity to general structure in artificial grammar learning, and there are also different viewpoints concerning how to measure the conscious status of the knowledge acquired in artificial grammar learning. Investigating these different theories is important, partly because it may provide an understanding of the extent to which complex learning and abstraction of structure proceeds unconsciously. Study I of this thesis investigated artificial grammar learning and the use of a fluency heuristic, which involves relying on the surprising ease of processing an item as a basis for making a judgment. Other studies have shown that the fluency heuristic is used in a wide variety of judgments (e.g., recognition and preference). Study I showed that participants rely on a fluency heuristic in artificial grammar learning as well, but mainly under non-analytic pro¬ces¬sing conditions when participants were encouraged to respond rapidly and thereby make global judgments about items without processing details to any large extent. This is consistent with the idea that fluency may provide a cue for indirect sensitivity to general structure. Study II investigated the effect of non-analytic processing on the conscious status of knowledge as assessed by confidence judgments. It was found that non-analytic processing increased the availability of conscious knowledge, consistent with the idea that part of the knowledge acquired in artificial grammar learning may be, not inherently unconscious, but of a kind that is available through a non-analytic form of introspection. One possibility is that, relative to more analytic forms of introspection, non-analytic introspection may be more sensitive to the non-focal peripheral contents of consciousness, the so called “fringe consciousness”. This could explain why the knowledge acquired in artificial grammar learning often seems intuitive, even though it is not necessarily unconscious. Study III investigated whether artificial grammar learning gives rise to knowledge that is independent from the surface features of the exposure material. A number of claims have been offered in the literature for such surface-independent knowledge, particularly as a result of extended exposure to regularities. The results clearly suggested that the knowledge formed under observational learning conditions in artificial grammar learning is not independent from the surface features of the exposure material. The results are consistent with a variety of computational models of artificial grammar learning that rely on surface-dependent perceptual representations. Finally, Study IV investigated whether the knowledge acquired in artificial grammar learning is unconscious in the sense that it may be expressed unintentionally. The results showed that, to the extent that knowledge was expressed, it was expressed intentionally. However, the low levels of performance in Study IV limit the generality of the findings. Possible reasons for the low performance are discussed in the context of different models of artificial grammar learning. Taken together, the studies in this thesis illuminate issues regarding both knowledge representation and the conscious status of knowledge in artificial grammar learning. In general, the studies are in line with an episodic framework according to which the general abstract structure of a domain is not automatically extracted. Instead, both learning and awareness proceeds as a function of task demands, intentions, expectations, and processing strategies

Machine Learning Based Methods for Virtual Validation of Autonomous Driving

During the last decade, automotive manufacturers have introduced increasingly capable driving automation functions in consumer vehicles. As the functionality becomes more advanced, the task of driving moves from the human to the car. Hence, making sure that autonomous driving (AD) functions are reliable and safe is of high importance. Often, increased levels of automation result in more complex safety validation procedures, that may be both expensive, time consuming, and dangerous to perform. One way to address these problems is to move parts of the validation to the virtual domain. In this thesis, we investigate methods for validating AD functionality in virtual simulation environments, using methods from machine learning and statistics. The main focus is on how to make virtual simulations resemble real-world conditions as closely as possible. We tackle this with an approach based on sensor error modeling. Specifically, we develop a statistical sensor error model that can be used to make ideal object measurements from simulations resemble measurements obtained from the perception system of a real-world vehicle. The model, which is based on autoregressive recurrent mixture density networks, was trained on sensor error data collected on European roads. The second part considers system falsification using reinforcement learning (RL); a flexible framework for validation of system safety, which naturally allows for the integration of, e.g., sensor error models. We compare results of system falsification using RL to an exact approach based on reachability analysis.With this thesis, we take steps towards more realistic statistical sensor error models for virtual simulation environments. We also demonstrate that approximate methods based on reinforcement learning may serve as an alternative to reachability analysis for validation of high-dimensional systems. Finally, we connect the RL falsification application to sensor error modeling as a possible direction for future research

Financial implications of relationship breakdown: Does marriage matter?

In raw data in the UK, the income loss on separation for women who were cohabiting is less than the loss for those who were married. Cohabitants lose less even after controlling for observable characteristics including age and the number of children. This difference is not explained by differences in access to benefits or labor supply responses after separation. In contrast, there is no difference in the change in household income experienced by cohabiting and married men who do better on average than both groups of women. We show that the difference for women arises because of differences in the use of family support networks: cohabitants’ standard of living falls by less because they are more likely to live with other adults, particularly their family, following separation, even after controlling for age and children. Divorced women do not return to living with their extended families. The greater legal protection offered by marriage does not appear to translate into economic protection.Fisher acknowledges the support of the Australian Research Council Discovery Project (DP150101718) and Australian Research Council Centre of Excellence for Children and Families over the Life Course (CE140100027). The Centre is administered by the Institute for Social Science Research at The University of Queensland, with nodes at The University of Western Australia, The University of Melbourne and The University of Sydney. The views expressed in this paper are not necessarily those of the Australian Research Council. Low thanks funding from the ESRC as a Research Fellow, grant number RES-063-27-0211.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s11150-015-9292-

