Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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First year courses

Research Master Specialisation Neuroeconomics Year 1School of Business and Economics

Mathematical Research Tools

Full course description

Multi-variable calculus, static optimisation methods in particular Lagrange and Kuhn-Tucker,connection with linear and non-linear programming, dynamic (discrete and non-discrete)optimisation methods (Bellman principle, calculus of variations, optimal control, Pontryaginmaximum principle), basic elements of difference and differential equations and of dynamic systems.

Course objectives

This 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 andFinance Research (EFR) master program.

Prerequisites

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

Recommended reading

Jehle, Geoffrey A. and Philip J. Reny : Advanced Microeconomic Theory (2nd edition).Sydsaetter, K., Hammond, P., Seierstad, A. and A. Strom : Further Mathematics for EconomicAnalysis (Financial Times / Prentice Hall, 2008).

EBC4182Period 14 Sep 201727 Oct 2017Print course descriptionECTS credits: 6.5Coordinator:

H.J.M. Peters

Teaching methods: PBL, Lecture(s), Assignment(s)Assessment methods:

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Participation, Written examFaculty of Psychology and Neuroscience

Psychology meets Neuroscience meets Economics

Full course description

During this course, the students from different backgrounds will receive an introduction into themain topics of Psychology, Neuroscience and Economics. They will receive an overview about wherethose fields come together and why there is a need for the new research direction ofNeuroeconomics. The students will gain insight into the anatomy and functioning of the brain andwill receive an introduction into cognitive and social neuroscience. The focus will be on usingneuroscience methodology to empirically address relevant research questions in psychology anddecision-making science. Specifically, the following topics will be addressed: economical models ofhuman decision making, including predictions based on game theory as well as other models used inbehavioural economics, using functional brain imaging and brain interference techniques to studythe neurobiology underlying human cognition and decision making, as well as examples from appliedsocial neuroscience research. Lecturers representing these topics will be recruited from all threeparticipating UM departments, namely the School of Business and Economics, the CognitiveNeuroscience department and the Work and Social Psychology department of the Faculty ofPsychology and Neuroscience. The course will thus aim at providing a bridge between classicalresearch themes in cognitive neuroscience, behavioural economics and neuroeconomics.

Course objectives

Knowledge of: Basic understanding of selected neuroscientific research tools including functionalbrain imaging and functional brain interference. Basic understanding of structural and functionalarchitecture of the brain applications of neuroscience methodology in cognitive and socialpsychology, as well as (neuro)economics.

PSY4711Period 14 Sep 201727 Oct 2017Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinators:

T. SchuhmannM. Wibral

Teaching methods: Assignment(s), Lecture(s), Paper(s), PBLAssessment methods: Attendance, Presentation, Written examKeywords: Cognitive Neuroscience, social neuroscience, neuroeconomics, cognitive psychology

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Faculty of Psychology and Neuroscience

Advanced Statistics I

Full course description

The course consists of six units. In the first four units, participants will be given an in-depth trainingin the following standard statistical methods: factorial ANOVA for between-subject designs, analysisof covariance (ANCOVA), multivariate ANOVA (MANOVA), discriminant analysis and multiple linearregression. Students are assumed to have background knowledge of balanced two-way factorialANOVA and multiplere 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 identifyingmodels, model equivalence, global and local goodness of fit indices, parsimony, model modificationand cross-validation. Some concepts from matrix algebra are needed for SEM, and these will bebriefly discussed without going into technical detail.

Course objectives

Knowledge of: Oneway analysis of variance, contrast analysis, unbalanced designs, multivariateanalysis of variance, discriminant analysis, linear regression with interaction terms, linearregression with dummy variables, data transformations, simple slope analysis, analysis ofcovariance, path analysis, structural equation modeling, confirmatory factor analysis, structuralmodels with latent variables.

PSY4106Period 14 Sep 201722 Dec 2017Print course descriptionECTS credits: 3.0Instruction language: EnglishCoordinator:

J. Schepers

Teaching methods: Assignment(s), Lecture(s), Skills, Training(s)Assessment methods: Attendance, Written examKeywords: Univariate analysis of variance, multivariatie analysis of variance, regression analysis, structural

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equation modelingFaculty of Psychology and Neuroscience

Practical Training: SPSS I and Lisrel

Full course description

In 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 traditionalSPSS program, but also the specialised LISREL software. LISREL is a statistical program that allowsstructural equations models to be tested.

Course objectives

Knowledge of: Defining contrasts, building regression models, doing multivariate analyses,transforming data, testing simple slopes, creating and testing SEM models.

PSY4119Period 14 Sep 201722 Dec 2017Print course descriptionECTS credits: 0.0Instruction language: EnglishCoordinator:

J. Schepers

Teaching methods: Assignment(s), Training(s)Assessment methods: AttendanceKeywords: SPSS, LISREL, statistical softwareFaculty of Psychology and Neuroscience

EEG and ERP

Full course description

Electroencephalography (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 ERP

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research, 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 typesof artefacts. Finally, students must interpret the resulting data. Successful measurement requires anunderstanding of the basics of EEG and ERP signal analysis techniques, such as artefactmanagement, spectral analysis, filtering, ERP averaging, time-frequency analysis etc. Students alsoreceive hands-on training in smaller groups in running an ERP experiment, including electrodeapplication, minimising artefacts, and health and safety in the lab. A number of simple experimentalparadigms will be used that provide interesting and reliable results. Data processing will include anumber of common EEG analyses, e.g. analyses in the time and frequency domain.

Course objectives

Knowledge of: Basic EEG/ERP paradigms, EEG recording systems, measurement settings, electrodeapplication, data quality verification, analogue-digital conversion, basic EEG / ERP components,interpreting topographical plots, neural origins of EEG, time domain analysis, frequency domainanalysis, time-frequency analysis, filtering, ocular artefact control, muscle artefact control, choice ofreference, re-referencing.

PSY4221Period 14 Sep 201727 Oct 2017Print course descriptionECTS credits: 2.0Instruction language: EnglishCoordinator:

F.T.Y. Smulders

Teaching methods: Lecture(s), Paper(s), Skills, Training(s), Work in subgroupsAssessment methods: Attendance, Final paperKeywords: Electroencephalography (EEG), Event-related potentials (ERP), electrophysiology, measurement,analysis of brain potentialsSchool of Business and Economics

Microeconomics I

Full course description

The 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.

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Examination method: Assessment method depends on number of participants.

Course objectives

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

Prerequisites

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

Recommended reading

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

EBC4061Period 230 Oct 201722 Dec 2017Print course descriptionECTS credits: 6.5Instruction language: EnglishCoordinator:

A. Predtetchinski

Teaching methods: Lecture(s), Assignment(s), Work in subgroupsAssessment methods: Oral exam, Written examFaculty of Psychology and Neuroscience

Noninvasive Brain Stimulation (NIBS)

Full course description

This course will provide students with an in-depth knowledge of; noninvasive brain stimulationtechniques, including transcranial magnetic stimulation (TMS) and transcranial direct currentstimulation (tDCS). Students will learn more about the mechanisms of action; the physico-physiological principles; various application protocols; functional brain stimulation paradigms andapproaches for combining brain stimulation with brain imaging techniques both within and betweenexperimental session(s).

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Since the very beginning of experimental brain research, neuro- scientists 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 of experimental and clinical applications. Incombination with methods of functional imaging, it is not only possible to measure the brain activityduring the execution of a particular function, but NIBS enables the researcher or clinician toincrease or decrease the neuronal activity in the task-related brain area and reveal behaviouralchanges in the actual task performance. This enables identification of those brain areas that arefunctionally relevant to a particular function. In a clinical context, NIBS has also been used to treatneurological, psychiatric, and psychological disorders that are accompanied by a pathologicallyincreased or decreased activity in a specific brain region. Since NIBS offers the possibility toincrease or decrease neuronal activity beyond the stimulation itself, it might, in the future, become apowerful therapeutic tool to help treat diseases like depression, epilepsy, or schizophrenia.

Course objectives

Knowledge of: Physics and mechanisms of action of NIBS, physiological effects of NIBS, NIBSprotocols and application paradigms, NIBS in human cognitive neuroscience, combining NIBS withfunctional imaging, clinical applications of NIBS.

PSY4216Period 38 Jan 20182 Feb 2018Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinators:

T. SchuhmannT.A. de Graaf

Teaching 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 imagingFaculty of Psychology and Neuroscience

Neuroanatomy

Full course description

The aim of this practical training is to make you acquainted with the neuroanatomical terminology

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and 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 orneurobiology. Many specific brain areas can be linked to particular functions. Thus, knowledge ofthe brain anatomy and its main functions allows direct linkage of specific neurological or psychiatricdisorders to particular brain areas. After a short theoretical introduction, you will study whole brainsand brain material of mammals at both macroscopical (visual inspection) and microscopical level.The emphasis will be on major brain systems, including the basal ganglia and limbic system.

Course objectives

Knowledge of: Limbic system, basal ganglia, plastinated human brains, brain dissection,microscopical slices.

PSY4108Period 38 Jan 20182 Feb 2018Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:

J.H.H.J. Prickaerts

Teaching methods: Lecture(s), Skills, Work in subgroupsAssessment methods: Attendance, Written examKeywords: Neuroanatomy, limbic system, basal gangliaFaculty of Psychology and Neuroscience

Methods of Deactivation

Full course description

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. Some

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studies 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.

Course objectives

Knowledge of: Transcranial magnetic stimulation, application of TMS, motor thresholddetermination, phosphene threshold determination, Neuronavigation, transcranial electricstimulation, cooling, various other deactivation methods.

PSY4233Period 38 Jan 20182 Feb 2018Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:

T. Schuhmann

Teaching methods: Assignment(s), Lecture(s), Skills, Training(s)Assessment methods: Attendance, AssignmentKeywords: Transcranial Magnetic Stimulation, Non-invasive Brain Stimulation, fMRI-guided neuronavigationFaculty of Psychology and Neuroscience

Colloquia

Full course description

Each specialisation organizes maximally two colloquia, in which senior researchers from MaastrichtUniversity or visiting lecturers present their scientific insights. Each colloquium focuses in depth onone of a wide range of topics, with issues transcending the courses and specialisations. Eachcolloquium lecture will be followed by active discussion, chaired by the lecturer or the host of theguest lecturer. A total of eleven colloquia will be offered.

Course objectives

Knowledge of: Key research domains from different specialisations, interdisciplinary research,interacting with students from different specialisations.

PSY4100

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Period 38 Jan 20181 Jun 2018Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinators:

W.J. RiedelG. ValenteS.Z. Stapert

Teaching methods: Lecture(s)Assessment methods: AttendanceKeywords: interdisciplinary knowledgeSchool of Business and Economics

Microeconomics II

Full course description

The 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 objectives

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

Prerequisites

Mathematical Research Tools, EBC4182.

Recommended reading

Main text:

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

Recommended text:

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

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EBC4204Period 45 Feb 20186 Apr 2018Print course descriptionECTS credits: 6.5Coordinator:

A. Predtetchinski

Teaching methods: Assignment(s), Lecture(s)Assessment methods: Written exam, Oral exam, Final paperFaculty of Psychology and Neuroscience

Social Neuroscience

Full course description

Social 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.

By contrast, social psychology emphasizes the first and second, while cognitive neuroscienceemphasizes the second and third of these three levels. SN researchers are interested in questionssuch as: How does the brain enable us to know what other people feel? Is there a neural substratefor cooperation or aggression? Which brain circuits suppress prejudices? Are social emotionsrepresented by the brain in a different way compared to basic emotions?

Although research on the biological correlates of social processes has been ongoing for decades, thisapproach has gone through a period of rapid expansion with the advent of new techniques likefunctional Magnetic Resonance Imaging (fMRI) and transcranial magnetic stimulation (TMS).

Course objectives

Knowledge of:

Neuroscience: fMRI, TMS, EEG.

Social Neuroscience: social decision making, social cognition, emotion-regulation, empathy, moraljudgment, aggression.

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PSY4712Period 45 Feb 20186 Apr 2018Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinator:

A. Heijne

Teaching methods: Assignment(s), PBL, Presentation(s)Assessment methods: Attendance, Presentation, Final paper, Written examKeywords: social neuroscience, Social cognition, Research proposal, neuroeconomicsFaculty of Psychology and Neuroscience

Functional Brain Imaging in Neuroeconomics

Full course description

In 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 ofFMRI principles and relevant literature for topics in Neuroeconomics and 2) hands-on experiencewith conducting, measuring, analyzing and reporting on FMRI data. In the first part (period 4),students will take part in lectures on principles and applications of FMRI, and in tutorial meetings todiscuss relevant empirical neuroscience papers on relevant topics, such as decision making, gametheory, social neuroscience and individual differences. In the second part (period 5), students willdesign and conduct an FMRI experiment, analyse and interpret the FMRI data, and write a researchreport about the findings. This part of the course will be conducted in collaboration with a number ofstaff members who are knowledgeable about FMRI. Assessment includes both periods and will bebased on personal performance in the course, quality of the presentations (period 4) and the gradingof the final research report (period 5).

Course objectives

Knowledge of: Functional brain imaging techniques and principles; experimental design; exposure toFMRI environment; hands-on experience in designing, conducting and analyzing an FMRIexperiment; fMRI in neuroeconomics and decision making.

Prerequisites

Basic knowledge of Brain anatomy, experimental design and statistics.

PSY4713

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Period 45 Feb 20189 May 2018Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinators:

V.G. van de VenB. Sorger

Teaching methods: Lecture(s), Paper(s), Presentation(s), Skills, PBL, Training(s)Assessment methods: Attendance, Final paper, PresentationKeywords: Magnetic Resonance Imaging (MRI), functional MRI, structural MRI, neuroimaging, experimentaldesign, data analysis, brain activity, neuroeconomics, decision makingFaculty of Psychology and Neuroscience

Advanced Statistics II

Full course description

The 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) thechoice between 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 ofincluding covariates into repeated measures ANOVA; and d) the choice between different methods ofanalysis 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 appliedto a 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 oforganisations).

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

Course objectives

Knowledge of: Repeated measures ANOVA for within-subject and split-plot (between x within)designs, including factorial designs and covariates in repeated measures ANOVA; Mixed (multilevel)linear regression with random effects and autocorrelation; Optimal design and sample sizecalculations for experimental and observational studies.

Prerequisites

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

PSY4107Period 419 Feb 20188 Jun 2018Print course descriptionECTS credits: 3.0Instruction language: EnglishCoordinator:

G.J.P. van Breukelen

Teaching methods: Assignment(s), Lecture(s), Training(s)Assessment methods: Attendance, Written examKeywords: Within-subject designs, repeated measures ANOVA, mixed (multilevel) regression, marginal versusrandom effects models, optimal design, sample size, powerFaculty of Psychology and Neuroscience

Practical Training: SPSS II

Full course description

This practical training forms part of the PSY4107 Advanced Statistics II course. The practicalconsists of seven sessions in the computer rooms. In the first six sessions SPSS procedures forrepeated measures and multilevel data are practised. The goal is to understand how proper analysesof such data can be done using SPSS. In the last session GPower will be used to practice sample size(power) calculations for some elementary research designs.

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Course objectives

Knowledge of:

How to run with SPSS: repeated measures ANOVA for within-subject and split-plot (between xwithin) 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, grantapplication)

Prerequisites

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

PSY4117Period 419 Feb 20188 Jun 2018Print course descriptionECTS credits: 0.0Instruction language: EnglishCoordinator:

G.J.P. van Breukelen

Teaching methods: Training(s)Assessment methods: AttendanceKeywords: Within-subject designs, repeated measures ANOVA, mixed (multilevel) regression, marginal versusrandom effects modelsFaculty of Psychology and Neuroscience

Neuroeconomics Meetings

Full course description

The neuroeconomics meetings are organised in seminar style, where students and researcherspresent and discuss original research articles, methodology and research ethics in neuroeconomicsas well as the relevance of neuroeconomics to neuroscience and economics. Meetings includepresentations and in-depth discussion. The meetings will take place on a bi-weekly basis. Themeetings are organised the whole year through. They are compulsory in periods 4-6 of the first yearbut are also highly recommended for second year students and first year students in period 1-3.

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Course objectives

Knowledge of: Key research domains in neuroeconomics, critical assessment of research projects,critical assessment of published research, interaction with other students and more seniorresearchers.

PSY4731Period 45 Feb 201826 Aug 2018Print course descriptionECTS credits: 1.5Instruction language: EnglishCoordinators:

A.M. RiedlA. Heijne

Teaching methods: Lecture(s), Presentation(s), Assignment(s)Assessment methods: Attendance, PresentationKeywords: neuroeconomics, methodology; decision making; critical analysisFaculty of Psychology and Neuroscience

