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As the commentaries reveal, cognitive neuroscience's first steps toward a theory of development are marked by vigorous debate, ranging from basic points of definition to the fine details of mechanism. In this Response, we present the neural constructivist position on this broad spectrum of issues, from basic questions such as what sets constructivism apart from other theories (particularly selectionism) to its relation to behavioral theories and to its underlying mechanisms. We conclude that the real value of global theories at this stage of cognitive neuroscience is not just their answers but the new set of research questions they pose.

How do minds emerge from developing brains? According to 'neural constructivism,' the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible of learning: 'constructive learning' minimizes the need for prespecification in accordance with recent neurobiological evidence that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. Keywords: cognitive development; constructivism; evolution; learnability; mathematical learning theory; neural development; selectionism

The Shattered Self: The End of Natural Evolution. Pierre Baldi. xiv + 245 pp. The MIT Press, 2001. $24.95. In the 20th century, science and technology helped shape modernism and [...]

Van Gelder seeks to distinguish between the computational and the dynamical hypotheses primarily on the basis of ontic criteria - the kind of systems cognitive agents really are. I suggest that this meets with mixed success. By shifting to epistemic criteria - what kind of explanations we require to understand cognitive agents - I suggest there is an easier and more intuitive way to distinguish between these two competing views of cognitive agents.

The modular theory of cognitive development can be disproved by observations of neurobiological processes which show how areas in the brain acquire specific abilities due to patterns of stimulation imposed by the environment. These areas become centers of knowledge or ability only when afferent neural activity produce patterns that influence the creation of specific functional areas. Thus, abilities are not derived from prespecified areas of the cortex, and their development can be altered at early stages when infant brains are highly plastic and require environmental stimuli to acquire organization.

Understanding how organisms deal with probabilistic stimulus-reward associations has been advanced by a convergence between reinforcement learning models and primate physiology, which demonstrated that the brain encodes a reward prediction error signal. However, organisms must also predict the level of risk associated with reward forecasts, monitor the errors in those risk predictions, and update these in light of new information. Risk prediction serves a dual purpose: (1) to guide choice in risk-sensitive organisms and (2) to modulate learning of uncertain rewards. To date, it is not known whether or how the brain accomplishes risk prediction. Using functional imaging during a simple gambling task in which we constantly changed risk, we show that an early-onset activation in the human insula correlates significantly with risk prediction error and that its time course is consistent with a role in rapid updating. Additionally, we show that activation previously associated with general uncertainty emerges with a delay consistent with a role in risk prediction. The activations correlating with risk prediction and risk prediction errors are the analogy for risk of activations correlating with reward prediction and reward prediction errors for reward expectation. As such, our findings indicate that our understanding of the neural basis of reward anticipation under uncertainty needs to be expanded to include risk prediction. [ABSTRACT FROM AUTHOR]

Despite much progress in brain and cognitive sciences, attempts to connect brain function to cognition are hampered by the large explanatory gap between psychology and neurobiology. In recent years, a neurocomputational perspective has emerged as the most promising approach to integrating brain and mind. According to this perspective, the brain is a special sort of computer, a system of many parallel neural networks whose operation underlies cognition. In this paper, we present this neurocomputational perspective and examine the ways in which this new approach to explaining our mental skills differs from earlier ones. In particular, we examine its emerging insights into two domains. First, we explore the neurocomputational approach to decision-making, the adaptive guidance of behavior in the satisfaction of life maintenance goals. Decision-making is central to all mobile creatures in an uncertain environment, and this approach reveals a surprising conservation of decision-making strategies across many species. We then examine the neurocomputational approach's new insights into characterizing cognitive development. In particular, this approach offers the new framework of self-organization to characterize the complex interaction between neural developmental programs and the environment, a framework that has important implications for understanding early intervention. MRDD Research Reviews 1999;5:86–99. © 1999 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Measures of intelligence, when broadcast, serve as salient signals of social status, which may be used to unjustly reinforce low-status stereotypes about out-groups' cultural norms. Herein, we investigate neurobehavioural signals manifest in small (n = 5) groups using functional magnetic resonance imaging and a 'ranked group IQ task' where implicit signals of social status are broadcast and differentiate individuals based on their expression of cognitive capacity. We report an initial overall decrease in the expression of cognitive capacity in the small group setting. However, the environment of the 'ranked group IQ task' eventually stratifies the population into two groups ('high performers', HP and 'low performers', LP) identifiable based on changes in estimated intelligence quotient and brain responses in the amygdala and dorsolateral prefrontal cortex. In addition, we demonstrate signals in the nucleus accumbens consistent with prediction errors in expected changes in status regardless of group membership. Our results suggest that individuals express diminished cognitive capacity in small groups, an effect that is exacerbated by perceived lower status within the group and correlated with specific neurobehavioural responses. The impact these reactions have on intergroup divisions and conflict resolution requires further investigation, but suggests that low-status groups may develop diminished capacity to mitigate conflict using non-violent means.

It has been shown that human combination of crossmodal information is highly consistent with an optimal Bayesian model performing causal inference. These findings have shed light on the computational principles governing crossmodal integration/segregation. Intuitively, in a Bayesian framework priors represent a priori information about the environment, i.e., information available prior to encountering the given stimuli, and are thus not dependent on the current stimuli. While this interpretation is considered as a defining characteristic of Bayesian computation by many, the Bayes rule per se does not require that priors remain constant despite significant changes in the stimulus, and therefore, the demonstration of Bayes-optimality of a task does not imply the invariance of priors to varying likelihoods. This issue has not been addressed before, but here we empirically investigated the independence of the priors from the likelihoods by strongly manipulating the presumed likelihoods (by using two drastically different sets of stimuli) and examining whether the estimated priors change or remain the same. The results suggest that the estimated prior probabilities are indeed independent of the immediate input and hence, likelihood.