Age of acquisition in facial identification: A connectionist approach
Age of Acquisition in Facial Identification: A Connectionist Approach Brenden M. Lake (brenden@stanford.edu) Torrey Pines High School, 710 Encinitas Blvd. Encinitas, CA 92024 USA Garrison W. Cottrell (gary@ucsd.edu) UCSD Computer Science and Engineering, 9500 Gilman Dr. La Jolla, CA 92093-0114 USA Abstract Age of Acquisition (AoA) is the phenomenon that acquiring a certain piece of information earlier than another results in a faster response time in adulthood. AoA has been shown to have a significant role in a variety of human studies. Recently, it has been demonstrated that connectionist networks that abstractly model reading and arbitrary mappings can also show AoA effects, and we extend this to facial identification. We present a connectionist model of facial identification that demonstrates strong AoA effects by allowing faces to be acquired in their natural order and by staging face presentation. This extends previous work by showing that a network that simply classifies its inputs also shows AoA effects. We manipulate the staged model in two ways, by either assuming outputs for the late set are trained to be off early in learning (or not) and by assuming the representation developed for the early set is used for the late set (or not). In three of these cases, we find strong AoA effects, and in the fourth, we find AoA effects with a recency control. Introduction The latency between a visual stimulus (an object, a face, or a printed word) and the correct response has been the subject of numerous studies. In an object-naming task, Oldfield & Wingfield (1964) reported naming latency to be dependent upon the frequency of the object’s name in the word corpora. Carroll & White (1973) reanalyzed Oldfield & Wingfield’s data, establishing that word frequency was not significant when AoA was accounted for. Since then, both AoA and frequency have drawn considerable attention. Despite many conflicting reports, it is generally concluded that both play significant roles. In a celebrity face-naming study, Moore & Valentine (1998) showed independent effects of both AoA and frequency on naming latency. Facial distinctiveness, surname frequency, and number of phonemes in the celebrity’s full name were not significant predictors of naming latency. Additionally, Moore & Valentine (1999) found AoA effects when frequency was not significant in a variety of tasks. They proposed, along with Morrison & Ellis (1995), that connectionist networks would be incapable of showing similar AoA effects due to “catastrophic interference” from more recently presented material. Ellis & Lambon Ralph (2000) demonstrated AoA effects in a connectionist network that abstractly models reading. This task mapped patterns of random binary bits to similar patterns with some bits flipped. Since orthography and phonology are highly correlated but not identical (Jared, McRae, & Seidenberg, 1990), the model was designed to represent the inconsistencies in skilled reading. Ellis & Lambon Ralph were able to show AoA effects by training the network on some patterns initially and adding an additional set further into training. They found that the network is more “plastic” at the beginning of training, allowing the earlier patterns to have a larger impact on the weights. They also provide evidence that “catastrophic interference” does not occur unless the earlier patterns are completely removed from training. In a similar task, Smith, Cottrell, & Anderson (2001) and Anderson & Cottrell (2001) demonstrated AoA effects by presenting all patterns at the beginning of training and measuring the naturally occurring AoA for each pattern. They found that earlier acquired patterns had a stronger correlation with the rest of the training set and had lower final errors than later acquired patterns. Smith et al. also found independent effects of frequency and AoA when the simulation was subjected to a frequency manipulation. Zevin & Seidenberg (2002) did not find AoA effects in a connectionist network modeling reading with normal spelling-sound regularities. However, when they used early words that were completely unrelated to later words (like the arbitrary binary mappings of Ellis & Lambon Ralph and Smith et al.), AoA effects were shown. Zevin & Seidenberg claimed that AoA effects only exist if little information is carried over from the early patterns to the late patterns. Additionally, they suggested that learning the names associated with faces would produce genuine AoA effects, because no information would be shared between the stages of learning. Anderson and Cottrell (2004) have found in replicating their work that if one measures AoA in their simulations one still finds AoA effects. This is because words that they considered to be “late” by their manipulation were actually acquired early through generalization. Hence, we continue to use measured AoA in our first simulation below. In this study, we provide a connectionist model of a facial identification task. Our model demonstrates strong AoA effects. We show that the model produces “natural” AoA effects (patterns that happen to be learned early show AoA effects), and we investigate the effects of staging the presentation of the faces. Unlike the reading simulations discussed above, in this case there is no opportunity for generalization to the late set early in training, as the network