Дисертації з теми "Mechanism of attention"
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Fitzgerald, Marilyn. "Are attention bias and interpretation bias reflections of a single common mechanism or multiple independent mechanisms?" University of Western Australia. School of Psychology, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0052.
Повний текст джерелаYan, Shiyang. "Visual attention mechanism in deep learning and its applications." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3028892/.
Повний текст джерелаParker, Amanda Louise. "A cross-modal investigation into the relationships between bistable perception and a global temporal mechanism." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9545.
Повний текст джерелаRaykos, Bronwyn C. "Attentional and interpretive biases : independent dimensions of individual difference or expressions of a common selective processing mechanism?" University of Western Australia. School of Psychology, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0018.
Повний текст джерелаWang, Jing. "Hyperspectral Image Classification Based on Deep Learning and Module Inspired by Human Attention Mechanism." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/397634.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
DAL, MOLIN Anna. "Interaction between mechanism of attention selection in space and time: Behavioural and electrophysiological evidence." Doctoral thesis, Università degli Studi di Verona, 2009. http://hdl.handle.net/11562/337444.
Повний текст джерелаThe study of mechanisms involved in spatial attention is one of the most investigated field inmodern neuroscience, but in the last years a growing interest has been devoted to unveil themechanisms concerning also the temporal aspects of attention. In this thesis three experiment arereported that tried to cast more light on the temporal aspects of attention and on the relationshipbetween spatial and temporal attentional mechanisms.In the first experiment the relationship between spatial and temporal deficit in selective visualattention has been investigated in a group of neglect patients using a temporal order judgement task(TOJ). The main finding is a stronger impairment in temporal selection for spatial position in whichthe attention selection is more impaired, suggesting an interaction between the two aspects in themodulation of the deficit.The second and the third experiment investigated temporal expectations generated by a regularrhythm. In particular, the impact of exogenous and endogenous temporal expectation has beencompared in a discrimination task, revealing the pervasive effect of regularity of movement andspeed in orienting attention in time. Moreover, it has been confirmed the combined effect of spatialand temporal expectations in modulation of electrophysiological response.These results suggest the existence of an interaction between spatial and temporal mechanisms ofattention.
Isunza, Navarro Abgeiba Yaroslava. "Evaluation of Attention Mechanisms for Just-In-Time Software Defect Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288724.
Повний текст джерелаJust-In-Time Defect Prediction (JIT-DP) fokuserar på att förutspå fel i mjukvara vid ändringar i koden, med målet att hjälpa utvecklare att identifiera defekter medan utvecklingsprocessen fortfarande är pågående, och att förbättra kvaliteten hos applikationsprogramvara. Detta arbete studerar djupinlärningstekniker genom att tillämpa attentionmekanismer som har varit framgångsrika inom, bland annat, språkteknologi (NLP). Vi introducerar två nätverk vid namn Convolutional Neural Network with Bidirectional Attention (BACNN), och Bidirectional Attention Code Network (BACoN), som använder en tvåriktad attentionmekanism mellan koden och meddelandet om en mjukvaruändring. Dessutom undersöker vi BERT [17] och RoBERTa [57], attentionarkitekturer för JIT-DP. Mer specifikt studerar vi hur effektivt dessa attentionbaserade modeller kan förutspå defekta ändringar, och jämför dem med de bästa tillgängliga arkitekturerna DeePJIT [37] och TLEL [101]. Våra experiment utvärderar modellerna genom att använda mjukvaruändringar från det öppna källkodsprojektet OpenStack. Våra resultat visar att attentionbaserade nätverk överträffar referensmodellen sett till träffsäkerheten i de olika scenarierna. De attentionbaserade modellerna, framför allt BERT och RoBERTa, demonstrerade lovade resultat när det kommer till att identifiera defekta mjukvaruändringar och visade sig vara effektiva på att förutspå defekter i ändringar av nya mjukvaruversioner.
PUTELLI, LUCA. "Attention Mechanism e Interpretabilità del Deep Learning per il Natural Language Processing in Ambito Biomedico." Doctoral thesis, Università degli studi di Brescia, 2021. http://hdl.handle.net/11379/548259.
