Finite automata and S−R models

Suppes (1969, Journal of Mathematical Psychology, 6) apparently proved that any finite automaton could be mimicked by the asymptotic behavior of a stimulussampling S?R model, broadly implying that cognitive theory could be reduced to S?R theory. Here it is shown that the finite automata used by Suppes are more restricted and less powerful than finite automata in general. Furthermore, the S?R models proposed by Suppes are limited to producing the behavior of only these restricted automata, and cannot mimic all finite automata. Hence, the formal claim that S?R models are adequate to produce all behaviors obtainable from finite automata, and the informal claim that cognitive theory reduces to S?R theory, do not follow from Suppes's (1969) result. Some alternative S?R models and their problems are also briefly discussed.     

The "God Module" and the Complexifying Brain

Recent reports of the discovery of a "God module" in the human brain derive from the fact that epileptic seizures in the left temporal lobe are associated with ecstatic feelings sometimes described as an experience of the presence of God. The brain area involved has been described as either (a) the seat of an innate human faculty for experiencing the divine or (b) the seat of religious delusions.
In fact, religious experience is extremely various and involves many parts of the brain, including some that are prehuman in their evolutionary history and some that are characteristically human. In the continuing integration of such experiences, spiritual formation takes place. Thus the entire human brain might be described as a "God module."
Such a process is only possible because of the brain's complexity. The human brain is the most complex entity for its size that we know of. As used here, complexity is a specialized term denoting the presence of a web of interlinked and significant connections—the more intricate the web, the more complex the entity. Complex systems develop only in a milieu that provides both lawfulness and freedom, and they tend to be self-organizing, becoming more complex and more effective as a result of both inward and outward experience. The evidence suggests that both personal growth and spiritual growth are processes of complexification of character, and of the brain itself. This thesis is tested in light of the work of William James and James W. Fowler.     

Realization for Causal Nondeterministic Input-Output Systems

There are two well-developed formalizations of discrete time dynamic systems that evidently share many concerns but suffer from a lack of mutual awareness. One formalization is classical systems and automata theory. The other is the logic of actions in which the situation and event calculi are the strongest representatives. Researchers in artificial intelligence are likely to be familiar with the latter but not the former. This is unfortunate, for systems and automata theory have much to offer by way of insight into problems raised in the logics of action. This paper is an outline of how the input-output view of systems and its associated solution of state realization may be applied to the formalization of dynamics that uses a situation calculus approach. In particular, because the latter usually admits incompletely specified dynamics, which induces a non-deterministic input-output system behavior, we first show that classical state realization can still be achieved if the behavior is causal. This is a novel systems-theoretic result. Then we proceed to indicate how situation calculi dynamic specifications can be understood in systems-theoretic terms, and how automata can be viewed as models of such specifications. As techniques for reasoning about automata are abundant, this will provide yet more tools for reasoning about actions.     

Mimic automata: A novel formal model for mimic computing

In this paper, we aim to establish a mathematical model for mimic computing. To this end, we present a novel automaton called a mimic automaton. First, a one-dimensional cellular automaton is used for expressing some dynamic changes in the structure of a computing unit, a sequential automaton is employed to describe some state transitions, a hierarchical automaton is employed to express the different granularities of some computing units, and a probabilistic automaton is used to depict some random changes of a computing unit. Second, a mimic automaton is obtained by combining the various types of automata mentioned above in a certain logical relationship. Compared with the existing models of mimic computing, the new model portrays the operational semantics for a mimic computing system, and it can directly describe the behaviors of a system. Furthermore, the new model possesses a stronger generality.     

Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders

The dual-systems model of a ventral affective system, whose reactivity confers risks and liabilities, and a prefrontal control system, whose regulatory capacities buffer against these vulnerabilities, is an intuitive account that pervades many fields in the cognitive neurosciences--especially in the study of populations that differ from neurotypical adults, such as adolescents or individuals with affective or impulse regulation disorders. However, recent evidence that is inconsistent with dual-systems models illustrates the complexity of developmental and clinical variations in brain function. Building new models to account for this complexity is critical to progress in these fields, and will be facilitated by research that emphasizes network-based approaches and maps relationships between structure and function, as well as brain and behavior, over time.     

Does a rock implement every finite-state automaton?

Hilary Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. I argue that Putnam's argument fails, but that it points out the need for a better understanding of the bridge between the theory of computation and the theory of physical systems: the relation of implementation. It also raises questions about the class of automata that can serve as a basis for understanding the mind. I develop an account of implementation, linked to an appropriate class of automata, such that the requirement that a system implement a given automaton places a very strong constraint on the system. This clears the way for computation to play a central role in the analysis of mind.     

Computer models of cognitive processes

Conclusion From the small sample of achievements that I have had time to mention, we can only conclude that automation is here to stay. Nor is there any doubt that more powerful automata will be built. A great many of the higher human abilities will be given to machines. The great rush to automation is sure to stimulate psychologists to learn more about the human symbolic processes being mimicked by the machines. And the computers, which are the ultimate cause of the feverish scramble toward automation, are providing both the framework for describing complex models of behavior and also the means for testing these models. With both the means and the motivation at hand, psychologists are sure to make rapid progress in understanding complex human behavior.Operated with support from the United States Army, Navy, and Air Force.     

有关进化心理学局限性的理论思考

进化心理学是现代西方心理学的新取向之一。这一新取向吸引了众多的追随者,但是它自身的核心假设、方法论和认识论方面却存在着局限性。文章从三个方面分析了这种局限性：第一,进化心理学的核心假设是心理的模块性观点。依据这种观点,心灵由大量功能各异的心理模块组成,但是来自于心理学和生物学的研究都表明这种观点是缺乏科学依据的。第二,进化心理学从过去的角度理解现在,所依据的事实大多是推测性的,更多的是一种历史叙事,从方法论的角度来说,这种研究不具备可证伪性,不符合科学方法论的基本原则。第三,在认识论方面,进化心理学继承了社会生物学的传统,有意或无意地夸大了基因的作用,贬低了个体发展过程中其它因素,现代生命科学的研究已经证明了基因决定论的虚假性     

Searching for the variables that control human rule‐governed “insensitivity”

Verbal rules or instructions often exert obvious and meaningful control over human behavior. Sometimes instructions benefit the individual by enabling faster acquisition of a skill or by obviating an aversive consequence. However, research has also suggested a clear disadvantage: “insensitivity” to changing underlying contingencies. The two experiments described here investigated the variables that control initial rule‐following behavior and rule‐following insensitivity. When the initial rule was inaccurate, behavior was consistent with the rule for approximately half of participants and all participants' behavior was mostly insensitive to changing contingencies. When the initial rule was accurate, behavior of all participants was consistent with it and behavior for nearly all participants was insensitive to changes in underlying contingencies. These findings have implications for how best to establish and maintain rule‐following behavior in applied settings when deviant behavior would be more reinforcing to the individual.     

LOGIQUE ET PSYCHOLOGIE DE L'APPRENTISSAGE DES CONCEPTS

In the field of concept learning, there are very few studies in which logícal conditions are used as independent variables. A logical analysis of concept learning phenomena is presented: it is an application of some elementary theoremas of logics and set theory. To learn a concept means to identify relevant attributes of the phenomena observed; the theoretical analysis of the situation shows that concept learning can be seen as a problem, the solution of which depends on a precise algorithm. Numerous experiments have been carried out on the basis of such an analysis, some of which are described here. Results provide evidence that time and quality of performance depend on the length and logical structure of the set of successive stimuli presented to subjccts. So logical features of the situation influence concept learning; but, there are various other factors which control subject's performance : for instance, number of redundant examples, and subject's memory and perceptual-motor activity. Current interpretations of concept learning, i.e., stochastic models of learning and logical and mathematical interpretation are discussed and the need of an approach which would combine both to fit the complexity of concept learning phenomena is emphasized.     

