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This paper presents our approach towards realizing a robot which can bootstrap itself towards higher complexity through embodied interaction dynamics with the environment including other agents. First, the elements of interaction dynamics are extracted from conceptual analysis of embodied interaction and its emergence, especially of behavioral imitation. Then three case studies are made, presenting our neural architecture and the robotic experiments on some of the important elements discussed above: self exploration and entrainment, emergent coordination, and categorizing self behavior. Finally we propose that integrating all these elements will be the important step towards realizing the bootstrapping agent envisaged above.

How does the visual system generate percepts of moving forms? H ow does this happen when the forms are emergent percepts (such as illusory contours or segregated textures) and the motion percept is apparent motion between the emergent forms? A neural model of form-motion interactions is developed to explain parametric properties of psychophysical motion data and to make predictions about the parallel cortical processing streams VI -> M X and VI ->^ V 2 -> MT. The model simulates many parametric psychophysical data arising from form-motion interactions. A key linkage between form and motion data is articulated in terms of properties of visual persistence and properties of apparent motion. The model explains how an illusory contour can move in apparent motion to another illusory contour or to a luminance-derived contour; how illusory contour persistence relates to the upper ISl threshold for apparent motion; and how upper and lower ISI thresholds for seeing apparent motion between two flashes decrease with stimulus duration and narrow with spatial separation (Korte's laws). Psychophysical data are derived from an analysis of how orientationally tuned form perception mechanisms and directionally tuned motion perception mechanisms interact to generate consistent percepts of moving forms.

A key problem in cognitive science concerns how the brain binds together parts of an object into a coherent visual object representation. One difficulty that this binding process needs to overcome is that different parts of an object may be processed by the brain at different rates and may thus become desynchronized. Perceptual framing is a mechanism that resynchronizes cortical activities corresponding to the same retinal object A neural network model based on cooperation between oscillators via feedback from a subsequent processing stage is presented that is able to rapidly resynchronize desynchronized featural activities. Model properties help to explain perceptual framing data, including psychophysical data about temporal order judgments. These cooperative model interactions also simulate daU concerning the reduction of threshold contrast as a function of stimulus length. The model hereby provides a unified explanation of temporal order and threshold contrast data as manifestations of a cortical binding process that can rapidly resynchronize image parts which belong together in visual object representations.

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the "where" cortical stream coordinates view-invariant object category learning in the "what" cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how "attentional shrouds" are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. (Contains 10 figures.)