In the Absence of Animacy: Superordinate Category Structure Affects Subordinate Label Verification

Abstract

Theoretical accounts as well as behavioral studies reporting animacy effects offer inconsistent and sometimes contradictory results. A possible explanation for these inconsistencies may be inadvertent biases in the stimuli selected for test – with category-specific effects driven by characteristics of test stimuli other than animacy per se. In this study, we pit animacy against feature structure (intra-item variability), in a picture-word matching task. For unimpaired adults, regardless of whether objects were from animate (mammals; insects) or inanimate (clothes; musical instruments) superordinate categories, participants were faster to match basic level labels with objects from categories with low intra-item variability (mammals; clothes) than from categories with high intra-item variability (insects; instruments). Thus, pitting animacy against variability allowed us to clarify that observable differences in processing speed between animals and instruments are systematically driven by the intra-item variability of the superordinate categories, and not by animacy itself.

Quantifying semantic animacy: How much are words alive?

“…Some studies have found a processing difference between animate and inanimate entities, at both a behavioral and a neural level (Martin, Wiggs, Ungerleider, & Haxby, 1996; Perani et al, 1995, 1999). Other studies have failed to replicate this finding (Devlin et al, 2002; Ilić, Ković, & Styles, 2013; Pilgrim, Fadili, Fletcher, & Tyler, 2002; Tyler, Bright, et al, 2003).…”

“…Studies that reported differences in processing of animate and inanimate entities used only these two categories (Martin, Wiggs, Ungerleider, & Haxby, 1996; Perani et al, 1995, 1999). Studies that have failed to demonstrate processing differences between animates and inanimates typically have used several categories of objects to instantiate animate and inanimate classes (Devlin et al, 2002; Ilić et al, 2013; Pilgrim et al, 2002; Tyler, Bright, et al, 2003). For example, animals and fruits were treated as living or natural kinds, while tools and vehicles were nonliving, that is, manmade objects (Devlin et al, 2002).…”

A direct replication and extension of Popp and Serra (2016, experiment 1): better free recall and worse cued recall of animal names than object names, accounting for semantic similarity

“…Such an explanation is also consistent with the overlapping semantic feature hypothesis (McRae et al, 1997) that suggests animals may be more memorable than objects because animals share more overlapping features (e.g., fur, four legs, teeth) relative to objects, which tend to have wider-ranging features (e.g., features of different musical instruments such as trumpets and guitars have less featural overlap). This hypothesis is supported by studies showing benefits of greater feature and neural global pattern overlap for subsequent memory (Ilic et al, 2013;Xiao et al, 2016). Overlapping semantic features can explain animacy and reverse animacy effects by drawing on the spreading-activation theory of semantic memory (Collins and Loftus, 1975) that posits concepts activate other semantically-related concepts in memory proportional to the degree of relatedness.…”

Why are human animacy judgments continuous rather than categorical? A computational modeling approach

“…Some studies have reported behavioral and/or neurological differences in response to animate and inanimate stimuli (Perani et al, 1995;Martin et al, 1996;Perani et al, 1999). Other studies have failed to replicate these findings (Devlin et al, 2002;Pilgrim et al, 2002;Tyler et al, 2003;Ilić et al, 2013). The linguistic encoding of animacy has been shown to affect many different aspects of psychological functioning, including the processing of relative clauses (Mak et al, 2002;Traxler et al, 2005;Gennari et al, 2012); attentional mechanisms (Bugaiska et al, 2019); the detection of semantic violations in language (Grewe et al, 2006;Szewczyk and Schriefers, 2011); the learning of artificial languages (Vihman et al, 2018); word recognition (Bonin et al, 2019) and the ability to recall words (Bonin et al, 2015;VanArsdall et al, 2015;Bugaiska et al, 2016;Serra, 2016, 2018;Nairne et al, 2017;Kazanas et al, 2020).…”