Verbal interference suppresses exact numerical representation : online lexical encoding as an account of cross-linguistic differences in cognition

Language for number is an important case study of the relationship between language and cognition because the mechanisms of non-verbal numerical cognition are well-understood. When the Pirahã (an Amazonian hunter-gatherer tribe who have no exact number words) are tested in non-verbal numerical tasks, they are able to perform one-to-one matching tasks but make errors in more difficult tasks. The pattern of errors suggests that they are using analog magnitude estimation, an evolutionarilyand developmentally-conserved mechanism for estimating quantities. Here we show that English-speaking participants rely on the same mechanisms when verbal number representations are unavailable due to concurrent verbal interference tasks. Followup experiments demonstrate that the effects of verbal interference are primarily manifest during encoding of quantity information, and—using a new procedure for matching difficulty of interference tasks for individual participants—that the effects are restricted to verbal interference. These results are consistent with the hypothesis that number words are used online to encode, store, and manipulate numerical information. This linguistic strategy complements, rather than altering or replacing, non-verbal representations. We review evidence supporting the extension of this proposal (the online linguistic encoding hypothesis) to the domains of color, navigation, theory of mind, and object individuation. Online lexical encoding of number 3 When a name is once annexed to [an idea] wherein the parts of that complex idea have a settled and permanent union then is the essence as it were established . John Locke, An Essay Concerning Human Understanding, 1690 How does knowing a language affect the way you perceive, act, and reason? Do differences between languages cause systematic differences in the cognition of their speakers? Questions about the relationship between language and thought are among the most controversial in cognitive science (Boroditsky, 2001; Davidoff, Davies, & Roberson, 1999; Gentner & Goldin-Meadow, 2003; Gumperz & Levinson, 1996; Levinson, Kita, Haun, & Rasch, 2002; Li & Gleitman, 2002; Pinker, 1994; Rosch Heider, 1972). Theoretical proposals concerning the nature of this relationship run the gamut from suggestions that individual languages strongly influence their speakers cognition (Davidoff et al., 1999; Levinson, 2003; Whorf, 1956) to suggestions that there is no causal relationship between speakers’ language and their cognition (Fodor, 1975; Li & Gleitman, 2002; Pinker, 1994), with a number of more moderate proposals falling between these extremes (Gentner, 2003; Kay & Kempton, 1984; Slobin, 1996). Recently, across the domains of color, number, navigation, theory of mind, and object individuation, there has been a convergence of empirical results addressing this question. In each of these domains, meaningful cognitive differences have been demonstrated between people who have words for particular concepts and those who don’t, either because their language does not encode those concepts (Frank, Everett, Fedorenko, & Gibson, 2008; Gordon, 2004; Pica, Lemer, Izard, & Dehaene, 2004; Pyers & Senghas, 2009; Winawer et al., 2007) or because they haven’t yet learned the relevant words (Hermer-Vazquez, Spelke, & Katsnelson, 1999; de Villiers & de Villiers, 2000; Hermer-Vazquez, Moffet, & Munkholm, 2001; Le Corre, Walle, Brannon, & Carey, 2006). These group differences disappear when the people who do know the relevant words cannot access these words (for example, when they are required to occupy their verbal resources with Online lexical encoding of number 4 interfering material or when tasks are speeded) (Hermer-Vazquez et al., 1999; Newton & de Villiers, 2007; Winawer et al., 2007). These similarities across domains point towards a unified account of the relationship between language and cognition that falls midway between the two theoretical extremes of strong interaction and no interaction. On the one hand, the data suggest that languages do change the cognition of their speakers: they help their speakers accomplish difficult cognitive tasks by creating abstractions for the efficient processing and storage of information. On the other hand, the data also suggest the hypothesis that these abstractions complement rather than replace pre-existing non-verbal representations. When linguistic abstractions are temporarily inaccessible, language users seem to fall back on the representations used by other animals, children, and speakers of languages without those abstractions. By analogy to physical technologies—which are invented to facilitate a range of tasks and which complement their users’ pre-existing abilities—we have described this pattern of results as the use of language as a “cognitive technology” (Dascal, 2002; Frank et al., 2008) to create new abstractions. In the current manuscript we elaborate this view. The work reported here makes three contributions. First, we attempt to unify previous theoretical claims about (i) the use of language to create abstractions and (ii) how this process could create cross-linguistic differences in cognition. Although the account proposed here—which we call the “online lexical encoding” hypothesis (OLE)—is intended to be a restatement and synthesis of a number of previous views, rather than a wholly novel theoretical development, we believe that it clarifies some issues in previous literature. Second, we test predictions of the OLE in the domain of numerical cognition: specifically, that in the absence of access to number words even English speakers should make errors consistent with non-verbal numerical mechanisms. Third, we review how ideas about linguistic abstraction—and specifically, their formulation in the OLE—can help to unify the pattern of results on cross-linguistic cognitive differences across a variety of domains. Online lexical encoding of number 5 The Online Lexical Encoding Hypothesis We begin by giving a direct statement of our hypothesis, which we then unpack into its