Implicit signals in small group settings and their impact on the expression of cognitive capacity and associated brain responses

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.

Bayesian priors are encoded independently from likelihoods in human multisensory perception

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.

Reason, emotion and decision-making: risk and reward computation with feeling

Many models of judgment and decision-making posit distinct cognitive and emotional contributions to decision-making under uncertainty. Cognitive processes typically involve exact computations according to a cost-benefit calculus, whereas emotional processes typically involve approximate, heuristic processes that deliver rapid evaluations without mental effort. However, it remains largely unknown what specific parameters of uncertain decision the brain encodes, the extent to which these parameters correspond to various decision-making frameworks, and their correspondence to emotional and rational processes. Here, I review research suggesting that emotional processes encode in a precise quantitative manner the basic parameters of financial decision theory, indicating a reorientation of emotional and cognitive contributions to risky choice.

The right and the good: distributive justice and neural encoding of equity and efficiency

Distributive justice concerns how individuals and societies distribute benefits and burdens in a just or moral manner. We combined distribution choices with functional magnetic resonance imaging to investigate the central problem of distributive justice: the trade-off between equity and efficiency. We found that the putamen responds to efficiency, whereas the insula encodes inequity, and the caudate/septal subgenual region encodes a unified measure of efficiency and inequity (utility). Notably, individual differences in inequity aversion correlate with activity in inequity and utility regions. Against utilitarianism, our results support the deontological intuition that a sense of fairness is fundamental to distributive justice but, as suggested by moral sentimentalists, is rooted in emotional processing. More generally, emotional responses related to norm violations may underlie individual differences in equity considerations and adherence to ethical rules.

Neural Differentiation of Expected Reward and Risk in Human Subcortical Structures

In decision-making under uncertainty, economic studies emphasize the importance of risk in addition to expected reward. Studies in neuroscience focus on expected reward and learning rather than risk. We combined functional imaging with a simple gambling task to vary expected reward and risk simultaneously and in an uncorrelated manner. Drawing on financial decision theory, we modeled expected reward as mathematical expectation of reward, and risk as reward variance. Activations in dopaminoceptive structures correlated with both mathematical parameters. These activations differentiated spatially and temporally. Temporally, the activation related to expected reward was immediate, while the activation related to risk was delayed. Analyses confirmed that our paradigm minimized confounds from learning, motivation, and salience. These results suggest that the primary task of the dopaminergic system is to convey signals of upcoming stochastic rewards, such as expected reward and risk, beyond its role in learning, motivation, and salience.

Agent-specific responses in the cingulate cortex during economic exchanges

Interactions with other responsive agents lie at the core of all social exchange. During a social exchange with a partner, one fundamental variable that must be computed correctly is who gets credit for a shared outcome; this assignment is crucial for deciding on an optimal level of cooperation that avoids simple exploitation. We carried out an iterated, two-person economic exchange and made simultaneous hemodynamic measurements from each player's brain. These joint measurements revealed agent-specific responses in the social domain ("me" and "not me") arranged in a systematic spatial pattern along the cingulate cortex. This systematic response pattern did not depend on metrical aspects of the exchange, and it disappeared completely in the absence of a responding partner.

Getting to know you: reputation and trust in a two-person economic exchange

Using a multiround version of an economic exchange (trust game), we report that reciprocity expressed by one player strongly predicts future trust expressed by their partner-a behavioral finding mirrored by neural responses in the dorsal striatum. Here, analyses within and between brains revealed two signals-one encoded by response magnitude, and the other by response timing. Response magnitude correlated with the "intention to trust" on the next play of the game, and the peak of these "intention to trust" responses shifted its time of occurrence by 14 seconds as player reputations developed. This temporal transfer resembles a similar shift of reward prediction errors common to reinforcement learning models, but in the context of a social exchange. These data extend previous model-based functional magnetic resonance imaging studies into the social domain and broaden our view of the spectrum of functions implemented by the dorsal striatum.

Getting to Know You: Reputation and Trust in a Two-Person Economic Exchange

Using a multiround version of an economic exchange (trust game), we report that reciprocity expressed by one player strongly predicts future trust expressed by their partner-a behavioral finding mirrored by neural responses in the dorsal striatum. Here, analyses within and between brains revealed two signals-one encoded by response magnitude, and the other by response timing. Response magnitude correlated with the "intention to trust" on the next play of the game, and the peak of these "intention to trust" responses shifted its time of occurrence by 14 seconds as player reputations developed. This temporal transfer resembles a similar shift of reward prediction errors common to reinforcement learning models, but in the context of a social exchange. These data extend previous model-based functional magnetic resonance imaging studies into the social domain and broaden our view of the spectrum of functions implemented by the dorsal striatum.

The neural basis of cognitive development: a constructivist manifesto

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 type 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 of 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.

Neural networks, nativism, and the plausibility of constructivism

Recent interest in PDP (parallel distributed processing) models is due in part to the widely held belief that they challenge many of the assumptions of classical cognitive science. In the domain of language acquisition, for example, there has been much interest in the claim that PDP models might undermine nativism. Related arguments based on PDP learning have also been given against Fodor's anti-constructivist position--a position that has contributed to the widespread dismissal of constructivism. A limitation of many of the claims regarding PDP learning, however, is that the principles underlying this learning have not been rigorously characterized. In this paper, I examine PDP models from within the framework of Valiant's PAC (probably approximately correct) model of learning, now the dominant model in machine learning, and which applies naturally to neural network learning. From this perspective, I evaluate the implications of PDP models for nativism and Fodor's influential anti-constructivist position. In particular, I demonstrate that, contrary to a number of claims, PDP models are nativist in a robust sense. I also demonstrate that PDP models actually serve as a good illustration of Fodor's anti-constructivist position. While these results may at first suggest that neural network models in general are incapable of the sort of concept acquisition that is required to refute Fodor's anti-constructivist position, I suggest that there is an alternative form of neural network learning that demonstrates the plausibility of constructivism. This alternative form of learning is a natural interpretation of the constructivist position in terms of neural network learning, as it employs learning algorithms that incorporate the addition of structure in addition to weight modification schemes. By demonstrating that there is a natural and plausible interpretation of constructivism in terms of neural network learning, the position that nativism is the only plausible model of acquisition can no longer be defended. Indeed, I briefly discuss a number of learning-theoretic reasons indicating that constructivist models so characterized uniquely possess a number of important learning characteristics.