Timing Neural Processing with EEG and MEG

Full course description

Cognitive 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,which determine its suitability for studying a particular research question. Bothelectroencephalography (EEG) and magnetoencephalography (MEG) are important in characterisingthe time course of activation of neural systems involved in perceptual and cognitive processes.Relevant topics include auditory and visual perception, attention, language, memory and theirdevelopment. EEG and MEG signals reflect complementary aspects of brain activity, with MEGhaving some advantages over EEG in the localisation of underlying neural sources. This courseprovides detailed knowledge on EEG and MEG, both of which have a clear advantage over otherneuroimaging methods in terms of temporal precision. The study of EEG and MEG experimentaldesign, data acquisition and data analysis will be combined with detailed literature discussions ontheoretical and methodological issues. Based on different types of empirical questions, there will bediscussion of the potential of a range of methods for advanced EEG and MEG analysis, includinganalysis in the time and frequency domain, source localisation, the combination with functionalmagnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) methods,independent component analysis and analyses of functional connectivity.

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Course objectives

Knowledge of: Electro-encephalography, event-related potentials, magneto-encephalography, dipolesource analysis, distributed source analysis, Fourier analysis, wavelet analysis, independentcomponent analysis, connectivity analysis, application: mental chronometry, attention, lateralisedevent-related potentials, combination electro-encephalography and functional magnetic resonanceimaging, combination electro-encephalography and trans-cranial magnetic stimulation.

PSY4256Period 517 Apr 201824 May 2018Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinator:

F.T.Y. Smulders

Teaching methods: Lecture(s), Paper(s), Presentation(s), PBLAssessment methods: Attendance, Final paper, PresentationKeywords: electroencephalography, magnetoencephalography, biological signal analysis, source localisationFaculty of Psychology and Neuroscience

Programming in Matlab Basic Course

Full course description

Matlab provides a powerful environment for numerical computation, data analysis and visualisation.It is, 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)and 2D or 3D plots for visualisation of data or analysis-results. Many complete packages for theanalysis of cognitive neuroimaging data (e.g. fMRI data or EEG/MEG data) are implemented inMatlab. Thus, usage of these packages requires at least a basic understanding of Matlab.Furthermore, if more advanced analysis or visualisation is needed than what is offered by existingpackages, developing new functionalities in Matlab is often the most convenient option. The firstpart of the course will deal with how Matlab primarily represents and processes data, i.e. asmatrices. Subsequently, attention is focused on the usage of the environment: the prompt; theworkspace; the help options; and loading, saving and visualising data. The principles behindprogramming will be introduced, with particular emphasis on neuroimaging applications.

Course objectives

Knowledge of: Matlab environment, Matlab variables, vectors, matrices, matrix algebra, 2D and 3D

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plots, conditional loops, scripts, functions, file Input-Output, structures, cells.

PSY4224Period 59 Apr 20188 Jun 2018Print course descriptionECTS credits: 2.0Instruction language: EnglishCoordinator:

G. Valente

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

Scientific Writing

Full course description

The 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 aimsto cover 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. Individual feedback onparallel block assignments is given at the end of the course by the instructor.

Course objectives

Knowledge of: Principles of scientific writing, conventions in scientific writing, the structure ofscientific texts, ethics in scientific writing, plagiarism, editing skills, ethics, language in scientificwriting, academic writing style, coherence in scientific writing, reporting sources.

PSY4110Period 59 Apr 20188 Jun 2018Print course descriptionECTS credits: 1.0

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Instruction language: EnglishCoordinator:

P.P.C. Wilms van Kersbergen

Teaching methods: Assignment(s), Lecture(s), Paper(s), Research, Skills, Training(s), Work in subgroupsAssessment methods: Attendance, Final paperKeywords: Scientific writing, Research proposal, empirical research article, literature review, peer review,language awarenessSchool of Business and Economics

Experimental Economics Methods

Full course description

This 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 inexperimental economics research, as well as the use of task related incentives and the no-deceptionparadigm. On the practical side the course will deal with the questions of: What makes anexperimental design good or bad? What are the different degrees of independent observations?What are efficient dialogues with the data? Part of the course will be devoted to practical designissues and students will develop their own experiments as part of a final paper. Time will also bedevoted to research ethics.

Course objectives

Knowledge of:Theory of experimental economics, controlled economic environments, induced value theory,internal validity, 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.

Prerequisites

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

Recommended reading

There 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

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research articles.