Judicial Review and the Uncertain Appeal of Certainty on Appeal

Syftet med denna studie är att samla kunskap om lärares uppfattningar om och värdering av estetiska uttrycksformer i undervisningen. Studiens empiriska material har samlats in genom en kvalitativ intervjustudie där åtta lärare på sex olika skolor har intervjuats. De medverkande lärarna arbetar alla på lågstadiet i samma kommun och har ansvar för en egen klass. Lärarna har inte behövt speciella kompetenser eller behörigheter inom de estetiska uttrycksformerna för att delta i studien och informanternas ålder och kön har inte haft någon betydelse. Resultaten i studien visar att lärarna uppfattar de estetiska uttrycksformerna som lustfyllda och stimulerande för eleverna. Vidare framkommer det att de estetiska uttrycksformerna i stor utsträckning uppfattas som verktyg för att främja lärandet i skolans andra ämnen. Lärarna anser att bild är lättast att integrera med andra ämnen. En gemensam uppfattning hos lärarna är att de estetiska uttrycksformerna prioriteras bort till förmån för andra teoretiska ämnen. Resultatet i vår studie visar att lärarnas egen trygghet och kunskap är det som påverkar undervisningen. Andra faktorer som påverkar arbetssätt som innefattar estetiska uttrycksformer är tillgång på material och lokaler

The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models

Large Language Models (LLMs) make natural interfaces to factual knowledge, but their usefulness is limited by their tendency to deliver inconsistent answers to semantically equivalent questions. For example, a model might predict both "Anne Redpath passed away in Edinburgh." and "Anne Redpath's life ended in London." In this work, we identify potential causes of inconsistency and evaluate the effectiveness of two mitigation strategies: up-scaling and augmenting the LM with a retrieval corpus. Our results on the LLaMA and Atlas models show that both strategies reduce inconsistency while retrieval augmentation is considerably more efficient. We further consider and disentangle the consistency contributions of different components of Atlas. For all LMs evaluated we find that syntactical form and other evaluation task artifacts impact consistency. Taken together, our results provide a better understanding of the factors affecting the factual consistency of language models.Comment: Accepted at EMNLP 202

Trädslagsjämförelser på Omberg

Det här examensarbetet är skrivet på C-nivå, inom ämnet skogshushållning på institutionen för Sydsvensk skogsvetenskap. Omfattningen är 10 poäng (15 hp), vilket motsvarar 10 veckors heltidsstudier. Handledare för arbetet har varit Ulf Johansson och Per Magnus Ekö, examinator var Eric Agestam. Studien handlar om skogliga försöksytor på Omberg i Östergötland. Bakgrunden till att jag valde att skriva det här arbetet är att skogarna på Omberg sedan låg tid tillbaka har varit föremål för skoglig forskning och försöksverksamhet. Redan i början av 1900-talet anlade dåvarande Statens Skogsförsöksanstalt de första skogliga försöksytorna där. Det har inte på senare tid gjorts någon samlad redovisningen av dessa försök. Idén till studien kom ursprungligen från Per Pettersson (Sveaskog). År 2003 avsattes skogarna på Omberg av Sveaskog till en eko-park. Sveaskog satsar på informationsverksamhet inom ekoparken och i detta sammanhang finns ett önskemål om att tillgängliggöra resultat och data från befintliga skogliga långtidsförsök. Arbetet handlar om följande • Sammanställning av försöksresultat • Produktionsjämförelser mellan olika trädslag • Utreda om det finns försöksytor som går att använda i informationssyfte. Ett varmt tack riktas till Ulf Johansson och Per-Magnus Ekö, som på ett utmärkt sätt fungerat som handledare till det här arbetet. Jag vill även tacka Sverker Svensson (Sveaskog) för värdefull hjälp under fältdatainsamlingen.The purpose of this study was (i) to describe a number of long term forest experiments at Omberg, in the region of Östergötland, (ii) to compare the production between different tree species and (iii) to examine the possibilities of using permanent sample plot data for information activities at Omberg. The sample plots were visited in the field and a photos were taken. The tree species comparisons used data from the permanent sample plots together with newly collected data (site index comparisons) from temporary sample plots in nearby stands of Norway spruce. The results indicated that Norway spruce (Picea abies (L.) Karst.) had a high production over the whole of Omberg. However, one problem was that many Norvay spruce stands at Omberg were damaged by wind. Furthermore, stands of Silver fir (Abies alba L.) performed well and had in many cases higher production than Norway spruce. Also some other tree species had high production levels, for exampel beech (Fagus sylvatica L.) and larch (Larix sp.). Ash (Fraxinus excelsior L.) and mixed stands of Norway spruce and birch (Betula sp.), as well as mixtures of Norway spruce and common alder (Alnus glutinosa (L.) Gaertn.), had a low production compared to other tree species studied. The permanent sample plots 260, 262, 280, 574, 837 and 838 are suitable for information purpose. The data can be exposed on permanent field posters at the sample plots

Urban Navigation with LTE using a Large Antenna Array and Machine Learning

Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block

Graph Convolutional Networks for Complex Traffic Scenario Classification

A scenario-based testing approach can reduce the time required to obtain statistically significant evidence of the safety of Automated Driving Systems (ADS). Identifying these scenarios in an automated manner is a challenging task. Most methods on scenario classification do not work for complex scenarios with diverse environments (highways, urban) and interaction with other traffic agents. This is mirrored in their approaches which model an individual vehicle in relation to its environment, but neglect the interaction between multiple vehicles (e.g. cut-ins, stationary lead vehicle). Furthermore, existing datasets lack diversity and do not have per-frame annotations to accurately learn the start and end time of a scenario. We propose a method for complex traffic scenario classification that is able to model the interaction of a vehicle with the environment, as well as other agents. We use Graph Convolutional Networks to model spatial and temporal aspects of these scenarios. Expanding the nuScenes and Argoverse 2 driving datasets, we introduce a scenario-labeled dataset, which covers different driving environments and is annotated per frame. Training our method on this dataset, we present a promising baseline for future research on per-frame complex scenario classification.Comment: Netherlands Conference on Computer Vision (NCCV) 2023 camera-ready + supplementary materia