Повний текст джерелаRaykos, Bronwyn C. "Attentional and interpretive biases : independent dimensions of individual difference or expressions of a common selective processing mechanism? /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0018.
Повний текст джерелаMa, Tengfei. "A Graph Attention plus Reinforcement Learning Method for Antenna Tilt Optimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300111.
Повний текст джерелаOptimering av fjärrlutning är en effektiv metod för att nå optimala nyckeltal genom fjärrstyrning av den vertikala lutningen av en antenn i en basstation. Att förbättra nyckeltalen innebär att förbättra sammarbetseffekten mellan antenner eftersom nyckeltalen är mått på kvalitén av sammarbetet mellan den antenn som optimeras och dess angränsande antenner. Förstärkande Inlärning (FI) är en lämplig metod för att lära sig en optimal strategi för reglering av antennlutningen eftersom agenten inom FI kan generera den optimala epsilongiriga optimeringsstrategin genom att observera miljön och lära sig från par av tillstånd och aktioner. Nuvarande modeller genererar dock endast lutningsstrategier genom att tolka egenskaperna hos den antenn som ska optimeras, vilket inte är tillräckligt för att karatärisera mobilnätverket bestående av antennen som ska optimeras samt dess angränsande antenner. Därav är inkluderingen av de angränsande antennernas egenskaper i modellen viktig för att förbättra optimeringsstrategin. Detta arbete introducerar Graf- Uppmärksammat Nätverk för att modellera de angränsande antennernas påverkan på den antenn som ska optimeras genom uppmärksamhetsmekanismen. Metoden genererar en lågdimensionell vektor med större förmåga att representera den optimerade antennens tillstånd i FI modellen genom att hantera data i struktur av en graf. Den nya modellen, Graf- Uppmärksammat Q- Nätverk (GUQ), är en modell baserad på DQN med mål att nå bättre prestanda än en standard DQN- modell, utvärderat efter samma mätvärde –– förbättring av nyckeltalen. Eftersom GUQ har en större upfattning av miljön så överträffar metoden DQN- modellen genom en fjorton procent bättre prestandaökning. Dessutom, så överträffar GUQ även DQN i form av snabbare konvergens.
Dou, Tianyu. "Multi-Kernel Deformable 3D Convolution for Video Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42682.
Повний текст джерелаChen, Meihong. "Real-Time Video Object Detection with Temporal Feature Aggregation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42790.
Повний текст джерелаDronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.
Повний текст джерелаShi, Fangzhou. "Towards Molecule Generation with Heterogeneous States via Reinforcement Learning." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22335.
Повний текст джерелаAhmad, Fawad. "Role of Cognitive Processes, Emotional Regulation, Attention, and Intrinsic Motives in explaining the underlying Mechanism and Dynamics of Value Premium: A Mispricing Perspective." Doctoral thesis, Luiss Guido Carli, 2020. http://hdl.handle.net/11385/203452.
Повний текст джерелаLee, Yun K. "Unveiling the underlying mechanism for the matching effect between construal level and message frames: how and why do matches between gain versus loss frames and construal level enhance persuasion?" Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3332.
Повний текст джерелаWilson, Steven. "Mechanisms of attention and awareness : parameters and assessment of pre-attentional awareness using change blindness task." Thesis, University of Lincoln, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496078.
Повний текст джерелаKerr, John H. "Arousal mechanisms, attention and sports performance." Thesis, University of Nottingham, 1988. http://eprints.nottingham.ac.uk/10947/.
Повний текст джерелаGama, Nuno. "Mechanisms of multisensory integration and attention." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/40400/.
Повний текст джерелаFurlan, Michele. "Neural mechanisms of attention to motion." Doctoral thesis, Università degli studi di Trento, 2011. https://hdl.handle.net/11572/368674.
Повний текст джерелаGalashan, Fingal Orlando [Verfasser], Detlef [Akademischer Betreuer] Wegener, Andreas [Akademischer Betreuer] Kreiter, and Michael [Akademischer Betreuer] Koch. "Selective visual attention: A mechanism for optimal adjustment of sensory processing to task requirements - from method development to human psychophysics and monkey single cell recordings / Fingal Orlando Galashan. Gutachter: Andreas Kreiter ; Michael Koch. Betreuer: Detlef Wegener." Bremen : Staats- und Universitätsbibliothek Bremen, 2011. http://d-nb.info/1071842129/34.
