(Vytvorená stránka „= Deep Learning and Cognition – <span style="display:inline-block;"></span> <small style="white-space:nowrap;">2-IKV-239a/22</small>= __TOC__ The course introduces th...“)
 
(Course schedule)
Riadok 30: Riadok 30:
 
|Tuesday
 
|Tuesday
 
|09:50 - 11:20
 
|09:50 - 11:20
|I-23
+
|I-9
 
|[[Igor Farkas|Igor Farkaš]]
 
|[[Igor Farkas|Igor Farkaš]]
 
|-
 
|-
Riadok 36: Riadok 36:
 
|Tuesday
 
|Tuesday
 
|11:40 - 13:05
 
|11:40 - 13:05
|I-23
+
|I-9
 
|students
 
|students
 
|}
 
|}

Verzia zo dňa a času 14:17, 4. september 2023

Deep Learning and Cognition – 2-IKV-239a/22

The course introduces the field of a highly popular machine learning approach focused on deep learning in artificial neural networks. Aiming at master’s students with diverse backgrounds of bachelor’s studies (such as students of cognitive science), the course will guide them through different areas of DL, to highlight its successful applications. To better understand the mechanistic principles of DL models, the students will learn the basics about the underlying mathematical concepts of DL and will be shown a few simpler examples of functioning neural network models. Throughout the course, the discussions will also focus on virtues and vice of deep learning and its relation to human cognition.

The course is a part of Master Programme in Cognitive Science.


Course schedule

Type Day Time Room Lecturer
Lecture Tuesday 09:50 - 11:20 I-9 Igor Farkaš
Student presentation Tuesday 11:40 - 13:05 I-9 students

Syllabus

Date Topic References
(1)
27.09.
Introduction to language and concepts

slides1 slides2

♦ Wiki: Language
♦ Margolis E., Laurence S. (2014) Concepts, The Stanford Encyclopedia of Philosophy.

(2)
04.10.
Towards embodied cognition

♦ Wilson M. (2002) Six views of embodied cognition. Psychonomics Bulletin Review, 9(4), 625-636.
♦ Ziemke T. (2003) What's that thing called embodiment? Proc. of the 25th Annual Conf. of the Cog. Sci. Society, 1134-1139.

(3)
11.10.
Mirror neuron system and its role(s) in cognition

♦ Rizzolatti G. & Sinigaglia C. (2010) The functional role of the parieto-frontal mirror circuit: Interpretations and misinterpretations. Nature Rev. Neurosci., 11, 264-274.
♦ Bonini L. et al. (2022) Mirror neurons 30 years later: implications and applications, Trends in Cognitive Sciences.

(4)
18.10.
Common coding theory, motor simulation, mental simulation

♦ Smith A.H. (2006) Motor cognition and mental simulation. Chapter in Smith E. & Kosslyn S. (eds.): Cognitive Psychology: Mind and Brain, Prentice Hall, pp. 451-481.
♦ van der Wel R., Sebanz N., Knoblich G. (2013) Action perception from a common coding perspective. Chapter in K. Johnson and M. Schiffrar (Eds.), People Watching: Social, Perceptual, and Neurophysiological Studies of Body Perception, Oxford University Press

(5)
25.10.
Language as action

♦ Fischer M.H., Zwaan R.A. (2008) Embodied language: A review of the role of the motor system in language comprehension. The Quarterly Journal of Exp. Psych., 61 (6), 825-850
♦ Arbib M., Gasser B., Barrès V. (2014) Language is handy but is it embodied?. Neuropsychologia, 55, 57–70. </b>


01.11.
no class autumn break / holiday
(6)
08.11.
Conceptual and linguistic systems - two theories

♦ Barsalou L. et al. (2008) Language and simulation in conceptual processing. In: de Vega, Glenberg & Graesser (eds), Symbols and Embodiment: Debates on Meaning and Cognition, OUP, 245-283.
♦ Evans V. (2016) Design features for linguistically-mediated meaning construction: The relative roles of the linguistic and conceptual systems in subserving the ideational function of language. Frontiers in Psychology. </b>

(7)
15.11.
Meaning as statistical covariation

♦ Landauer T., Dumais D. (2008) Latent semantic analysis, Scholarpedia, 3(11):4356. wiki
♦ Glenberg, A. M., & Mehta, S. (2008) Constraint on covariation: It’s not meaning. Italian Journal of Linguistics, 20, 33-53.
♦ Bruni E., Tran N.K., Baroni M. (2014) Multimodal distributional semantics. Journal of Artificial Intelligence Research, 49, 1-47

(8)
22.11.
Symbol grounding problem

♦ Steels L. (2008) The symbol grounding problem has been solved, so what’s next?. In: de Vega, Glenberg & Graesser (eds), Symbols and Embodiment: Debates on Meaning and Cognition, OUP, 223-244.
♦ Coradeschi S., Loutfi A., Wrede B. (2013) A short review of symbol grounding in robotic and intelligent systems. Künstliche Intelligenz, 27:129–136

(9)
29.11.
Unification attempts

♦ Louwerse M. (2010) Symbol interdependency in symbolic and embodied cognition. Topics in Cognitive Science, 1-30.
♦ Dove G. (2011) On the need for embodied and dis-embodied cognition. Frontiers in Psychology, 1:242

(10)
06.12.
Role(s) of language in cognition and thought

♦ Mirolli M., Parisi D. (2009) Towards a Vygotskyan cognitive robotics: The role of language as a cognitive tool. New Ideas in Psychology, doi:10.1016/j.newideapsych.2009.07.001
♦ Hendricks R. K., Boroditsky L. (2017). New Space–Time Metaphors Foster New Nonlinguistic Representations. Topics in Cognitive Science.

(11)
13.12.
Grounding abstract concepts. Summary.

♦Borghi A.M., Barca L., Binkofski F., Tummolini L. (2018) Varieties of abstract concepts: development, use and representation in the brain. Phil. Trans. R. Soc. B, 373: 20170121
♦ Pulvermüller F. (2018) The case of CAUSE: neurobiological mechanisms for grounding an abstract concept. Phil. Trans. R. Soc. B, 373: 20170129

(12)
TBD
Group presentations Prepare a 20-minute presentation of the chosen topic.

Grading

  • Weekly activity during the semester (40%). This includes weekly submitting inputs to the moderator and active participation during discussions.
  • Paper presentation and discussion moderation (30%). You will present a selected topic (one of the papers in the syllabus), collect by email the inputs (one question or a discussion point) from other students in advance (until Sunday, 20:00). The inputs should will sent to the moderator, with the subject "author" (use the first author's surname). The moderator organizes the questions for discussion that he/she will moderate in the class.
  • Final group presentation (30%). You will be organized in small groups (3-4 students) and will prepare a final presentation on the topic of your choice relevant for the course.
  • Overall grading (in %): A > 90, B > 80, C > 70, D > 60, E > 50, else Fx.

Attendance

You are expected to regularly attend the class. In case of absence, inform the teacher about the reason. Max. two absences are ok (i.e. without losing points).