Buchstaben und Wörter im Kontext

Acknowledgments VI Zusammenfassung VIII Summary XIV General Introduction 1 Extending the theoretical base: Interactive Activation Models 2 The original interactive activation model and the identification of letters 3 The Multiple Read-Out Model and the recognition of words 5 Dual-route models and phonological representations in visual word recognition and reading aloud 11 How to model (semantic) associations during word recognition? 17 Overview of the present studies and their methods 22 Study 1: Sub-lexical frequency measures provided by corpus analyses 22 Study 2: Word frequency, lexicality and optical imaging 24 Study 3: Modeling electrophysiological responses to conflicting lexical representations 29 Study 4: Affective connotation of lexical representations, ERPs, and pupillometry 31 Study 5: Modeling associations between lexical representations and Receiver Operation Characteristics 34 Study 1: Sub-lexical frequency measures for orthographic and phonological units in German 41 Introduction 42 Grain sizes, domains, databases, and measures 46 Grain sizes: Syllable, dual unit, or single unit 47 Processing domains: Orthography or phonology 49 Databases: Lemma or word form 50 Measures: Type or token 51 Method 53 Results 55 Syllable frequencies of the lemma database 56 Syllable frequencies of the word form database 56 Dual unit frequencies of the lemma database 57 Dual unit frequencies of the word form database 58 Single unit frequencies of the lemma database 58 Single unit frequencies of the word form database 59 Discussion 60 Study 2: Differential activation of frontal and parietal regions during visual word recognition: An optical topography study 65 Introduction 67 Methods 71 Participants 71 Materials 71 Experimental procedure 72 Data acquisition 73 Data analysis 73 Results 76 Behavioral results 76 fNIRS (optical topography) 78 Discussion 82 The lexicality effect 83 The word frequency effect 85 Optical topography as a tool to investigate word recognition 86 Appendix 90 Study 3: Conflict monitoring engages the mediofrontal cortex during nonword processing 91 Introduction 92 Methods 95 Participants 95 Materials 95 Procedure 95 Data acquisition 96 Data analysis 97 Results 98 Behavioral 98 ERPs 98 sLORETA 98 Discussion 101 Conclusion 103 Study 4: Affective processing within 1/10th of a second: High-Arousal is necessary for early facilitative processing of negative but not positive words 105 Introduction 107 Method 112 Participants 112 Materials 112 Procedure 113 Data acquisition 114 Data analysis 114 Results 117 Behavioral 117 ERPs 117 sLORETA 118 Discussion 122 Study 5: Remembering words in context as predicted by an Associative Read-Out Model 127 Introduction 129 Does associative spreading activate ‘false memories’? 130 Can each item’s signal be detected in an explicit memory task? 134 Simulation methods: The AROM and its predictions 137 Experimental methods: Testing the AROM’s predictions 140 Participants 140 Corpus 140 Stimuli 140 Procedure 143 Experimental and modeling results 144 Discussion 148 Conclusions 155 General Discussion: Summary and outlook 159 Sub-lexical frequencies 160 Sub-lexical frequency measures constrained the interpretation of effects! 160 Can the matching of global features be replaced by specific ones? 161 Word frequency and optical imaging 163 Optical imaging revealed greater “neural activations” to low frequency words! 163 What is “neural activation” in the IFG? 164 Lexical conflicts 167 Lexical conflicts predicted behavioral data and ACC activation! 167 Does associative-semantic competition predict IFG activation? 170 Affective word features 171 Affective lexical features elicited behavioral and ERP, but no pupil dilation effects! 171 Can semantic cohesiveness account for affective effects? 173 Associative-semantic representations 176 Modeling associations between the word stimuli of an experiment predicted false and veridical memories! 176 Going beyond measurement models of familiarity and recollection? 179 The rebirth of a mental lexicon: How to answer the challenge of fixing the structure of time? 182 Does the mind construct semantic taxonomies from associations? 186 Conclusions 189 References 191 Erklärung I Curriculum Vitae III Wissenschaftlicher Werdegang III Lehre III Vorträge III Poster V Publizierte Konferenzbeiträge und Buchkapitel VI Gutachtertätigkeiten VI Zeitschriftenartikel VIIThis dissertation investigated visual word recognition based on the theoretical framework of interactive activation models (IAMs, McClelland & Rumelhart, 1981). Study 1 provided sub-lexical frequency measures for German, which were used as control variables in the Studies 2, 4, and 5. Study 2 was the first of three studies using the lexical decision task. The optical imaging results showed that words elicit greater neural responses than nonwords in the left inferior parietal gyrus, which suggests a role of this region during the integration of orthographic, phonological and semantic representations. Greater activations for word stimuli in the superior frontal gyrus can be interpreted in terms of decision-related processes during visual word recognition. Moreover, rare words elicited greater neural activation than common words in the left inferior frontal gyrus. This word frequency effect suggests a role of this region during the selection of a semantic representation from many co-activated semantic candidates. Study 3 used an IAM to calculate the conflicts between orthographic representations, and set this model-generated measure of lexical competition into a direct relation with an event-related potentials (ERP) negativity between 400 and 600 ms post- stimulus. The electric sources of the ERP-conflict effects were attributed to the anterior cingulate cortex. The model accounted for a significant portion of item-level variance in reaction times, error rates and mean amplitudes. Study 4 showed that positive and high-arousal negative words elicit response facilitation and an early ERP effect between 80 and 120 ms post-stimulus, when compared to neutral words. The ERP-effect in high-arousal negative words was source-localized in the left fusiform and middle temporal gyri. The latter finding may be explained by the larger amount of associative relations of affective words. Study 5 captures associative relations between words for IAMs. Two words were defined as 'associated', when they co-occur significantly often together in the sentences of a large corpus. This corresponds to Hebbian learning: Items being repeatedly presented together are likely to be associated. The results of a recognition memory task showed that learned and non-learned words elicit greater 'yes' response rates when they provide a larger amount of associated items in the stimulus set. The co-occurrence statistics were further used to implement associations between words in a contextual representation layer. This IAM-model predicted which word is recognized with which probability on an item-level. Because many of the most strongly co-activated words revealed a semantic relation to the presented word (e.g., synonymy), the resulting 'Associative Read-Out Model' is the first IAM with a fully implemented semantic representation layer.Diese Dissertation untersucht die visuelle Worterkennung auf der theoretischen Grundlage des 'Interactive Activation Models' (IAM, McClelland und Rumelhart, 1981). Studie 1 stellt sub-lexikalische Häufigkeitsmaße für das Deutsche zur Verfügung: Orthographische und phonologische Silbenfrequenzen, Bigramm- Biphonemfrequenzen, sowie Buchstaben- und Phonemfrequenzen. Solche Maße dienen in den Studien 2, 4, und 5 als Kontroll-Variablen. Studie 2 ist die erste von drei Studien, welche die visuelle Worterkennung mit der lexikalischen Entscheidungsaufgabe untersucht. Die optischen Bildgebungsbefunde zeigen, dass Wörter höhere neuronale Aktivierungen im linken inferioren Parietallappen auslösen, was auf die Rolle dieser Region bei der Integration orthographischer, phonologischer und semantischer Repräsentationen hinweist. Die höheren Aktivierungen für Wörter im superioren Frontallappen weisen auf die entscheidungsrelevanten Prozesse der Worterkennung hin. Seltene Wörter lösten höhere Aktivierungen im linken inferioren Frontallappen im Vergleich zu häufigen Wörtern aus, was die Beteiligung dieser Region bei der Auswahl einer semantischen Repräsentation aus verschiedenen konfligierenden Kandidaten nahelegt. Studie 3 setzt simulierte Konflikte zwischen orthographischen Repräsentationen bei der Verarbeitung von Nichtwörtern in eine direkte Beziehung zu einer Negativierung des ereigniskorrelierten Potentials (EKP) zwischen 400 und 600 ms. Die Quellen dieser Aktivierungen wurden im anterioren cingulären Cortex verortet. Das Modell klärte signifikante Varianzanteile für Reaktionszeiten, Fehlerraten und EKP-Negativierungen auf. Studie 4 zeigt, dass positive und hocherregend negative Wörter Antworterleichterungen im Vergleich zu neutralen Wörtern und eine frühen EKP-Negativierung zwischen 80 und 120 ms nach Reizdarbietung auslösen. Der EKP-Effekt hocherregend negativer Wörter konnte im linken fusiformen und im mittleren temporalen Gyrus verortet werden, was dafür spricht, dass affektiv konnotierte Wörter mehr assoziativ verknüpfte Wörter aufweisen. Studie 5 erschließt assoziative Verknüpfungen zwischen Wörtern für IAMs. Zwei Wörter wurden als 'assoziiert' definiert, wenn sie in den Sätzen eines großen Satzkorpus signifikant häufig gemeinsam auftreten. Dies entspricht der Hebb'schen Lernregel: Wörter, die häufig gemeinsam auftreten, sind wahrscheinlich assoziiert. Die Ergebnisse einer Wiedererkennens-Gedächtnisaufgabe zeigen, dass gelernte und nicht-gelernte Wörter mehr 'Ja-'Antworten auslösen, wenn sie eine größere Anzahl assoziierte Wörter im Reizmaterial aufweisen. Die ko-okkurrenzstatistischen Maße wurden benutzt, um eine kontextuelle Modell-Repräsentationsebene mit assoziativen Verbindungsgewichten auszustatten. Das Modell sagt auf dem Item-level voraus, welches Wort mit welcher Wahrscheinlichkeit wiedererkannt wird. Da viele der am stärksten assoziierten Wörter zum präsentierten Wort eine semantische Verknüpfung aufweisen (z.B. Synonymie), ist das so gewonnene 'Associative Read-Out Model', das erste IAM mit einer semantischen Repräsentationsebene

Learning to recognize novel words and novel objects

Reading seems as easy and natural as listening. It is still not clear how we acquire this skill, and how visual word identification mechanisms are refined through reading experience. Theoretical models of word recognition describe general principles of skilled reading behaviour. However, these models have been based on averaged data from relatively small samples of skilled readers, mainly English native speakers, and are based on the assumption that skilled reading involves a specialized system of word identification. In this thesis it is proposed that expert reading requires the development and refinement of basic visual processing mechanisms originally employed to identify everyday objects, and then adapted to reading. To test this hypothesis, I carried out three experiments investigating: (i) how L2 visual word recognition changes with growing proficiency; (ii) how novel lexical memories are integrated into the lexicon, i.e., how they interact with previously existing words; and (iii) how sensitivity to the lexicon statistics plays out in the process of learning a novel set of visual stimuli, either in the language and non--language domain

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years