Повний текст джерелаOwen, Adrian Mark. "Fronto-striatal mechanisms in planning and attention." Thesis, King's College London (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302625.
Повний текст джерелаGontrum, Johannes. "Attention Mechanisms for Transition-based Dependency Parsing." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395491.
Повний текст джерелаSantangelo, Valerio. "Multimodal investigation on spatial attention mechanisms: a model of shared attention resources (ShAR)." Doctoral thesis, La Sapienza, 2005. http://hdl.handle.net/11573/917227.
Повний текст джерелаKennett, Steffan Anthony. "Links in spatial attention between touch and vision." Thesis, Birkbeck (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343623.
Повний текст джерелаDas, Srijan. "Mécanismes d'attention spatio-temporels pour la reconnaissance d'activité." Thesis, Université Côte d'Azur, 2020. https://tel.archives-ouvertes.fr/tel-03177892.
Повний текст джерелаThis thesis targets recognition of human actions in videos. Action recognition is a complicated task in the field of computer vision due to its high complex challenges. With the emergence of deep learning and large scale datasets from internet sources, substantial improvements have been made in video understanding. For instance, state-of-the-art 3D convolutional networks like I3D pre-trained on huge datasets like Kinetics have successfully boosted the recognition of actions from internet videos. But, these networks with rigid kernels applied across the whole space-time volume cannot address the challenges exhibited by Activities of Daily Living (ADL). We are particularly interested in discriminative video representation for ADL. Besides the challenges in generic videos, ADL exhibits - (i) fine-grained actions with short and subtle motion like pouring grain and pouring water, (ii) actions with similar visual patterns differing in motion patterns like rubbing hands and clapping, and finally (iii) long complex actions like cooking. In order to address these challenges, we have made three key contributions. The first contribution includes - a multi-modal fusion strategy to take the benefits of multiple modalities into account for classifying actions. However the question remains, how to combine multiple modalities in an end-to-end manner? How can we make use of the 3D information to guide the current state-of-the-art RGB networks for action classification? To this end, we propose articulated pose driven attention mechanisms for action classification. We propose, three variants of spatio-temporal attention mechanisms exploiting RGB and 3D pose modalities to address the aforementioned challenges (i) and (ii) for short actions. Our third main contribution is a Temporal Model on top of our attention based model. The video representation retaining dense temporal information enables the temporal model to model long complex actions which is crucial for ADL.We have evaluated our first contribution on three small-scale public datasets: CAD-60, CAD-120 and MSRDailyActivity3D. On the other hand, we have evaluated our remaining two contributions on four public datasets: a large scale human activity dataset: NTU-RGB+D 120, its subset NTU-RGB+D 60, a real-world challenging human activity dataset: Toyota Smarthome and a small scale human-object interaction dataset Northwestern UCLA. Our experiments show that the methods proposed in this thesis outperform the state-of-the-art results
Tattersall, A. J. "Divided attention and the structure of temporary memory." Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382717.
Повний текст джерелаBattistoni, Elisa. "Attentional Mechanisms in Natural Scenes." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/369086.
Повний текст джерелаPaltoglou, Aspasia Eleni. "Mechanisms of spatial and non-spatial auditory selective attention." Thesis, University of Nottingham, 2009. http://eprints.nottingham.ac.uk/10697/.
Повний текст джерелаBeuth, Frederik. "Visual attention in primates and for machines - neuronal mechanisms." Universitätsverlag Chemnitz, 2017. https://monarch.qucosa.de/id/qucosa%3A35655.
