A Model of infant Causal Perception and its Development

The acquisition of infant causal perception has been the center of considerable debate, and some have attributed this phenomenon to an innate causal module. Recent studies, however, suggest that causal knowledge may develop in infants through experience with the environment. We present a computational model of causal knowledge acquisition built using the Constructivist Learning Architecture, a hierarchical self-organizing system. This system does a remarkably good job of developing causal perception from a component view to a holistic view in a way that mirrors data from habituation studies with human infants. Causal Perception in Infants Causal perception has been the focus of philosophical inquiry for centuries, but it received its first notable psychological investigation by Michotte (1963). He presented adults with a scene in which one billiard ball struck another stationary ball, resulting in the launching of the stationary ball, and the halting of the moving ball. The subjects, naturally, described this scene as a “causal” event. But by manipulating the causal event (along spatial or temporal dimensions), Michotte could affect a subjects’ likeliness of perceiving causality. One can alter the spatial component of the event by introducing a gap between the two balls, so that agent and the object never actually touch. Also, one can alter the temporal component by introducing a delay between the moment of contact and the moment of launching. As these components deviated from zero gap and zero delay, adult subjects were less likely to classify the event as “causal. ” These events are illustrated in Figure 1. More recently, researchers have combined these events with habituation techniques to demonstrate the presence of causal perception in infants. One such study, by Leslie (1984), was able to demonstrate 6 1/2month-old infants’ ability to discriminate between different launching events. In addition, Leslie also argued that infants’ responses were based, in part, on the causality of the event. That is, infants habituated to a causal event would dishabituate to a non-causal event (e.g. from direct to gap), or vice versa. But infants would not dishabituate to the same degree if the causality remained constant (e.g. from delay to gap). Leslie then claimed that these results, since they came from such young infants, were the product of an innate “causal module”. Figure 1: Four different launching events. Recent studies, though, have cast doubt on a nativist view of causal perception. Cohen and Amsel (1998) ran a similar experiment on 6 1/4-month-old infants, and re-affirmed their ability to discriminate causal events from non-causal events. But they then ran the same experiment on 5 1/2-month-old and 4-month-old infants and found that infants were not able to discriminate between launching events strictly on the basis of causality. Rather, younger infants responded to the spatial and temporal components of the event. Unlike the older infants, younger infants would respond to the introduction or removal or either a delay or a gap, regardless of how this change impacted the causality of the event. Cohen and Amsel (1998) posited that these results indicated a developmental component to causal perception. This developmental process is characterized by a more general part-to-whole sequence, whereby infants first respond to the spatial and temporal components of the events, then combine these components to create the higher-level concept of causality. Moreover, Cohen (1998) pointed out numerous studies of developmental cognition that fit this general framework, and proposed an information processing approach to cognitive development as an alternative to nativism. Rather than being born with a set of innate perceptual modules, infants start with very low-level perceptual capabilities. This approach is summarized by the following propositions (Cohen & Cashon, 2000): 1. Perceptual/cognitive development follows a set of domain-general information processing principles. 2. Information in the environment can be processed at a number of different levels of organization. 3. Higher (more holistic) levels can be defined in terms of the types of relations among lower (parts) levels. 4. Development involves progressing to higher and higher levels. 5. There is a bias to initiate processing at the highest level available. 6. If an information overload occurs (such as when movement is added or when the task involves forming a category), the optimal strategy is to fall back to a lower level of processing. At the very least, long term development appears to play an important role in the perception of high-level concepts such as causality, regardless of the concept’s origin. There are countless learning systems which model knowledge acquisition. But we know of no such model that conforms to the six propositions of developmental information processing described above. There are also very few computational models of developmental cognition that differentiate between long-term learning and short-term habituation, let alone use one to determine the other. Constructivist Learning Architecture The Constructivist Learning Architecture (CLA) (Chaput, 2001) is a hierarchical self-organizing system designed to generate concepts at multiple levels of abstraction through the organization of sensory input. Using the six propositions of cognitive development listed above as a design specification, CLA was built to produce the kind of short-term habituation effects that infants exhibit through the kind of long-term exposure to the environment that infants experience. The information processing approach to cognitive development described above does not use any kind of corrective feedback, and thus suggests an unsupervised learning system. For this reason, CLA uses one such system, Self-Organizing Map (Kohonen, 1997), as a building block. The Self-Organizing Map (SOM) is a two-dimensional matrix of nodes, each of which stores a feature vector. As stimuli are repeatedly presented to the SOM (as feature vectors), the SOM adjusts the feature vectors of its nodes to represent the observed stimuli. A trained SOM exhibits the following attributes: 1) the feature vectors of nodes in a SOM reflect the stimuli presented to the SOM (environmental representation); 2) nodes which are close to each other in the network have similar feature vectors (similarity topography); and 3) stimuli which occur more often will be represented by a larger number of nodes (frequency effect). But although the SOM performs the kind of unsupervised category formation that appears to be at work in developmental cognition, it does not by itself form the kind of hierarchical knowledge representation that also appears to be present in infants. CLA achieves hierarchical representation by connecting multiple SOMs into a hierarchy. At the lowest layer, the SOMs receive the raw input from the environment. Each node in these SOMs becomes activated proportional to how close the node’s feature vector is to the incoming stimulus. These activation values are then collected for each layer into an activation matrix, which in turn becomes the input to the layer directly above. This process then repeats for as many layers as are contained in the whole system. For an illustration, see Figure 2. Figure 2: The first two layers of an example CLA. Of course, two SOMs connected in this fashion can learn no more or less than a single SOM would. But the stratification of the SOMs allows for processing to occur at intermediate stages. There are three types of intermediate processing that are relevant to the present discussion: activation decay, activation blurring, and multi-modal layers. Activation decay allows the activation of a given node to decay over time, rather than reset at each time step. This is useful as a rudimentary representation of time and sequence. Activation blurring will “blur” the activation matrix by applying a Gaussian filter to the matrix. This has the effect of spreading the activation of a given node to surrounding nodes, which is particularly useful given the SOM’s similarity topography feature. Finally, a layer can receive input from two or more lower-level layers. We call these layers multi-modal layers. A CLA system will have the properties that are described by Cohen and Cashon (2000). First, CLA is not designed for any particular domain, but is a domain-general learning architecture that can readily be applied to any number of areas in developmental cognition. Second, when a stimulus is introduced to the system, it will create activation patterns at multiple layers, which are actually different levels of organization; processing of these patterns can occur at any of these layers. Third, because each layer is organizing activation patterns of the layer beneath it, higher-level representations can be defined in terms of the types of relations among lower-level representations. Fourth, learning in CLA involves progressing to higher and higher levels. When CLA is learning, each layer is trying to build categories out of the activation patterns of the layer beneath it. While one layer is organizing, all the layers above it cannot form stable categories because the underlying activation patterns keep changing. Only when a layer “settles” can the layers above it organize. The result is a progression from level to level. Propositions 5 and 6 involve the resulting behavior of the system, rather than its organization. This paper does not address these propositions directly, but we do consider their ramifications in the discussion section. Experiment: Learning a Causal Event We conducted an experiment designed to show whether CLA could model the data produced by human infant subjects in the Cohen and Amsel (1998) study, given the same experiment design and starting set of assumptions.