Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming

Often we can use different names to refer to the same object (e.g., pony vs. horse) and naming choices vary among people. In the present study we explore factors that affect naming variation for visually presented objects. We analyse a large dataset of object naming with realistic images and focus on two factors: (a) the visual typicality of objects and their context for the names used by human annotators and (b) the lexical frequency of these names. We use a novel computational approach to estimate visual typicality by calculating the visual similarity of a given object (or context) and the average visual information of other objects which were given the same name (in an independent dataset). In difference to previous studies, we not only consider the name used by most annotators for a given object (top name) but explore also the role of the second most frequently used name (alternative name). Our results show that naming variation decreases the more typical an object is for its top name and the higher the lexical frequency of this name. For alternative names the opposite is found. Context typicality does not show a general effect in our analysis. Overall our results show that visual and lexical characteristics relating to name candidates beyond the top name are informative for predicting variability in object naming. On a methodological level, our results demonstrate the potential of using large scale datasets with realistic images in conjunction with computational methods to inform models of human object naming

Ease of processing constrains the activation flow in the conceptual-lexical system during speech planning

In three picture-word interference experiments, speakers named a target object in the presence of an unrelated not-to-be-named context object. Distractor words, which were phonologically related or unrelated to the context object’s name, were used to determine whether the context object had become phonologically activated. All objects had high frequency names, and ease of processing of these objects was manipulated by a visual degradation technique. In Experiment 1, both objects were non-degraded, in Experiment 2 both objects were degraded, and in Experiment 3 either the target object or the context object was degraded. Distractor words, which were phonologically related to the context objects, interfered with the naming response when both objects were presented non-degraded, indicating that the context objects had become phonologically coactivated. The effect vanished, when both objects were degraded, when only the context object was degraded, and when only the target object was degraded. These data demonstrate that the amount of available processing resources constrains the forward-cascading of activation in the conceptual-lexical system. Context objects are likely to become phonologically coactivated, if they are easily retrieved and if prioritized target processing leaves sufficient resources

What does the articulatory output buffer know about alternative picture names?

Here we provide provide data and R-scripts to reproduce the main analysis reported in: Hantsch, A., & Mädebach, A. (2011). What does the articulatory output buffer know about alternative picture names? Evidence against the response-exclusion hypothesis. Language and Cognitive Processes, 28(5), 684–700. https://doi.org/10.1080/01690965.2011.59572

Many-Analysts: Gender-Inclusive Language (Team-ID: 46)

This document details our analysis plan for the “Many-Analysts: Gender-Inclusive Language” project

What does the articulatory output buffer know about alternative picture names? Evidence against the response-exclusion hypothesis

When participants name pictures in the presence of a distractor word, a semantic relation between distractor word and picture name interferes with the naming response. Some models take this to reflect a lexical-competition process, while other models assume it to result from a post-lexical response-exclusion mechanism. According to the latter view, the distractor word has privileged access to an articulatory output buffer and has to be purged from it before the picture name can be produced. This buffer is assumed to have access to information which is relevant within a given task, such as gross semantic category information. Any (semantic) similarity between the picture name and the distractor word then should render removal of the distractor more difficult and thus prolong naming latencies. However, more fine-grained semantic information is not accessible to the articulatory output buffer, and thus should not affect naming performance. We tested this assumption by comparing the effect of two semantic distractor conditions keeping the semantic relation between distractor words and the to-be-produced (basic-level) picture names constant, while manipulating only the relation between the distractor and the pictures’ subordinate-level name. Contrary to the predictions of the response-exclusion hypothesis, this manipulation, determined whether or not semantic interference was obtained

Explaining semantic facilitation and interference effects in the picture-word interference task - a rejoinder to Navarrete and Mahon (2013)

In our original article, we presented a picture-word interference experiment in which pictures had to be named with basic level names although their naming preference was at the subordinate level (e.g., picture: rose, response: flower). We observed semantic interference for distractor words denoting the subordinate name of the picture, but no effect for distractor words denoting another subordinate name corresponding to the same basic level. We argued that this pattern is inconsistent with the assumption that semantic interference effects in picture-word interference tasks are caused by a post-lexical response-exclusion mechanism which operates over coarse semantic properties of distractor words, but that lexical competition models can account for these results. In their commentary on our article, Navarrete and Mahon (2012, this issue) come to the opposite conclusion. Here we are addressing the principal objections raised by Navarrete and Mahon regarding our interpretation of our results. We elaborate on our view that lexical competition models can account for our data and agree that a response-exclusion mechanism which is part of a general monitoring system and operates over fine-grained semantic information would be compatible with our data