Повний текст джерелаVisuelle Aufmerksamkeit ist ein wichtiges kognitives Konzept für das tägliche Leben des Menschen. Es ist aber immer noch nicht komplett verstanden, so dass es ein langjähriges Ziel der Neurowissenschaften ist, das Phänomen grundlegend zu durchdringen. Gleichzeitig wird es aufgrund des mangelnden Verständnisses nur selten in maschinellen Sehsystemen in der Informatik eingesetzt. Das Verständnis von visueller Aufmerksamkeit ist jedoch eine komplexe Herausforderung, da Aufmerksamkeit äußerst vielfältige und scheinbar unterschiedliche Aspekte besitzt. Sie verändert multipel sowohl die neuronalen Feuerraten als auch das menschliche Verhalten. Daher ist es sehr schwierig, eine einheitliche Erklärung von visueller Aufmerksamkeit zu finden, welche für alle Aspekte gleichermaßen gilt. Um dieses Problem anzugehen, hat diese Arbeit das Ziel, einen gemeinsamen Satz neuronaler Mechanismen zu identifizieren, welche sowohl den neuronalen als auch den verhaltenstechnischen Aspekten zugrunde liegen. Die Mechanismen werden in neuro-computationalen Modellen simuliert, wodurch ein einzelnes Modellierungsframework entsteht, welches zum ersten Mal viele und verschiedenste Phänomene von visueller Aufmerksamkeit auf einmal erklären kann. Als Aspekte wurden in dieser Dissertation multiple neurophysiologische Effekte, Realwelt Objektlokalisation und ein visuelles Maskierungsparadigma (OSM) gewählt. In jedem dieser betrachteten Felder wird gleichzeitig der State-of-the-Art verbessert, um auch diesen Teilbereich von Aufmerksamkeit selbst besser zu verstehen. Die drei gewählten Gebiete zeigen, dass der Ansatz grundlegende neurophysiologische, funktionale und verhaltensbezogene Eigenschaften von visueller Aufmerksamkeit erklären kann. Da die gefundenen Mechanismen somit ausreichend sind, das Phänomen so umfassend zu erklären, könnten die Mechanismen vielleicht sogar das essentielle neuronale Substrat von visueller Aufmerksamkeit im Cortex darstellen. Für die Informatik stellt die Arbeit damit ein tiefergehendes Verständnis von visueller Aufmerksamkeit dar. Darüber hinaus liefert das Framework mit seinen neuronalen Mechanismen sogar eine Referenzimplementierung um Aufmerksamkeit in zukünftige Systeme integrieren zu können. Aufmerksamkeit könnte laut der vorliegenden Forschung sehr nützlich für diese sein, da es im Gehirn eine Aufgabenspezifische Optimierung des visuellen Systems bereitstellt. Dieser Aspekt menschlicher Wahrnehmung fehlt meist in den aktuellen, starken Computervisionssystemen, so dass eine Integration in aktuelle Systeme deren Leistung sprunghaft erhöhen und eine neue Klasse definieren dürfte.:1. General introduction 2. The state-of-the-art in modeling visual attention 3. Microcircuit model of attention 4. Object localization with a model of visual attention 5. Object substitution masking 6. General conclusion
Greco, Claudio. "Transfer Learning and Attention Mechanisms in a Multimodal Setting." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/341874.
Повний текст джерелаElshafei, Hesham. "Neurophysiological Mechanisms of Auditory Distractibility in the Healthy, Aging or Damaged Human Brain." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1255/document.
Повний текст джерелаTop-down (TD) and bottom-up (BU) mechanisms of attention are supported by dorsal and ventral networks that mainly overlap in the lateral prefrontal cortex (lPFC). A balance between these mechanisms is essential, yet rarely investigated. Increased distractibility observed during ageing or after frontal damage could result from jeopardizing this balance. It has been proposed that distinct oscillatory frequencies support the activation of these two attention networks. Our main aim was to test, in the auditory modality, whether (1) alpha oscillations would coordinate activity within the dorsal TD network, (2) gamma activity would index the activation of the ventral BU network, (3) the lPFC would support the balance between these networks through oscillatory coupling. We also aimed to investigate the oscillatory correlates of the increased distractibility associated with ageing or frontal damage. MEEG data were recorded while participants performed the Competitive Attention Test, which enables simultaneous investigation of BU and TD attention mechanisms. We showed that alpha oscillations indexed facilitatory and suppressive mechanisms of TD attention, and communication within the dorsal network; while gamma oscillations indexed the ventral network activation. Moreover, the lPFC subtended communication in the two networks; with the TD/BU interaction occurring in the medial PFC. We also showed that ageing-related distractibility was of TD deficit origin. Finally, preliminary results suggest that lPFC damage can impact both TD and BU attention. This thesis provides novel insights into the brain oscillatory dynamics of the TD/BU attentional balance supporting distractibility
Magimairaj, Beula M. "Attentional Mechanisms in Children’s Complex Memory Span Performance." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1267650640.
