Computational Cognitive Science (CCS) Lab

Our lab, led by Andrew Perfors, is part of the Complex Human Data Hub at the Melbourne School of Psychological Sciences. Our research focuses on quantitative approaches to higher-order cognition: categorisation, concepts, language acquisition and evolution, decision-making, and social learning and transmission. We use mathematical and computational models to understand the why and the what within these topics. What goals are human learners and reasoners trying to achieve in particular situations? What constraints (cognitive, informational, environmental) do they operate under? How do these factors shape their behaviour?

Below is a complete list of all publications from members of the CCS Lab. It includes links to the journal versions of the paper (often behind a paywall) as well as links to the author version of the manuscripts (documents freely available). We've taken some care to ensure that the files that we are sharing do not violate copyright laws: see our copyright notes page for details. We hope you find our work interesting!

Books
- N Chater, A Clark, J Goldsmith and A Perfors (2015). Empiricism and language learnability. Oxford University Press. (published version)
Journal Articles
- A Hendrickson and A Perfors (2019) Cross-situational learning in a Zipfian environment. Cognition 189: 11-22 (psyarxiv) (osf) (published version)
- A Hendrickson, A Perfors, DJ Navarro, and K Ransom (2019) Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization. Cognitive Psychology 111: 80-102 (psyarxiv) (published version)
- Y Kashima, P Bain, and A Perfors (2019) The psychology of cultural dynamics: What is it, what do we know, and what is yet to be known? Annual Review of Psychology 70: 499-529 (published version)
- WK Vong, A Hendrickson, DJ Navarro, and A Perfors (2019) Do additional features help or hurt category learning? The curse of dimensionality in human learners. Cognitive Science 43: e12724 (psyarxiv) (osf) (published version)
- C Pryor, A Perfors, and P Howe (2019) Even arbitrary norms influence moral decision-making. Nature Human Behaviour 3: 57-62 (published version)
- S De Deyne, DJ Navarro, A Perfors, M Brysbaert, and G Storms (2019). The "Small World of Words" English word association norms for over 12,000 cue words. [Best article of the year] Behaviour Research Methods 51, 987-1006. (psyarxiv) (published version) (dataset)
- DJ Navarro, A Perfors, A Kary, S Brown, and C Donkin (2018). When extremists win: Cultural transmission via iterated learning when populations are heterogeneous. Cognitive Science 42, 2108-2149 (psyarxiv) (osf) (published version)
- P Howe and A Perfors (2018). An argument for how (and why) to incentivise replication. Commentary. Behavioral and Brain Sciences, 41. (published version)
- C Pryor, A Perfors, and P Howe (2018). Reversing the endowment effect. Judgment and Decision Making, 13(3), 275-286. (published version)
- S Langsford, A Perfors, A Hendrickson, L Kennedy, and DJ Navarro (2018). Quantifying sentence acceptability measures: Reliability, bias, and variability. Glossa: A Journal of General Linguistics, 3(1), 37 (psyarxiv) (published version)
- L Kennedy, DJ Navarro, A Perfors, and N Briggs (2018). Not every credible interval is credible: On the importance of robust methods in Bayesian data analysis. Behavioral Research Methods, 49(6), 2219-2234 (psyarxiv) (published version)
- K Smith, A Perfors, O Feher, A Samara, K Swoboda and E Wonnacott (2017). Language learning, language use, and the evolution of linguistic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 372 (published version)
- S Tauber, DJ Navarro, A Perfors, and M Steyvers (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124(4), 410-441 (psyarxiv) (published version)
- S De Deyne, DJ Navarro, A Perfors and G Storms (2016). Structure at every scale: A semantic network account of the similarities between very unrelated concepts. Journal of Experimental Psychology: General, 145(9), 1228-54 (psyarxiv) (published version)
- D Gokaydin, DJ Navarro, A Ma-Wyatt and A Perfors (2016). The structure of sequential effects. Journal of Experimental Psychology: General, 145, 110-123 (psyarxiv) (supplementary materials) (published version)
- A Hendrickson, DJ Navarro and A Perfors (2016). Sensitivity to hypothesis size during information search. Decision, 3, 62-80 (psyarxiv) (published version)
- A Perfors (2016). Adult regularization of inconsistent input depends on pragmatic factors. [Peter Jusczyk Best Paper Award Winner] Language Learning and Development, 12, 138-155 (published version)
- A Perfors (2016). Piaget, probability, causality, and contradiction. Human Development, 59: 26-33 (published version)
- K Ransom, A Perfors and DJ Navarro (2016). Leaping to conclusions: Why premise relevance affects argument strength. Cognitive Science, 40(7), 1775-1796 (psyarxiv) (published version)
- WK Vong, A Perfors and DJ Navarro (2016). The helpfulness of category labels in semi-supervised learning depends on category structure. Psychonomic Bulletin and Review, 23: 230-238 (psyarxiv) (published version)
- W Voorspoels, DJ Navarro, A Perfors, K Ransom and G Storms (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 1-25 (psyarxiv) (published version)
- A Perfors (2014). Representations, approximations, and limitations within a computational framework for cognitive science: Commentary on article by Tecumseh Fitch. Physics of Life Reviews, 11, 369-370 (published version)
- A Perfors and DJ Navarro (2014). Language evolution can be shaped by the structure of the world. Cognitive Science (psyarxiv) (published version)
- DJ Navarro, A Perfors and WK Vong (2013). Learning time-varying categories. Memory and Cognition, 41, 917-927 (published version)
- A Perfors (2012). Bayesian models of cognition: What's built in after all? Philosophy Compass, 7, 127-138 (published version)
- A Perfors (2012). Levels of explanation and the workings of science. Australian Journal of Psychology, 64, 52-59 (published version)
- A Perfors (2012). When do memory limitations lead to regularization? An experimental and computational investigation. Journal of Memory and Language, 67, 486-506 (published version)
- P Shafto, B Eaves, DJ Navarro and A Perfors (2012). Epistemic trust: Modeling children's reasoning about others' knowledge and intent. Developmental Science, 15, 436-447 (psyarxiv) (published version)
- DJ Navarro and A Perfors (2011). Hypothesis generation, the positive test strategy, and sparse categories Psychological Review, 118, 120-34 (psyarxiv) (published version)
- DJ Navarro and A Perfors (2011). Enlightenment grows from fundamentals: Comment on Jones and Love Behavioral and Brain Sciences, 34, 207-208 (published version)
- A Perfors, J Tenenbaum, TL Griffiths and F Xu (2011). A tutorial introduction to Bayesian models of cognitive development Cognition, 120, 302-321 (published version)
- A Perfors, JB Tenenbaum and T Regier (2011). The learnability of abstract syntactic principles Cognition, 118, 306-338 (published version)
- T Griffiths, N Chater, C Kemp, A Perfors and J Tenenbaum (2010). Probabilistic models of cognition: Exploring representations and inductive biases Trends in Cognitive Sciences, 14, 357-364 (published version)
- DJ Navarro and A Perfors (2010). Similarity, feature discovery, and the size principle Acta Psychologica, 133, 256-268 (psyarxiv) (published version)
- A Perfors, JB Tenenbaum and E Wonnacott (2010). Variability, negative evidence, and the acquisition of verb argument constructions Journal of Child Language, 37, 607-642 (published version)
- S Foraker, T Regier, N Khetarpal, A Perfors and JB Tenenbaum (2009). Indirect evidence and the poverty of the stimulus: The case of anaphoric one Cognitive Science, 33, 287-300
- C Kemp, A Perfors and JB Tenenbaum (2007). Learning overhypotheses with hierarchical Bayesian models Developmental Science, 10, 307-321 (published version)
- A Fernald, A Perfors and V Marchman (2006). Picking Up Speed in Understanding: Speech Processing Efficiency and Vocabulary Growth Across the 2nd Year Developmental Psychology, 42, 98-116
- A Perfors (2002). Simulated Evolution of Language: A Review of the Field Journal of Artificial Societies and Social Simulation, 5, 2 (published version)
Refereed Conference Publications
- A Perfors and DJ Navarro (2019) Why do echo chambers form? The role of trust, population heterogeneity, and objective truth. In A Goel, C Seifert, and C Freksa (Eds.) Proceedings of the 41st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society
- K Ransom and A Perfors (2019) Exploring the role that encoding and retrieval play in sampling effects. In A Goel, C Seifert, and C Freksa (Eds.) Proceedings of the 41st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society
- S Mehrotra and A Perfors (2019) Generic noun phrases in child speech. In A Goel, C Seifert, and C Freksa (Eds.) Proceedings of the 41st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. (supplemental)
- YH Khoe, A Hendrickson, and A Perfors (2019) Modeling individual performance in cross-situational word learning. In A Goel, C Seifert, and C Freksa (Eds.) Proceedings of the 41st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society
- S De Deyne and A Perfors and DJ Navarro (2018). Learning word meaning with little means: An investigation into the inferential capacity of paradigmatic information. In C Kalish, M Rau, J Zhu and T Rogers (Ed.) Proceedings of the 40th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
- A Perfors and N Van Dam (2018). Human decision making in black swan situations. In C Kalish, M Rau, J Zhu and T Rogers (Ed.) Proceedings of the 40th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
- A Perfors, DJ Navarro, and P Shafto (2018). Stronger evidence isn't always better: A role for social inference in evidence selection and interpretation. In C Kalish, M Rau, J Zhu and T Rogers (Ed.) Proceedings of the 40th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
- K Ransom, A Hendrickson, A Perfors, and DJ Navarro (2018). Representational and sampling assumptions drive individual differences in single category generalisation. In C Kalish, M Rau, J Zhu and T Rogers (Ed.) Proceedings of the 40th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
- S De Deyne, A Perfors and DJ Navarro (2017). Predicting human similarity judgments with distributional models: The value of word associations. Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 4806:4810 (published version)
- S Langsford, A Hendrickson, A Perfors and DJ Navarro (2017). When do learned transformations influence similarity and categorization? In G Gunzelmann, A Howes, T Tenbrink, and E Davelaar (Ed.) Proceedings of the 39th Annual Conference of the Cognitive Science Society: 2530-2535 (published version)
- DJ Navarro, A Perfors, A Kary, S Brown and C Donkin (2017). When extremists win: On the behavior of iterated learning chains when priors are heterogeneous. In G Gunzelmann, A Howes, T Tenbrink, and E Davelaar (Ed.) Proceedings of the 39th Annual Conference of the Cognitive Science Society: 847-852 (published version)
- K Ransom, W Voorspoels, A Perfors, and DJ Navarro (2017). A cognitive analysis of deception without lying. In G Gunzelmann, A Howes, T Tenbrink, and E Davelaar (Ed.) Proceedings of the 39th Annual Conference of the Cognitive Science Society: 992-997 (published version)
- WK Vong, A Hendrickson, A Perfors, and DJ Navarro (2016). Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners. [Marr Prize Winner] In A Papafragou, D Grodner, D Mirman and JC Trueswell (Ed.) Proceedings of the 38th Annual Conference of the Cognitive Science Society: 2471-2476 (published version)
- S De Deyne, A Perfors, and DJ Navarro (2016). Predicting human similarity judgments with distributional models: The value of word associations. [Best Paper Award] 26th International Conference on Computational Linguistics, Osaka, Japan: 1861–1870 (published version)
- S De Deyne, S Verheyen, A Perfors and DJ Navarro (2015). Evidence for widespread thematic structure in the mental lexicon. In DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock, CD Jennings and PP Maglio (Ed.) Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 518-523) (supplementary materials, published version)
- A Hendrickson, A Perfors and DJ Navarro (2014). Adaptive information source selection during hypothesis testing. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 607-612 (published version)
- S Langsford, A Hendrickson, A Perfors and DJ Navarro (2014). People are sensitive to hypothesis sparsity during category discrimination. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 2531-2536 (published version)
- A Perfors, K Ransom and DJ Navarro (2014). People ignore token frequency when deciding how widely to generalize. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 2759-2764 (supplementary materials, published version)
- WK Vong, A Perfors and DJ Navarro (2014). The relevance of labels in semi-supervised learning depends on category structure. In P Bellow, M Guarani, M McShane and B Scassellati (Ed.) Proceedings of the 36th Annual Conference of the Cognitive Science Society: 1718-1723 (published version)
- WK Vong, A Hendrickson, A Perfors and DJ Navarro (2013). The role of sampling assumptions in generalization with multiple categories. In M Knauff, M Pauen, N Sebanz and I Wachsmuth (Ed.) Proceedings of the 35th Annual Conference of the Cognitive Science Society: 3699-3704 (published version)
- S De Deyne, DJ Navarro, A Perfors and G Storms (2012). Strong structure in weak semantic similarity: A graph based account. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 1464-1469 (published version)
- DJ Navarro and A Perfors (2012). Anticipating changes: Adaptation and extrapolation in category learning. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 809-814 (supplementary materials, published version)
- A Perfors (2012). Probability matching vs over-regularization in language: Participant behavior depends on their interpretation of the task. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 845-850(published version)
- A Perfors and JH Ong (2012). Musicians are better at learning non-native sound contrasts even in non-tonal languages. In N Miyake, D Peebles and RP Cooper (Ed.) Proceedings of the 34th Annual Conference of the Cognitive Science Society: 839-844 (published version)
- D Gokaydin, D., A Ma-Wyatt, A., DJ Navarro and A Perfors (2011). Humans use different statistics for sequence analysis depending on the task. Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 543-548 (published version)
- R Montague, DJ Navarro, A Perfors, R Warner and P Shafto (2011). To catch a liar: The effects of truthful and deceptive testimony on inferential learning. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 1312-1317 (published version)
- A Perfors (2011). Memory limitations alone do not lead to over-regularization: An experimental and computational investigation. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 3274-3279 (published version)
- A Perfors and DJ Navarro (2011). Language evolution is shaped by the structure of the world: An iterated learning analysis. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 477-482 (published version)
- S Yuan, A Perfors, J Tenenbaum and F Xu (2011). Learning individual words and learning about words simultaneously. In L Carlson, C Hoelscher and TF Shipley (Ed.) Proceedings of the 33rd Annual Conference of the Cognitive Science Society: 3280-3285 (published version)
- L Maurits, A Perfors and DJ Navarro (2010). Why are some word orders more common than others? A uniform information density account. In J Lafferty, CKI Williams, J Shawe-Taylor, RS Zemel and A Culotta (Ed.) Advances in Neural Information Processing Systems: 1585-1593 (supplementary materials, published version)
- A Perfors and N Burns (2010). Adult language learners under cognitive load do not over-regularize like children. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society: 2524-2529 (published version)
- A Perfors and D Dunbar (2010). Phonetic training makes word learning easier. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society: 1613-1618 (published version)
- A Perfors and DJ Navarro (2010). How does the presence of a label affect attention to other features?. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1834-1839 (published version)
- RG Stephens, A Perfors and DJ Navarro (2010). Social context effects on the impact of category labels. In S Ohlsson and R Catrambone (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (pp. 1411-1416) (published version)
- A Ejova, DJ Navarro and AF Perfors (2009). When to walk away: The effect of variability on keeping options viable. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1258-1263 (published version)
- L Maurits, AF Perfors and DJ Navarro (2009). Joint acquisition of word order and word reference. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 1728-1733 (published version)
- DJ Navarro and A Perfors (2009). Learning time-varying categories. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 31st Annual Conference of the Cognitive Science Society: 412-424 (published version)
- A Perfors and DJ Navarro (2009). Confirmation bias is rational when hypotheses are sparse. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 32nd Annual Meeting of the Cognitive Science Society: 2471-2476 (published version)
- A Perfors and JB Tenenbaum (2009). Learning to learn categories. In N Taatgen, H Rijn, L Schomaker and J Nerbonne (Ed.) Proceedings of the 31st Annual Conference of the Cognitive Science Society: 136-141 (published version)
- S Foraker, T Regier, N Khetarpal, A Perfors and JB Tenenbaum (2007). Indirect evidence and the poverty of the stimulus: The case of anaphoric one. In D McNamara and J Trafton (Ed.) Proceedings of the 29th Annual Conference of the Cognitive Science Society: 275-281 (published version)
- C Kemp, A Perfors and JB Tenenbaum (2006). Learning overhypotheses. In R Sun and N Miyake (Ed.) Proceedings of the 28th Annual Conference of the Cognitive Science Society: 417-422 (published version)
- A Perfors, J Tenenbaum and T Regier (2006). Poverty of the Stimulus? A rational approach. In R Sun and N Miyake (Ed.) Proceedings of the 28th Annual Conference of the Cognitive Science Society: 663-668 (published version)
- A Perfors, C Kemp and JB Tenenbaum (2005). Modeling the acquisition of domain structure and feature understanding. In B Bara, L Barsalou and M Bucciarelli (Ed.) Proceedings of the 27th Annual Conference of the Cognitive Science Society: 1720-1725 (published version)
- C Kemp, A Perfors and J Tenenbaum (2004). Learning domain structures.. In K Forbus, D Gentner and T Regier. (Ed.) Proceedings of the 26th Annual Conference of the Cognitive Science Society: 672-677
Encyclopedia Articles
- A Perfors (in press). Bayesian techniques in linguistics. Oxford Research Encyclopaedia of Linguistics
- A Perfors (2014). Induction in language learning. In PJ Brooks and V Kempe (Ed.) Encyclopedia of Language Development (pp. 281-283)
- A Perfors (2014). Bayesian inference in word learning. In PJ Brooks and V Kempe (Ed.) Encyclopedia of Language Development (pp. 46-49)
Book Chapters
- A Perfors and DJ Navarro (2012). What Bayesian modelling can tell us about statistical learning: What it requires and why it works. In P Rebuschat and J Williams (Ed.) Statistical learning and language acquisition: 383-408 (published version)
- A Perfors (2011). Simplicity and fit in grammatical theory. In E Bender and J Arnold (Ed.) Language from a cognitive perspective (pp. 99-120)
- A Perfors and E Wonnacott (2011). Bayesian modeling of sources of constraint in language acquisition. In I Arnon and E Clark (Ed.) Experience, Variation, and Generalization: Learning a first language (pp. 277-294)
- A Perfors, JB Tenenbaum, E Gibson and T Regier (2010). How recursive is language? A Bayesian exploration. In H Hulst (Ed.) Recursion and human language (pp. 159-175)
- F Xu, K Dewar and A Perfors (2009). Induction, overhypotheses, and the shape bias: Some arguments and evidence for rational constructivism. In BM Hood and L Santos (Ed.) The origins of object knowledge
- T Wasow, A Perfors and D Beaver (2005). The Puzzle of Ambiguity. In O Orgun and P Sells (Ed.) Morphology and the Web of Grammar
Theses
- A Perfors (2008). Learnability, representation, and language: A Bayesian approach. PhD Thesis. Massachusetts Institute of Technology Department of Brain and Cognitive Sciences. Cambridge, MA
- A Perfors (2000). Simulated Evolution of Communication: The Emergence of Meaning. MA Thesis. Stanford University Department of Linguistics. Stanford, CA
- A Perfors (1999). Slow and steady doesn't win the race: The relation between infant information processing skills and language comprehension. Honours Thesis. Stanford University Symbolic Systems Department. Stanford, CA
Posters (not already included)
- Wonnacott, E., Perfors, A., and Tenenbaum, J. (2008) Higher order inference in verb argument structure acquisition. 14th Annual Conference on Architectures and Mechanisms for Language Processing. Cambridge, UK.
- Perfors, A., Kemp, C., Tenenbaum, J., and Wonnacott, E. (2007) Learning inductive constraints: The acquisition of verb argument constructions. Machine Learning and Cognitive Science of Language Acquisition Workshop. University College London.
- Perfors, A., Kemp, C., Tenenbaum, J., and Wonnacott, E. (2007) Learning inductive constraints: The acquisition of verb argument constructions. Proceedings of the 29th Annual Meeting of the Cognitive Science Society.
- Perfors, A. (2004) What's in a Name? The effect of sound symbolism on perception of facial attractiveness. Proceedings of the 26th Annual Conference of the Cognitive Science Society. Chicago, Illinois
- Perfors, A., Magnani, K., and Fernald, A. (2002) Speed and accuracy in on-line comprehension are related to vocabulary growth in 15- to 25-month old children. 15th Annual CUNY Conference on Human Sentence Processing. New York, NY
- Magnani, K., Perfors, A., and Fernald, A. (1999). Are developmental changes in the speed and accuracy of word recognition related to vocabulary growth in the second year? Society for Research in Child Development Conference. Albuquerque, NM
- Perfors, A., Magnani, K., Fernald, A., and Pinto, J. (1999). How do infant information processing skills relate to later linguistic performance? Society for Research in Child Development Conference. Albuquerque, NM
- Fernald, Anne, Pinto, J., Swingley, D., Perfors, A., Magnani, K., and Bradley, A. (1998) Infants can recognize words using partial phonetic information. 11th International Conference on Infant Studies. Atlanta, GA.

Content is still being added here so this is mostly just a stub, but here are some good resources for learning R at least.

Learning Statistics with R is a great (free) online textbook that was designed to introduce beginning psychology students to statistics using R. As such it is an excellent resource for both statistics (seriously it is one of the clearest intro stats books I know of) and R. Since it was written a while ago it doesn't have much on ggplot or tidyverse, although the author, Danielle Navarro, is creating newer materials more focused on using R, which do incorporate ggplot and tidyverse, here in R for Psychological Science.

Interactive tutorials in R offer introductions to both statistics and R, and are meant to work alongside Andy Field's statistics textbook called An adventure in statistics: The reality enigma. The book is not free but I believe the tutorials are.

R for Data Science is a great (free) online textbook that walks you through using R for data science. The focus isn't on psychological tests but on data wrangling, visualisation, and so forth, with chapters on tidyverse and ggplot2.

RYouWithMe is a collection of introductory learning resources designed specifically for newbies from the fabulous folks at RLadiesSydney, which include ggplot2 and tidyverse. Not finished yet.

Data Skills for Reproducible Science contains the materials from a course which aims to teach students the basic principles of reproducible research and to provide practical training in data processing and analysis in the statistical programming language R. With weekly assignments if that kind of thing helps!

RStudioPrimers looks like a great set of primers on all sorts of important topics, including ggplot2 and tidyverse amongst other things.

Data Visualisation: A practical introduction walks you through how to make nice graphs with R, but also talks about general principles of good visualisation more theoretically.

Data Analysis and Visualisation in R for ecologists says it's for ecologists but most of it is relevant to anybody.

Here is a basic intro tutorial for ggplot.

The R cheat sheets are exactly what it sounds like -- great cheat sheets for a lot of useful packages. Not great for teaching yourself but great for reminding yourself. I use them a lot.

One of the PhD students at UniMelb, Christina van Heer, here ran two workshops on tidyverse and ggplot for beginners. The wiki can be found here, and the downloads here.

R-graphics cookbook here and here are good resources for creating graphs once you understand the basics.

The CCS Lab is part of:

Faculty of Medicine, Dentistry, and Health Sciences
Melbourne School of Psychological Sciences
Complex Human Data Hub