He Hit First : An Analysis of Mixed Martial Arts Matches to Determine The Significance of the First Strike

Tämän tutkielman tavoite on selvittää ensimmäisen lyönnin merkitys vapaaotteluerässä. Teoreettisena pohjana tutkimukselle käytetään psykologinen liikevoima -ilmiötä kuvailevia teorioita. Näiden teorioiden mukaan, jos ensimmäinen osuma on riittävä lähtökohta positiiviselle ja/tai negatiiviselle psykologiselle liikevoimalle, vaikutuksen tulisi näkyä lyöntimäärässä ensimmäisen hyökkäyksen jälkeen. Näkökulmia tarkastellaan sekä itsepuolustuksen että kamppailu-urheilun näkökulmista. Ilmiönä psykologinen liikevoima on edelleen kiistanalainen. Sitä on aiemmin tutkittu urheiluympäristöissä, joissa vastapelaajan toiminta vaikuttaa toiseen pelaajaan. Vapaaottelussa tämä vaikutus on välittömämpi, koska kamppailijat ovat fyysisesti huomattavasti läheisemmässä kontaktissa. Dataa kerättiin sekä katsomalla Ultimate Fighting Championships -otteluita että Fight Metric -sivustolta, joka kokoaa tiedot kaikista Ultimate Fighting Championships -otteluista. Data koostuu yhteensä 104 ottelusta. Ensimmäisen hyökkääjän kokonaislyöntimäärää ennustetaan sarjalla lineaarisia sekamalleja ja reaktiotilanteita verrataan kontrastianalyysilla. Päätulos on, että jos ensimmäinen hyökkäys torjutaan ottelun kolmannessa erässä, lyö ensimmäisenä hyökännyt ottelija enemmän erän aikana. Tästä voidaan päätellä, että ensin hyökkäävä ottelija lyö enemmän, jos ensimmäinen hyökkäys torjutaan, mutta vain kolmannessa erässä. Tämä tarkoittaa, että kolmas erä eroaa kahdesta ensimmäisestä erästä, mutta syy siihen on epäselvä. Tätä havaintoa voidaan käyttää hyväksi kamppailu-urheilun valmennusstrategioissa, ja sen tulisi motivoida lisätutkimuksia ensimmäisen hyökkäyksen merkitykseen sekä itsepuolustustilanteissa että kamppailu-urheilussa.The aim of this thesis is to investigate the effects of the first hit in a round of mixed martial arts competition. The theoretical background comes from theories of psychological momentum. Following these theories, if the first hit is a robust starting point for positive and/or negative psychological momentum, the effect should show in the amount of hitting following the first attack. Perspectives for both self defence and combat sports are considered. Psychological momentum as a phenomenon remains controversial. It has previously been investigated in sports contexts where the opposing player’s actions have an effect on the other player. In a mixed martial arts match that effect is more immediate due to the physical proximity of the fighters to each other. Data was gathered both by viewing Ultimate Fighting Championships matches and from the Fight Metric website, which holds records for all Ultimate Fighting Championships matches. The data consists of a total of 104 matches. A series of linear mixed models is fitted to predict the first attacker’s total strikes based on the opponent’s reaction, and a contrast analysis is used to compare the conditions based on reaction. The main result is that on the third round of the match, if the fighter who hits first is blocked, he or she will hit more during that round. The conclusion is that the fighter attacking first strikes more if his or her attack is blocked, but only on the third round. While this implies that the third round is different from the first two, the reason for that is unclear. This finding can be used to inform combat sports coaches’ strategies, and should motivate further investigations to the significance of the first attack in both self defence situations and in combat sports

Mismatch negativity (MMN) elicited by abstract regularity violations in two concurrent auditory streams

The study investigated whether violations of abstract regularities in two parallel auditory stimulus streams can elicit the MMN (mismatch negativity) event-related potential. Tone pairs from a low (220-392 Hz) and a high (1319-2349 Hz) stream were delivered in an alternating order either at a fast or a slow pace. With the slow pace, the pairs were perceptually heard as a single stream obeying an alternating low pair-high pair pattern, whereas with the fast pace, an experience of two separate auditory streams, low and high, emerged. Both streams contained standard and deviant pairs. The standard pairs were either in both streams ascending in the direction of the within-pair pitch change or in the one stream ascending and in the other stream descending. The direction of the deviant pairs was opposite to that of the same-stream standard pairs. The participant's task was either to ignore the auditory stimuli or to detect the deviant pairs in the designated stream. The deviant pairs elicited an MMN both when the directions of the standard pairs in the two streams were the same or when they were opposite. The MMN was present irrespective of the pace of stimulation. The results indicate that the preattentive brain mechanisms, reflected by the MMN, can extract abstract regularities from two concurrent streams even when the regularities are opposite in the two streams, and independently of whether there perceptually exists only one stimulus stream or two segregated streams. These results demonstrate the brain's remarkable ability to model various regularities embedded in the auditory environment and update the models when the regularities are violated. The observed phenomena can be related to several aspects of auditory information processing, e.g., music and speech perception and different forms of attention.Peer reviewe

Evidence for a Behaviourally Measurable Perseverance Trait in Humans

The aim was to create and study a possible behavioural measure for trait(s) in humans that reflect the ability and motivation to continue an unpleasant behaviour, i.e., behavioural perseverance or persistence (BP). We utilised six different tasks with 54 subjects to measure the possible BP trait(s): cold pressor task, hand grip endurance task, impossible anagram task, impossible verbal reasoning task, thread and needle task, and boring video task. The task performances formed two BP factors. Together, the two-factor solution is responsible for the common variance constituting 37.3% of the total variance in the performances i.e., performance times. Excluding the impossible anagram task, the performance in any given task was better explained by performances in the other tasks (i.e., “trait”, η2 range = 0.131–0.253) than by the rank order variable (“depletion”, i.e., getting tired from the previous tasks, η2 range = 0–0.096)

