Category verbal fluency tasks, where participants are asked to produce words according to a semantic category, are typically noun-based (e.g., animals). While insights about the integrity and retrieval of semantic knowledge have been obtained by analyzing the ordinal variances of word production in these noun-based fluency tasks, focusing exclusively on noun categories ignores the semantic knowledge contributed by other semantic/grammatical categories, especially verbs. To better understand the representational differences of nouns and verbs within the mental lexicon, the current study conducted and contrasted different noun- and verb-based fluency tasks. By analyzing the use of different lexical information sources, including word frequency, and context and order similarity derived from a computational model of lexical semantics (BEAGLE; Jones & Mewhort, 2007), and the use of perceptual information derived from the recently released sensorimotor norms (Lynott, Connell, Brysbaert, Brand, & Carney, 2020), it was found that these information sources consistently distinguished noun and verb retrieval, signaling the underlying distributional and sensorimotor representational differences for these two semantic/grammatical categories. The results demonstrate the essential and integral role that distributional and grounded/embodied models play in understanding language and cognition, and highlight the usefulness of verbal fluency tasks in exploring theoretical questions in memory and psycholinguistics.