EBS4026Period 611 Jun 20186 Jul 2018Print course descriptionECTS credits: 4.0Coordinator:

M. Wibral

Teaching methods: PBL, Work in subgroups, Assignment(s), Lecture(s)Assessment methods: Participation, Attendance, Final paperFaculty of Psychology and Neuroscience

Research Grant Writing Workshop

Full course description

During 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 fundingwill be discussed. Several researchers who have experience in applying for different types of grantswill provide students with first-hand knowledge and tips. Students will learn fundamentals of goodgrant writing, general preparation of the grant application and how to deal with reviewer comments.Ethical issues including feasibility and acceptability of the research, and the role of the localresearch ethics committee will be discussed. These skills will be practiced during the workshop.Students will subsequently choose a topic (provided by senior researchers) on which they will writea research proposal during the second-year Research Grant Writing Course (see description ofPSY5112).

Course objectives

Knowledge of: Opportunities for funding, how grants can be acquired, grant writing skills.

PSY4112Period 611 Jun 20186 Jul 2018Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinators:

P. AaltenS. Köhler

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Teaching methods: Assignment(s), Lecture(s)Assessment methods: Attendance, Final paperKeywords: Funding possibilities, grant applications, proposal writingSecond year courses

Research Master Specialisation Neuroeconomics Year 2Faculty of Psychology and Neuroscience

Programming in Matlab Advanced Course

Full course description

This 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 howto put the principles of efficient programming, such as debugging and profiling, into practice.Advanced topics in graphics and user interfaces will also be discussed.

Course objectives

Knowledge of: Debugging, efficient programming, graphical objects, graphical user interfaces.

Prerequisites

PSY4224 Programming in Matlab Basic Course.

PSY5223Period 14 Sep 201727 Oct 2017Print course descriptionECTS credits: 1.0Instruction language: EnglishCoordinator:

G. Valente

Teaching methods: Assignment(s), Lecture(s), Skills, Work in subgroupsAssessment methods: Attendance, Take home examKeywords: Efficient programming, debugging, graphical user interfacesSchool of Business and Economics

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Behavioural Economics

Full course description

Starting from the observation that human behaviour often deviates from traditional homoeconomicus models, behavioural economists try to model how people actually behave. In this coursewe will discuss why they do that, how they do that and what the results of their efforts are. We startby discussing the reasons to build models of human behaviour and the challenges faced when tryingto do so. Next we will critically examine different general approaches to this endeavour. Having setthe stage we will study behavioural economics models of choice, belief updating, decision makingunder risk, intertemporal choice, strategic decision making, and social preferences. Finally weexplore the implications of behavioural economics for policy making and macro-economic modelling.Although we discuss the interplay between theory and empirics we will focus on theories, ratherthan empirical observations, of human behaviour.

Course objectives

Form an opinion on the why and how of modelling human behaviour, in particular as itdeviates from the rational and selfish homo economics.Become acquainted with some of the most important behavioural economics models on avariety of topics.Understand, build and expand models of human behaviour.Understand the interplay between theory and data, including the ability to build a theorybased on observed behavioural regularities, assess a theory based on observed behaviour, andconceive of possible 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.

Prerequisites

Research 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.

Recommended reading

There 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 contemporaryresearch articles.

EBC4200Period 14 Sep 201727 Oct 2017Print course descriptionECTS credits: 6.5

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Coordinator:

J. Linde

Teaching methods: PBL, Lecture(s)Assessment methods: Participation, Final paperFaculty of Psychology and Neuroscience

Research Grant Writing Course

Full course description

In this course, students will apply what they have learned during the Research Grant WritingWorkshop (PSY4112). Students will work together (groups of max. 5-6 students) to write a researchproposal on their selected topic, including an original research hypothesis, design, methods andvalorization. Students are encouraged to think across boundaries of different scientific fields. Asenior researcher will guide students during this writing process. The students will write theirproposal in 3 steps, and they will receive feedback from their mentor and peers. The resultingproposals will be presented during a symposium by way of an oral presentation.

Course objectives

Knowledge of how to:

Review literature, formulate a research hypothesis, design a research study, write a researchproposal, present a proposal at a symposium.

Prerequisites

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

PSY5112Period 14 Sep 201727 Oct 2017Print course descriptionECTS credits: 3.0Instruction language: EnglishCoordinators:

P. AaltenS. Köhler

Teaching methods: Work in subgroupsAssessment methods: Attendance, Final paper, Presentation

Research Master Cognitive and Clinical Neuroscience Specialisation Neuroeconomics

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Keywords: Research proposal, Interdisciplinary, hypothesis, design, methods, research symposium, peer review