A ganglionic model of “learned helplessness”

The phenomenon known as “learned helplessness” (LH) is seen broadly across the animal kingdom. Some of the basic characteristics of this behavior are: failure to escape shock when it is possible to do so following non-escapable shock; reversion to non-escape behavior even after successful escape; if the animal is given escape/avoidance training prior to being given inescapable shocks, the latter will not interfere with its ability to later show normal escape/avoidance behavior (generally described as an immunization effect); following inescapable shock training the animals often become “passive and still” when confronted with an escapable shock. These behaviors are seen in intact mammals, lower vertebrates, and invertebrates. In fact, the basic characteristics are even seen in a spinal rat and, with the exception of one characteristic not yet examined, in an isolated thoracic ganglion of an insect. The brain is evidently not essential either in mammals or in invertebrates for demonstrating this behavior. Not only can an insect ganglion show the behavioral characteristics of LH, but the neural information underlying the phenomenon of LH can be shown to transfer from one ganglion innervating one pair of legs to another ganglion innervating a different pair of legs. Thus, how CNS information underlying LH is coded and transferred from one site to another within the CNS can be examined in such a system. The LH model has provided valuable insights into the physiology of depression. This model suggests that human depression is caused by one’s lack of control over traumatic events. It is supported by a number of parallels between depression and LH behavior. Tricyclic antidepressants, MAO inhibitors, and ECT, which are effective in treating depression, also can prevent and reverse LH in mammals. It would be important to find out if they are also effective in invertebrate models. The fact that the characteristics of the behavior called LH are seen in invertebrates such as slugs, cockroaches, and locusts provokes other intriguing questions about the presence of cognition at these phylogenetic levels, as well as what animal or preparation constitutes an appropriate model for human depression.     

Where is the randomness for the human computer?

Psychologists traditionally have employed both statistical and process assumptions in models of human learning and performance. The corresponding tradition in the field of artificial intelligence is to minimize or eliminate the use of statistical assumptions. This article reviews some stochastic and nonntochastic models of human memory, probability learning, medical diagnosis, and concept identification. Some stochastic models are found to have a larger deterministic component than was previously realized; deterministic models applicable with random selection of stimuli can be represented in stochastic form. A policy of methodological determinism is recommended in which the model builder originally assumes that no random processes take place within the organism. If necessary, and as a last resort, such processes can be appended to an otherwise deterministic model for predictive convenience.     

Environmental complexity and central nervous system development and function

Environmental restriction or deprivation early in development can induce social, cognitive, affective, and motor abnormalities similar to those associated with autism. Conversely, rearing animals in larger, more complex environments results in enhanced brain structure and function, including increased brain weight, dendritic branching, neurogenesis, gene expression, and improved learning and memory. Moreover, in animal models of CNS insult (e.g., gene deletion), a more complex environment has attenuated or prevented the sequelae of the insult. Of relevance is the prevention of seizures and attenuation of their neuropathological sequelae as a consequence of exposure to a more complex environment. Relatively little attention, however, has been given to the issue of sensitive periods associated with such effects, the relative importance of social versus inanimate stimulation, or the unique contribution of exercise. Our studies have examined the effects of environmental complexity on the development of the restricted, repetitive behavior commonly observed in individuals with autism. In this model, a more complex environment substantially attenuates the development of the spontaneous and persistent stereotypies observed in deer mice reared in standard laboratory cages. Our findings support a sensitive period for such effects and suggest that early enrichment may have persistent neuroprotective effects after the animal is returned to a standard cage environment. Attenuation or prevention of repetitive behavior by environmental complexity was associated with increased neuronal metabolic activity, increased dendritic spine density, and elevated neurotrophin (BDNF) levels in brain regions that are part of cortical-basal ganglia circuitry. These effects were not observed in limbic areas such as the hippocampus.     

Analyzing the RULEX model of category learning

Recent approaches to human category learning have often (re)invoked the notion of systematic search for good rules. The RULEX model of category learning is emblematic of this renewed interest in rule-based categorization, and is able to account for crucial findings previously thought to provide evidence in favor of prototype or exemplar models. However, a major difficulty in comparing RULEX to other models is that RULEX is framed in terms of a stochastic search process, with no analytic expressions available for its predictions. The result is that RULEX predictions can only be found through time consuming simulations, making model-fitting very difficult, and all but prohibiting more detailed investigations of the model. To remedy this problem, this paper describes an algorithmic method of calculating RULEX predictions that does not rely on numerical simulation, and yields some insight into the behavior of the model itself.     

New lessons from knockout mice: The role of serotonin during development and its possible contribution to the origins of neuropsychiatric disorders

Serotonin (5-HT) modulates numerous processes in the central nervous system that are relevant to neuropsychiatric function and dysfunction. It exerts significant effects on anxiety, mood, impulsivity, sleep, ingestive behavior, reward systems, and psychosis. Serotonergic dysfunction has been implicated in several psychiatric conditions but efforts to more clearly understand the mechanisms of this influence have been hampered by the complexity of this system at the receptor level. There are at least 14 distinct receptors that mediate the effects of 5-HT as well as several enzymes that control its synthesis and metabolism. Pharmacologic agents that target specific receptors have provided clues regarding the function of these receptors in the human brain. 5-HT is also an important modulator of neural development and several groups have employed a genetic strategy relevant to behavior. Several inactivation mutations of specific 5-HT receptors have been generated producing interesting behavioral phenotypes related to anxiety, depression, drug abuse, psychosis, and cognition. In many cases, knockout mice have been used to confirm what has already been suspected based on pharmacologic studies. In other instances, mutations have demonstrated new functions of serotonergic genes in development and behavior.     

What Are the Brain Mechanisms on Which Psychological Processes Are Based?

ABSTRACT— That the human brain is the organ of the mind is not in dispute, but we know remarkably little about the brain mechanisms underlying the mind. What are the functional structures and computational processes of the human brain that subserve cognition, emotion, and behavior? Given the complexity of the human brain, progress in understanding the functional organization and structure of the human brain depends on sophisticated theoretical specifications of the psychological representations and processes that differentiate two or more comparison conditions. Psychological scientists, therefore, are well positioned to lead the search for brain mechanisms underlying psychological processes. Doing so constitutes an expansion of the purview of psychological science beyond a science of behavior, and beyond a science of the mind, to include a science of the brain. Such an expansion of the mission of psychological science has implications for the infrastructure and training needs of the discipline.     

The weight of being: Psychological perspectives on the existential moment

As the prefrontal cortex expanded in human evolution, so too did the capacity for nesting basic biological goals within more complex systems of behavioral organization. This increased ability for abstraction brought with it the challenge of deciding how to interpret the personal significance of any given experience. The human brain appears to manage this increased complexity by defining meaning in relation to one's currently adopted goals. When encountering goal-related information, arousal and exploratory systems become engaged, such that information is processed more thoroughly. As a consequence of this enhanced attention and arousal, neural plasticity is facilitated, allowing motivationally relevant experiences to have a stronger influence on an individual's neural organization. To borrow a gravitational metaphor, the existential weight, or significance, of a particular moment will determine the strength of that moment's influence on an individual's life. Human experience thus appears to be curved around fluctuations in the existential weight of being.     

The ABC’s of LGM: An Introductory Guide to Latent Variable Growth Curve Modeling

In recent years, we have witnessed an increase in the complexity of theoretical models that attempt to explain behavior from both contextual and developmental perspectives. This increase in the complexity of our theoretical propositions regarding behavior parallels recent methodological advances for the analysis of change. These new analysis techniques have fundamentally altered how we conceptualize and study change. Researchers have begun to identify larger frameworks to integrate our knowledge regarding the analysis of change. One such framework is latent growth modeling, perhaps the most important and influential statistical revolution to have recently occurred in the social and behavioral sciences. This study presents a basic introduction to a latent growth modeling approach for analyzing repeated measures data. Included is the specification and interpretation of the growth factors, primary extensions such as the analysis of growth in multiple populations, and structural models including both precursors of growth, and subsequent outcomes hypothesized to be influenced by the growth functions.     

Neural modeling,functional brain imaging,and cognition

The richness and complexity of data sets acquired from PET or fMRI studies of human cognition have not been exploited until recently by computational neural-modeling methods. In this article, two neural-modeling approaches for use with functional brain imaging data are described. One, which uses structural equation modeling, estimates the functional strengths of the anatomical connections between various brain regions during specific cognitive tasks. The second employs large-scale neural modeling to relate functional neuroimaging signals in multiple, interconnected brain regions to the underlying neurobiological time-varying activities in each region. Delayed match-to-sample (visual working memory for form) tasks are used to illustrate these models.