The Role of Space in Children’s Rule-Use: A Dynamic Neural Field Model of the DCCS

1. The Role of Space in Children’s Rule-Use: A Dynamic Neural Field Model of the DCCS Aaron T. Buss1 & John P. Spencer1,2 Department of Psychology, University of Iowa1 and Iowa Center for Developmental and Learning Sciences2 • The DCCS • DCCS is used to study the development of cognitive/attentional flexibility between 3 and 5 years; children must flexibly switch between using shape and color rules • Current theories either lack process and neural plausibility (see Zelazo et al., 2003) or extensive formal coverage of children’s behavior and development in this task (see Morton & Munakata, 2002) • We have built upon an existing DNF model (Johnson, Spencer, & Schoner, 2008) that captures how objects are neurally represented. We use a simple mechanism of resting-level modulation to get rule-use and development. • This yields extensive formal coverage of the literature within a neurally plausible framework. Test cards and target cards used in the DCCS Dynamic Field Theory Dorsal { Pathway • The DFT uses fields of neurons tuned to continuous metric dimensions and a basic two-layer architecture initially explored by Amari & Arbib (1977) • Inputs come into a WM field which becomes self-excitatory at threshold; activation is passed to and from associated locations in an inhibitory field. The interaction among these layers achieves a locally excitatory/laterally inhibitory form of interaction. A LTM field implements a form of Hebbian-learning. } h-level Ventral { Pathway • A 1-D spatial WM field captures activity of the dorsal pathway of the visual system. Peaks of activation here represent the spatial location of objects. Two 2-D feature WM fields capture activity of the ventral pathway of the visual system. Peaks of activation here represent the visual features of objects as well as where those features are located. All three WM fields have their own inhibitory and LTM fields and are coupled along spatial dimensions. Object Representation The ‘Binding-Problem’ (Treisman, 1996): different dimensions of visual features are represented by different populations of neurons and spatial input to these populations is coarse; there is no a priori way to determine which features go with which other features when representing an object. Spatial Coupling: As activation reaches threshold, spatial activation is shared among the three WM fields, anchoring features to space and giving rise to a representation of multi-dimensional objects. In the DFT, an object is a pattern of peaks for different feature dimensions all coupled along the dimension of space. As inputs reach threshold, spatial activation is shared between the WM fields, anchoring features to space • Emergence of Rule-use and Executive Function • To get rule-use, we boost the resting level of the relevant feature WM field  it is now closer to threshold, will share its spatial information sooner, will bind the other feature at that spatial location, and will drive a response based on that dimension. • Because of the LTM accumulated from sorting by the pre- switch dimension, stronger boosting of the post-switch feature field is needed in order to sort by that dimension • Over development children are learning what the labels ‘shape’ and ‘color’ mean  the strength of resting level modulation depends on how well the label is mapped to different feature fields. h h Fits for 3- and 4-year-olds in NP, ANP, and Standard versions. Above: h-boost distribution for 3- and 4-yo; right: WM fields going into post-switch for NP and ANP • Tests of the Model • NP-SpaceSwap: Swapping the locations of the target cards in the post-switch phase should allow weak levels of h-boosts to be sufficient for rule-switching • ANP-SpaceSwap: Swapping the locations of the target cards in the post-switch phase should make weak levels of h-boost insufficient for rule-switching Behavioral Results WM fields going into post-switch for ANPS (left) and NPS (right) Thus, space plays a fundamental role in children’s rule-use. Executive control is an emergent property of language learning and object representation — The Emergent Executive This research was supported by NSF HSD0527698 awarded to JPS. And a special thanks to the members of the SPAM Lab at the University of Iowa