Denis Mareschal

Categorization in infancy.

Human infants display complex categoriztion abilities. Results from studies of visual preference, object examination, conditioned leg-kicking, sequential touching, and generalized imitation reveal different patterns of category formation, with different levels of exclusivity in the category representations formed by infants at different ages. We suggest that differences in levels of exclusivity reflect the degree to which the various tasks specify the relevant category distinction to be drawn by the infant. Performance in any given task might reflect prior learning or within-task learning, or both. The extent to which either form of learning is deployed could be determined by task context.

The Role of Bottom-Up Processing in Perceptual Categorization by 3- to 4-Month-Old Infants: Simulations and Data

Disentangling bottom-up and top-down processing in adult category learning is notoriously difficult. Studying category learning in infancy provides a simple way of exploring category learning while minimizing the contribution of top-down information. Three- to 4-month-old infants presented with cat or dog images will form a perceptual category representation for cat that excludes dogs and for dog that includes cats. The authors argue that an inclusion relationship in the distribution of features in the images explains the asymmetry. Using computational modeling and behavioral testing, the authors show that the asymmetry can be reversed or removed by using stimulus images that reverse or remove the inclusion relationship. The findings suggest that categorization of nonhuman animal images by young infants is essentially a bottom-up process.

A connectionist account of asymmetric category learning in early infancy.

Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. A connectionist model is described that shows similar exclusivity asymmetries when categorizing the same stimuli presented to infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. The model was used to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. An experiment with 3-4-month-olds showed that asymmetric exclusivity persisted in the presence of mixed-exemplar familiarization, thereby confirming the models prediction.

Modeling Cognitive Development on Balance Scale Phenomena

We used cascade-correlation to model human cognitive development on a well studied psychological task, the balance scale. In balance scale experiments, the child is asked to predict the outcome of placing certain numbers of equal weights at various distances to the left or right of a fulcrum. Both stage progressions and information salience effects have been found with children on this task. Cascade-correlation is a generative connectionist algorithm that constructs its own network topology as it learns. Cascade-correlation networks provided better fits to these human data than did previous models, whether rule-based or connectionist. The network model was used to generate a variety of novel predictions for psychological research.

A computational and neuropsychological account of object‐oriented behaviours in infancy

Infants under 7 months of age fail to reach behind an occluding screen to retrieve a desired toy even though they possess sufficient motor skills to do so. However, even by 3.5 months of age they show surprise if the solidity of the hidden toy is violated, suggesting that they know that the hidden toy still exists. We describe a connectionist model that learns to predict the position of objects and to initiate a response towards these objects. The model embodies the dual-route principle of object information processing characteristic of the cortex. One route develops a spatially invariant surface feature representation of the object whereas the other route develops a feature blind spatial–temporal representation of the object. The model provides an account of the developmental lag between infants’ knowledge of hidden objects and their ability to demonstrate that knowledge in an active retrieval task, in terms of the need to integrate information across multiple object representations using (associative) connectionist learning algorithms. Finally, the model predicts the presence of an early dissociation between infants’ ability to use surface features (e.g. colour) and spatial–temporal features (e.g. position) when reasoning about hidden objects. Evidence supporting this prediction has now been reported.

Analogy as relational priming: A developmental and computational perspective on the origins of a complex cognitive skill

The development of analogical reasoning has traditionally been understood in terms of theories of adult competence. This approach emphasizes structured representations and structure mapping. In contrast, we argue that by taking a developmental perspective, analogical reasoning can be viewed as the product of a substantially different cognitive ability - relational priming. To illustrate this, we present a computational (here connectionist) account where analogy arises gradually as a by-product of pattern completion in a recurrent network. Initial exposure to a situation primes a relation that can then be applied to a novel situation to make an analogy. Relations are represented as transformations between states. The network exhibits behaviors consistent with a broad range of key phenomena from the developmental literature, lending support to the appropriateness of this approach (using low-level cognitive mechanisms) for investigating a domain that has normally been the preserve of high-level models. Furthermore, we present an additional simulation that integrates the relational priming mechanism with deliberative controlled use of inhibition to demonstrate how the framework can be extended to complex analogical reasoning, such as the data from explicit mapping studies in the literature on adults. This account highlights how taking a developmental perspective constrains the theory construction and cognitive modeling processes in a way that differs substantially from that based purely on adult studies, and illustrates how a putative complex cognitive skill can emerge out of a simple mechanism.

Modeling developmental cognitive neuroscience

In the past few years connectionist models have greatly contributed to formulating theories of cognitive development. Some of these models follow the approach of developmental cognitive neuroscience in exploring interactions between brain development and cognitive development by integrating structural change into learning. We describe two classes of these models. The first focuses on experience-dependent structural elaboration within a brain region by adding or deleting units and connections during learning. The second models the gradual integration of different brain areas based on combinations of experience-dependent and maturational factors. These models provide new theories of the mechanisms of cognitive change in various domains and they offer an integrated framework to study normal and abnormal development, and normal and impaired adult processing.

An Interacting Systems Model of Infant Habituation

Habituation and related procedures are the primary behavioral tools used to assess perceptual and cognitive competence in early infancy. This article introduces a neurally constrained computational model of infant habituation. The model combines the two leading process theories of infant habituation into a single functional system that is grounded in functional brain circuitry. The HAB model (for Habituation, Autoassociation, and Brain) proposes that habituation behaviors emerge from the opponent, complementary processes of hippocampal selective inhibition and cortical long-term potentiation. Simulations of a seminal experiment by Fantz Visual experience in infants: Decreased attention familiar patterns relative to novel ones. Science, 146, 668670, 1964 are reported. The ability of the model to capture the fine detail of infant data (especially age-related changes in performance) underlines the useful contribution of neurocomputational models to our understanding of behavior in general, and of early cognition in particular.

Fusion of visual cues is not mandatory in children

Human adults can go beyond the limits of individual sensory systems’ resolutions by integrating multiple estimates (e.g., vision and touch) to reduce uncertainty. Little is known about how this ability develops. Although some multisensory abilities are present from early infancy, it is not until age ≥8 y that children use multiple modalities to reduce sensory uncertainty. Here we show that uncertainty reduction by sensory integration does not emerge until 12 y even within the single modality of vision, in judgments of surface slant based on stereoscopic and texture information. However, adults’ integration of sensory information comes at a cost of losing access to the individual estimates that feed into the integrated percept (“sensory fusion”). By contrast, 6-y-olds do not experience fusion, but are able to keep stereo and texture information separate. This ability enables them to outperform adults when discriminating stimuli in which these information sources conflict. Further, unlike adults, 6-y-olds show speed gains consistent with following the fastest-available single cue. Therefore, whereas the mature visual system is optimized for reducing sensory uncertainty, the developing visual system may be optimized for speed and for detecting sensory conflicts. Such conflicts could provide the error signals needed to learn the relationships between sensory information sources and to recalibrate them while the body is growing.

Models of habituation in infancy

Research on infant cognition using habituation methods has sparked considerable controversy in recent years. At the core of the debates is the issue of whether infants have early (and possibly innate) conceptual understandings. This article reviews a range of computational models of habituation that might provide insights into such discussions. The models are assessed against key behavioral and neural features of habituation: temporal unfolding, exponential decrease, familiarity-to-novelty shift, habituation to repeated testing, discriminability of habitual items, selective inhibition and cortical-subcortical interactions. The review suggests that current models fail to offer comprehensive explanations of the behavioral phenomena.