Повний текст джерелаHahn, Britta. "Mechanisms of nicotine-induced attentional enhancement." Thesis, King's College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400578.
Повний текст джерелаCohen, Jason C. "Attention mechanisms and inhibition of return in the somatosensory system." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
Повний текст джерелаHighlander, Tyler Clayton. "Conditional Dilated Attention Tracking Model - C-DATM." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1564652134758139.
Повний текст джерелаSock, Ching Low. "Giving centre stage to top-down inhibitory mechanisms for selective attention." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670753.
Повний текст джерелаL’atenció selectiva determina els senyals sensorials que es processen a nivells superiors a costa dels altres. Està esbiaixada per regions cerebrals d’ordre superior que anticipen estímuls rellevants per a la tasca i augmenten la sensibilitat neuronal a l’escorça sensorial. Sovint, es creu que això es produeix mitjançant l'excitació de neurones seleccionades, però alguns estudis han suggerit que no és la descripció completa del procés. Cada vegada més, l’evidència apunta cap a un mecanisme alternatiu de polarització inhibitiva de dalt a baix. Aquí hem investigat, aleshores, un model d’atenció inhibitori. Primer, vam demostrar com es redueix la sensibilitat a les funcions d’estímul irrellevants per tasques mitjançant la supressió de dalt a baix. En segon lloc, vam demostrar la capacitat d’un model d’espiga basat en la biologia per modular el processament de la informació i l’hem comparat amb la fisiologia. Per últim, hem explorat la interacció entre els models excitadors i inhibidors d’atenció de dalt a baix en un agent de cerca d’aliments. Els nostres resultats donen suport al model inhibitori de l’atenció de dalt a baix com a mecanisme d’atenció biològica i mostren com s’adapta al ‘zeitgeist’ actual dels mecanismes d’atenció de dalt a baix.
Burton, Pamela Ann. "Physiological evidence of interactive object-based and space-based attention mechanisms." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 50.79Mb,139 p, 2005. http://wwwlib.umi.com/dissertations/fullcit/3157279.
Повний текст джерелаCristescu, Tamara C. "Flexible mechanisms for orienting attention to words in the human brain." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442831.
Повний текст джерелаAustin, Alison J. "Mechanisms of attention for cues associated with rewarding and aversive outcomes." Thesis, University of Sussex, 2010. http://sro.sussex.ac.uk/id/eprint/6265/.
Повний текст джерелаKulke, L. V. "Cortical mechanisms of visual attention in typically developing infants and adults." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1471117/.
Повний текст джерелаBaker, Christopher A. "Differentiating attention and motor system-based mechanisms underlying concealed knowledge detection /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.
Повний текст джерелаLisi, Matteo. "Mechanisms of top-down visual spatial attention: computational and behavioral investigations." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3423038.
