Causal Cognition (PSYL10160)

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Causal Cognition (PSYL10160)

Semester 2, Friday 2:10pm-4:00pm

BPS core areas - Developmental Psychology, Cognitive Psychology 

Overview

What will be covered?

This course will explore how humans and animals form beliefs about the causal structure of the world, and how these beliefs guide their thoughts and actions. It will cover a range of topics related to causality in thought, including (but not limited to): (a) A history of theories of causality from Hume's dual definition to Pearl's causal model framework for inference; (b) The role of causal learning in development; (c) How causal models support reasoning and simulation; (d) Learning from actively intervening vs. passively observing; (e) Intro to probability theory; causal Bayesian networks and Bayesian model selection; (f) The role of time in causal learning and reasoning; (g) Causality and responsibility; (h) From causality to control.

How will it be delivered?

Primarily lectures with discussion breaks built-in as time/progress allow. Depending on size of the course, small group discussions may be used to explore particular topics.

Lecture recording policy: Lectures will be recorded.

What skills will be gained?

The course will develop students' skills at analytic writing and thinking. It will teach students how to critically evaluate theories and construct computational models. It will take a broader more philosophical perspective than many other courses so will give students opportunities to reflect on cognition as a whole and to link different topics under a unifying philosophical perspective.

Learning Outcomes:

1. Evaluate behavioural evidence for causal model based cognition.

2. Understand the major recent advances, debates, and challenges in the causal cognition literature.

3. Appreciate roles of probability theory and information theory as tools for studying learning and the structure of beliefs.

4. Understand and reason about the evidential value of interventions for revealing causal structure.

5. Construct and use simple causal models to analyse data.

Assessment

30% Mid-term essay (1000 words limit; submission deadline Thursday 13th February, 12.00 noon)

A short (1000 word) essay, on a question selected from 4-5 alternatives or a question of the students own choice agreed with the course organiser. Feedback from the mid-term assessment will help students prepare for final assessment by improving writing skills and understanding of course material.

70% Final exam (May Exam Diet)

The exam will comprise two sections. Section 1 will require students to complete 5 short answer questions. Section 2 will require students to answer 1 essay question from a choice of 5. Questions will test the understanding and evaluation of theories in one or several of the core topics.

Relationship Between Assessment and Learning Outcomes

Mid-term will assess LO 1

Final exam will assess all LO

Reading resources

Indicative reading:

Books

Causal Models: How people think about the world and its alternatives - Steven Sloman

The Book of Why - Judea Pearl

Articles

Sloman, S. A. & Lagnado, D. (2015). Causality in thought. Annual Review of Psychology, 66(1), 223–247.

Schulz L. & A. Gopnik (2004). Causal learning across domains. Developmental Psychology 40(2):162-176.

Bramley, N. R., Lagnado, D. A. & Speekenbrink, M. (2015). Conservative forgetful scholars: How people learn causal structure through interventions. Journal of Experimental Psychology: Learning, Memory & Cognition, Vol 41(3), 708-731.

Wolff, P. (2007). Representing causation. Journal of Experimental Psychology: General, 136(1), 82-111.

Bechlivanidis, C. & Lagnado, D. A. (2016). Time reordered: Causal perception guides the interpretation of temporal order. Cognition, 146, 58–66.

Frosch, C. A., McCormack, T., Lagnado, D. A., & Burns, P. (2012). Are causal structure and intervention judgments inextricably linked? A developmental study. Cognitive Science, 36, 261–285.

Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation. In Causal learning: Psychology, philosophy, and computation (pp. 154–172). Oxford University Press.

T. Kushnir & A. Gopnik (2005). Young children infer causal strength from probabilities and interventions. Psychological Science 16(9):678-683.

Note: There will be a full pdf reading list provided on Learn to accompany the lecture schedule.

 

28/08/2019 - 2:20pm