Cognitive Modeling Gary Cottrell

Cognitive Modeling Gary Cottrell
12/9/2018 Cognitive Modeling Gary Cottrell Computer Science and Engineering Department Institute for Neural Computation Temporal Dynamics of Learning Center UCSD 12/9/2018 OSHER OSHER

Cognitive Modeling …with a brief introduction to neural networks
12/9/2018 Cognitive Modeling …with a brief introduction to neural networks Gary Cottrell Computer Science and Engineering Department Institute for Neural Computation Temporal Dynamics of Learning Center UCSD 12/9/2018 OSHER OSHER

12/9/2018 Cognitive Modeling …with a brief introduction to neural networks …and the Interactive Activation Model Gary Cottrell Computer Science and Engineering Department Institute for Neural Computation Temporal Dynamics of Learning Center UCSD 12/9/2018 OSHER OSHER

COGS 200 Cognitive Modeling
Outline 12/9/2018 I. Motivation: Why build cognitive models? II. Human-style computation: What's it like? III. Neural nets: What are they like? IV. The Interactive Activation Model V. Conclusions 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Why model? Models rush in where theories fear to tread. Models can be manipulated in ways people cannot Models can be analyzed in ways people cannot. 12/9/2018 COGS 200 Cognitive Modeling OSHER

Models rush in where theories fear to tread
12/9/2018 Models rush in where theories fear to tread Theories are high level descriptions of the processes underlying behavior. They are often not explicit about the processes involved. They are difficult to reason about if no mechanisms are explicit -- they may be too high level to make explicit predictions. Theory formation itself is difficult. 12/9/2018 COGS 200 Cognitive Modeling OSHER

Models rush in where theories fear to tread
12/9/2018 Models rush in where theories fear to tread Using machine learning techniques, one can often build a working model of a task for which we have no theories or algorithms (e.g., expression recognition). A working model provides an “intuition pump” for how things might work, especially if they are “neurally plausible” (e.g., development of face processing - Dailey and Cottrell). A working model may make unexpected predictions (e.g., the Interactive Activation Model and SLNT). 12/9/2018 COGS 200 Cognitive Modeling OSHER

Models can be manipulated in ways people cannot
12/9/2018 Models can be manipulated in ways people cannot We can see the effects of variations in cortical architecture (e.g., split (hemispheric) vs. non-split models (Shillcock and Monaghan word perception model)). We can see the effects of variations in processing resources (e.g., variations in number of hidden units in Plaut et al. models). 12/9/2018 COGS 200 Cognitive Modeling OSHER

Models can be manipulated in ways people cannot
12/9/2018 Models can be manipulated in ways people cannot We can see the effects of variations in environment (e.g., what if our parents were cans, cups or books instead of humans? I.e., is there something special about face expertise versus visual expertise in general? (Sugimoto and Cottrell, Joyce and Cottrell, Tong & Cottrell)). We can see variations in behavior due to different kinds of brain damage within a single “brain” (e.g. Juola and Plunkett, Hinton and Shallice). 12/9/2018 COGS 200 Cognitive Modeling OSHER

Models can be analyzed in ways people cannot
12/9/2018 Models can be analyzed in ways people cannot In the following, I specifically refer to neural network models. We can do single unit recordings. We can selectively ablate and restore parts of the network, even down to the single unit level, to assess the contribution to processing. We can measure the individual connections -- e.g., the receptive and projective fields of a unit. We can measure responses at different layers of processing (e.g., which level accounts for a particular judgment: perceptual, object, or categorization? (Dailey et al. J Cog Neuro 2002). 12/9/2018 COGS 200 Cognitive Modeling OSHER

How (I like) to build Cognitive Models
12/9/2018 How (I like) to build Cognitive Models Choose an area where there is a lot of data - even better if there is controversy! I like to be able to relate them to the brain, so “neurally plausible” models are preferred -- neural nets. The model should be a working model of the actual task, rather than a cartoon version of it. Of course, the model should nevertheless be simplifying (i.e. it should be constrained to the essential features of the problem at hand): Do we really need to model the (supposed) translation invariance and size invariance of biological perception? As far as I can tell, NO! Then, take the model “as is” and fit the experimental data: 0 fitting parameters is preferred over 1, 2 , or 3. 12/9/2018 COGS 200 Cognitive Modeling OSHER

The other way (I like) to build Cognitive Models
12/9/2018 The other way (I like) to build Cognitive Models Same as above, except: Use them as exploratory models -- in domains where there is little direct data (e.g. no single cell recordings in infants or undergraduates) to suggest what we might find if we could get the data. These can then serve as “intuition pumps.” Examples: Why we might get specialized face processors (Dailey & Cottrell, 1999) Why those face processors get recruited for other tasks (Sugimoto & Cottrell, 2001, and following papers) 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 So, Why model? Models rush in where theories fear to tread. Models can be manipulated in ways people cannot Models can be analyzed in ways people cannot. 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints People are able to combine lots of different kinds of knowledge quickly in understanding English - For example, in understanding the relationships given in a sentence: Syntax (structure) gives us some information: The boy kissed the girl. 12/9/2018 COGS 200 Cognitive Modeling OSHER

Mutual Constraints But usually we need semantics (meaning) too:
12/9/2018 Mutual Constraints But usually we need semantics (meaning) too: I saw the man on the hill with the big hat. I saw the man on the hill with my telescope. These have identical parts: [Pronoun] [Verb] [Noun Phrase] [Prep. Phrase] [Prep. Phrase] But how those parts go together depends on the meanings… I saw [the man [on the hill] [with the big hat]]. I saw [the man [on the hill]] with my telescope. 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints Ditto for pronoun reference: “The city council refused the demonstrators a permit because they were communists.” Who are they? 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints “The city council refused the demonstrators a permit because they were communists.” Who are they? In San Diego, it is the demonstrators who are likely to be the communists 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints “The city council refused the demonstrators a permit because they were communists.” Who are they? In Berkeley, it is the city council who are likely to be the communists! ;-) 12/9/2018 COGS 200 Cognitive Modeling OSHER

Mutual Constraints: Word Sense Disambiguation
12/9/2018 Mutual Constraints: Word Sense Disambiguation The most frequent words (in any language) are the most ambiguous! How do we deal with this ambiguity? Answer: We combine constraints from many sources… 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints: Word Sense Disambiguation Things that people do well involve integrating constraints from multiple sources: Discourse Context: "I'm taking the car” Grammar: "The carpenter saw the wood” Meaning frequency: "Bob threw a ball” 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints: Word Sense Disambiguation Semantics (meaning): Associations between word senses: "dog's bark" "deep pit” The fit between roles and role fillers: "Bob threw the fight" 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Mutual Constraints: Word Sense Disambiguation Pragmatic: "The man walked on the deck” "Nadia swung the hammer at the nail and the head flew off” (Hirst, 1983) 12/9/2018 COGS 200 Cognitive Modeling OSHER

Computers are different…
12/9/2018 Computers are different… They are fine with sentences like: The boy the girl the dog bit liked cried. You don’t think that’s a sentence? 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Computers are different… The boy cried. The boy the girl liked cried. The boy the girl the dog bit liked cried. 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Computers are different… But… U CN REED THIIS CANDT YU? 12/9/2018 COGS 200 Cognitive Modeling OSHER

Mutual Constraints: Audience Participation! Read this aloud with me:
12/9/2018 Mutual Constraints: Audience Participation! Read this aloud with me: 12/9/2018 COGS 200 Cognitive Modeling OSHER

Read these aloud with me:
12/9/2018 Read these aloud with me: 12/9/2018 COGS 200 Cognitive Modeling OSHER

But… 12/9/2018 12/9/2018 COGS 200 Cognitive Modeling OSHER

Who do you see? Context influences perception 12/9/2018 COGS 200 Cognitive Modeling

How do humans compute? They are fast at using mutual constraints to: Understand sentences Disambiguate words Read ambiguous letters Recognize faces (and sometimes the constraints lead us astray!) 12/9/2018 COGS 200 Cognitive Modeling

12/9/2018 Motivation Why are people (still) smarter than machines? Is it because we have faster hardware? No Is it because we have better programs? It’s because we have brains! ;-) Brains are massively parallel machines 12/9/2018 COGS 200 Cognitive Modeling OSHER

