Fig 1.
Histograms showing the rating distributions of novel time-to-perceive, space-to-perceive, and confusability variables.
Each variable is rated on a 1–7 scale.
Table 1.
Mean, standard deviation, and range for time, space, and confusability ratings.
Table 2.
Correlation matrix for all variables.
Table 3.
Model results for response times in word recognition, lexical decision, and semantic decision tasks.
Fig 2.
Correlations between time-to-perceive and decision latencies (left panels) and concreteness and decision latencies (right panels; to facilitate comparison with time-to-perceive, we have reversed the concreteness scale [the signs on the r values correspond to the reversed scale] so that more abstract items appear to the right side). Plots show the positive relationship between decision latencies and time-to-perceive in each experiment (left panels), and that this relationship is different for concreteness (right panels). For ease of interpretation, we use raw scores on the x-axis in both cases. Note that although effects of concreteness are less evident in these first-order correlations, this does not mean that concreteness has no bearing on decision latencies (as Table 3 indicates, there is an effect of concreteness after accounting for word length, frequency, and age of acquisition); rather, plots illustrate that time-to-perceive and concreteness have disparate effects on decision latencies. The full range of concreteness is not present in the data because we wanted to test the same set of words on all three tasks, and Pexman et al. [35] only included words with concreteness values > 3.5 and < 2.5 for their semantic decision (abstract or concrete?) task. To provide examples of the items, data points corresponding to a random set of words (5% of the total word list) are labeled in each panel. However, all data points appear in each panel (as light gray points). The dashed gray line over panel F shows the inverted U-shaped function originally reported in Pexman et al. [35], which we fit here as a LOESS (locally estimated scatterplot smoothing) curve, showing that decision latencies are indeed slowest in the abstract/concrete boundary cases.