Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non‐neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.     

The Modulation of Visual and Task Characteristics of a Writing System on Hemispheric Lateralization in Visual Word Recognition—A Computational Exploration

Through computational modeling, here we examine whether visual and task characteristics of writing systems alone can account for lateralization differences in visual word recognition between different languages without assuming influence from left hemisphere (LH) lateralized language processes. We apply a hemispheric processing model of face recognition to visual word recognition; the model implements a theory of hemispheric asymmetry in perception that posits low spatial frequency biases in the right hemisphere and high spatial frequency (HSF) biases in the LH. We show two factors that can influence lateralization: (a) Visual similarity among words: The more similar the words in the lexicon look visually, the more HSF/LH processing is required to distinguish them, and (b) Requirement to decompose words into graphemes for grapheme‐phoneme mapping: Alphabetic reading (involving grapheme‐phoneme conversion) requires more HSF/LH processing than logographic reading (no grapheme‐phoneme mapping). These factors may explain the difference in lateralization between English and Chinese orthographic processing.     

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.     

Deep learning and punctuated equilibrium theory

Deep learning is associated with the latest success stories in AI. In particular, deep neural networks are applied in increasingly different fields to model complex processes. Interestingly, the underlying algorithm of backpropagation was originally designed for political science models. The theoretical foundations of this approach are very similar to the concept of Punctuated Equilibrium Theory (PET). The article discusses the concept of deep learning and shows parallels to PET. A showcase model demonstrates how deep learning can be used to provide a missing link in the study of the policy process: the connection between attention in the political system (as inputs) and budget shifts (as outputs).     

Behavior analysis and neuroscience: Complementary disciplines

Behavior analysis and neuroscience are disciplines in their own right but are united in that both are subfields of a common overarching field—biology. What most fundamentally unites these disciplines is a shared commitment to selectionism, the Darwinian mode of explanation. In selectionism, the order and complexity observed in nature are seen as the cumulative products of selection processes acting over time on a population of variants—favoring some and disfavoring others—with the affected variants contributing to the population on which future selections operate. In the case of behavior analysis, the central selection process is selection by reinforcement; in neuroscience it is natural selection. The two selection processes are inter‐related in that selection by reinforcement is itself the product of natural selection. The present paper illustrates the complementary nature of behavior analysis and neuroscience through considering their joint contributions to three central problem areas: reinforcement—including conditioned reinforcement, stimulus control—including equivalence classes, and memory—including reminding and remembering.     

Comparing different kinds of words and word-word relations to test an habituation model of priming

Huber and O'Reilly (2003) proposed that neural habituation exists to solve a temporal parsing problem, minimizing blending between one word and the next when words are visually presented in rapid succession. They developed a neural dynamics habituation model, explaining the finding that short duration primes produce positive priming whereas long duration primes produce negative repetition priming. The model contains three layers of processing, including a visual input layer, an orthographic layer, and a lexical-semantic layer. The predicted effect of prime duration depends both on this assumed representational hierarchy and the assumption that synaptic depression underlies habituation. The current study tested these assumptions by comparing different kinds of words (e.g., words versus non-words) and different kinds of word-word relations (e.g., associative versus repetition). For each experiment, the predictions of the original model were compared to an alternative model with different representational assumptions. Experiment 1 confirmed the prediction that non-words and inverted words require longer prime durations to eliminate positive repetition priming (i.e., a slower transition from positive to negative priming). Experiment 2 confirmed the prediction that associative priming increases and then decreases with increasing prime duration, but remains positive even with long duration primes. Experiment 3 replicated the effects of repetition and associative priming using a within-subjects design and combined these effects by examining target words that were expected to repeat (e.g., viewing the target word ‘BACK' after the prime phrase ‘back to'). These results support the originally assumed representational hierarchy and more generally the role of habituation in temporal parsing and priming.     

发展性协调障碍的神经机制

发展性协调障碍是一种特殊的发育障碍, 它的一个显著特征是动作协调困难, 这种障碍影响着5%~8%学龄儿童的学业成绩和日常生活。发展性协调障碍常常与其他类型的学习困难并存。发展性协调障碍的病因非常复杂, 这种障碍与一定程度的视觉空间认知能力受损有关, 早期精细运动技能发育与脑神经发育进程可能存在时间的重合。近年来, 研究者们试图探索影响发展性协调障碍的神经基础, 并先后提出不同的神经关联假说。这些理论假说主要涉及到小脑、顶叶、胼胝体、基底神经节和大脑白质等脑区。本文全面回顾了该领域研究的最新进展, 并提出未来研究应该关注的方向与问题。     

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.     

Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume

Aiming at the existing problems in the production and export scale prediction of aquaculture, a model of yield prediction based on BP Neural network algorithm is proposed, and a set of algorithms is proposed to optimize BP neural network (BPNN). Based on the traditional BP neural network, it is easy to get into the local optimal problem due to the long training time of the model. By using the simple Johnson algorithm, the dimensionality of the input neuron is reduced, and then the hidden layer neural network is determined by this method. At the same time, the data mining method is used to filter the Data.Particle swarm optimization algorithm is used to optimize the parameters. At the same time, based on the domestic e-commerce Sales network data, the results show that the average square root error of the model is less than the traditional BP neural network and the learning efficiency is higher than the traditional BP neural network. The results show that the model has a great advantage in building up a large number of historical data, and it can shorten the modeling time and get good prediction result by combining the sales data of e-commerce. It provides a new feasible method for the export prediction of aquatic products.     

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.