Cultural boundaries have often been the basis for discrimination, nationalism, religious wars, and genocide. Little is known, however, about how cultural groups form or the evolutionary forces behind group affiliation and in-group favoritism. Hence, we examine these forces and show that arbitrary symbolic markers, though initially meaningless, evolve to play a key role in cultural group formation and in-group favoritism because they enable a population of heterogeneous individuals to solve important coordination problems. This process requires that individuals differ in some critical but unobservable way and that their markers are freely and flexibly chosen. If these conditions are met, markers become accurate predictors of behavior. The resulting social environment
includes strong incentives to bias interactions toward others with the same marker, and subjects accordingly show strong in-group favoritism. When markers do not acquire meaning as accurate predictors of behavior, players show a dramatically reduced taste for in-group favoritism. Our results support the prominent evolutionary hypothesis that cultural processes can reshape the selective pressures facing individuals and so favor the evolution of behavioral traits not previously advantaged.
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers. We expect that this new random effects method will prove useful for a wide range of group studies, not only in the context of DCM, but also for other modelling endeavours, e.g. comparing different source reconstruction methods for EEG/MEG or selecting among competing computational models of learning and decision-making.
Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.
In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.
Distinct regions in the left inferior frontal gyrus (IFG) preferentially support the processing of different word-types (e.g., real words, pseudowords) and tasks (e.g., lexical decisions, phonological decisions) in visual word recognition. However, the functional connectivity underlying the task-related specialisation of regions in the left IFG is not yet well understood. In this study we investigated the neural mechanisms driving the interaction of WORD-TYPE (real word vs. pseudoword) and TASK (lexical vs. phonological decision) in Brodmann's area (BA) 45 in the left IFG using dynamic causal modelling (DCM). Four different models were compared, all of which included left BA44, left BA45, and left inferior temporal gyrus (ITG). In each model, the visual presentation of words and pseudowords is assumed to directly evoke activity in the ITG and is then thought to be subsequently propagated to BA45 and to BA44 via direct intrinsic connections. The models differed with regard to which connections were modulated by the different tasks. Both tasks were assumed to either modulate the ITG_BA45 connection (Model #1), or the BA44_BA45 connection (Model #2), or both connections in parallel (Model #3). In Model #4 lexical decisions modulated the ITG_BA45 connection, whereas phonological decisions modulated the BA44_BA45 connection. Bayesian model selection revealed a superiority of Model #1. In this model, the strength of the ITG_BA45 connection was enhanced during lexical decisions. This model is in line with the hypothesis that left BA 45 supports explicit lexical decisions during visual word recognition based on lexical access in the ITG.
In this paper, we provide evidence for functional asymmetries in forward and backward connections that
define hierarchical architectures in the brain. We exploit the fact that modulatory or nonlinear influences of
one neuronal system on another (i.e., effective connectivity) entail coupling between different frequencies.
Functional asymmetry in forward and backward connections was addressed by comparing dynamic causal
models of MEG responses induced by visual processing of normal and scrambled faces.We compared models
with and without nonlinear (between-frequency) coupling in both forward and backward connections.
Bayesian model comparison indicated that the best model had nonlinear forward and backward connections.
Using the best model we then quantified frequency-specific causal influences mediating observed spectral
responses. We found a striking asymmetry between forward and backward connections; in which high
(gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This
suppression was significantly greater than the homologous coupling in the forward connections.
Furthermore, exactly the asymmetry was observed when we examined face-selective coupling (i.e., coupling
under faces minus scrambled faces). These results highlight the importance of nonlinear coupling among
brain regions and point to a functional asymmetry between forward and backward connections in the human
brain that is consistent with anatomical and physiological evidence from animal studies. This asymmetry is
also consistent with functional architectures implied by theories of perceptual inference in the brain, based
on hierarchical generative models.
This paper presents models for search behavior and provides experimental evidence that behavioral heterogeneity in search is linked to heterogeneity in individual preferences. Observed search behavior is more consistent with a new model that assumes dynamic updating of utility reference points than with models that are based on expected-utility maximization. Specifically, reference point updating and loss aversion play a role for more than a third of the population. The findings are of practical relevance as well as of interest for researchers who incorporate behavioral heterogeneity into models of dynamic choice behavior in, for example, consumer economics, labor economics, finance, and decision theory.
There is an extensive literature claiming that it is often di*cultnto make use of arbitrage opportunities in *nancial markets. Thisnpaper provides a new reason why existing arbitrage opportunitiesnmight not be seized. We consider a world with short-lived securities,nno short-selling constraints and no transaction costs. We show thatnto exploit all existing arbitrage opportunities, traders should paynattention to all *nancial markets simultaneously. It gives a generalnresult stating that failure to do so will leave some arbitrage oppor-ntunies unexploited with probability one.
This paper shows that a stock market is evolutionary stable if andnonly if stocks are evaluated by expected relative dividends. Any othernmarket can be invaded by portfolio rules that will gain market wealthnand hence change the valuation. In the model the valuation of assetsnis given by the wealth average of the portfolio rules in the market. Thenwealth dynamics is modelled as a random dynamical system. Necessary and sufficient conditions are derived for the evolutionary stabilitynof portfolio rules when (relative) dividend payoffs form a stationarynMarkov process. These local stability conditions lead to a unique evolutionary stable strategy according to which assets are evaluated bynexpected relative dividends.
Tobin (1958) has argued that in the face of potential capital losses on bonds it is nreasonable to hold cash as a means to transfer wealth over time. It is shown that this assertion cannot be sustained taking into account the evolution of wealth of cash holders versus non cash holders. Cash holders will be driven out of the market in the long run by traders who only use a (risky) long-lived asset to transfer wealth.
