Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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First year courses

Research Master Specialisation NeuroeconomicsYear 1School of Business and Economics

Mathematical Research ToolsFull course descriptionMulti-variable calculus, static optimisation methods in particular Lagrange and Kuhn-Tucker,connection with linear and non-linear programming, dynamic (discrete and non-discrete) optimisationmethods (Bellman principle, calculus of variations, optimal control, Pontryagin maximum principle),basic elements of difference and differential equations and of dynamic systems.

Course objectivesThis course offers basic mathematical methods for economic research. The focus is on static anddynamic optimisation and on the underlying mathematics, necessary to understand and apply theseoptimisation methods. These tools are relevant for all specialisations within the Economic and FinanceResearch (EFR) master program.

PrerequisitesBasic level of mathematics (e.g. Sydsaetter et.al, Mathematics for Economic Analysis).

Recommended readingJehle, Geoffrey A. and Philip J. Reny : Advanced Microeconomic Theory (2nd edition).Sydsaetter, K., Hammond, P., Seierstad, A. and A. Strom : Further Mathematics for Economic

Analysis (Financial Times / Prentice Hall, 2008).

EBC4182Print course descriptionECTS credits: 6.5Teaching methods: PBL, Lecture(s), Assignment(s)Assessment methods: Participation, Written exam

Faculty of Psychology and Neuroscience

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Psychology meets Neuroscience meets EconomicsFull course descriptionDuring this course, the students from different backgrounds will receive an introduction into the maintopics of Psychology, Neuroscience and Economics. They will receive an overview about where thosefields come together and why there is a need for the new research direction of Neuroeconomics. Thestudents will gain insight into the anatomy and functioning of the brain and will receive anintroduction into cognitive and social neuroscience. The focus will be on using neurosciencemethodology to empirically address relevant research questions in psychology and decision-makingscience. Specifically, the following topics will be addressed: economic models of human decision-making, including predictions based on game theory as well as other models used in behaviouraleconomics, using functional brain imaging and brain interference techniques to study theneurobiology underlying human cognition and decision making, as well as examples from appliedsocial neuroscience research. Lecturers representing these topics will be recruited from the School ofBusiness and Economics and the Faculty of Psychology and Neuroscience. The course will thus aim atproviding a bridge between classical research themes in cognitive neuroscience, behaviouraleconomics and neuroeconomics.

Course objectivesStudents are able to understand:

selected neuroscientific research tools including functional brain imaging and functional braininterference;

structural and functional architecture of the brain, applications of neuroscience methodology incognitive and social psychology, as well as (neuro)economics.

PSY4711Period 12 Sep 201925 Oct 2019

Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinators:T. SchuhmannM. WibralTeaching methods: Assignment(s), Lecture(s), Paper(s), PBLAssessment methods: Attendance, Presentation, Written examKeywords: Cognitive Neuroscience, social neuroscience, neuroeconomics, cognitive psychology

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Faculty of Psychology and Neuroscience

Advanced Statistics IFull course descriptionThe course consists of six units. In the first four units, participants will be given an in-depth training inthe following standard statistical methods: factorial ANOVA for between-subject designs, analysis ofcovariance (ANCOVA), multivariate ANOVA (MANOVA), discriminant analysis and multiple linearregression. Students are assumed to have background knowledge of balanced two-way factorialANOVA and multiple regression. These methods will be briefly reviewed. The following advancedtopics will then be covered: unbalanced factorial designs, contrast analysis, interaction, simple slopeanalysis, dummy coding, centring covariates, different coding schemes, collinearity and residualschecks and data transformation. The distinction between confounders and mediators in regressionand ANCOVA is also discussed, forming a bridge from regression to structural equations modelling(SEM). The latter is an advanced multivariate method that is gaining importance in psychology butstill requires special software (such as Lisrel, EQS, AMOS or Mplus). SEM is introduced in two units,starting with causal modelling and mediation analysis in cross-sectional research and then extendingto longitudinal research and latent variables (factors). Special attention is given to identifying models,model equivalence, global and local goodness of fit indices, parsimony, model modification and cross-validation. Some concepts from matrix algebra are needed for SEM, and these will be brieflydiscussed without going into technical detail.

Course objectivesStudents are able to understand:

oneway analysis of variance, contrast analysis, unbalanced designs, multivariate analysis of variance,discriminant analysis, linear regression with interaction terms, linear regression with dummyvariables, data transformations, simple slope analysis, analysis of covariance, path analysis, structuralequation modeling, confirmatory factor analysis, structural models with latent variables.

PSY4106Period 12 Sep 201920 Dec 2019

Print course descriptionECTS credits: 3.0Instruction language: EnglishCoordinator:J. SchepersTeaching methods: Assignment(s), Lecture(s), Skills, Training(s)Assessment methods: Attendance, Written examKeywords:

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Univariate analysis of variance, multivariatie analysis of variance, regression analysis, structuralequation modeling

Faculty of Psychology and Neuroscience

Practical Training: SPSS I and LisrelFull course descriptionIn order to make practical use of the statistical models that form the topic of the Advanced Statisticscourse, researchers must make use of statistical software. This course will utilise the traditional SPSSprogram, but also the specialised LISREL software. LISREL is a statistical program that allowsstructural equations models to be tested.

Course objectivesStudents are able to understand:

defining contrasts;building regression models;doing multivariate analyses;transforming data;testing simple slopes;creating and testing SEM models.

PSY4119Period 12 Sep 201920 Dec 2019

Print course descriptionECTS credits: 0.0Instruction language: EnglishCoordinator:J. SchepersTeaching methods: Assignment(s), Training(s)Assessment methods: AttendanceKeywords: SPSS, LISREL, statistical software

Faculty of Psychology and Neuroscience

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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EEG and ERPFull course descriptionElectroencephalography (EEG) and Event Related Potentials (ERP) offer a combination of precisemeasurements for the time course of brain processes. These are low cost, non-invasivemeasurements and are widely available. For these reasons they make a unique contribution tocognitive neuroscience. Scientific interest in EEG and ERP is growing, and results have beenincreasingly integrated with other neuro-imaging techniques during the last few decades.

Lectures and basic literature provide an introduction for students to the basics of EEG and ERPresearch, EEG and ERP terminology and the possibilities and limitations of EEG and ERP. For aMidterm paper students study an empirical data article from the literature and answer questionsabout its EEG and ERP methods and interpretation based on lectures, basic literature and othersources. Students also study practical measurement issues, such as electrode placement and types ofartefacts. Finally, students must interpret the resulting data. Successful measurement requires anunderstanding of the basics of EEG and ERP signal analysis techniques, such as artefact management,spectral analysis, filtering, ERP averaging, time-frequency analysis etc. Students also receive hands-on training in smaller groups in running an ERP experiment, including electrode application,minimising artefacts, and health and safety in the lab. A number of simple experimental paradigmswill be used that provide interesting and reliable results. Data processing will include a number ofcommon EEG analyses, e.g. analyses in the time and frequency domain.

Course objectivesStudents are able to understand:

basic EEG/ERP paradigms, EEG recording systems, measurement settings, electrode application, dataquality verification, analogue-digital conversion, basic EEG / ERP components, interpretingtopographical plots, neural origins of EEG, time domain analysis, frequency domain analysis, time-frequency analysis, filtering, ocular artefact control, muscle artefact control, choice of reference, re-referencing.

PSY4221Period 12 Sep 201925 Oct 2019

Print course descriptionECTS credits: 2.0Instruction language: EnglishCoordinator:F.T.Y. SmuldersTeaching methods: Lecture(s), Paper(s), Skills, Training(s), Work in subgroupsAssessment methods:

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Attendance, Final paperKeywords: Electroencephalography (EEG), Event-related potentials (ERP), electrophysiology, measurement,analysis of brain potentials

School of Business and Economics

Microeconomics IFull course descriptionThe course provides an exposition of key elements of modern microeconomic theory at an advancedlevel. Topics include consumer theory and general equilibrium theory. Note that this is a researchmaster course. We treat the material rigorously, and a substantial amount of time will be spent onmathematical proofs.

Examination method: Assessment method depends on number of participants.

Course objectivesStudents learn to apply mathematical tools to model economic problems, to develop the theoreticalframework of microeconomics and to prove its results.

PrerequisitesMathematical Research Tools (EBC4182) or knowledge equivalent to the mathematical appendix ofJehle and Reny (2011).

Recommended readingMain text: Jehle GA and PJ Reny (2011), Advanced Microeconomic Theory.Recommended text: Mas-Colell A, MD Whinston and JR Green (1995), Microeconomic Theory.

EBC4061Period 228 Oct 201920 Dec 2019

Print course descriptionECTS credits: 6.5Instruction language: EnglishCoordinator:A. Predtetchinski

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Teaching methods: Lecture(s), Assignment(s), Work in subgroupsAssessment methods: Oral exam, Written exam

Faculty of Psychology and Neuroscience

Noninvasive Brain Stimulation (NIBS)Full course descriptionThis course will provide students with an in-depth knowledge of; noninvasive brain stimulationtechniques, including transcranial magnetic stimulation (TMS) and transcranial electrical stimulation(TES). Students will learn about the mechanisms of action; the physical-physiological principles;various application protocols; functional brain stimulation paradigms and approaches for combiningbrain stimulation with brain imaging techniques both within and between experimental session(s).

Since the very beginning of experimental brain research, neuroscientists have dreamed about notonly observing the brain at work, but actually changing and modulating the neuronal activity in thebrain without causing harm to patients or subjects. With the development of noninvasive brainstimulation (NIBS) it is now possible to reach into the skull of a patient or healthy subject and totemporarily alter brain activity at a specific location. This possibility opens the door to a wide range ofexperimental and clinical applications. New protocols and technologies allow researchers to modulatenot only the level, but also the type of brain processes that occur. For instance, brain oscillations canbe entrained to an external stimulation frequency.

NIBS enables the researcher or clinician to change neuronal activity in the task-related brain area andreveal behavioural changes in actual task performance. This enables identification of those brainareas, or brain mechanisms, that are functionally relevant to a particular function. In a clinicalcontext, NIBS has also been used to treat neurological, psychiatric, and psychological disorders thatare accompanied by a pathologically increased or decreased activity, or pathological changes in brainoscillations, in a specific brain region or network. Since NIBS offers the possibility to change neuronalactivity beyond the stimulation period itself, it is increasingly applied as a therapeutic tool, forinstance to treat diseases like depression.

Course objectivesStudents are able to understand:

physics and mechanisms of action of NIBS;physiological effects of NIBS;NIBS protocols and application paradigms;NIBS in human cognitive neuroscience;combining NIBS with functional imaging;clinical applications of NIBS.

PSY4216Period 36 Jan 202031 Jan 2020

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinators:T.A. de GraafA.T. SackTeaching methods: Assignment(s), Lecture(s), Presentation(s), Work in subgroups, PBLAssessment methods: Attendance, Presentation, Written examKeywords: Non-invasive Brain Stimulation, functional magnetic brain interference, multi-modal imaging

Faculty of Psychology and Neuroscience

NeuroanatomyFull course descriptionThe aim of this practical training is to make you acquainted with the neuroanatomical terminologyand to gain insight into the spatial and functional organisation of the brain. It is essential to have abasic knowledge of the brain anatomy when working in the field of neuropsychology or neurobiology.Many specific brain areas can be linked to particular functions. Thus, knowledge of the brain anatomyand its main functions allows direct linkage of specific neurological or psychiatric disorders toparticular brain areas. After a short theoretical introduction, you will study whole brains and brainmaterial of mammals at both macroscopical (visual inspection) and microscopical level. The emphasiswill be on major brain systems, including the basal ganglia and limbic system.

Course objectivesStudents are able to understand:

organisation of the brain in particular the limbic system and basal ganglia;brain dissection;microscopical staining techniques.

PSY4108Period 36 Jan 202031 Jan 2020

Print course descriptionECTS credits: 1.0Instruction language: English

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Coordinator:J.H.H.J. PrickaertsTeaching methods: Lecture(s), Skills, Work in subgroupsAssessment methods: Attendance, Written examKeywords: Neuroanatomy, limbic system, basal ganglia

Faculty of Psychology and Neuroscience

Methods of DeactivationFull course descriptionIn three consecutive practical training sessions, students acquire direct hands-on experience withnon-invasive magnetic brain stimulation (transcranial magnetic stimulation (TMS) and transcranialelectric stimulation (TES)). Students learn how to use the brain stimulator devices, how to evokemuscle responses and how to induce visual experiences. Students act as both the experimenter,applying the brain stimulation, and the participant, receiving the magnetic pulses.

Practical I: Technical introduction/motor thresholds/motor excitability

In three consecutive practical training sessions, students acquire direct hands-on experience withnon-invasive magnetic brain stimulation (transcranial magnetic stimulation (TMS) and transcranialelectric stimulation (TES)). Students learn how to use the brain stimulator devices, how to evokemuscle responses and how to induce visual experiences. Students act as both the experimenter,applying the brain stimulation, and the participant, receiving the magnetic pulses.

Practical I: Technical introduction/motor thresholds/motor excitability

Practical II: TMS-induced visual experiences (phosphenes)

Practical III: TMS Neuronavigation (frameless stereotaxy)

There are a variety of ways in which activity in a brain region can be prevented or influenced. Somestudies use anatomical lesion methods (in animals), while others use reversible methods such ascooling, and pharmacological or genetic manipulations in animals, or TMS in human participants.

The training will end with a lecture that provides an overview of these different methodologies,including a discussion of the advantages and limitations of the different techniques and of the issuesrelated to data interpretation.

Practical II: TMS-induced visual experiences (phosphenes)

Practical III: TMS Neuronavigation (frameless stereotaxy)

There are a variety of ways in which activity in a brain region can be prevented or influenced. Somestudies use anatomical lesion methods (in animals), while others use reversible methods such ascooling, and pharmacological or genetic manipulations in animals, or TMS in human participants.

The training will end with a lecture that provides an overview of these different methodologies,including a discussion of the advantages and limitations of the different techniques and of the issues

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related to data interpretation.

Course objectivesStudents are able to understand:

Transcranial Magnetic Stimulation, application of TMS, motor threshold determination, phosphenethreshold determination, neuronavigation, transcranial electric stimulation, cooling, various otherdeactivation methods.

PSY4233Period 36 Jan 202031 Jan 2020

Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:T. SchuhmannTeaching methods: Assignment(s), Lecture(s), Skills, Training(s)Assessment methods: Attendance, AssignmentKeywords: Transcranial Magnetic Stimulation, Non-invasive Brain Stimulation, fMRI-guided neuronavigation

Faculty of Psychology and Neuroscience

ColloquiaFull course descriptionEach specialisation organizes two colloquia, in which senior researchers from Maastricht University orvisiting lecturers present their scientific insights. Each colloquium focuses in depth on one of a widerange of topics, with issues transcending the courses and specialisations. Each colloquium lecture willbe followed by active discussion, chaired by the lecturer or the host of the guest lecturer. A total oftwelve colloquia will be offered.

Course objectivesStudents are able to understand:

key research domains from different specialisations;interdisciplinary research.

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Students are able to interact with students from different specialisations.

PSY4100Period 36 Jan 20203 Jul 2020

Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:R. SchreiberTeaching methods: Lecture(s)Assessment methods: AttendanceKeywords: interdisciplinary knowledge

School of Business and Economics

Microeconomics IIFull course descriptionThe course provides an exposition of key elements of modern microeconomic theory at an advancedlevel. Topics include information economics and mechanism design. Note that this is a researchmaster course. We treat the material rigorously, and a substantial amount of time will be spent onmathematical proofs.

Course objectivesStudents learn to apply mathematical tools to model economic problems, to develop the theoreticalframework of microeconomics and to prove its results.

PrerequisitesMathematical Research Tools, EBC4182.

Recommended readingMain text:

Jehle GA and PJ Reny (2011), Advanced Microeconomic Theory.

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Recommended text:

Mas-Colell A, MD Whinston and JR Green (1995), Microeconomic Theory.

EBC4204Period 43 Feb 20203 Apr 2020

Print course descriptionECTS credits: 6.5Coordinator:A. PredtetchinskiTeaching methods: Assignment(s), Lecture(s)Assessment methods: Written exam, Oral exam, Final paper

Faculty of Psychology and Neuroscience

Social NeuroscienceFull course descriptionSocial neuroscience (SN) is an interdisciplinary field that asks questions about topics traditionally ofinterest to social psychology (such as emotion regulation, attitude change, or stereotyping),economics and political science. To answer these questions, it uses methods traditionally employedby cognitive neuroscientists, such as functional brain imaging and neuropsychological patientanalysis. By integrating the theories and methods of its parent disciplines, SN seeks to explain socialand emotional behavior in terms of the interaction between three levels of analysis:

The social level, which includes descriptions of experience, behavior, and context;The cognitive level, which specifies information processing mechanisms;The neural level, which specifies neural systems that instantiate these processes.

SN researchers are interested in questions such as: How can a group of brains be social? How doesour brain enable us to know what other people think and feel? Is there a neural substrate forcooperation or aggression? Which brain circuits suppress prejudices? Are social emotions representedby the brain in a different way compared to basic emotions?

Although research on the biological correlates of social processes has been ongoing fordecades, this approach has gone through a period of rapid expansion with the advent of newtechniques like functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG) and itsderivative, the Event Related Potential (ERP). In addition, Non-Invasive Brain Stimulation techniques(NIBS) are used to study causal relations in cognitive processing, but also to “entrain” the brain tooperate in a more optimal way.

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Course objectivesStudents are able to understand:

social decision-making, social cognition, emotion-regulation, empathy, moral judgment, aggression,social norms.

