Welcome! This repository contains course materials for the Dartmouth undergraduate course Human Memory (PSYC 51.09). The syllabus may be found here. Feel free to follow along with the course materials (whether you are officially enrolled in the course or just visiting!), submit comments and suggestions, etc. If you are a course instructor, you may feel free to use these materials in your own courses (attribution is appreciated).

This course, and many of the course materials, were inspired by (and in some cases copied from!), similar content by Michael Kahana, Sean Polyn, and Per Sederberg. These materials have also been heavilty influenced by feedback from students who enrolled in prior offerings of this course.

While I strive for 100% accuracy in my courses, I recognize that I am very unlikely to achieve that goal. If you notice inaccuracies, inefficiencies, and/or if you have any other suggestions, feature requests, questions, comments, concerns, etc. pertaining to this course, I encourage you to open an issue and/or submit a pull request. This course is continually evolving as I attempt to maintain its currency and relevance in a rapidly developing field; your help, feedback, and contributions are much appreciated!

0. Orientation
1. Assignments
2. Background
3. Recognition memory
4. Attribute models
5. Associative memory
6. Free recall
7. Sequence memory
8. Context reinstatement and advanced topics

Start here! The materials for each module below are organized sequentially. Work your way from section to section (and from top to bottom within each section). The recorded lectures (in bold) typically cover preceding material (after the previous lecture, within the same module). Take notes on questions you have as you are reviewing the material, along with any comments, concerns, etc. that would like to bring them up for discussion during our synchronous class meetings. I'll leave time at the beginning of most classes to quickly recap the key ideas from the prior lecture, and for students to bring up discussion topics related to the readings and/or course materials.

Each of the sections below (except the next one) covers a specific aspect of human learning and memory. Most of the sections (all but the last) correspond to specific chapters in our course textbook. You should read the given chapter(s) prior to our course meeting on that topic. Note: the outline below reflects my current best guess about the material we will cover this term. The content is subject to change based on students' interests and backgrounds.

• Course introduction and overview [PDF][KEY]
  • Supplemental guest lecture: a perspective on the knowns and unknowns of human memory

All assignments should be submitted via the course Canvas page unless otherwise specified. Point values are indicated in parentheses. Note that all problem sets are graded as credit (1 point) or no credit (0 points). To recieve credit for a problem set you must turn in the complete problem set by the due date. (There is no credit for late assignments and/or partially completed assignments.)

The exam links will become active when they go live (they are not available in advance). Exams are open-book and must be completed within 24 hours their respective start times. Collaboration and cooperation on problem sets is encouraged, but exams must be completed individually.

Note: Only assignments marked active are guarantee to be in their final form-- inactive assignments are provided to help set expectations about future assignments, but they may be edited or changed prior to be formally assigned. Expired assignments are past their due date (and therefore may no longer be handed in for credit).

• Required reading: Chapter 1
• Optional materials: Tulving (1972), Memento (film), The Golden Man
• Assignment: Problem set 1
• Lecture 1 recording: defining and thinking about memory [PDF][KEY]
• Lecture 2 recording: spaced versus massed repetition, recency, sleep, and context [PDF][KEY]

• Note: this topic will take two weeks to cover
• Required reading: Chapter 2
• Lecture 3 recording: introducing recognition memory and strength theory [PDF][KEY]
• Lecture 4 recording: strength theory continued (distributions, Gaussian mean and variance) [PDF][KEY]
• Lecture 5 recording: strength theory continued, ROC curves [PDF][KEY]
• Lecture 6 recording: strength theory continued, practice with ROC curves, familiarity versus recollection [PDF][KEY]
• Lecture 7 recording: Yonelinas familiarity-recollection model, conditional probability, reasoning with ROC curves and strength distributions [PDF][KEY]
• Lecture 8 recording: remember/know judgements, familiarity vs. fluency, false fame and cryptomnesia [PDF][KEY]
• Lecture 9 recording: the Sternberg paradigm, scanning models, serial versus parallel search [PDF][KEY]
• Assignment: Problem set 2

• Required readings: Chapter 3, Mitchell et al. (2008), Huth et al. (2016), Blei (2012)
• Lecture 10 recording: introduction to attribute models, distance-based similarity, cosine-based similarity, visualizing high-dimensional spaces [PDF][KEY]
• Optional materials: Semantic maps (Gallant Lab)
• Lecture 11 recording: semantic spaces, brain spaces, multiple trace hypothesis, summed similarity [PDF][KEY]
• Lecture 12 recording: empirical evidence for summed similarity, mirror effect explained, frequency versus contextual variability [PDF][KEY]
• Lecture 13 recording: noisy memories, variable encoding, drift diffusion model, contextual drift, temporal judgements [PDF][KEY]
• Assignment: Problem set 3

• Note: this topic will take two weeks to cover
• Required readings: Chapter 4, Chapter 5
• Optional materials: Deep neural networks tutorial, Owen et al. (2021), The Science of Remembering: How to Forget, Sievers and Momennejad (2019)
• Lecture 14 recording: introduction to associative memory and cued recall, paired associates learning, incremental versus all-or-none learning, priming, free associations, elaborative encoding [PDF][KEY]
• Lecture 15 recording: decay vs. interference, competition, proactive interference [PDF][KEY]
• Lecture 16 recording: retroactive interference, modified free recall, modified modified free recall, interference and context, the attribute similarity model of recall, retrieval induced forgetting [PDF][KEY]
• Assignments: Problem set 4, Midterm exam
  • Midterm review session: open Q&A about any course material up through Chapter 4
• Lecture 17 recording: introduction to models of associative memory and the learning rule of Hopfield networks [PDF][KEY]
• Lecture 18 recording: Hopfield networks part II: the learning rule (continued) [PDF][KEY]
• Lecture 19 recording: Hopfield networks part III: the dynamic rule, Hopfield network intuitions [PDF][KEY]
• Lecture 20 recording: Hopfield networks part IV: further intuitions, extensions of Hopfield networks-- deep neural networks, connectionist models, links to biological brain networks [PDF][KEY]
• Assignment: Problem set 5

• Note: this topic will take more than a week to cover
• Required readings: Chapter 6, Chapter 7, Owen et al., 2020, Manning et al. (2016)
• Optional materials: Manning (2015), Memory Booster: Episodic Memory
• Lecture 21 recording: free recall (and variants), probability of first recall, clustering, the role of context in free recall [PDF][KEY]
• Lecture 22 recording: clustering scores and memory fingerprints, serial position curves, intrusions, directed forgetting, event boundaries, situation models [PDF][KEY]
• Lecture 23 recording: dual store model (SAM) [PDF][KEY]
• Lecture 24 recording: single store (context-based) model (TCM), neural signature of temporal context [PDF][KEY]
• Assignment: Problem set 6

• Note: this topic will take two weeks to cover (and we will likely skip this topic for the Spring 2021 offering of this course)
• Required readings: Chapter 8, Chapter 9

• Note: we will cover material in this module as time allows
• Required readings: Manning (2021), Manning (2020), Baldassano et al. (2017)
• Lecture 25 recording: multi-timescale models, scale invariance, time cells, temporal receptive windows, concluding thoughts [PDF][KEY]
  • Extra slides (not covered in class): [PDF][KEY]
  • Supplementary lecture that covers material mentioned on extra slides (source: talk given at Berkeley in 2020): thought spaces, thought trajectories, multi-timescale models and their neural correlates, geometric models of memory, where we "go" when we remember, modeling classroom learning, AI teachers, models of conversation
• Assignments: Problem set 7, Final exam