Повний текст джерелаQuesta tesi verte sull’indagine dei meccanismi alla base dell'attenzione visuo-‐spaziale e In particolare sull'attenzione top-‐down. Con questo termine si intende la capacità di selezionare le informazioni rilevanti e scartare quelle irrilevanti in maniera volontaria e sulla base dei nostri obiettivi. Il cervello umano non è in grado di processare allo stesso livello tutte le informazioni disponibili nell’ambiente in un dato momento, per questo una selezione corretta dell’informazione da elaborare è fondamentale anche per l’esecuzione delle più semplici attività quotidiane. Prestare attenzione ad informazioni irrilevanti può farci trascurare altre informazioni di importanza cruciale, con conseguenze potenzialmente gravi. Nel primo studio (capitolo 2) I affronterò con un approccio computazionale la questione dei meccanismi neurali che sottendono l’attenzione visuo-‐spaziale: quali sono le basi neurali dell’attenzione visuo-‐spaziale? Secondo la teoria premotoria, orientare l'attenzione verso una specifica posizione spaziale equivale a preparare un movimento oculare verso la medesima posizione, un’ipotesi supportata dai risultati di molteplici studi di neuroimaging e neurofisiologici, i quali hanno mostrato una notevole sovrapposizione tra i circuiti dedicati all’attenzione visiva e la programmazione di movimenti oculari. In questo capitolo presenterò un modello computazionale in grado di spiegare diversi effetti attentivi senza richiedere l’aggiunta di meccanismi specifici oltre ai circuiti oculomotori. Inoltre include un meccanismo, modellato sulla base di dati neurofisiologici, che consente di anticipare le conseguenze sensoriali di un movimento oculare sulla rappresentazione spaziale interna al modello, e di spiegare alcune recenti dimostrazioni di dissociazione tra attenzione e movimenti oculari che possono essere utilizzate per confutare la teoria premotoria. Nel capitolo successivo presenterò un secondo modello computazionale (capitolo 3) con lo scopo di investigare ulteriormente i meccanismi computazionali alla base delle trasformazioni sensorimotorie, cioè i processi che traducono l’informazione sensoriale in appropriati comandi motori. In particolare mostrerò che una rappresentazione spaziale costituita da neuroni con campi recettivi retinocentrici ,modulati in ampiezza da un segnale posturale, è sia efficiente (al fine di trasformare l’informazione visiva in coordinate motorie centrate su un effettore) che plausibile, in quanto emerge in un modello di rete neurale addestrato in maniera non supervisionata (usando cioè solo segnali disponibili localmente a livello  del singolo neurone). Questo risultato supporta inoltre l’approccio utilizzato nel primo modello presentato. Successivamente presenterò una serie di studi comportamentali: nel primo (capitolo 4), mostrerò che la costanza spaziale dell’attenzione visiva rispetto ai movimenti oculari (cioè la capacità di mantenere stabilmente l'attenzione in un punto nello spazio attraverso successivi movimenti oculari), dipende fortemente da alcune proprietà dell'immagine, vale a dire la presenza continua di punti di riferimento visivi. Questo risultato aiuta a risolvere recenti controversie sull’orientamento dell’attenzione durante movimenti oculari. Nel secondo studio comportamentale (capitolo 5), indagherò un aspetto spesso trascurato relativo al paradigma di cueing spaziale (probabilmente la tecnica più utilizzata nello studio dell’attenzione spaziale): la predittività del cue (cioè la misura in cui il cue spaziale indica correttamente la posizione in cui apparirà lo stimolo bersaglio). I risultati mostrano che, indipendentemente dalla consapevolezza dei partecipanti, variazioni nella predittività producono corrispondenti variazioni degli effetti di validità del cue, e che effetti significativi di validità possono comparire anche in assenza di un cue predittivo o direzionale. Questi risultati mettono in dubbio l’appropriatezza dell’uso di cue predittivi per indagare spostamenti volontari dell’attenzione spaziale. Infine, nell'ultimo studio userò una misura psicofisiologica, il diametro della pupilla, per indagare gli aspetti relativi all’intensità del processamento visuospaziale. In particolare mostrerò come dilatazioni della pupilla evento-‐relate riflettano accuratamente variazioni nella performance in un compito di monitoraggio spaziale provocate dall’aggiunta di un doppio-‐compito. Inoltre, i risultati del compito primario spaziale rivelano la presenza di un bias consistente verso l’emispazio di destra, indicato da una percentuale maggiore di bersagli omessi nell’emispazio di sinistra. In particolare il pattern di errori rispecchia il fenomeno dell’estinzione (mancata risposta a uno stimolo quando è presentata simultaneamente con un secondo stimolo, tipicamente nell’emispazio opposto) che si trova spesso in pazienti con danno cerebrale unilaterale. In conclusione, dagli studi presentati emerge un quadro dell’attenzione volontaria visuo-‐spaziale come un meccanismo complesso, che, anche nei suoi aspetti volitivi è fortemente influenzato da altri fattori, non volitivi, sia esterni che interni all'individuo
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