PSY4712Period 43 Feb 20203 Apr 2020

Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinator:B.M. JansmaTeaching methods: Assignment(s), PBL, Presentation(s)Assessment methods: Attendance, Presentation, Written examKeywords: social neuroscience, Social cognition, neuroeconomics

Faculty of Psychology and Neuroscience

Functional Brain Imaging in Neuroeconomics I & IIFull course descriptionIn Neuroeconomics, FMRI plays a pivotal role in studying human decision-making. Neuroeconomicsstudents must therefore know about relevant FMRI principles and experimental design, and haveexperience in conducting an FMRI study. This course consists of two main parts: 1) discussion of FMRIprinciples and relevant literature for topics in Neuroeconomics and 2) hands-on experience withconducting, measuring, analyzing and reporting on FMRI data. In the first part (period 4), students willtake part in lectures on principles and applications of FMRI, and in tutorial meetings to discussrelevant empirical neuroscience papers on relevant topics, such as decision making, game theory,social neuroscience and individual differences. In the second part (period 5), students will design andconduct an FMRI experiment, analyse and interpret the FMRI data, and write a research report aboutthe findings. This part of the course will be conducted in collaboration with a number of staff memberswho are knowledgeable about FMRI. Assessment includes both periods and will be based on personalperformance in the course, quality of the presentations (period 4) and the grading of the finalresearch report (period 5).

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Course objectivesStudents are able to understand or have:

functional brain imaging techniques and principles;experimental design;exposure to FMRI environment;hands-on experience in designing, conducting and analyzing an FMRI experiment;fMRI in neuroeconomics and decision-making.

PrerequisitesBasic knowledge of Brain anatomy, experimental design and statistics.

PSY4713Period 43 Feb 202024 Apr 2020

Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinators:V.G. van de VenB. SorgerTeaching methods: Lecture(s), Paper(s), Presentation(s), Skills, PBLAssessment methods: Attendance, Final paper, PresentationKeywords: Magnetic Resonance Imaging (MRI), functional MRI, structural MRI, neuroimaging, experimentaldesign, data analysis, brain activity, neuroeconomics, decision-making

Faculty of Psychology and Neuroscience

Advanced Statistics IIFull course descriptionThe course consists of seven units.

The first three units cover classical repeated measures ANOVA for the one- and two-way within-subject design and the split-plot (between x within) design. Special attention is given to: a) the choicebetween multivariate and univariate data formats and method of analysis, and the sphericityassumption; b) the distinction between the within-subjects and between-subjects part of a split-plotANOVA, and how to obtain both using regression analysis; c) the surprising consequences of includingcovariates into repeated measures ANOVA; and d) the choice between different methods of analysis

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for randomised versus non-randomised group comparisons.

Subsequently, a further three units are devoted to mixed (multilevel) regression for nested designsand longitudinal studies. This mixed regression starts with a unit on marginal models for repeatedmeasures as an alternative to repeated measures ANOVA in cases of missing data or within-subjectcovariates. Students are shown the pros and cons of various models for the correlational structure ofrepeated measures, such as compound symmetry and AR1. The second unit covers the randomintercept model for repeated measures as a method to include individual effects in marginal modelsfor longitudinal data (growth curves) or single trial analyses of lab data (response times, ERP, fMRI).Students learn how this can be combined with e.g. ARMA modelling to distinguish betweeninterpersonal and intrapersonal outcome variation. The random intercept model will also be applied toa cluster randomised trial, i.e. an RCT where organisations like schools or companies instead ofindividuals are randomised. The third and last unit on mixed regression covers random slope modelsfor longitudinal data (individual differences in change over time), single trial analysis (individualdifferences in stimulus effects) and multicentre trials (RCT within each of a number of organisations).

Finally, the topic of optimal design, sample size and power calculations is introduced in a seventhunit.

Course objectivesStudents are able to understand:

repeated measures ANOVA for within-subject and split-plot (between x within) designs, includingfactorial designs and covariates in repeated measures ANOVA;

mixed (multilevel) linear regression with random effects and autocorrelation;optimal design and sample size calculations for experimental and observational studies.

More specifically, students are able to choose the correct method of analysis, and specify a statisticalmodel, for repeated measurements, to compare different models and choose the best model (basedon checking assumptions, model fit and parsimony on top of plausibility), and to interpret effectestimates and significance tests obtained with that model. Students are furthermore able to choosethe correct formula for computing the sample size for basic and often used research designs, and tocompute the sample size with that formula.

PrerequisitesGood understanding of descriptive and inferential statistics at the elementary and intermediate level,including t-tests, factorial ANOVA and multiple linear regression. Skilled in the use of SPSS forstatistical data analyses.

PSY4107Period 43 Feb 20205 Jun 2020

Print course descriptionECTS credits: 3.0Instruction language: English

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Coordinator:G.J.P. van BreukelenTeaching methods: Assignment(s), Lecture(s), Training(s)Assessment methods: Attendance, Written examKeywords: Within-subject designs, repeated measures ANOVA, mixed (multilevel) regression, marginalversus random effects models, optimal design, sample size, power

Faculty of Psychology and Neuroscience

Practical Training: SPSS IIFull course descriptionThis practical training forms part of the PSY4107 Advanced Statistics II course. The practical consistsof seven sessions in the computer rooms. In the first six sessions SPSS procedures for repeatedmeasures and multilevel data are practised. The goal is to understand how proper analyses of suchdata can be done using SPSS. In the last session GPower will be used to practice sample size (power)calculations for some elementary research designs.

Course objectivesStudents are able to understand and apply:

how to run with SPSS: repeated measures ANOVA for within-subject and split-plot (between x within)designs, including factorial designs and covariates;

how to run SPSS for: mixed (multilevel) linear regression with random effects and autocorrelation;how to use GPower for sample size (power) calculations for your own research (master thesis, grant

application).PrerequisitesGood understanding of descriptive and inferential statistics at the elementary and intermediate level,including t-tests, factorial ANOVA and multiple linear regression. Skilled in the use of SPSS forstatistical data analyses.

PSY4117Period 43 Feb 20205 Jun 2020

Print course descriptionECTS credits: 0.0Instruction language: English

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Coordinator:G.J.P. van BreukelenTeaching methods: Training(s)Assessment methods: AttendanceKeywords: Within-subject designs, repeated measures ANOVA, mixed (multilevel) regression, marginalversus random effects models, sample size, power, effect size

Faculty of Psychology and Neuroscience

Neuroeconomics MeetingsFull course descriptionThe neuroeconomics meetings are organised in seminar style, where students and researcherspresent and discuss original research articles, methodology and research ethics in neuroeconomics aswell as the relevance of neuroeconomics to neuroscience and economics. Meetings includepresentations and in-depth discussion. The meetings will take place on an approximately bi-weeklybasis in periods 4-6 and less frequently in periods 1-3. They are compulsory in periods 4-6 of the firstyear but are also highly recommended for second year students and first year students in period 1-3.

Course objectivesStudents will be able to understand:

key research domains in neuroeconomics;critical assessment of research projects;critical assessment of published research;interaction with other students and more senior researchers.

PSY4731Period 49 Mar 20203 Jul 2020

Print course descriptionECTS credits: 1.5Instruction language: EnglishCoordinator:A.M. RiedlTeaching methods: Lecture(s), Presentation(s), Assignment(s)Assessment methods:

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Attendance, PresentationKeywords: neuroeconomics, methodology; decision making; critical analysis

Faculty of Psychology and Neuroscience

Timing Neural Processing with EEG and MEGFull course descriptionCognitive neuroscientists can currently choose from a range of different imaging methods toinvestigate human brain function. Each of these methods has its own strengths and limitations, whichdetermine its suitability for studying a particular research question. Both electroencephalography(EEG) and magnetoencephalography (MEG) are important in characterising the time course ofactivation of neural systems involved in perceptual and cognitive processes. Relevant topics includeauditory and visual perception, attention, language, memory and their development. EEG and MEGsignals reflect complementary aspects of brain activity, with MEG having some advantages over EEGin the localisation of underlying neural sources. This course provides detailed knowledge on EEG andMEG, both of which have a clear advantage over other neuroimaging methods in terms of temporalprecision. The study of EEG and MEG experimental design, data acquisition and data analysis will becombined with detailed literature discussions on theoretical and methodological issues. Based ondifferent types of empirical questions, there will be discussion of the potential of a range of methodsfor advanced EEG and MEG analysis, including analysis in the time and frequency domain, sourcelocalisation, the combination with functional magnetic resonance imaging (fMRI) and transcranialmagnetic stimulation (TMS) methods, independent component analysis and analyses of functionalconnectivity.

Course objectivesStudents are able to understand:

electro-encephalography, event-related potentials, magneto-encephalography, dipole source analysis,distributed source analysis, Fourier analysis, wavelet analysis, independent component analysis,connectivity analysis, application: mental chronometry, attention, lateralised event-related potentials,combination electro-encephalography and functional magnetic resonance imaging, combinationelectro-encephalography and trans-cranial magnetic stimulation.

PSY4256Period 514 Apr 202015 May 2020

Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinator:

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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F.T.Y. SmuldersTeaching methods: Lecture(s), Paper(s), Presentation(s), PBLAssessment methods: Attendance, Final paper, PresentationKeywords: electroencephalography, magnetoencephalography, biological signal analysis, source localisation

Faculty of Psychology and Neuroscience

Programming in Matlab Basic CourseFull course descriptionMatlab provides a powerful environment for numerical computation, data analysis and visualisation. Itis, in essence, a programming environment that has built-in primitives for common scientific tasksthat in other languages, such as C or Delphi, require many operations. Examples are tasks such asmatrix algebra (used in statistical analysis of data), Fourier transforms (used in signal processing) and2D or 3D plots for visualisation of data or analysis-results. Many complete packages for the analysis ofcognitive neuroimaging data (e.g. fMRI data or EEG/MEG data) are implemented in Matlab. Thus,usage of these packages requires at least a basic understanding of Matlab. Furthermore, if moreadvanced analysis or visualisation is needed than what is offered by existing packages, developingnew functionalities in Matlab is often the most convenient option. The first part of the course will dealwith how Matlab primarily represents and processes data, i.e. as matrices. Subsequently, attention isfocused on the usage of the environment: the prompt; the workspace; the help options; and loading,saving and visualising data. The principles behind programming will be introduced, with particularemphasis on neuroimaging applications.

Course objectivesStudents are able to understand:

Matlab environment, Matlab variables, vectors, matrices, matrix algebra, 2D and 3D plots, conditionalloops, scripts, functions, file Input-Output, structures, cells.

PSY4224Period 56 Apr 20205 Jun 2020

Print course descriptionECTS credits: 2.0Instruction language: EnglishCoordinator:G. Valente

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Teaching methods: Assignment(s), Lecture(s), Skills, Work in subgroupsAssessment methods: Attendance, Take home examKeywords: Programming principles, scripts and functions, data analysis

Faculty of Psychology and Neuroscience

Scientific WritingFull course descriptionThe course is delivered in a series of one lecture and four tutorials, during which students produceand revise a short research proposal, literature research paper or research article. The lecture aims tocover the structure of the three genres, and ethical issues surrounding the production of scientifictexts (for example, plagiarism and non-biased writing). In tutorials, students apply principles in thelinguistic sense and discover how these apply to their own writing. In particular, the 'doors andwindows' (abstracts, introductions, hypotheses and discussions) of scientific papers are analysed fortheir linguistic and stylistic content. Furthermore, students develop the language awareness andcritical skills required to review their own work as well as that of their peers. The instructor givesindividual feedback on parallel block assignments at the end of the course.

Course objectivesStudents are able to understand:

principles of scientific writing, conventions in scientific writing, the structure of scientific texts, ethicsin scientific writing, plagiarism, editing skills, ethics, language in scientific writing, academic writingstyle, coherence in scientific writing, reporting sources.

PSY4110Period 56 Apr 20205 Jun 2020

Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:P.P.C. Wilms van KersbergenTeaching methods: Assignment(s), Lecture(s), Paper(s), Research, Skills, Training(s), Work in subgroupsAssessment methods: Attendance, Final paper

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Keywords: Scientific writing, Research proposal, empirical research article, literature review, peer review,language awareness

School of Business and Economics

Experimental Economics MethodsFull course descriptionThis course will cover the theoretical and methodological background, as well as practical issues ofexperimental work in economics and finance. The course will discuss methodological issues in thedomain of economics experiments, internal validity, external validity, the role of experiments intheory testing and theory suggesting. It will also critically discuss norms and customs in experimentaleconomics research, as well as the use of task related incentives and the no-deception paradigm. Onthe practical side the course will deal with the questions of: What makes an experimental design goodor bad? What are the different degrees of independent observations? What are efficient dialogueswith the data? Part of the course will be devoted to practical design issues and students will developtheir own experiments as part of a final paper. Time will also be devoted to research ethics.

Course objectivesKnowledge of:Theory of experimental economics, controlled economic environments, induced value theory, internalvalidity, external validity, domain of experimental economics, methodological foundations ofexperimental design, paradigm of no deception, direct experimental control, between-subject design,within-subject design, indirect experimental control (randomisation), preference elicitation methods,selected topics from experimental economics, human-subjects research ethics.

PrerequisitesResearch master’s students only; Microeconomics at a research master’s level (e.g. EBC4061Microeconomics I and EBC4204 Microeconomics II).

Recommended readingThere is no one textbook that will cover the course. The literature will consist of a wide variety ofreadings including chapters from textbooks aimed at advanced graduates and contemporary researcharticles.

EBS4026Period 68 Jun 20203 Jul 2020

Print course descriptionECTS credits:

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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4.0Coordinator:M. WibralTeaching methods: PBL, Work in subgroups, Assignment(s), Lecture(s)Assessment methods: Participation, Attendance, Final paper

Faculty of Psychology and Neuroscience

Research Grant Writing WorkshopFull course descriptionDuring this workshop students will learn why and how to apply for research grants. The need foracquiring funding for research, the opportunities for, and availability of grant application funding willbe discussed. Several researchers who have experience in applying for different types of grants willprovide students with first-hand knowledge and tips. Students will learn fundamentals of good grantwriting, general preparation of the grant application and how to deal with reviewer comments. Ethicalissues including feasibility and acceptability of the research, and the role of the local research ethicscommittee will be discussed. Students will subsequently choose a topic (from a list of topics) and workin teams to develop a research idea based on abstracts that will serve as a basis for writing a fullresearch proposal during the second-year Research Grant Writing Course with guidance of a mentor(see description of PSY5112).

Course objectivesstudents will learn about the importance of grant writing for an academic career;students will recognize opportunities for funding, ethical aspects of grants, how grants can be

acquired, and grant writing skills;students will develop a first outline of a grant proposal with peers.

PSY4112Period 68 Jun 20203 Jul 2020

Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinators:S. KöhlerR.L.H. HandelsTeaching methods: Assignment(s), Lecture(s), Work in subgroupsAssessment methods:

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Attendance, Final paperKeywords: Funding possibilities, grant applications, proposal writing, team science

Second year courses

Research Master Specialisation NeuroeconomicsYear 2Faculty of Psychology and Neuroscience

Research Grant Writing CourseFull course descriptionIn this course, students will apply what they have learned during the Research Grant WritingWorkshop (PSY4112) by going through a full grant proposal writing and review process. Students willwork together (groups of max. 5-6 students) to write a research proposal on their selected topic,including an original research hypothesis, design, methods and valorization. Students are encouragedto think across boundaries of different scientific fields. A mentor (senior researcher) will guidestudents during this writing process. The students will write their proposal in 3 steps, and they willreceive feedback from their mentor and peers. The resulting proposals will be reviewed by twoassessors and presented during a symposium by way of an oral presentation.

Course objectivesStudents are able to:

review literature;formulate a research hypothesis;design a research study;write a final research proposal;present and illustrate a research proposal at a symposium.

PrerequisitesThis course is a continuation of the Research Grant Writing Workshop (PSY4112).

PSY5112Period 12 Sep 201925 Oct 2019

Print course descriptionECTS credits: 3.0Instruction language: English

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Coordinators:S. KöhlerR.L.H. HandelsTeaching methods: Work in subgroupsAssessment methods: Attendance, Final paper, PresentationKeywords: Research proposal, Interdisciplinary, hypothesis, design, methods, research symposium

School of Business and Economics

Behavioural EconomicsFull course descriptionStarting from the observation that human behaviour often deviates from traditional homo economicusmodels, behavioural economists try to model how people actually behave. In this course we willdiscuss why they do that, how they do that and what the results of their efforts are. We start bydiscussing the reasons to build models of human behaviour and the challenges faced when trying todo so. Next we will critically examine different general approaches to this endeavour. Having set thestage we will study behavioural economics models of choice, belief updating, decision making underrisk, intertemporal choice, strategic decision making, and social preferences. Finally we explore theimplications of behavioural economics for policy making and macro-economic modelling. Although wediscuss the interplay between theory and empirics we will focus on theories, rather than empiricalobservations, of human behaviour.

Course objectivesForm an opinion on the why and how of modelling human behaviour, in particular as it deviates

from the rational and selfish homo economics.Become acquainted with some of the most important behavioural economics models on a variety of

topics.Understand, build and expand models of human behaviour.Understand the interplay between theory and data, including the ability to build a theory based on

observed behavioural regularities, assess a theory based on observed behaviour, and conceive ofpossible empirical tests of a theory.

Acquire intimate knowledge of the theoretical and empirical literature related to the chosenresearch topic.

Design a research project related to behavioural economics.PrerequisitesResearch masters students only: Microeconomics at a research Master’s level (e.g.: EBC4061Microeconomics I and EBC4204 Microeconomics 2); exposure to experimental economicsmethodology (e.g. EBS4026 Experimental Economics Methods) is an advantage but not a formalrequirement.

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Recommended readingThere is no one textbook that will cover the course. The literature will consist of a wide variety ofreadings including chapters from textbooks aimed at advanced graduates and contemporary researcharticles.

EBC4200Period 12 Sep 201925 Oct 2019

Print course descriptionECTS credits: 6.5Coordinator:J. LindeTeaching methods: PBL, Lecture(s)Assessment methods: Participation, Final paper

Faculty of Psychology and Neuroscience

Programming in Matlab Advanced CourseFull course descriptionThis course deals with advanced topics in Matlab programming. In particular, it will focus on how toimplement efficient and re-usable programs for neuroimaging applications. Students will learn how toput the principles of efficient programming, such as debugging and profiling, into practice. Advancedtopics in graphics and user interfaces will also be discussed.

Course objectivesStudents are able to understand:

debugging, efficient programming, graphical objects, graphical user interfaces.

PrerequisitesPSY4224 Programming in Matlab Basic Course.

PSY5223Period 12 Sep 201925 Oct 2019

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:G. ValenteTeaching methods: Assignment(s), Lecture(s), Skills, Work in subgroupsAssessment methods: Attendance, AssignmentKeywords: Efficient programming, debugging, graphical user interfaces