First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
Course unit
PSP4065487, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
PS1932, Degree course structure A.Y. 2017/18, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination NEW CONCEPTS IN COGNITIVE PSYCHOLOGY
Department of reference Department of General Psychology
E-Learning website
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit is available ONLY for students enrolled in COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY

Teacher in charge MARCO ZORZI M-PSI/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses M-PSI/01 General Psychology 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 42 108.0 No turn

Start of activities 07/10/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Board From To Members of the board
6 2019 01/10/2019 30/09/2020 ZORZI MARCO (Presidente)
BONATO MARIO (Membro Effettivo)
CUTINI SIMONE (Membro Effettivo)
TESTOLIN ALBERTO (Membro Effettivo)
VALLESI ANTONINO (Membro Effettivo)

Prerequisites: Basic knowledge of cognitive psychology, statistics, and cognitive neuroscience methods.
Target skills and knowledge: The course objective is to present a multidisciplinary approach to the study of cognition, based on a variety of empirical research methods and integrated by computational modeling. Using numerical cognition as a case study, the course presents a coherent path to gain an understanding how cognition is shaped by evolution, learning, and culture.
Examination methods: Type of examination: Written (duration: 1:30 h)
Written examination: Open questions (5 questions, up to 4 points per question; max 20 points) + paper assignment (max 10 points)
Paper assignment: Each student will be required to write an essay (3-4 pages, about 1500 words) that reviews and discusses one of the topics assigned during the course. The paper must be submitted on the day of the written exam.
Assessment criteria: Evaluation of student's learning outcome will be based on the understanding of the topics covered by the course in terms of concepts and methods.
Course unit contents: The course presents empirical studies based on a variety of methods and subject populations (animals, children, adults, neurological patients) to build an understanding of cognition that bridges the gap between various levels of analysis (from neurons to behaviour) and can be formalized in computational models. Numerical cognition is used as a case study to show how human cognition is shaped by evolution, learning, and culture.
Topics: animal cognition; cognitive development; neural bases of cognition; cultural effects on cognition; embodied cognition; computational modeling of cognition.
Planned learning activities and teaching methods: Teaching is based on frontal lectures with discussion of scientific findings from the most important studies on a given topic. Attendance to lectures is compulsory and active involvement of the students is encouraged.

Students can deepen their knowledge of computational modeling through hands-on experience by enrolling into the course/laboratory "Computational Neuroscience"
Additional notes about suggested reading: Reading material available on Moodle platform:
- Lectures slides
- Scientific articles
Agrillo et al (2012). PloS One, 7(2), e31923.
Ansari (2008). Nature Reviews Neuroscience, 9, 278-291.
Brannon (2006). In: J. Campbell (Ed), Handbook of Mathematical Cognition (chapter 6, pp. 85-107).
Feigenson et al (2004). Trends in Cognitive Sciences, 8, 307-314.
Göbel et al (2011). Journal of Cross-Cultural Psychology, 42, 543-565.
Halberda et al. (2008). Nature, 455, 665-668.
Hubbard et al (2005). Nature Reviews Neuroscience, 6, 435-448.
Nieder & Dehaene (2009). Annual Review of Neuroscience, 32,185–208.
Pica et al (2004). Science, 306, 499-503.
Sella et al (2017). Cognition, 158, 56-67
Stoianov & Zorzi (2012). Nature Neuroscience, 15, 194-196
Umiltà et al (2009). Experimental Brain Research, 192, 561-569.
Zorzi et al (2013). Frontiers in Psychology, 4:515
Zorzi & Testolin (2018). Phil Trans Roy Soc B, 373:20170043.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Quality Education