12/9/2018 Motivation Why are people (still) smarter than machines? Basic differences in architecture Hence some of us study brain-like computational models: Neural nets “Cartoon versions“ of how the brain works 12/9/2018 COGS 200 Cognitive Modeling OSHER

How do neural networks compute?
The 100-step program constraint (Feldman, 1985): Neurons (brain cells) are slow: they take about 10ms to respond Yet mental events take about half a second (500ms) So there are no more than 50 steps possible to arrive at an answer! This suggests: Massive parallelism Simple steps 12/9/2018 COGS 200 Cognitive Modeling

Constraints on a brain model (Francis Crick)
Computer Brain speed Fast Slow Order Serial Parallel component reliability Good Poor fault tolerance No degradation signals Precise, symbolic Imprecise, terse Programming Needs it Does it 12/9/2018 COGS 200 Cognitive Modeling

A real neuron (Ramon Y Cajal, 1899)
Axon (output) Cell body (soma) Dendrites (input) 12/9/2018 COGS 200 Cognitive Modeling

A model “neuron” Axon (output) Cell body (soma) Dendrites (input) 12/9/2018 COGS 200 Cognitive Modeling

A model “neuron” Inputs (from another unit or the outside world) Connection strengths (or weights) Internal “potential” Output, representing firing frequency 12/9/2018 COGS 200 Cognitive Modeling

Neural nets (PDP nets, connectionist networks)
Networks of simple units Connected by weighted links Compute by spreading activation and inhibition 12/9/2018 COGS 200 Cognitive Modeling

The Interactive Activation Model: A model of reading from print
Assumptions: Levels of abstraction Spatially parallel Temporally parallel (all levels at the same time) Interactive A neural net 12/9/2018 COGS 200 Cognitive Modeling

The Interactive Activation Model: A model of reading from print
Word level Letter level Feature level 12/9/2018 COGS 200 Cognitive Modeling

The Interactive Activation Model: Data it was built to explain
The word-superiority effect (Cattell, 1886; Reicher, 1969): Time Word Stimulus Non-word Stimulus Letter Alone 70ms WALK XDYK K MASK #### #### #### ^ ^ 2AFC L or K? L or K? L or K? Word stimulus results in best accuracy. Note that the result can’t be due to post-perceptual guessing, since both letters fit the word. 12/9/2018 COGS 200 Cognitive Modeling

The Interactive Activation Model: Data it was built to explain
Feature Relevant? Word shape? No Patterned Mask? Yes! No mask-> smaller effect Contrast? Reversed effect with low contrast and no mask Constraint? No! In high contrast: _HIP no better than _INK 12/9/2018 COGS 200 Cognitive Modeling

Operation of the model 12/9/2018 COGS 200 Cognitive Modeling

Example of data accounted for…
This is from an experiment where subjects either saw a word or a letter in a mask: e.g., ##B# vs DEBT Contrast and mask vs. no mask were varied. Main point: difference in size of effect in the two conditions: much bigger for bright display&mask Model: 81 66 78 68 12/9/2018 COGS 200 Cognitive Modeling

Example of data accounted for…
Results for bright target, bright mask 12/9/2018 COGS 200 Cognitive Modeling

Example of data accounted for… Pseudoword effect
12/9/2018 COGS 200 Cognitive Modeling

Example of data predicted
What about non-pronounceable non-words like SLNT? SLNT has a lot of friends at the word level The model predicts that there should be a superiority effect for SLNT. The tested this in UCSD Psychology sophomores and got the predicted effect 12/9/2018 COGS 200 Cognitive Modeling

Summary Why model? Models make assumptions explicit Models (because they are run on a computer and can be highly non-linear) can make unexpected predictions While no model is “correct”, the more data a model predicts, the more we “believe” that model… 12/9/2018 COGS 200 Cognitive Modeling

Cognitive Modeling Gary Cottrell

Similar presentations

Presentation on theme: "Cognitive Modeling Gary Cottrell"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Cognitive Modeling Gary Cottrell

Similar presentations

Presentation on theme: "Cognitive Modeling Gary Cottrell"— Presentation transcript:

Similar presentations

About project

Feedback