This is our wonderfully ambitious schedule.
We'll probably keep with it, but it's occasionally subject to modification.
| Date | Topic | Readings (* = to be read by this class. All others are reference readings) |
Notices & Reading Questions |
| 1/5/16 |
Admin, Selection of articles, and Intro to Learning & Development [Mimi & Lisa] |
(no required reading) Marr's Levels (A1) Marr 1982: Ch.1 (especially pp. 24-29 [pp.12-14 of pdf]) (A2) Bechtel & Shagrir 2015: contributions of Marr's three levels (A3) Griffiths et al. 2015: between computational and algorithmic (A4) Cooper & Peebles 2015: integrated cognitive architectures that use the three levels (A5) Love 2015: algorithmic level (A6) French & Thomas 2015: emergent structures and Marr’s levels |
Introductory message board discussion points due |
| 1/7/16 |
Theoretical Approaches: Bayesian [Colin] |
* (1) Perfors et al. 2011: Bayesian tutorial for cognitive development
Theoretical (A1) Perfors 2012: Thoughts on how to use Bayesian modeling (A2) Pearl & Goldwater (forthcoming): Bayesian approaches to language acquisition (A3) Kemp, Perfors, & Tenenbaum 2007: hierachical Bayesian overview (A4) Tenenbaum et al. 2011: Bayesian inference for cognition (A5) Jones & Love 2011: Bayesian fundamentalism or enlightenment + Marcus & Davis 2013 criticism Computational (B1) Tenenbaum et al. 2006: Bayesian approaches to inductive learning & reasoning (B2) Bonawitz et al. 2011: simple sequential algorithm for approximating Bayesian inference (B3) Abbott et al. 2012: Bayesian inference approximation Neuro (C1) Ma et al. 2006: Bayesian inference with population codes |
Message board discussion points due |
| 1/12/16 |
Theoretical Approaches: Associative & Reinforcement Learning [Mac] |
* (1) Niv
2009 Background (A1) Pearce & Bouton 2001: Theories of associative learning in animals (A2) Roesch et al. 2012: neuro review (A3) Liljeholm & O'Doherty 2012: neuro review Computational (B1) Kuvayev & Sutton 1997: Model-based reinforcement learning (B2) Harmon & Harmon 1996: A tutorial on reinforcement learning (B3) Gershman 2015: probabilistic view of associative learning Neuro (C1) Hampton et al. 2006: HMM of reversal learning (C2) Kakade & Dayan 2002: Novelty bonus for exploration in RL (C3) Ostlund & Maidment 2012: Tonic dopamine and behavioral vigor |
Message board discussion points due |
| 1/14/16 |
Theoretical Approaches: Connecionist [Ryan] |
* (1) McClelland 1988 Approach (A1) Page 2000: connectionist overview (A2) McClelland et al. 1995: aproach + neuro Computational (B1) Browne & Sun 2001: connectionist inference models (B2) Holyoak & Hummel 2000: symbols in connectionist architectures (B3) Munakata & McClelland 2003: Connectionist models of development (B4) Hinton 2007: Deep belief networks (B5) Griffiths et al. 2012: Neural nets vs Bayes |
Message board discussion points due |
| 1/19/16 |
Perceptual: Sounds [Katie] |
* (1) Curtin
& Zamuner 2014 Background (A1) Casserly & Pisoni 2010: general overview of speech perception & production Neuro (B1) Kuhl 2010: neural mechanisms for phonetic acquisition Behavioral (C1) Swingley 2009: word context for phonetic acquisition (among other topics) (C2) Feldman et al. 2011, 2013: human learner sensitivity to word context of a sound (C3) Maye et al. 2002, 2008: phonetic acquisition in infants (C4) Dietrich, Swingley, & Werker 2007: 18-month-old sound discrimination when in word context (C5) Yoshida et al. 2010: 10-month-old infant phoneme discrimination abilities Computational (D1) Feldman et al. 2009, 2013: word context in phonetic acquisition (D2) Vallabha et al. 2007: identifying vowels from acoustic data (D3) Elsner et al. 2012: learning phonetic categories and words from child-directed speech (D4) Adriaans & Swingley 2012: useful cues to phonetic categories (D5) Martin et al. 2013: learning phonemes with proto-lexicons (simultaneous problem solving) (D6) Dillon et al. 2013: joint learning of phonetic categories and phonemes |
Message board discussion points due |
| 1/21/16 |
Perceptual: Speech Segmentation [Stephen] |
* (1) Phillips
& Pearl 2015 Behavioral (A1) Frank et al. 2010: Bayesian model matching human word seg performance (A2) Saffran, Aslin, & Newport 1996: infant transitional probability tracking (A3) Gomez & Gerken 2000: artificial language expts (A4) Finn & Hudson Kam 2008, Onnis et al. 2005: issues with adults in artificial language expts (A5) Johnson & Tyler 2010: issues with infant sensitivity to transitional probability (A6) Lew-Williams et al. 2011: utility of isolated words for word seg (A7) Mersad & Nazzi 2012: utility of familiar words in word seg (A8) Willits et al. 2009: morpheme tracking Computational (B1) Phillips & Pearl 2012: constrained Bayesian word seg over syllables (shorter version of Phillips & Pearl 2015) (B2) Goldwater et al. 2009: ideal learner Bayesian model (B3) Johnson & Goldwater 2009: ideal learner Bayesian model (B4) Pearl et al. 2010, 2011: more cognitively plausible algorithms (B5) McInnes & Goldwater 2011: using acoustic input (B6) Borschinger & Johnson 2011: particle filter for Bayesian seg (B7) Phillips & Pearl 2014a, 2014b, 2015 Ms.: cross-linguistic Bayesian segmentation (B8) Phillips & Pearl 2015: utility of segmentation output (B9) Doyle & Levy 2013: learning stress patterns and segmenting at the same time (Bayesian) (B10) Blanchard et al. 2010: cognitively plausible inference with phonotactic constraints (B11) Gambell & Yang 2006 Ms, Lignos 2011, Lignos 2012: algebraic learning + stress + probabilistic memory (B12) Swingley 2005: using mutual information over syllables (B13) Jarosz & Johnson 2013: comp analysis of distributional cues utility (useful when combined, but not separately) (B14) Ketrez 2014: vowel harmony as statistical word seg cue (B15) Daland & Pierrehumbert 2011: model based on diphones |
Message board discussion points due |
| 1/26/16 |
Perceptual: Vision [Christina] |
* (1) Lu et
al. 2011 Background (A1) Fahle 2005: perceptual learning overview (A2) Censor et al. 2012: perceptual & motor learning mechanism (A3) Gilbert et al. 2001: (neuro review) neural basis of perceptual learning Neuro (B1) Kahnt et al. 2011: behavioral & neuro perceptual learning Computational (C1) Dosher et al. 2013: integrated reweighting (C2) Yuille & Kersten 2006: vision as Bayesian inference (C3) Liu & Weinshall 2000: mechanisms of generalization (computational + experimental) (C4) Bejjanki et al. 2011: perceptual learning as improved probabilistic inference |
Message board discussion points due |
| 1/28/16 |
Categories: Objects [Prachi] |
* (1) Palmeri &
Gauthier 2004 Background (A1) Edelman 1997: object recognition (A2) Seger & Miller 2010: neuro + category learning in the brain (A3) Shohamy et al. 2008: overview + neuro Neuro (B1) Rever et al. 2003: implicit vs. explicit category learning (B2) Nomura et al. 2007: rule-based and information-integration category learning Computational (C1) Gluck & Bower 1988: adaptive network model (C2) Love et al. 2004: network model of learning (C3) Kruschke 1992: exemplar-based model of learning (C4) Rehder & Murphy 2003: knowledge-based category learning (C5) Tenenbaum et al. 2006: Bayesian approaches to inductive category learning & reasoning |
Message board discussion points due |
| 2/2/16 |
Categories: Words I [Alandi] |
* (1) Yurovsky
et al. 2013 Background (A1) Swingley 2012: intro to word meaning learning, from the cog dev perspective Behavioral (B1) Bergelson & Swingley 2012, 2014, 2015: early word learning (B2) Smith & Yu 2008: infant cross-situational learning (B3) Yu & Smith 2007: adult cross-situational learning (B4) Medina et al. 2011: against cross-situational learning (B5) Ramscar et al. 2011: for cross-situational learning, but with differences between kids and adults (B6) Kachergis et al. 2012: active vs passive learning for word-meaning mapping (B7) Yurovsky et al. 2012: speech segmentation + word-meaning mapping in parallel (B8) Smith & Yu 2013: visual attention & local effects in cross-situational learning (B9) Kachergis & Yu 2013: cross sit learning without 1-1 mapping (B10) Romberg & Yu 2013: rich info structure in cross-sit learning (B11) Romberg & Yu 2014: cross-situational learning vs. hypothesis-testing (B12) Trueswell et al. 2013: fast mapping & cross-situational word learning Computational (C1) Frank et al. 2012: using social cues for word learning (C2) Fazly et al. 2010: probabilistic model of word-meaning mapping for more than just nouns (C3) Stevens et al. 2013: pursuit of word meanings (word-meaning mapping) + word-learning commentary (C4) Nematzadeh 2010: multi-word acq model (C5) Nematzadeh et al. 2011: word learning + sem cat in late talkers (C6) Nematzadeh et al. 2012: memory, attention, & word learning (C7) Mollica & Piantadosi 2015: word learning cross-sit with recursion |
Message board discussion points due |
| 2/4/16 |
Categories: Words II [K.J.] |
* (1) Lewis
& Frank 2013 Behavioral + Computational (A1) Xu & Tenenbaum 2007: learning overlapping concept (Bayesian) (A2) Jenkins et al. 2015: learning overlapping concepts (non-Bayesian) Computational (B1) Frank, Goodman, & Tenenbaum 2009: including speaker intentions (B2) Carstensen et al. 2014: learning spatial relationships (extension of Frank et al. 2009) (B3) Gagliardi et al. 2012: incorporating grammatical category information (B4) Meylan & Griffiths 2015: learning words from multiword utterances - Xu & Tenenbaum 2007 extension |
Message board discussion points due |
| 2/9/16 |
Categories: Numerical Cognition [Galia] |
* (1) Sarnecka
& Negen 2012 Behavioral (A1) Sarnecka & Wright 2013: cardinality & equinumerosity Computational (B1) Lee & Sarnecka 2011: Bayesian number learning (B2) Piantadosi et al. 2012: Bayesian number learning |
Message board discussion points due |
| 2/11/16 |
Conditioning & Contingency Learning: Cue Competition [Percy] (pdf, including paper errata) |
* (1) De Houwer &
Beckers 2002 Background (A1) Melchers et al. 2008: parts & wholes Experimental (B1) Vogel et al. 2015 (B2) Liljeholm & Balleine 2009: physical and functional similarity of cues (B3) Kruschke & Blair 2000: blocking & backward blocking (B4) McCormack et al. 2013: blocking in children's causal learning (B5) Becker et al. 2005: outcome additivity & maximality (B6) Mitchell & Lovibund 2002: blocking & outcome additivity (B7) Jones et al. 1997: Kamin blocking effects & schizophrenia Neuro (C1) Turner et al. 2004: preventative & super-learning |
Message board discussion points due |
| 2/16/16 |
Conditioning & Contingency Learning: Fear - Acquisition & Extinction [Alandi] |
* (1) Sehlmeyer et
al. 2009 Background (A1) Tronson et al. 2012: background + neuro (A2) Hermans et al. 2006: extinction in human fear conditioning Experimental (B1) Schiller et al. 2010: reconsolidation update mechanisms Neuro (C1) Milad et al. 2009: extinction memory & PTSD (C2) Ahs et al. 2014: seratonin & fear extinction |
Message board discussion points due |
| 2/18/16 |
Conditioning & Contingency Learning: Goals & Habits [Karen] |
* (1) Dolan & Dayan
2013 Neuro (A1) Tricomi et al. 2009: habit learning (A2) Wunderlich et al. 2012: value-based planning & extensively trained choice (A3) McNamee et al. 2015: associative content of brain structures (A4) Liljeholm et al. 2015: goal-directed & habitual behavioral control (A5) Liljeholm et al. 2013: instrumental probability distributions & neural correlates Computational (B1) Daw et al. 2005: Uncertainty-based competition (B2) Dezfoule & Balleine 2012: habits (B3) Solway & Botvinick 2012: goal-directed decision-making as probabilistic inference |
Message board discussion points due |
| 2/23/16 |
Conditioning & Contingency Learning: Pavlovian-Instrumental Transfer [Prachi] |
* (1) Nadler et
al. 2011 Experimental (A1) Allman et al. 2010: transfer following reinforced devaluation (A2) Lewis et al. 2013: avoidance-based transfer in humans Neuro (B1) Bray et al. 2008: neural mechanisms underlying pavlovian cues (B2) Talmi et al. 2008: in humans (B3) Prevost et al. 2012: neural correlates Computational (C1) Huys et al. 2011: computational + experimental (C2) Cartoni et al. 2013: computational + theory |
Message board discussion points due |
| 2/25/16 | Structure: Syntax [Galia] |
* (1) Pearl &
Sprouse 2015 Theoretical (A1) Phillips 2013: response to Pearl & Sprouse 2013 Behavioral (B1) Gagliardi et al. 2012 Ms: acquisition of filler-gap dependencies by young children Computational (C1) Pearl & Sprouse 2013: version of Pearl & Sprouse 2015 focused on debates in linguistics (C2) Pearl & Sprouse 2013 book chapter: version of Pearl & Sprouse 2015 focused on relationship to language processing |
Message board discussion points due |
| 3/1/16 | Structure: Causality I [Percy] |
* (1) Cheng 1997 Background (A1) Holyoak & Cheng 2013: causal learning & inference as a rational process Experimental (B1) Liljeholm & Cheng 2007: coherent generalization across contexts (B2) Cheng et al. 2013: logical consistency in causal learning (B3) Liljeholm 2015: independence constraints on causal inference Computational (C1) Danks et al. 2003: dynamical causal learning (C2) Griffiths & Tenenbaum 2005: causal induction (C3) Lu et al. 2008: Bayesian generic priors |
Message board discussion points due |
| 3/3/16 |
Structure: Causality II [Emily] |
* (1) Goodman
et al. 2011 Behavioral (A1) Gopnik & Wellman 2014: Bayesian approach to causal inference (A2) Denison et al. 2013: children sampling causal hypotheses about in a Bayesian-compliant manner Computational (B1) Tenenbaum et al. 2006: Bayesian approaches to causal learning & reasoning (B2) Kemp et al. 2010: causal schemas |
Message board discussion points due |
| 3/8/16 |
Structure: Relational Reasoning [Jacky] |
* (1) Halford et
al. 2010 Theoretical (A1) Johnson-Laird 2010: overview of human reasoning (A2) Goodwin & Johnson-Laird 2013: boolean concepts Computational (B1) Hummel & Holyoak 2005: relational reasoning in a neural network (B2) Doumas et al. 2008: discovery and predication of relational concepts (B3) Morrison et al. 2011: analogical reasoning development & working memory (B4) Chen et al. 2014: symbolic magnitudes Neuro (C1) Morrison et al. 2004: analogical reasoning (C2) Krawczyk 2010: relational reasoning overview |
Message board discussion points due |
| 3/10/16 |
Peer review [Everyone] |
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| 3/15/16 |
Final presentations (@ 2:00pm in SBSG 2200) [Everyone] |
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