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Modelling the Virtual Machine in Simple Rating and Categorization Tasks Simon Dennis School of Psychology University of Adelaide.

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Presentation on theme: "Modelling the Virtual Machine in Simple Rating and Categorization Tasks Simon Dennis School of Psychology University of Adelaide."— Presentation transcript:

1 Modelling the Virtual Machine in Simple Rating and Categorization Tasks Simon Dennis School of Psychology University of Adelaide

2 The Fundamental Fraud of Cognitive Modelling In higher cognitive tasks, the primary determinants of performance are the instructions When faced with a novel situation, the cognitive system must first construct a task virtual machine that captures: the spectrum of allowable responses the memory cues that must be generated the sequence in which operations will occur etc. (c.f. ACT-R Anderson, SOAR Simon & Newell) The failure to address this construction process renders current cognitive models impotent and model selection pointless

3 Instructions Task Dimensions Discriminability Pace... Background Experience Individual Differences Yes/No Recognition Yes/No Performance Forced Choice Recognition Cued Recall Free Recall Forced Choice Performance Cued Recall Performance Free Recall Performance

4 Does it bite? Nonword recognition (Whittlesea) get a pronounceable nonword like PHRAWG must pronounce then give recognition decision Knowledge partitioning (Lewandowsky, Kalish) subjects divide on whether they use an irrelevant context cue to divide categorization and function learning problems

5 Virtual Machine Assembly Instructions 1)Develop a comprehensive theory of human language use 2)Apply it to categorization

6 Is it that hard? Perhaps we don't need a complete theory of human language to get started Perhaps in understanding what language must accomplish in order to create a virtual machine we will inform our models of both the phenomena and language Enter: The Syntagmatic Paradigmatic Model

7 The SP Model in a Nutshell Assumes that people store a large number of sentence instances When trying to interpret a new sentence they retrieve similar traces from memory and resolve the constraints in these traces in working memory Traces consist of: syntagmatic associations between serially presented items paradigmatic associations between items that fill similar slots in different sentences

8 Learning Sequential traces are sets of syntagmatic associations Relational traces are sets of paradigmatic associations Learning occurs by: adding new traces to sequential and relational memory inferring word to word similarities from corpus statistics using EM

9 The SP Architecture Sequential Long-Term Memory the lion searched for prey the mouse ate the leaves the hamster nibbled the leaves is a lion fierce ? yes is a mouse fierce ? no Relational Long-Term Memory lion: mouse hamster searched: ate nibbled mouse: lion hamster ate: searched nibbled hamster: lion mouse nibbled: searcher ate lion: mouse yes:no mouse: lion no:yes Working Memory ## ?? fierce hamster aa Is

10 Sequential Retrieval Sequential Long-Term Memory the lion searched for prey the mouse ate the leaves the hamster nibbled the leaves is a lion fierce ? yes is a mouse fierce ? no Relational Long-Term Memory lion: mouse hamster searched: ate nibbled mouse: lion hamster ate: searched nibbled hamster: lion mouse nibbled: searcher ate lion: mouse yes:no mouse: lion no:yes Working Memory ## ?? fierce hamster aa Is

11 Sequential Resolution Sequential Long-Term Memory the lion searched for prey the mouse ate the leaves the hamster nibbled the leaves is a lion fierce ? yes is a mouse fierce ? no Relational Long-Term Memory lion: mouse hamster searched: ate nibbled mouse: lion hamster ate: searched nibbled hamster: lion mouse nibbled: searcher ate lion: mouse yes:no mouse: lion no:yes Working Memory yes no# ?? fierce lion mousehamster aa Is

12 Relational Retrieval Sequential Long-Term Memory the lion searched for prey the mouse ate the leaves the hamster nibbled the leaves is a lion fierce ? yes is a mouse fierce ? no Relational Long-Term Memory lion: mouse hamster searched: ate nibbled mouse: lion hamster ate: searched nibbled hamster: lion mouse nibbled: searcher ate lion: mouse yes:no mouse: lion no:yes Working Memory yes no# ?? fierce lion mousehamster aa Is

13 Relational Resolution Sequential Long-Term Memory the lion searched for prey the mouse ate the leaves the hamster nibbled the leaves is a lion fierce ? yes is a mouse fierce ? no Relational Long-Term Memory lion: mouse hamster searched: ate nibbled mouse: lion hamster ate: searched nibbled hamster: lion mouse nibbled: searcher ate lion: mouse yes:no mouse: lion no:yes Working Memory no yes# ?? fierce mouse lionhamster aa Is

14 Background Corpus the lion searched for prey the lion walked across the savannah the mouse ate the leaves the mouse scurried through the hole the giraffe ambled across the savannah the giraffe ate the leaves the tiger looked for prey the tiger ran across the savannah the camel ambled across the dessert the camel ate the leaves the hamster nibbled the leaves the hamster ran through the hole a giraffe is a herbivore a lion is a carnivore is a lion fierce ? yes is a mouse fierce ? no is a giraffe fierce ? no is a lion big ? yes is a mouse big ? no is a giraffe big ? yes

15 A X is a Y a giraffe is a herbivore a lion is a carnivore. a: a (1.00) camel: giraffe (.98) lion (.49) is: is (1.00) a: a (1.00) #: herbivore (.98) carnivore (.49).:. (1.00) a giraffe is a herbivore a lion is a carnivore the hamster nibbled the leaves the hamster ran through the hole is a mouse fierce ? no is a mouse big ? no the mouse ate the leaves. a: a (1.00) hamster: giraffe (.97) lion (.37) is: is (1.00) a: a (1.00) #: herbivore (.97) carnivore (.37).:. (1.00) a lion is a carnivore a giraffe is a herbivore. a: a (1.00) tiger: lion (.83) giraffe (.66) is: is (1.00) a: a (1.00) #: carnivore (.83) herbivore (.66).:. (1.00) Relational MemoryWorking Memory

16 Is a X big? is a giraffe big ? yes is a mouse big ? no is a giraffe fierce ? no is a mouse fierce ? no is a lion big ? yes is a lion fierce ? yes is: is (1.00) a: a (1.00) camel: giraffe (.71) mouse (.61) lion (.34) big: big (.97) fierce (.52) ?: ? (1.00) #: yes (.78) no (.72) is a mouse big ? no is a giraffe big ? yes is a mouse fierce ? no is a giraffe fierce ? no is a lion big ? yes is: is (1.00) a: a (1.00) hamster: mouse (.90) giraffe (.46) lion (.28) big: big (.98) fierce (.50) ?: ? (1.00) #: no (.93) yes (.54) is a lion big ? yes is a giraffe big ? yes is a lion fierce ? yes is a giraffe fierce ? no is a mouse big ? no is: is (1.00) a: a (1.00) tiger: lion (.82) giraffe (.53) mouse (.30) big: big (.97) fierce (.51) ?: ? (1.00) #: yes (.99) no (.48) Relational MemoryWorking Memory

17 Is a X fierce? is a giraffe fierce ? no is a mouse fierce ? no is a giraffe big ? yes is a mouse big ? no is a lion fierce ? yes is a lion big ? yes is: is (1.00) a: a (1.00) camel: giraffe (.71) mouse (.61) lion (.34) fierce: fierce (.97) big (.52) ?: ? (1.00) #: no (.97) yes (.52) is a mouse fierce ? no is a giraffe fierce ? no is a mouse big ? no is a giraffe big ? yes is a lion fierce ? yes is: is (1.00) a: a (1.00) hamster: mouse (.91) giraffe (.44) lion (.29) fierce: fierce (.97) big (.51) ?: ? (1.00) #: no (.99) yes (.47) is a lion fierce ? yes is a giraffe fierce ? no is a lion big ? yes is a giraffe big ? yes is a mouse fierce ? no a: a (1.00) tiger: lion (.80) giraffe (.55) mouse (.30) fierce: fierce (.98) big (.51) ?: ? (1.00) #: yes (.86) no (.63) Relational MemoryWorking Memory

18 Discussion model constructs virtual machines for categorization or yes/no rating tasks depending on the instructions model stores information in a derivable relational form that can be used by a variety of tasks (current models are too task driven) shows how "attention” could be manipulated on a trial by trial basis if instructions are alternated (e.g. between fierceness and size) exemplar and rule based reasoning are just different ways of looking at the same mechanism

19 Conclusions hamsters are not fierce not necessary to have a comprehensive model of language use to address questions of how the virtual machine is constructed Dennis, S. (2005). A memory-based theory of verbal cognition. Cognitive Science. 29:2. Dennis, S. (2004). An unsupervised method for the extraction of propositional information from text. Proceedings of the National Academy of Sciences, 101,

20 Phenomena on which the model has been illustrated SENTENCE PROCESSING Long distance dependencies Structure sensitivity Generativity Transformations and Systematicity Garden pathing Structural priming in comprehension (L1 & L2) SEMANTIC MEMORY Categorization Rating SHORT TERM MEMORY The serial position curve Intra-list intrusions Inter-list intrusions Grouping effects Fillin Confusable lists Lag CRPs INFERENCE Analogical inference Rule based inference Question answering Inference by coincidence

21 A Thought Experiment Consider a simple two alternative forced choice categorization experiment Suppose we can predict exactly which alternative a given subject will choose on any given trial with 100% accuracy At best we will have accounted for 1 bit of information per trial The instructions, however, decrease the possible outputs from the entire repertoire of human responses (at least 20 bits) to two


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