Disambiguating Different Covariation Types

Covariations in neuronal latency or excitability can lead to peaks in spike train covariograms that may be very similar to those caused by spike timing synchronization (see companion article). Two quantitative methods are described here. The first is a method to estimate the excitability component of a covariogram, based on trial-by-trial estimates of excitability. Once estimated, this component may be subtracted from the covariogram, leaving only other types of contributions. The other is a method to determine whether the covariogram could potentially have been caused by latency covariations

Correlations Without Synchrony

Peaks in spike train correlograms are usually taken as indicative of spike timing synchronization between neurons. Strictly speaking, however, a peak merely indicates that the two spike trains were not independent. Two biologically plausible ways of departing from independence that are capable of generating peaks very similar to spike timing peaks are described here: covariations over trials in response latency and covariations over trials in neuronal excitability. Since peaks due to these interactions can be similar to spike timing peaks, interpreting a correlogram may be a problem with ambiguous solutions. What peak shapes do latency or excitability interactions generate? When are they similar to spike timing peaks? When can they be ruled out from having caused an observed correlogram peak? These are the questions addressed here. The previous article in this issue proposes quantitative methods to tell cases apart when latency or excitability covariations cannot be ruled out

The Roles of Implicit Causality and Discourse Context in Pronoun Resolution

Some interpersonal verbs show a bias in the proportion of times their subject and object arguments are rementioned in a sample of explanations for the eventuality the verb describes. This bias is known as the implicit causality bias. Several studies have shown that readers and listeners rapidly use the implicit causality bias during pronoun resolution. Whether listeners also rapidly incorporate relevant contextual information during pronoun resolution, is an open question. In the current paper, we report two visual world eye-tracking studies intended to answer this question. Participants listened to stories that included implicit causality verbs followed by a “because” clause with an ambiguous pronoun in its subject position. During the story, the participants looked at a screen on which potential referents of the ambiguous pronoun were displayed. In Experiment 1, a simple main effect of implicit causality bias on looks toward the character that was congruent with the bias was found among items in one of the two discourse conditions. Discourse context, however, only affected looks for a subset of verbs and in the opposite direction of what was hypothesized. In Experiment 2, no main effects of IC Bias or discourse context were found, but there was a marginally significant interaction which was not hypothesized. In both experiments, discourse context influenced looks only for a subset of verbs and never in the predicted direction. The results favor an account in which the influence of lexical semantics is, at least initially, stronger than the influence of world knowledge, and discourse context. Additional exploratory analyses suggested that eye movements already reveal remention biases at an early point in the sentence, whereas the causal potency of the subject argument is predicted by looks starting from the onset of the causal connective

Human Conscious Experience is Four-Dimensional and has a Neural Correlate Modeled by Einstein's Special Theory of Relativity

In humans, knowing the world occurs through spatial-temporal experiences and interpretations. Conscious experience is the direct observation of conscious events. It makes up the content of consciousness. Conscious experience is organized in four dimensions. It is an orientation in space and time, an understanding of the position of the observer in space and time. A neural correlate for four-dimensional conscious experience has been found in the human brain which is modeled by Einstein’s Special Theory of Relativity. Spacetime intervals are fundamentally involved in the organization of coherent conscious experiences. They account for why conscious experience appears to us the way it does. They also account for assessment of causality and past-future relationships, the integration of higher cognitive functions, and the implementation of goal-directed behaviors. Spacetime intervals in effect compose and direct our conscious life. The relativistic concept closes the explanatory gap and solves the hard problem of consciousness (how something subjective like conscious experience can arise in something physical like the brain). There is a place in physics for consciousness. We describe all physical phenomena through conscious experience, whether they be described at the quantum level or classical level. Since spacetime intervals direct the formation of all conscious experiences and all physical phenomena are described through conscious experience, the equation formulating spacetime intervals contains the information from which all observable phenomena may be deduced. It might therefore be considered expression of a theory of everything

Incremental Mutual Information: A New Method for Characterizing the Strength and Dynamics of Connections in Neuronal Circuits

Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e. g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system

A Connectionist Theory of Phenomenal Experience

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways