Doing cognitive neuroscience: a third way

The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.     

The role of the magnocellular and parvocellular pathways in the attentional blink

The attentional blink refers to the transient impairment in perceiving the 2nd of two targets presented in close temporal proximity in a rapid serial visual presentation (RSVP) stream. The purpose of this study was to examine the effect on human attentional-blink performance of disrupting the function of the magnocellular pathway--a major visual-processing pathway specialized in temporal segregation. The study was motivated by recent theories that relate the attentional blink to the limited temporal resolution of attentional responses, and by a number of poorly understood empirical findings, including the effects on the attentional blink of luminance adaptation and distraction. The attentional blink was assessed for stimuli on a red background (Experiment 1), stimuli on an equiluminant background (Experiment 2), and following flicker or motion adaptation (Experiment 3), three psychophysical manipulations known to disrupt magnocellular function. Contrary to our expectations, the attentional blink was not affected by these manipulations, suggesting no specific relationship between the attentional blink and magnocellular and/or parvocellular processing.     

A short proof of a theorem of Falmagne

Falmagne (J. Math. Psychol. 18 (1978) 52) proved the sufficiency of the Block-Marschak inequalities (in: I. Olkin, S. Ghurye, W. Hoefding, W. Madow, H. Mann (Eds.), Contributions to Probability and Statistics, Stanford University Press, Stanford, CA, 1960, pp. 97-132) and normalization equalities for a complete system of choice probabilities to be induced by rankings. Here, we give a considerably shorter proof of this result. Our approach combines Möbius inversion and network flows.     

Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands (task-negative network, TNN) and compares effects of intelligence on neural effort in the TPN and the TNN. In a sample of 52 healthy subjects, functional magnetic resonance imaging was used to determine changes in neural effort associated with the processing of a working memory task. The task comprised three conditions of increasing difficulty: (a) maintenance, (b) manipulation, and (c) updating of a four-letter memory set. Neural effort was defined as signal increase in the TPN and signal decrease in the TNN, respectively. In both functional networks, TPN and TNN, neural effort increased with task difficulty. However, intelligence, as assessed with Raven's Matrices, was differentially associated with neural effort in the TPN and TNN. In the TPN, we observed a positive association, while we observed a negative association in the TNN. In terms of neural efficiency (i.e., task performance in relation to neural effort expended on task processing), more intelligent subjects (as compared to less intelligent subjects) displayed lower neural efficiency in the TPN, while they displayed higher neural efficiency in the TNN. The results illustrate the importance of differentiating between TPN and TNN when interpreting correlations between intelligence and fMRI measures of brain activation. Importantly, this implies the risk of misinterpreting whole brain correlations when ignoring the functional differences between TPN and TNN.     

Neural circuitry of the bilingual mental lexicon: Effect of age of second language acquisition

Numerous studies have proposed that changes of the human language faculty caused by neural maturation can explain the substantial differences in ultimate attainment of grammatical competences between first language (L1) acquirers and second language (L2) learners. However, little evidence on the effect of neural maturation on the attainment of lexical knowledge in L2 is available. The present functional magnetic resonance study addresses this question via a cross-linguistic neural adaptation paradigm. Age of acquisition (AoA) of L2 was systematically manipulated. Concrete nouns were repeated across language (e.g., French–German, valise^suitcase–Koffer^suitcase). Whereas early bilinguals (AoA of L2 < 3 years) showed larger repetition enhancement (RE) effects in the left superior temporal gyrus, the bilateral superior frontal gyrus and the right posterior insula, late bilinguals (AoA of L2 > 10 years) showed larger RE effects in the middle portion of the left insula and in the right middle frontal gyrus (MFG). We suggest that, as for grammatical knowledge, the attainment of lexical knowledge in L2 is affected by neural maturation. The present findings lend support to neurocognitive models of bilingual word recognition postulating that, for both early and late bilinguals, the two languages are interconnected at the conceptual level.     

Using habituation in machine learning

Habituation, a decrement in response to a stimulus that is presented repeatedly without ill effect, can be identified in almost all animals. It can also be used in machine learning to provide a variety of different applications, such as novelty detection, recency encoding, and temporal signal pre-processing. This paper examines how habituation can be mathematically modelled, and discusses how well these models fit the revised characteristics of habituation. It then demonstrates how the models can be combined with neural networks in order to realise the various applications. Finally, some simple experimental results are presented that demonstrate the effectiveness of the methods.     

Brain and mind operational architectonics and man-made “machine” consciousness

To build a true conscious robot requires that a robot’s “brain” be capable of supporting the phenomenal consciousness as human’s brain enjoys. Operational Architectonics framework through exploration of the temporal structure of information flow and inter-area interactions within the network of functional neuronal populations [by examining topographic sharp transition processes in the scalp electroencephalogram (EEG) on the millisecond scale] reveals and describes the EEG architecture which is analogous to the architecture of the phenomenal world. This suggests that the task of creating the “machine” consciousness would require a machine implementation that can support the kind of hierarchical architecture found in EEG.

Neoclassical Economics and the Last Dogma of Positivism: Is the Normative-Positive Distinction Justified?

Neoclassical economic theory in its pretensions to scientific status is founded on one of the variants of a now discredited positivism. Neoclassical economic theory claims that there are two distinct areas of economic research: positive economics and normative economics. The former is assumed to deal with the cognitive as scientific content of economics while the later focuses on welfare or equity issues. I argue that the reliance of the whole theoretical structure of economics on the normative postulate of rationality renders neoclassical economics a normative discipline. I also argue that neoclassical economics should thus be viewed as an instance of applied ethics rather than as applied mathematics, say. Finally, it is suggested that neoclassical economics, if its scientific pretensions are to be taken seriously, should be absorbed theoretically into general anthropology.     

Neural computation as a tool to differentiate perceptual from emotional processes: The case of anger superiority effect

Research findings in social and cognitive psychology imply that it is easier to detect angry faces than happy faces in a crowd of neutral faces [Hansen, C. H., & Hansen, R. D. (1988). Finding the face in the crowd – An anger superiority effect. Journal of Personality and Social Psychology, 54(6), 917–924]. This phenomenon has been held to have evolved over phylogenetic development because it was adaptive to quickly and accurately detect a potential threat in the environment. However, across recent studies, a controversy has emerged about the underlying perceptual versus emotional factors responsible for this so-called anger superiority effect [Juth, P., Lundqvist, D., Karlsson, A., & Ohman, A. (2005). Looking for foes and friends: Perceptual and emotional factors when finding a face in the crowd. Emotion, 5(4), 379–395; Purcell, D. G., Stewart, A. L., & Skov, R. B. (1996). It takes a confounded face to pop out of a crowd. Perception, 25(9), 1091–1108]. To tease apart emotional and perceptual processes, we used neural network analyzes of human faces in two different simulations. Results show that a perceptual bias is probably acting against faster and more accurate identification of anger faces compared to happy faces at a purely perceptual level. We suggest that a parsimonious hypothesis related to the simple perceptual properties of the stimuli might explain these behavioral results without reference to evolutionary processes. We discuss the importance of statistical or connectionist analysis for empirical studies that seek to isolate perceptual from emotional factors, but also learned vs. innate factors in the processing of facial expression of emotion.     

A key role for experimental task performance: effects of math talent, gender and performance on the neural correlates of mental rotation

The neurophysiological mechanisms underlying superior cognitive performance are a research area of high interest. The majority of studies on the brain-performance relationship assessed the effects of capability-related group factors (e.g. talent, gender) on task-related brain activations while only few studies examined the effect of the inherent experimental task performance factor. In this functional MRI study, we combined both approaches and simultaneously assessed the effects of three relatively independent factors on the neurofunctional correlates of mental rotation in same-aged adolescents: math talent (gifted/controls: 17/17), gender (male/female: 16/18) and experimental task performance (median split on accuracy; high/low: 17/17). Better experimental task performance of mathematically gifted vs. control subjects and male vs. female subjects validated the selected paradigm. Activation of the inferior parietal lobule (IPL) was identified as a common effect of mathematical giftedness, gender and experimental task performance. However, multiple linear regression analyses (stepwise) indicated experimental task performance as the only predictor of parietal activations. In conclusion, increased activation of the IPL represents a positive neural correlate of mental rotation performance, irrespective of but consistent with the obtained neurocognitive and behavioral effects of math talent and gender. As experimental performance may strongly affect task-related activations this factor needs to be considered in capability-related group comparison studies on the brain-performance relationship.