Conceptual Hierarchies Arise from the Dynamics of Learning and Processing: Insights from a Flat Attractor Network Christopher M. O’ConnorKen McRaeGeorge S. Cree University of Western OntarioUniversity of Western OntarioUniversity of Toronto at Scarborough London, Ontario, CanadaLondon, Ontario, CanadaToronto, Ontario, Canada Superordinate & Basic-level Representations Superordinate Priming & Temporal Dynamics of Similarity  both spreading activation (Collins & Loftus, 1975) and attractor networks predict that magnitude of semantic priming is determined by degree of semantic similarity  supported experimentally using basic-level concepts (McRae & Boisvert, 1998)  simulated using feature-based attractor nets (Cree, McRae, & McNorgan, 1999)  therefore, the degree that an exemplar target is primed by its superordinate should vary as a function of typicality  high typicality > medium typicality > low typicality  however, Schwanenflugel and Rey (1986) found that short SOA superordinate priming does not vary as a function of target exemplar typicality  replicated and simulated their experiment Experiment  72 superordinate-exemplar pairs, e.g., vegetable paired with peas, turnip, garlic  12 superordinate primes with 2 exemplars each of low, medium, and high typicality  200ms superordinate prime, 50ms ISI, exemplar target until response (concrete object?) Results: replicated Schwanenflugel and Rey (1986)  main effect of relatedness, F1(1, 42) = 8.09, p <.01,F2(1, 66) = 3.52, p <.07  main effect of relatedness, F1(1, 42) = 8.09, p <.01, F2(1, 66) = 3.52, p <.07  no interaction between typicality & relatedness, F1 < 1, F2 < 1 Feature Verification Conclusions  semantic memory can be represented as a single layer of semantics  without a transparent hierarchical structure  accounts for graded structure of categories  predicts online superordinate verification latencies; novel result  due to the temporal dynamics of similarity, accounts for counterintuitive and seemingly inconsistent results regarding basic-level vs. superordinate priming  results counter to hierarchical spreading activation theories Introduction  people’s conceptual knowledge structure for concrete nouns traditionally viewed as hierarchical (Collins & Quillian, 1969)  superordinate concepts (vegetable) represented at a different level in hierarchy than basic-level concepts (carrot, or pumpkin)  flat attractor networks – i.e., models with a single layer of semantics – have provided insight to a number of phenomena regarding basic-level concepts  semantic priming  statistically-based feature correlations  concept-feature distributional statistics  unclear how these networks could learn and represent superordinate concepts  can such a network account for established results and provide novel insights? Goals  demonstrate that a flat attractor network can learn superordinate concepts  simulate typicality ratings to show model accounts for graded structure  simulate feature verification latencies to demonstrate superordinate representations may be computed similarly to basic-level concepts  simulate superordinate semantic priming to provide insight into the temporal dynamics of similarity ModelStructure  input:30 wordform units representing spelling/sound of a word  output:2349 semantic feature units representing features taken from McRae et al.’s (2005) feature production norms  e.g.,,,,  e.g.,,,,  single layer of semantics; taxonomic features removed; all semantic features were interconnected  thus, no hierarchy built into the model Training  model learned to map random 3-unit wordform for each concept to semantic features for that concept  basic-level concepts trained in 1-to-1 manner:  3-unit wordform paired with same set of semantic features on every learning trial  superordinate concepts trained in 1-to-many manner  wordform paired with semantic features of one of its exemplars on each trial  e.g., wordform for vegetable paired with features of carrot on one trial, spinach on another, etc.  each exemplar was presented equally often  thus, typicality was NOT built into the model Wordform (30 units) SemanticFeatures (2349 units)  activation of features influenced by:  Feature Frequency:  if many exemplars possess a feature, it is strongly activated  Category Cohesion:  degree of featural overlap of exemplars determines activation of superordinate features  more overlap = more activation  Feature Correlations:  activate one another during the computation of meaning  Superordinate representations:  most features have intermediate activations  Basic-level representations:  all features have activations close to 1 (on) FeatureActivation Vegetable FeatureActivation Celery CategoryNCosine/Fam Res/Cosine/ TypicalityTypicalityFam Res furniture17.76**.62**.78** fruit29.71**.69**.91** appliance14.61*.73**.89** weapon39.58**.70**.76** utensil22.57**.52**.68** bird29.57**.49**.69** insect13.52*.69**.77** carnivore19.52*.45*.83** container14.46*.50*.51** vegetable31.45**.50**.90** musical instrument18.44*.54*.94** instrument18.44*.54*.94** clothing39.43**.50**.73** tool34.41**.38*.65** fish ** animal133.18*.12.55** pet ** herbivore ** predator ** mammal ** vehicle ** * p <.05, ** p <.01 Fam Res = Family Resemblance Typicality Ratings  important for any semantic memory model to simulate graded structure Experiment  collected behavioral typicality ratings for all 20 categories (7-point scale) Simulation  superordinate wordform presented & representation recorded  basic-level wordform presented & representation recorded  computed cosine similarity between each superordinate & exemplar  computed correlation between typicality ratings & cosines for each category  correlation between typicality ratings & family resemblance served as baseline Results  models predicts typicality ratings at least as well as family resemblance  therefore, the model was successful in simulating graded structure Feature Verification  similar “flat” attractor networks have simulated basic-level feature verification  model can also simulate verification of superordinate features Experiment  54 superordinate-feature pairs such as: furniture & fruit  54 superordinate-feature pairs such as: furniture & fruit  superordinate name for 400 ms, feature name until participant responded  "Is the feature characteristic of the category?" Simulation  present superordinate wordform and record feature's activation over 20 time ticks  correlated model's feature activation with human verification latency  feature activation in model predicts human verification from ticks References Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, Cree, G. S., McRae, K, & McNorgan, C. (1999). An attractor model of lexical conceptual processing: Simulating semantic priming. Cognitive Science, 23, McRae, K. & Boivert, S. (1998). Automatic semantic similarity priming. Journal of Experimental Psychology: Learning, Memory and Cognition, 24, McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37, Schwanenflugel, P. J., & Rey, M. (1986). Interlingual semantic facilitation: Evidence for a common representational system in the bilingual lexicon. Journal of Memory and Language, 25, Simulation  superordinate prime wordform presented to model for 15 ticks  exemplar target presented for 20 ticks  cross entropy error recorded over last 20 ticks Results  typicality & relatedness did not interact, F < 1  main effect of relatedness, F(1, 66) = , p <.001  related lower than unrelated for ticks 1 to 13 Explanation  why is priming from superordinate to exemplar different than priming between basic-level concepts?  superordinate features have intermediate activations, which (due to the sigmoid activation function) require less change in net input to be turned on or off  basic-level priming: features in prime but not in target relatively difficult to turn off  prime & target must have high degree of featural overlap to produce priming  superordinate priming: activation of prime's features more easily changed  priming still results (vs. unrelated superordinate), but less sensitive to similarity  therefore, same amount of facilitation for exemplars of all typicality levels Acknowledgements NSERC grant OGP & NIH grant R01-MH to Ken McRae