Evidence for a Behaviourally Measurable Perseverance Trait in Humans

The aim was to create and study a possible behavioural measure for trait(s) in humans that reflect the ability and motivation to continue an unpleasant behaviour, i.e., behavioural perseverance or persistence (BP). We utilised six different tasks with 54 subjects to measure the possible BP trait(s): cold pressor task, hand grip endurance task, impossible anagram task, impossible verbal reasoning task, thread and needle task, and boring video task. The task performances formed two BP factors. Together, the two-factor solution is responsible for the common variance constituting 37.3% of the total variance in the performances i.e., performance times. Excluding the impossible anagram task, the performance in any given task was better explained by performances in the other tasks (i.e., “trait”, η2 range = 0.131–0.253) than by the rank order variable (“depletion”, i.e., getting tired from the previous tasks, η2 range = 0–0.096)

UEyes: Understanding Visual Saliency across User Interface Types

Funding Information: This work was supported by Aalto University’s Department of Information and Communications Engineering, the Finnish Center for Artifcial Intelligence (FCAI), the Academy of Finland through the projects Human Automata (grant 328813) and BAD (grant 318559), the Horizon 2020 FET program of the European Union (grant CHISTERA-20-BCI-001), and the European Innovation Council Pathfnder program (SYMBIOTIK project, grant 101071147). We appreciate Chuhan Jiao’s initial implementation of the baseline methods for saliency prediction and active discussion with Yao (Marc) Wang. Publisher Copyright: © 2023 Owner/Author.While user interfaces (UIs) display elements such as images and text in a grid-based layout, UI types differ significantly in the number of elements and how they are displayed. For example, webpage designs rely heavily on images and text, whereas desktop UIs tend to feature numerous small images. To examine how such differences affect the way users look at UIs, we collected and analyzed a large eye-tracking-based dataset, UEyes (62 participants and 1,980 UI screenshots), covering four major UI types: webpage, desktop UI, mobile UI, and poster. We analyze its differences in biases related to such factors as color, location, and gaze direction. We also compare state-of-the-art predictive models and propose improvements for better capturing typical tendencies across UI types. Both the dataset and the models are publicly available.Peer reviewe

Robust and Deployable Gesture Recognition for Smartwatches

Funding Information: This work was supported by the Department of Communications and Networking – Aalto University, Finnish Center for Artificial Intelligence (FCAI) and the Academy of Finland projects Human Automata (Project ID: 328813), BAD (Project ID: 318559), Huawei Technologies, and the Horizon 2020 FET program of the European Union (grant CHIST-ERA-20-BCI-001). Publisher Copyright: © 2022 ACM. Open Access fee has been paid, but the PDF version does not contain information on OA licence.Gesture recognition on smartwatches is challenging not only due to resource constraints but also due to the dynamically changing conditions of users. It is currently an open problem how to engineer gesture recognisers that are robust and yet deployable on smartwatches. Recent research has found that common everyday events, such as a user removing and wearing their smartwatch again, can deteriorate recognition accuracy significantly. In this paper, we suggest that prior understanding of causes behind everyday variability and false positives should be exploited in the development of recognisers. To this end, first, we present a data collection method that aims at diversifying gesture data in a representative way, in which users are taken through experimental conditions that resemble known causes of variability (e.g., walking while gesturing) and are asked to produce deliberately varied, but realistic gestures. Secondly, we review known approaches in machine learning for recogniser design on constrained hardware. We propose convolution-based network variations for classifying raw sensor data, achieving greater than 98% accuracy reliably under both individual and situational variations where previous approaches have reported significant performance deterioration. This performance is achieved with a model that is two orders of magnitude less complex than previous state-of-the-art models. Our work suggests that deployable and robust recognition is feasible but requires systematic efforts in data collection and network design to address known causes of gesture variability.Peer reviewe

Robust and Deployable Gesture Recognition for Smartwatches

Funding Information: This work was supported by the Department of Communications and Networking – Aalto University, Finnish Center for Artificial Intelligence (FCAI) and the Academy of Finland projects Human Automata (Project ID: 328813), BAD (Project ID: 318559), Huawei Technologies, and the Horizon 2020 FET program of the European Union (grant CHIST-ERA-20-BCI-001). Publisher Copyright: © 2022 ACM. Open Access fee has been paid, but the PDF version does not contain information on OA licence.Gesture recognition on smartwatches is challenging not only due to resource constraints but also due to the dynamically changing conditions of users. It is currently an open problem how to engineer gesture recognisers that are robust and yet deployable on smartwatches. Recent research has found that common everyday events, such as a user removing and wearing their smartwatch again, can deteriorate recognition accuracy significantly. In this paper, we suggest that prior understanding of causes behind everyday variability and false positives should be exploited in the development of recognisers. To this end, first, we present a data collection method that aims at diversifying gesture data in a representative way, in which users are taken through experimental conditions that resemble known causes of variability (e.g., walking while gesturing) and are asked to produce deliberately varied, but realistic gestures. Secondly, we review known approaches in machine learning for recogniser design on constrained hardware. We propose convolution-based network variations for classifying raw sensor data, achieving greater than 98% accuracy reliably under both individual and situational variations where previous approaches have reported significant performance deterioration. This performance is achieved with a model that is two orders of magnitude less complex than previous state-of-the-art models. Our work suggests that deployable and robust recognition is feasible but requires systematic efforts in data collection and network design to address known causes of gesture variability.Peer reviewe