Conceptual Hierarchies Arise from the Dynamics of Learning and Processing:
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Conceptual Hierarchies Arise from the Dynamics of Learning and Processing: Conceptual Hierarchies Arise from the Dynamics of Learning and Processing: Insights from a Flat Attractor Network Insights from a Flat Attractor Network Christopher M. O’Connor Christopher M. O’Connor Ken McRae Ken McRae George S. Cree George S. Cree University of Western Ontario University of Western Ontario University of Western Ontario University of Western Ontario University of Toronto at Scarborough University of Toronto at Scarborough London, Ontario, Canada London, Ontario, Canada London, Ontario, Canada London, Ontario, Canada Toronto, Ontario, Canada Toronto, Ontario, Canada [email protected] [email protected] [email protected] Superordinate & Basic-level Superordinate & Basic-level Representations Representations Superordinate Priming & Temporal Dynamics of Superordinate Priming & Temporal Dynamics of Similarity Similarity both spreading activation (Collins & Loftus, 1975) and both spreading activation (Collins & Loftus, 1975) and attractor networks predict that magnitude of semantic priming attractor networks predict that magnitude of semantic priming is determined by degree of semantic similarity is determined by degree of semantic similarity supported experimentally using basic-level concepts (McRae & supported experimentally using basic-level concepts (McRae & Boisvert, 1998) Boisvert, 1998) simulated using feature-based attractor nets (Cree, McRae, & simulated using feature-based attractor nets (Cree, McRae, & McNorgan, 1999) McNorgan, 1999) therefore, the degree that an exemplar target is primed by its therefore, the degree that an exemplar target is primed by its superordinate should vary as a function of typicality superordinate should vary as a function of typicality high typicality > medium typicality > low typicality high typicality > medium typicality > low typicality however, Schwanenflugel and Rey (1986) found that short SOA however, Schwanenflugel and Rey (1986) found that short SOA superordinate priming does not vary as a function of target superordinate priming does not vary as a function of target exemplar typicality exemplar typicality replicated and simulated their experiment replicated and simulated their experiment Experiment Experiment 72 superordinate-exemplar pairs, e.g., 72 superordinate-exemplar pairs, e.g., vegetable vegetable paired with paired with peas peas , , turnip turnip , , garlic garlic 12 superordinate primes with 2 exemplars each of low, 12 superordinate primes with 2 exemplars each of low, medium, and high typicality medium, and high typicality 200ms superordinate prime, 50ms ISI, exemplar target until 200ms superordinate prime, 50ms ISI, exemplar target until response (concrete object?) response (concrete object?) Results: replicated Schwanenflugel and Rey (1986) Results: replicated Schwanenflugel and Rey (1986) main effect of relatedness, main effect of relatedness, F F 1(1, 42) = 8.09, 1(1, 42) = 8.09, 2(1, 2(1, 66) = 3.52, 66) = 3.52, p p < .07 < .07 no interaction between typicality & relatedness, no interaction between typicality & relatedness, 2 < 2 < 1 1 Feature Verification Feature Verification Conclusions Conclusions semantic memory can be represented as a single layer of semantic memory can be represented as a single layer of semantics semantics without a transparent hierarchical structure without a transparent hierarchical structure accounts for graded structure of categories accounts for graded structure of categories predicts online superordinate verification latencies; novel predicts online superordinate verification latencies; novel result result due to the temporal dynamics of similarity, accounts for due to the temporal dynamics of similarity, accounts for counterintuitive and seemingly inconsistent results regarding counterintuitive and seemingly inconsistent results regarding basic-level vs. superordinate priming basic-level vs. superordinate priming results counter to hierarchical spreading activation results counter to hierarchical spreading activation theories theories Introduction Introduction people’s conceptual knowledge structure for concrete nouns people’s conceptual knowledge structure for concrete nouns traditionally viewed as hierarchical traditionally viewed as hierarchical (Collins & Quillian, 1969) (Collins & Quillian, 1969) superordinate concepts ( superordinate concepts ( vegetable vegetable ) represented at a ) represented at a different level in hierarchy than basic-level concepts different level in hierarchy than basic-level concepts ( ( carrot carrot , or , or pumpkin pumpkin ) ) flat attractor networks – i.e., models with a single layer of flat attractor networks – i.e., models with a single layer of semantics – have provided insight to a number of phenomena semantics – have provided insight to a number of phenomena regarding basic-level concepts regarding basic-level concepts semantic priming semantic priming statistically-based feature correlations statistically-based feature correlations concept-feature distributional statistics concept-feature distributional statistics unclear how these networks could learn and represent unclear how these networks could learn and represent superordinate concepts superordinate concepts can such a network account for established results and can such a network account for established results and provide novel insights? provide novel insights? Goals Goals demonstrate that a flat attractor network can learn demonstrate that a flat attractor network can learn superordinate concepts superordinate concepts simulate typicality ratings to show model accounts for graded simulate typicality ratings to show model accounts for graded structure structure simulate feature verification latencies to demonstrate simulate feature verification latencies to demonstrate superordinate representations may be computed similarly to superordinate representations may be computed similarly to basic-level concepts basic-level concepts simulate superordinate semantic priming to provide insight simulate superordinate semantic priming to provide insight into the temporal dynamics of similarity into the temporal dynamics of similarity Model Model Structure Structure input: input: 30 wordform units representing spelling/sound of a 30 wordform units representing spelling/sound of a word word output: output: 2349 semantic feature units representing features 2349 semantic feature units representing features taken from McRae et al.’s (2005) feature production norms taken from McRae et al.’s (2005) feature production norms e.g., <has wings>, <made of metal>, <is red>, <has e.g., <has wings>, <made of metal>, <is red>, <has seeds> seeds> single layer of semantics; taxonomic features removed; all single layer of semantics; taxonomic features removed; all semantic features were interconnected semantic features were interconnected thus, no hierarchy built into the model thus, no hierarchy built into the model Training Training model learned to map random 3-unit wordform for each concept model learned to map random 3-unit wordform for each concept to semantic features for that concept to semantic features for that concept basic-level concepts trained in 1-to-1 manner: basic-level concepts trained in 1-to-1 manner: 3-unit wordform paired with same set of semantic 3-unit wordform paired with same set of semantic features on every learning trial features on every learning trial superordinate concepts trained in 1-to-many manner superordinate concepts trained in 1-to-many manner wordform paired with semantic features of one of its wordform paired with semantic features of one of its exemplars on each trial exemplars on each trial e.g., wordform for e.g., wordform for vegetable vegetable paired with features of paired with features of carrot carrot on one trial, on one trial, spinach spinach on another, etc. on another, etc. each exemplar was presented equally often each exemplar was presented equally often thus, typicality was NOT built into the model thus, typicality was NOT built into the model Wordform Wordform (30 units) (30 units) Semantic Semantic Features Features (2349 units) (2349 units) activation of features activation of features influenced by: influenced by: Feature Frequency: Feature Frequency: if many exemplars if many exemplars possess a feature, it possess a feature, it is strongly activated is strongly activated Category Cohesion: Category Cohesion: degree of featural degree of featural overlap of exemplars overlap of exemplars determines activation determines activation of superordinate of superordinate features features more overlap = more more overlap = more activation activation Feature Correlations: Feature Correlations: activate one another activate one another during the computation during the computation of meaning of meaning Superordinate Superordinate representations: representations: most features have most features have intermediate intermediate activations activations Basic-level Basic-level representations: representations: all features have all features have activations close activations close to 1 (on) to 1 (on) Feature Feature Activation Activation <is edible> <is edible> .67 .67 <grows in gardens> <grows in gardens> .45 .45 <is green> <is green> .45 .45 <eaten by cooking> <eaten by cooking> .44 .44 <is nutritious> <is nutritious> .33 .33 <eaten in salads> <eaten in salads> .31 .31 <is round> <is round> .31 .31 <is small> <is small> .31 .31 <tastes good> <tastes good> .29 .29 <has seeds> <has seeds> .24 .24 <is crunchy> <is crunchy> .24 .24 <is white> <is white> .24 .24 <used for cooking> <used for cooking> .23 .23 <grows in ground> <grows in ground> .22 .22 <has leaves> <has leaves> .22 .22 Vegetable Vegetable Feature Feature Activation Activation <is crunchy> <is crunchy> .94 .94 <is edible> <is edible> .94 .94 <is green> <is green> .93 .93 <is nutritious> <is nutritious> .93 .93 <eaten with dips> <eaten with dips> .92 .92 <grows in gardens> <grows in gardens> .92 .92 <has fibre> <has fibre> .92 .92 <has leaves> <has leaves> .92 .92 <is stringy> <is stringy> .92 .92 <tastes bland> <tastes bland> .92 .92 <eaten in salads> <eaten in salads> .91 .91 <has stalks> <has stalks> .91 .91 <is long> <is long> .91 .91 <tastes good> <tastes good> .90 .90 Celery Celery Category Category N N Cosine/ Cosine/ Fam Res/ Fam Res/ Cosine/ Cosine/ Typicality Typicality Typicality Typicality Fam Fam Res Res furniture furniture 17 17 .76** .76** .62** .62** .78** .78** fruit fruit 29 29 .71** .71** .69** .69** .91** .91** appliance appliance 14 14 .61* .61* .73** .73** .89** .89** weapon weapon 39 39 .58** .58** .70** .70** .76** .76** utensil utensil 22 22 .57** .57** .52** .52** .68** .68** bird bird 29 29 .57** .57** .49** .49** .69** .69** insect insect 13 13 .52* .52* .69** .69** .77** .77** carnivore carnivore 19 19 .52* .52* .45* .45* .83** .83** container container 14 14 .46* .46* .50* .50* .51** .51** vegetable vegetable 31 31 .45** .45** .50** .50** .90** .90** musical musical instrument instrument 18 18 .44* .44* .54* .54* .94** .94** clothing clothing 39 39 .43** .43** .50** .50** .73** .73** tool tool 34 34 .41** .41** .38* .38* .65** .65** fish fish 11 11 .41 .41 .36 .36 .93** .93** animal animal 133 133 .18* .18* .12 .12 .55** .55** pet pet 22 22 .15 .15 -.01 -.01 .86** .86** herbivore herbivore 18 18 .04 .04 .21 .21 .78** .78** predator predator 17 17 -.14 -.14 .06 .06 .60** .60** mammal mammal 57 57 -.03 -.03 .14 .14 .64** .64** vehicle vehicle 27 27 -.14 -.14 .18 .18 .72** .72** * p * p < .05, ** < .05, ** p p < .01 < .01 Fam Res = Family Resemblance Fam Res = Family Resemblance Typicality Ratings Typicality Ratings important for any semantic important for any semantic memory model to simulate graded memory model to simulate graded structure structure Experiment Experiment collected behavioral typicality collected behavioral typicality ratings for all 20 categories ratings for all 20 categories (7-point scale) (7-point scale) Simulation Simulation superordinate wordform superordinate wordform presented & representation presented & representation recorded recorded basic-level wordform basic-level wordform presented & representation presented & representation recorded recorded computed cosine similarity computed cosine similarity between each superordinate & between each superordinate & exemplar exemplar computed correlation between computed correlation between typicality ratings & cosines for typicality ratings & cosines for each category each category correlation between typicality correlation between typicality ratings & family resemblance ratings & family resemblance served as baseline served as baseline Results Results models predicts typicality ratings at least as well as family models predicts typicality ratings at least as well as family resemblance resemblance therefore, the model was successful in simulating graded therefore, the model was successful in simulating graded structure structure Feature Verification Feature Verification similar “flat” attractor networks have simulated basic-level similar “flat” attractor networks have simulated basic-level feature verification feature verification model can also simulate verification of superordinate features model can also simulate verification of superordinate features Experiment Experiment 54 superordinate-feature pairs such as: 54 superordinate-feature pairs such as: furniture furniture <made of wood> <made of wood> & & fruit fruit <tastes sweet> <tastes sweet> superordinate name for 400 ms, feature name until participant superordinate name for 400 ms, feature name until participant responded responded "Is the feature characteristic of the category?" "Is the feature characteristic of the category?" Simulation Simulation present superordinate wordform and record feature's activation present superordinate wordform and record feature's activation over 20 time ticks over 20 time ticks correlated model's feature activation with human verification References References Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning Journal of Verbal Learning and Verbal Behavior, 8 and Verbal Behavior, 8 , 240-247. , 240-247. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82 Psychological Review, 82 , 407-428. , 407-428. Cree, G. S., McRae, K, & McNorgan, C. (1999). An attractor model of lexical conceptual processing: Cree, G. S., McRae, K, & McNorgan, C. (1999). An attractor model of lexical conceptual processing: Simulating semantic priming. Simulating semantic priming. Cognitive Cognitive Science, 23 Science, 23 , 371-414. , 371-414. McRae, K. & Boivert, S. (1998). Automatic semantic similarity priming. McRae, K. & Boivert, S. (1998). Automatic semantic similarity priming. Journal of Experimental Psychology: Journal of Experimental Psychology: Learning, Memory and Cognition, Learning, Memory and Cognition, 24 24 , 558-572. , 558-572. McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving for a large set of living and nonliving things. things. Behavior Research Methods, 37 Behavior Research Methods, 37 , 547-559. , 547-559. Schwanenflugel, P. J., & Rey, M. (1986). Interlingual semantic facilitation: Evidence for a common Schwanenflugel, P. J., & Rey, M. (1986). Interlingual semantic facilitation: Evidence for a common representational system in the bilingual representational system in the bilingual lexicon. lexicon. Journal of Memory and Language, 25 Journal of Memory and Language, 25 , 605-618. , 605-618. Simulation Simulation superordinate prime wordform superordinate prime wordform presented to model for 15 ticks presented to model for 15 ticks exemplar target presented for 20 exemplar target presented for 20 ticks ticks cross entropy error recorded cross entropy error recorded over last 20 ticks over last 20 ticks Results Results typicality & relatedness did not typicality & relatedness did not interact, interact, F F < 1 < 1 main effect of relatedness, main effect of relatedness, F F (1, (1, 66) = 187.27, 66) = 187.27, p p < .001 < .001 related lower than unrelated for related lower than unrelated for ticks 1 to 13 ticks 1 to 13 0 5 10 15 20 25 30 35 40 45 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tim e T ick R elated -Low Typicality Related -Medium Typicality Related -High Typicality U nrelated -Low Typicality Unrelated -M edium Typicality Unrelated -H igh Typicality Explanation Explanation why is priming from superordinate to why is priming from superordinate to exemplar different than priming exemplar different than priming between basic-level concepts? between basic-level concepts? superordinate features have superordinate features have intermediate activations, which (due intermediate activations, which (due to the sigmoid activation function) to the sigmoid activation function) require less change in net input to be require less change in net input to be turned on or off turned on or off basic-level priming: basic-level priming: features in prime but not in target features in prime but not in target relatively difficult to turn off relatively difficult to turn off prime & target must have high degree of featural overlap prime & target must have high degree of featural overlap to produce priming to produce priming superordinate priming: superordinate priming: activation of prime's features activation of prime's features more easily changed more easily changed priming still results (vs. unrelated superordinate), but priming still results (vs. unrelated superordinate), but less sensitive to similarity less sensitive to similarity therefore, same amount of facilitation for exemplars therefore, same amount of facilitation for exemplars of all typicality levels of all typicality levels Acknowledgements NSERC grant OGP0155704 & NIH grant R01-MH6051701 to Ken Mc
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Celery. Vegetable. FeatureActivation .94 .94 .93 .93 .92 .92 .92 .92 .92 .92 .91 .91 .91 - PowerPoint PPT Presentation
Transcript of Conceptual Hierarchies Arise from the Dynamics of Learning and Processing:
Conceptual Hierarchies Arise from the Dynamics of Learning and Processing:Conceptual Hierarchies Arise from the Dynamics of Learning and Processing:Insights from a Flat Attractor NetworkInsights from a Flat Attractor Network
Christopher M. O’ConnorChristopher M. O’Connor Ken McRaeKen McRae George S. CreeGeorge S. CreeUniversity of Western OntarioUniversity of Western Ontario University of Western OntarioUniversity of Western Ontario University of Toronto at ScarboroughUniversity of Toronto at Scarborough
London, Ontario, CanadaLondon, Ontario, Canada London, Ontario, CanadaLondon, Ontario, Canada Toronto, Ontario, CanadaToronto, Ontario, [email protected] [email protected] [email protected]
Superordinate & Basic-level Superordinate & Basic-level RepresentationsRepresentations
Superordinate Priming & Temporal Dynamics of Superordinate Priming & Temporal Dynamics of SimilaritySimilarity
both spreading activation (Collins & Loftus, 1975) and attractor both spreading activation (Collins & Loftus, 1975) and attractor networks predict that magnitude of semantic priming is networks predict that magnitude of semantic priming is determined by degree of semantic similaritydetermined by degree of semantic similarity supported experimentally using basic-level concepts (McRae & supported experimentally using basic-level concepts (McRae &
Boisvert, 1998) Boisvert, 1998) simulated using feature-based attractor nets (Cree, McRae, & simulated using feature-based attractor nets (Cree, McRae, &
McNorgan, 1999)McNorgan, 1999)
therefore, the degree that an exemplar target is primed by its therefore, the degree that an exemplar target is primed by its superordinate should vary as a function of typicalitysuperordinate should vary as a function of typicality high typicality > medium typicality > low typicalityhigh typicality > medium typicality > low typicality
however, Schwanenflugel and Rey (1986) found that short SOA however, Schwanenflugel and Rey (1986) found that short SOA superordinate priming does not vary as a function of target superordinate priming does not vary as a function of target exemplar typicalityexemplar typicality
replicated and simulated their experimentreplicated and simulated their experiment
ExperimentExperiment 72 superordinate-exemplar pairs, e.g., 72 superordinate-exemplar pairs, e.g., vegetablevegetable paired with paired with peaspeas, ,
turnipturnip, , garlicgarlic 12 superordinate primes with 2 exemplars each of low, 12 superordinate primes with 2 exemplars each of low,
medium, and high typicalitymedium, and high typicality 200ms superordinate prime, 50ms ISI, exemplar target until 200ms superordinate prime, 50ms ISI, exemplar target until
response (concrete object?)response (concrete object?)
Results: replicated Schwanenflugel and Rey (1986)Results: replicated Schwanenflugel and Rey (1986) main effect of relatedness, main effect of relatedness, FF1(1, 42) = 8.09, 1(1, 42) = 8.09, pp < .01, < .01, FF2(1, 66) = 2(1, 66) =
3.52, 3.52, pp < .07 < .07 no interaction between typicality & relatedness, no interaction between typicality & relatedness, FF1 < 1, 1 < 1, FF2 < 12 < 1
Feature VerificationFeature Verification
ConclusionsConclusions semantic memory can be represented as a single layer of semantic memory can be represented as a single layer of
semanticssemantics without a transparent hierarchical structurewithout a transparent hierarchical structure
accounts for graded structure of categoriesaccounts for graded structure of categories predicts online superordinate verification latencies; novel resultpredicts online superordinate verification latencies; novel result due to the temporal dynamics of similarity, accounts for due to the temporal dynamics of similarity, accounts for
counterintuitive and seemingly inconsistent results regarding counterintuitive and seemingly inconsistent results regarding basic-level vs. superordinate primingbasic-level vs. superordinate priming results counter to hierarchical spreading activation theoriesresults counter to hierarchical spreading activation theories
IntroductionIntroduction people’s conceptual knowledge structure for concrete nouns people’s conceptual knowledge structure for concrete nouns
traditionally viewed as hierarchical traditionally viewed as hierarchical (Collins & Quillian, 1969)(Collins & Quillian, 1969)
superordinate concepts (superordinate concepts (vegetablevegetable) represented at a ) represented at a different level in hierarchy than basic-level concepts different level in hierarchy than basic-level concepts ((carrotcarrot, or , or pumpkinpumpkin))
flat attractor networks – i.e., models with a single layer of flat attractor networks – i.e., models with a single layer of semantics – have provided insight to a number of phenomena semantics – have provided insight to a number of phenomena regarding basic-level conceptsregarding basic-level concepts
semantic primingsemantic priming statistically-based feature correlationsstatistically-based feature correlations concept-feature distributional statisticsconcept-feature distributional statistics
unclear how these networks could learn and represent unclear how these networks could learn and represent superordinate conceptssuperordinate concepts
can such a network account for established results and can such a network account for established results and provide novel insights?provide novel insights?
GoalsGoals demonstrate that a flat attractor network can learn demonstrate that a flat attractor network can learn
superordinate conceptssuperordinate concepts simulate typicality ratings to show model accounts for graded simulate typicality ratings to show model accounts for graded
structurestructure simulate feature verification latencies to demonstrate simulate feature verification latencies to demonstrate
superordinate representations may be computed similarly to superordinate representations may be computed similarly to basic-level conceptsbasic-level concepts
simulate superordinate semantic priming to provide insight into simulate superordinate semantic priming to provide insight into the temporal dynamics of similaritythe temporal dynamics of similarity
ModelModelStructureStructure input:input: 30 wordform units representing spelling/sound of a 30 wordform units representing spelling/sound of a
wordword output:output: 2349 semantic feature units representing features 2349 semantic feature units representing features
taken from McRae et al.’s (2005) feature production normstaken from McRae et al.’s (2005) feature production norms e.g., <has wings>, <made of metal>, <is red>, <has seeds>e.g., <has wings>, <made of metal>, <is red>, <has seeds>
single layer of semantics; taxonomic features removed; all single layer of semantics; taxonomic features removed; all semantic features were interconnectedsemantic features were interconnected
thus, no hierarchy built into the modelthus, no hierarchy built into the model
TrainingTraining model learned to map random 3-unit wordform for each model learned to map random 3-unit wordform for each
concept to semantic features for that conceptconcept to semantic features for that concept
basic-level concepts trained in 1-to-1 manner: basic-level concepts trained in 1-to-1 manner: 3-unit wordform paired with same set of semantic features 3-unit wordform paired with same set of semantic features
on every learning trialon every learning trial
superordinate concepts trained in 1-to-many mannersuperordinate concepts trained in 1-to-many manner wordform paired with semantic features of one of its wordform paired with semantic features of one of its
exemplars on each trialexemplars on each trial e.g., wordform for e.g., wordform for vegetablevegetable paired with features of paired with features of
carrotcarrot on one trial, on one trial, spinachspinach on another, etc. on another, etc.
each exemplar was presented equally ofteneach exemplar was presented equally often thus, typicality was NOT built into the modelthus, typicality was NOT built into the model
WordformWordform(30 units)(30 units)
SemanticSemanticFeaturesFeatures
(2349 units)(2349 units)
activation of features activation of features influenced by:influenced by:
Feature Frequency:Feature Frequency: if many exemplars if many exemplars
possess a feature, it is possess a feature, it is strongly activatedstrongly activated
Category Cohesion:Category Cohesion: degree of featural degree of featural
overlap of exemplars overlap of exemplars determines activation of determines activation of superordinate featuressuperordinate features
more overlap = more more overlap = more activationactivation
Feature Correlations:Feature Correlations: activate one another activate one another
during the computation during the computation of meaningof meaning
Superordinate Superordinate representations:representations: most features have most features have
intermediate intermediate activationsactivations
Basic-level Basic-level representations:representations: all features have all features have
activations close to activations close to 1 (on)1 (on)
FeatureFeature ActivationActivation
<is edible><is edible> .67.67<grows in gardens><grows in gardens> .45.45<is green><is green> .45.45<eaten by cooking><eaten by cooking> .44.44<is nutritious><is nutritious> .33.33<eaten in salads><eaten in salads> .31.31<is round><is round> .31.31<is small><is small> .31.31<tastes good><tastes good> .29.29<has seeds><has seeds> .24.24<is crunchy><is crunchy> .24.24<is white><is white> .24.24<used for cooking><used for cooking> .23.23<grows in ground><grows in ground> .22.22<has leaves><has leaves> .22.22
VegetableVegetableFeatureFeature ActivationActivation
<is crunchy><is crunchy> .94.94<is edible><is edible> .94.94<is green><is green> .93.93<is nutritious><is nutritious> .93.93<eaten with dips><eaten with dips> .92.92<grows in gardens><grows in gardens> .92.92<has fibre><has fibre> .92.92<has leaves><has leaves> .92.92<is stringy><is stringy> .92.92<tastes bland><tastes bland> .92.92<eaten in salads><eaten in salads> .91.91<has stalks><has stalks> .91.91<is long><is long> .91.91<tastes good><tastes good> .90.90
CeleryCelery
CategoryCategory NN Cosine/Cosine/ Fam Res/Fam Res/ Cosine/Cosine/TypicalityTypicality TypicalityTypicality Fam ResFam Res
furniturefurniture 1717 .76**.76** .62**.62** .78**.78**fruitfruit 2929 .71**.71** .69**.69** .91**.91**applianceappliance 1414 .61*.61* .73**.73** .89**.89**weaponweapon 3939 .58**.58** .70**.70** .76**.76**utensilutensil 2222 .57**.57** .52**.52** .68**.68**birdbird 2929 .57**.57** .49**.49** .69**.69**insectinsect 1313 .52*.52* .69**.69** .77**.77**carnivorecarnivore 1919 .52*.52* .45*.45* .83**.83**containercontainer 1414 .46*.46* .50*.50* .51**.51**vegetablevegetable 3131 .45**.45** .50**.50** .90**.90**musical musical instrumentinstrument 1818 .44*.44* .54*.54* .94**.94**clothingclothing 3939 .43**.43** .50**.50** .73**.73**tooltool 3434 .41**.41** .38*.38* .65**.65**fishfish 1111 .41.41 .36.36 .93**.93**
animalanimal 133133 .18*.18* .12.12 .55**.55**petpet 2222 .15.15 -.01-.01 .86**.86**herbivoreherbivore 1818 .04.04 .21.21 .78**.78**predatorpredator 1717 -.14-.14 .06.06 .60**.60**mammalmammal 5757 -.03-.03 .14.14 .64**.64**vehiclevehicle 2727 -.14-.14 .18.18 .72**.72**
* p* p < .05, ** < .05, ** pp < .01 < .01Fam Res = Family ResemblanceFam Res = Family Resemblance
Typicality RatingsTypicality Ratings important for any semantic important for any semantic
memory model to simulate graded memory model to simulate graded structurestructure
ExperimentExperiment collected behavioral typicality collected behavioral typicality
ratings for all 20 categories (7-ratings for all 20 categories (7-point scale)point scale)
SimulationSimulation superordinate wordform superordinate wordform
presented & representation presented & representation recordedrecorded
basic-level wordform basic-level wordform presented & representation presented & representation recordedrecorded
computed cosine similarity computed cosine similarity between each superordinate & between each superordinate & exemplarexemplar
computed correlation between computed correlation between typicality ratings & cosines for typicality ratings & cosines for each categoryeach category
correlation between typicality correlation between typicality ratings & family resemblance ratings & family resemblance served as baselineserved as baseline
ResultsResults
models predicts typicality ratings at least as well as family models predicts typicality ratings at least as well as family resemblanceresemblance
therefore, the model was successful in simulating graded structuretherefore, the model was successful in simulating graded structure
Feature VerificationFeature Verification similar “flat” attractor networks have simulated basic-level feature similar “flat” attractor networks have simulated basic-level feature
verificationverification model can also simulate verification of superordinate featuresmodel can also simulate verification of superordinate features
ExperimentExperiment 54 superordinate-feature pairs such as: 54 superordinate-feature pairs such as: furniturefurniture <made of wood> <made of wood>
& & fruitfruit <tastes sweet> <tastes sweet> superordinate name for 400 ms, feature name until participant superordinate name for 400 ms, feature name until participant
respondedresponded "Is the feature characteristic of the category?""Is the feature characteristic of the category?"SimulationSimulation present superordinate wordform and record feature's activation present superordinate wordform and record feature's activation
over 20 time ticksover 20 time ticks correlated model's feature activation with human verification correlated model's feature activation with human verification
latencylatency feature activation in model predicts human verification from ticks 6 feature activation in model predicts human verification from ticks 6
- 20- 20
ReferencesReferencesCollins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning Journal of Verbal Learning and Verbal Behavior, 8and Verbal Behavior, 8, 240-247., 240-247.Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Psychological Review, 82Review, 82, 407-428., 407-428.Cree, G. S., McRae, K, & McNorgan, C. (1999). An attractor model of lexical conceptual processing: Cree, G. S., McRae, K, & McNorgan, C. (1999). An attractor model of lexical conceptual processing: Simulating semantic priming. Simulating semantic priming. Cognitive Cognitive Science, 23Science, 23, 371-414., 371-414.McRae, K. & Boivert, S. (1998). Automatic semantic similarity priming. McRae, K. & Boivert, S. (1998). Automatic semantic similarity priming. Journal of Experimental Psychology: Journal of Experimental Psychology: Learning, Memory and Cognition,Learning, Memory and Cognition, 2424, 558-572., 558-572.McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving large set of living and nonliving things. things. Behavior Research Methods, 37Behavior Research Methods, 37, 547-559., 547-559.Schwanenflugel, P. J., & Rey, M. (1986). Interlingual semantic facilitation: Evidence for a common Schwanenflugel, P. J., & Rey, M. (1986). Interlingual semantic facilitation: Evidence for a common representational system in the bilingual representational system in the bilingual lexicon. lexicon. Journal of Memory and Language, 25Journal of Memory and Language, 25, 605-618., 605-618.
SimulationSimulation superordinate prime wordform superordinate prime wordform
presented to model for 15 tickspresented to model for 15 ticks exemplar target presented for 20 exemplar target presented for 20
ticksticks cross entropy error recorded over cross entropy error recorded over
last 20 tickslast 20 ticks
ResultsResults typicality & relatedness did not typicality & relatedness did not
interact, interact, FF < 1 < 1
main effect of relatedness, main effect of relatedness, FF(1, 66) (1, 66) = 187.27, = 187.27, pp < .001 < .001
related lower than unrelated for related lower than unrelated for ticks 1 to 13ticks 1 to 13
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Time Tick
Cross Entropy Error
Related - Low Typicality
Related - Medium Typicality
Related - High Typicality
Unrelated - Low Typicality
Unrelated - Medium Typicality
Unrelated - High Typicality
ExplanationExplanation why is priming from superordinate to why is priming from superordinate to
exemplar different than priming between exemplar different than priming between basic-level concepts?basic-level concepts?
superordinate features have superordinate features have intermediate activations, which (due to intermediate activations, which (due to the sigmoid activation function) require the sigmoid activation function) require less change in net input to be turned on less change in net input to be turned on or offor off
basic-level priming:basic-level priming: features in prime but not in target features in prime but not in target relatively difficult to turn offrelatively difficult to turn off prime & target must have high degree of featural overlap to prime & target must have high degree of featural overlap to
produce primingproduce priming superordinate priming:superordinate priming: activation of prime's features more activation of prime's features more
easily changedeasily changed priming still results (vs. unrelated superordinate), but less priming still results (vs. unrelated superordinate), but less
sensitive to similaritysensitive to similarity therefore, same amount of facilitation for exemplars of therefore, same amount of facilitation for exemplars of
all typicality levelsall typicality levels
AcknowledgementsNSERC grant OGP0155704 &NIH grant R01-MH6051701 to Ken McRae
