Cognitive Science Colloquium
Thursdays 15:30 to 17:00 - Building 57, Room 508
Welcome to the Wintersemester 2018-2019
November 08, 2018
Speaker: Prof. Dr. Fred Mast (Cognitve Psychology, Perception and Research Methods - Bern Universtiy, invited by Prof. Thomas Lachmann)
Topic: Sensory Inference and Cognitive Processes: The Case of Vestibular Perception
Abstract: Numerous studies have demonstrated that sensory information provided by the vestibular system is involved in cognitive processes such as mental rotation, body representation, numerical tasks and affective control. Despite these interesting empirical findings, the underlying mechanisms are not well understood. So far, only little consideration has been given to computational aspects. I will present empirical data from healthy participants and patients, and a new computational approach. The basic idea is that interactions between online sensorimotor processing and offline usage helps to better conceptualize the interplay between vestibular perception and cognitive processes.
November 15, 2018
Speaker: Prof. Dr. Jon Andoni Duñabeitia (LAELE, Lingüística Aplicada a la Enseñanza de Lenguas Extranjeras - Nebrija University, invited by Prof. Thomas Lachmann)
Topic: The real impact of learning foreign languages
Abstract: Native languages are typically acquired in emotionally rich family contexts, while foreign languages are often acquired in emotionally different academic environments. As a consequence of this difference, it has been suggested that bilinguals’ emotional reactivity in foreign language contexts is reduced as compared to native language contexts. In this talk I will present different studies that demonstrate how pervasive foreign language effects can be and how they can alter seemingly automatic responses that are of clear importance in our daily life. I will also discuss some of the limits of these effects and I’ll provide some ideas to counteract these effects by adopting new educational approaches in foreign language teaching.
November 29, 2018
Speaker: Prof. Dr. Julia Karbach (Cognition and Development Lab - Landau University, invited by Prof. Thomas Lachmann)
Topic: Cognitive plasticity across the lifespan: Individual differences, training and transfer effects
Abstract: The field of cognitive training research has evolved considerably over the last decade, especially in the domain of working memory (WM) training and executive functions (EF) training. There is no doubt that intensive cognitive training results in performance gains across a wide range of tasks and in participants of various ages. While most studies have also shown that these gains transfer to tasks measuring the same ability as the training task, transfer to other task domains and even to activities of daily living seems to be less consistent and has inspired heated debates in the field. Moreover, individual differences in training and transfer effects are usually substantial, indicating that some individuals benefit more from an intervention than others. Based on recent findings from WM and EF training, I will illustrate age differences and individual differences in training outcomes. I will discuss predictors of individual differences (such as motivation and cognitive performance at baseline) and highlight important issues that may be considered to disentangle the mechanisms underlying cognitive plasticity in order for the field to move forward.
December 06, 2018
Speaker: Prof. Dr. Ellen Aschermann (Department of Psychology - Cologne University, invited by Prof. Thomas Lachmann)
Topic: Children`s source identification – the impact of different interview conditions
Abstract: During school age children learn information from different sources. Although the content of the learned information is easily kept, the source of the information is often forgotten. In eyewitness context, the source of an information plays an important role and accuracy of source identification has been a major topic in children`s eyewitness research (cf. Roebers, Moga & Schneider, 2001). In our study (N=105, age 7-11) we investigated the role of accuracy motivation on primary school children’s event recall. The design was a 2 (recognition test: forced vs. withhold) x 2 (punishment: detracting token vs. informational feedback) between-subject design. All children heard two stories about circus visits and were interrogated after seven days about this situation. After a cognitive reinstatement instruction, they delivered a free report and were subsequently given a source recognition test. In the forced recognition condition, participants were forced to provide an answer to each question in the recognition test. In the withhold recognition condition, they were encouraged to withhold an uncertain answer by saying “I don`t know”, if they felt unsure about their answer. The second factor “punishment” was realised by either detracting a token from the initial stock for each incorrect answer or by an informational feedback about the correctness of each answer. In contrast to the results of Roebers and coworkers the answers in the forced recognition conditions were most accurate compared to the withhold condition. The punishment did not result in more accurate recognition indices. From these (partly unexpected) results we will discuss the impact of accuracy motivation and memory strategies in children’s source identification.
December 13, 2018
Speaker: Dr Paul Engelhardt (Lecturer in the School of Psychology, East Anglia University, invited by Dr. Leigh Fernandez)
Topic: Eye movements in dyslexia: An analysis of sentence comprehension and underlying risk factors
Abstract: Eye tracking is commonly used to investigate online processing in reading, but eye movement (EM) studies of sentence comprehension in dyslexia are shockingly absent in the literature. We examined several different types of sentences (garden paths, subject/object relatives, passives), which are often used to investigate mechanisms of language comprehension in typically-developing adults. We were also interested in how cognitive risk factors associated with dyslexia (e.g. working memory and processing speed) relate to EMs and comprehension. 50 dyslexic adults were compared to 50 adult controls. Participants read sentences, and answered comprehension questions. To investigate risk factors, participants completed a battery of cognitive/neuropsychological tests. Dyslexics, generally showed poorer comprehension and longer reading times, especially at difficult regions. They also made longer regression paths. Regarding risk factors, we found stronger relationships with working memory compared to processing speed for both reading times and regression paths. Results are interpreted by integrating EM findings and comprehension with theoretical psycholinguistic models and theories of dyslexia (e.g. Verbal Efficiency).
January 10, 2019
Speaker: Prof. Dr. Elke Teich (Department of Language Science and Technology, Saarland University, invited by Prof. Shanley Allen)
Topic: An optimal code for communication: the case of scientific English
Abstract: Language use is characterized by variation according to situational context, giving rise to registers that serve specific communicative purposes, such as scientific communication (Halliday and Martin, 1993). While there is plenty of descriptive, corpus-based work on registers, there are only few attempts at explaining why a given register settles on particular linguistic choices at the expense of others. Here, we pursue an information-theoretic explanation of register formation, according to which convergence in linguistic choices is beneficial for communication. In the talk, we sketch the linguistic development of Scientific English in the late modern period (1700-1900) on the basis of an electronic corpus of scientific text from the Transactions of the Royal Society (Kermes et al., 2016). Using selected information-theoretic measures, we (a) detect the linguistic patterns that become characteristic of scientific language over time and (b) evaluate their contribution to the formation of an "optimal" code for scientific communication (Degaetano-Ortlieb and Teich, 2018). The work reported on is carried out in a project on the linguistic development of Scientific English and supported by Deutsche Forschungsgemeinschaft (DFG) as one of 16 projects under grant 'SFB 1102: Information Density and Linguistic Encoding' (http://www.sfb1102.uni-saarland.de/).
- Degaetano-Ortlieb, Stefania and Elke Teich, 2018. Using relative entropy for detection and analysis of periods of diachronic linguistic change. Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature , COLING 2018, Santa Fe, NM, USA, ACL.
- Halliday, M.A.K. and J.R. Martin, 1993. Writing Science: Literacy and Discursive Power. The Falmer Press, London.
- Kermes, Hannah, Stefania Degaetano-Ortlieb, Ashraf Khamis, Jörg Knappen and Elke Teich, 2016. The Royal Society Corpus: From Uncharted Data to Corpus. Proceedings of the 10th Conference on Language Resources and Evaluation (LREC), Portoroz, Slovenia, ELRA.
January 17, 2019
January 24, 2019
Speaker: Dr. Mareike Bayer
January 31, 2019
February 07, 2019
Speaker: Prof. Dr. Jäkel Frank (Chair for Models of Higher Cognition at the Centre for Cognitive Science, Darmstadt University, invited by Thomas Lachmann)
Topic: Concepts and Categories: Combining Insights from Machine Learning and Experimental Psychology
Abstract: Categorization is a fundamental cognitive ability. Many, if not all, highercognitive functions, like language or problem-solving, crucially depend on categorization. Therefore, categorization has been studied by cognitive scientists and researchers in artificial intelligence alike. Early machine learning algorithms for categorization were inspired by psychology and neuroscience, but today machine learning is a mature field and more recent methods have been developed far beyond their original cognitive motivations. These methods, in turn, can be used to inform experimental studies of human categorization behavior. I will show several examples of how insights from machine learning can feed back into experimental psychology. This is, however, not a one way route: Cognitive models can still shed light on human conceptual behaviors that currently no computer can emulate. I will argue that a full understanding of concepts and categories will depend on a combination of insights from machine learning and experimental psychology.