Timing at the Start of Associative Learning

Some theories of associative learning imply that time plays a fundamental role in the acquisition process. Consistent with these theories, this paper presents evidence that the time from the onset of a conditioned stimulus (CS) until presentation of the unconditioned stimulus (US) is learned very rapidly at the start of training. We report two autoshaping studies and a study on aversive conditioning in goldfish in which we examine timing at the start of conditioning. We also review data from a number of other conditioning preparations, including fear-potentiated startle, appetitive conditioning in rats, and eyeblink conditioning in rabbits, that report conditioned response (CR) timing early in training. Acquisition speed and the very first expressions of conditioned responding often show sensitivity to the time of US presentation. In instances where temporal control is slowly expressed, it is likely due to performance factors, not to slow learning about time. In fact, the learning about time may be a necessary condition for associative learning.     

Olfaction,Emotion and Associative Learning: Effects on Motivated Behavior

Two experiments were conducted to investigate emotional associative learning to odors and subsequent behavioral effects. In Experiment 1, participants experienced a frustration mood induction in the presence of an unfamiliar ambient odor and later worked on puzzle tests in a room scented with either the same-odor, a different-odor, or no-odor. Participants in the same-odor condition spent significantly less time working on the tests than participants in the other conditions; however, test accuracy did not vary. To clarify the findings, Experiment 2 included a test-only control and an emotionally neutral same-odor conditions. Results were compatible with the conclusion that decreased time spent by participants in the negative-same-odor condition was due to emotions elicited by associative learning to the ambient odor, although alternative interpretations remain possible. These data extend our previous results with children and suggest that odors readily become associated to emotions and can thereby influence behavior.     

联想学习与工作记忆在流体智力发展中的作用

以115名7、10、13岁儿童为研究对象,采用配对联想学习、倒序背数、字母-数字排序、图画概念和矩阵推理等测验,考察联想学习与工作记忆在流体智力发展中的作用。结果发现:联想学习与工作记忆在流体智力发展中均起着重要的作用,然而,在不同年龄上,两者对流体智力的作用并不相同。具体来说,在7岁时,联想学习对流体智力的作用显著,而工作记忆的作用不显著;到10岁和13岁时,工作记忆对流体智力的作用显著,而联想学习的作用不再显著。从效应量来看,随着年龄的增长,联想学习对流体智力的作用逐渐减小,而工作记忆的作用则逐渐增加。     

Honeybee Memory: Navigation by Associative Grouping and Recall of Visual Stimuli

Studies of navigation in bees and ants are beginning to reveal that foraging insects traveling repeatedly to a food source navigate by using a series of visual images of the environment acquired en route (Collett, 1996; Collett et al., 1993; Judd & Collett, 1998; Wehner et al., 1990, 1996). By comparing the currently viewed scene with the appropriate stored image, the insect is able to ascertain whether or not it is on the correct path and make any necessary corrections. If a bee happens to forage at more than one site, then she needs not only to memorize a separate set of images for each route that she has learned but also to retrieve the set of images that is appropriate to each route. Here we examine the bee's capacity to learn and later retrieve from memory two different sets of visual stimuli. Bees were trained to fly through a compound Y-maze where they were presented alternately with two different sequences of visual stimuli on their route to a food reward. We find that bees can indeed store two different sequences of images simultaneously. Furthermore, the trained bees are able to classify the memorized images into two groups, one pertaining to each three-stimulus set. Exposure to any of the images pertaining to one set triggers recall of all of the other images associated with that set. Associative grouping and recall of visual stimuli, demonstrated here for the first time in honeybees, provide an effective means of retrieving the appropriate navigational information from memory.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.     

Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification

Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social‐trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human‐assigned trait ratings from the 512 dimensional representations of faces that emerged at the top‐layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top‐level DCNN features produced by the network predicted the human‐assigned social trait profiles with good accuracy. Human‐assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training.     

Associative links between discrete memories in early infancy

The specificity and independence of two successively established memories were assessed in two studies with 6-month-olds. On different days, infants learned two paired-associate tasks, each involving a specific cue-response pair, in a common distinctive context. Retention of each pair was tested either 3 days later, when both memories were still highly accessible (Experiment 1), or 22 days later, when a reminder was necessary to reactivate them (Experiment 2). In both instances, infants produced only the response associated with the test cue; neither retroactive nor proactive interference occurred. Surprisingly, the cue from one task indirectly reactivated the forgotten memory of the other task, revealing that they were linked in an associative network.     

婴幼儿词语习得中的社会性认知加工过程

有关婴幼儿词语习得的研究主要有三种理论假设:联想学习理论、制约限制理论和社会语用理论.本文将从以下四个方面着重介绍社会语用理论的相关研究:婴幼儿词语习得过程涉及哪些社会性认知加工过程,社会语用理论如何应对来自前两种理论的质疑和挑战,社会性认知加工过程的相对权重如何以及自闲症儿童词语习得存在怎样的社会性认知加工障碍.相关研究结果表明社会性认知加工过程在婴幼儿词语习得中具有基础性作用.最后对今后在该领域的研究中需要进一步深入探讨的问题提出了一些思考.     

The Utility of Connectionism for Motor Learning: A Reinterpretation of Contextual Interference in Movement Schemas

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.     

策略发现的元认知机制和联结机制的交互作用理论

本文主要介绍了策略发现的元认知机制和联结机制的三种交互作用理论,包括两种表征翻译理论和新近发展的竞争性谈判理论,并对这三种理论进行了评价。     

Measuring Associative Strength: Category-Item Associations and Their Activation from Memory

Three measures of the strength of association between a category and members of the category were investigated: (a) a naming measure, in which the participants (93 undergraduates) were asked to list the members of a category and the listing order was assumed to reflect associative strength; (b) a latency measure, which assessed the latency to correctly identify specific items as members or nonmembers of a given category; and (c) a facilitation measure, in which the spontaneous activation of an item upon presentation of a category label as a prime was assessed by considering the extent to which the prime facilitated recognition of an initially degraded (visually obscured) item. The three measures correlated substantially, thus validating the naming and latency measures as reasonable approximations of the likelihood that a given item will receive activation in memory when the category is presented. Many of the constructs of interest to survey researchers can be viewed similarly as associations in memory, and the naming and latency measures can be fruitfully used in surveys; research attesting to the utility of namingand latency data is reviewed.     

创造性问题解决中的联想能力及其作用

长期以来,研究者一直认为联想能力与创造力之间存在着紧密的联系,但相关的实证研究并不能很好地支持这一论断,这可能是由于联想能力的界定不清所造成的。本研究梳理了以往研究中联想能力与创造力的关系,在此基础上辨析了联想能力测验所反映的实质,进一步提出了一个创造性问题解决中联想能力的作用模型：（1）联想能力主要作用于创造性问题解决中的重构阶段;（2）存在三种相对独立的联想能力,它们对空间类顿悟问题和言语类顿悟问题的影响不同;（3）联想内容中经验关系的联想能力与领域一般创造力关联最密切。     

Learning Orthographic Structure With Sequential Generative Neural Networks

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high‐order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high‐quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non‐connectionist probabilistic models (n‐grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain.