Kilo-picture naming: An event-related potential (ERP) study

Poster Session B - Sunday, April 14, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Ian Martindale¹ (imartindale4976@sdsu.edu), Sofia Ortega¹, Emily Akers¹, Katherine Midgley¹, Phillip Holcomb¹; ¹San Diego State University

The neural processes implicated in effortlessly identifying and ascribing meaning to the visual objects we recognize are not fully understood. Previously, researchers have investigated the processing of various linguistic stimuli (e.g., visual words, auditory words, and sign language stimuli) in ERP megastudies with large participant samples and huge stimuli sets. In recent years, we investigated visual object recognition using picture stimuli, but with different task directives (i.e., object decision; semantic classification) and participants. The present study utilizes the ERP megastudy paradigm to explore the neural mechanisms of visual object recognition during naming. EEG has been collected for 40 right-handed English speakers. The current experiment employs a verbal production task wherein participants name 900 real-world objects. A variety of objects were presented to enable differential classification and comparison of ERP components. We expected ERPs generated would demonstrate modulation to visual and semantic variables that parallel effects in previous megastudies. Here we discuss differences between ERPs to objects high and low in complexity, objects high and low in familiarity, and animals and tools. The high and low complexity contrast showed higher P1 and N1 amplitudes, with trends persisting into the N300 and N400 time epochs. For the high and low familiarity dichotomy, low familiarity images displayed larger central-anterior N300s and central-posterior N400s when compared to high familiarity images. Tools (relative to animals) demonstrated greater bilateral posterior negativity in the N300 and N400 epochs. Alternatively, animals (relative to tools) elicited increased left anterior negativity only in the later N400 epoch.

Topic Area: LANGUAGE: Semantic