Activity as an Anthropological Category : On Distinguishing Between the Ontological and the Epistemological Status of Activity

This article is devoted to categorial and logical-semantic analysis of the concept "activity" in philosophy, methodology, and psychology. The basic activity-related subcategories are systematized and the normative structure of activity is revealed. The set of problems pertaining to the ontogenesis of activity-related abilities and to professional formation is defined in terms of the process of establishment of the subject of his own activity.     

The Merits of Unconscious Thought in Rule Detection

According to unconscious thought theory (UTT), unconscious thought is more adept at complex decision-making than is conscious thought. Related research has mainly focused on the complexity of decision-making tasks as determined by the amount of information provided. However, the complexity of the rules generating this information also influences decision making. Therefore, we examined whether unconscious thought facilitates the detection of rules during a complex decision-making task. Participants were presented with two types of letter strings. One type matched a grammatical rule, while the other did not. Participants were then divided into three groups according to whether they made decisions using conscious thought, unconscious thought, or immediate decision. The results demonstrated that the unconscious thought group was more accurate in identifying letter strings that conformed to the grammatical rule than were the conscious thought and immediate decision groups. Moreover, performance of the conscious thought and immediate decision groups was similar. We conclude that unconscious thought facilitates the detection of complex rules, which is consistent with UTT.     

Conscious and Unconscious Memory Systems

The idea that memory is not a single mental faculty has a long and interesting history but became a topic of experimental and biologic inquiry only in the mid-20th century. It is now clear that there are different kinds of memory, which are supported by different brain systems. One major distinction can be drawn between working memory and long-term memory. Long-term memory can be separated into declarative (explicit) memory and a collection of nondeclarative (implicit) forms of memory that include habits, skills, priming, and simple forms of conditioning. These memory systems depend variously on the hippocampus and related structures in the parahippocampal gyrus, as well as on the amygdala, the striatum, cerebellum, and the neocortex. This work recounts the discovery of declarative and nondeclarative memory and then describes the nature of declarative memory, working memory, nondeclarative memory, and the relationship between memory systems.The idea that memory is not a single faculty has a long history. In his Principles of Psychology, William James (1890) wrote separate chapters on memory and habit. Bergson (1910) similarly distinguished between a kind of memory that represents our past and memory that is not representational but nevertheless allows the effect of the past to persist into the present. One finds other antecedents as well. McDougall (1923) wrote about explicit and implicit recognition memory, and Tolman (1948) proposed that there is more than one kind of learning. These early proposals were often expressed as a dichotomy involving two forms of memory. The terminologies differed, but the ideas were similar. Thus, Ryle (1949) distinguished between knowing how and knowing that, and Bruner (1969) identified memory without record and memory with record. Later, the artificial intelligence literature introduced a distinction between declarative and procedural knowledge (Winograd 1975). Yet constructs founded in philosophy and psychology are often abstract and have an uncertain connection to biology, that is, to how the brain actually stores information. History shows that as biological information becomes available about structure and function, understanding becomes more concrete and less dependent on terminology.     

The Brain and the Mechanism of Psychosocial Phenomena

According to classic traditions, general psychology has always linked an intraindividual extrapsychic mechanism, the brain, with the intraindividual mental phenomena it examined. Social psychology, on the other hand, is handicapped by the fact that its world is that of interindividual mental phenomena, a disadvantage revealed by a compelling dilemma: this distinctive branch of psychology either fits into the classic tradition and presents the mechanism of interindividual phenomena in the brains of individuals (separately, if need be) or resigns itself to being unable to accommodate the mechanism level of its investigations to their phenomena level. All this is in spite of the fact that investigations focusing on the connection between the mind and the brain—whether they do so from the point of view of the specialized science or of philosophy—cannot begin to deal with the interindividual variety of spiritual phenomena.     

Classification of Self-Driven Mental Tasks from Whole-Brain Activity Patterns

During wakefulness, a constant and continuous stream of complex stimuli and self-driven thoughts permeate the human mind. Here, eleven participants were asked to count down numbers and remember negative or positive autobiographical episodes of their personal lives, for 32 seconds at a time, during which they could freely engage in the execution of those tasks. We then examined the possibility of determining from a single whole-brain functional magnetic resonance imaging scan which one of the two mental tasks each participant was performing at a given point in time. Linear support-vector machines were used to build within-participant classifiers and across-participants classifiers. The within-participant classifiers could correctly discriminate scans with an average accuracy as high as 82%, when using data from all individual voxels in the brain. These results demonstrate that it is possible to accurately classify self-driven mental tasks from whole-brain activity patterns recorded in a time interval as short as 2 seconds.     

The Problem of the Unconscious in the Psychological Concept of Set

In the pages of the three-volume anthology The unconscious: Its nature, function, and methods of investigation (Tbilisi, 1978), the following incorrect positions are presented concerning the relationship between the concept of set and the problem of the unconscious: (1) the unconscious is real only as a form of existence of physiological processes; (2) the unconscious is a category whose use is incompatible with dialectical materialist methodology in psychology; and (3) the identity of the concept of set (in D. N. Uznadze's conception) with the psychological concept of the unconscious makes the concept of set superfluous for psychology as an explanatory science (see [3. Vol. 1, p. 74]). Let us examine some aspects of these postulates.     

Accurate and Reliable Cancer Classification Based on Probabilistic Inference of Pathway Activity

With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.