Geoff Hollis, PhD, MA

Science Instructor, Faculty of Science - Computing Science


Science Instructor, Faculty of Science - Computing Science



What do a gourmet hotdog, a funny joke, and a good computer program have in common? I think they all require creativity to make. I like to create things. I create many things, but these are three of my favourite things to create.


My primary research interests are:

  • How human languages change over time.
  • How humans and other animals learn.
  • How learning processes can be simulated with computers.
  • How science measures complicated, unobservable ideas like feelings.


Learning new concepts requires large amounts of practice and a willingness to fail. For university students, this means doing regular homework, attempting to solve problems on your own before looking for answers, and always asking yourself "how would I know if I were wrong?" whenever you think you understand something. I try to design my courses with these beliefs in mind.

My teaching experience and interests are broad.

Currently I teach courses on:

  • Introductory computer programming (CMPUT 174, 175)
  • The ethics of modern technologies (CMPUT 300)
  • How to effectively transition from university to a career (INT D 400)

Previously I have taught courses on:

  • Introductory Psychology (PSYCO 104, 105)
  • How to design scientific experiments (PSYCHO 212)
  • How social situations affect our behavior (PSYCHO 241)
  • How we think (PSYCHO 258)
  • How our senses work (PSYCHO 267)
  • The history of psychology (PSYCHO 300)
  • The evolution of human behavior (PSYCHO 391)

I would like to teach courses on:

  • How humans learn language
  • How computers learn language
  • How science works


CMPUT 175 - Introduction to the Foundations of Computation II

A continuation of CMPUT 174, revisiting topics of greater depth and complexity. More sophisticated notions such as objects, functional programming, time and memory consumption, and user interface building are explored. Upon completion of this two course sequence, students from any discipline should be able to build programs to solve basic problems in their area, and will be prepared to take more advanced Computing Science courses. Prerequisite: CMPUT 174 or SCI 100. Credit cannot be obtained for CMPUT 175 if credit has been obtained for CMPUT 275, except with permission of the Department.

Winter Term 2021

Browse more courses taught by Geoff Hollis


Learning about things that never happened: a critique and refinement of the Rescorla-Wagner model when many outcomes are possible.
Author(s): Hollis, G.
Publication Date: 2019
Publication: Memory and Cognition
Volume: 47
Issue: 7
Page Numbers: 1-16
External Link:
Wriggly, squiffy, lummox, and boobs: What makes some words funny?
Author(s): Westbury C.F., Hollis G.
Publication Date: 2019
Publication: The Journal of Experimental Psychology: General
Volume: In Press
External Link:
Scoring best/worst data in unbalanced, many-item designs, with applications to crowdsourcing semantic judgments
Author(s): Hollis G.
Publication Date: 2018
Publication: Behavior Research Methods
Volume: 50
Issue: 2
Page Numbers: 711-729
External Link:
When is best-worst best?: A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms.
Author(s): Hollis G., Westbury C.F.
Publication Date: 2018
Publication: Behavior Research Methods
Volume: 50
Issue: 1
Page Numbers: 115-133
External Link:
Estimating the average need of semantic knowledge from distributional models of semantic memory
Author(s): Hollis G.
Publication Date: 2017
Publication: Memory and Cognition
Volume: 48
Issue: 8
Page Numbers: 1350-1370
External Link: