TY - JOUR

T1 - Similarity-induced interference or facilitation in language production reflects representation, not selection

AU - Oppenheim, Gary

AU - Nozari, Nazbanou

PY - 2024/4

Y1 - 2024/4

N2 - Researchers have long interpreted the presence or absence of semantic interference in picture naming latencies as confirming or refuting theoretical claims regarding competitive lexical selection. But inconsistent empirical results challenge any mechanistic interpretation. A behavioral experiment first verified an apparent boundary condition in a blocked picture naming task: when orthogonally manipulating association type, taxonomic associations consistently elicit interference, while thematic associations do not. A plausible representational difference is that thematic feature activations depend more on supporting contexts. Simulations show that contextsensitivity emerges from the distributional statistics that are often used to measure thematic associations: residual semantic activation facilitates the retrieval of words that share semanticfeatures, counteracting learning-based interference, and training a production model with greater sequential cooccurrence for thematically related words causes it to acquire stronger residual activation for thematic features. Modulating residual activation, either directly or through training, allows the model to capture gradient values of interference and facilitation, and in everysimulation competitive and noncompetitive selection algorithms produce qualitatively equivalent results.

AB - Researchers have long interpreted the presence or absence of semantic interference in picture naming latencies as confirming or refuting theoretical claims regarding competitive lexical selection. But inconsistent empirical results challenge any mechanistic interpretation. A behavioral experiment first verified an apparent boundary condition in a blocked picture naming task: when orthogonally manipulating association type, taxonomic associations consistently elicit interference, while thematic associations do not. A plausible representational difference is that thematic feature activations depend more on supporting contexts. Simulations show that contextsensitivity emerges from the distributional statistics that are often used to measure thematic associations: residual semantic activation facilitates the retrieval of words that share semanticfeatures, counteracting learning-based interference, and training a production model with greater sequential cooccurrence for thematically related words causes it to acquire stronger residual activation for thematic features. Modulating residual activation, either directly or through training, allows the model to capture gradient values of interference and facilitation, and in everysimulation competitive and noncompetitive selection algorithms produce qualitatively equivalent results.

U2 - 10.1016/j.cognition.2024.105720

DO - 10.1016/j.cognition.2024.105720

M3 - Article

VL - 245

JO - Cognition

JF - Cognition

SN - 0010-0277

M1 - 105720

ER -