Online research in philosophy

Memory researchers have discussed the relationship between consciousness and memory frequently in the last few decades. Beginning with research by Warrington and Weiskrantz (1968; 1970), memory has been shown to influence task performance even without awareness of retrieval. Data from amnesic patients show that a study episode influences task performance despite their lack of conscious memory for the study session. More recently, issues of intentionality, awareness, and the relationship between conscious and unconscious forms of memory have come to the forefront. Conscious memory has sometimes been defined by intention to retrieve and sometimes by awareness of retrieval. This distinction has been debated as measurement methodologies have developed. In addition, the functional relationship between conscious and automatic forms of memory has implications for measurement of memory processes and the development of models of memory task performance. Several measurement techniques for conscious and automatic memory are reviewed. The current state of these issues is also discussed.

Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the modal response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate. Key Words: biases; descriptive models; heuristics; individual differences; normative models; rationality; reasoning.

Through the continuous investigation of humans in organizations, we have learned much about motivation, attitudes, and performance. For example, Yukl and others have helped increase our understanding of influence tactics and the effect they have on the performance of subordinates, supervisors, and peers. Some tactics (and combinations of tactics) lead to resistance, some lead to compliance, and some lead to commitment. In this study, we raise the question of whether or not we incorporate our knowledge of these research findings into the design, implementation, and interpretation of our own research studies that require the participation of human subjects. In a survey of 134 subjects from a previous social science study, we found that performance varied across the sample, consistent with the concepts of resistance, compliance, and commitment. In addition, the variance in performance could be explained, in part, by task interest and perceived task demands. Implications are discussed.

The evolution of humans required performing increasingly hierarchically complex tasks within multiple domains. Hierarchical complexity increases task by task. Tasks occur within, and differ by, determinable domains, their stages of performance measurable using the Model of Hierarchical Complexity. How well one performs within single and multiple domains is considered to indicate intelligence. Original task-initiation is more difficult than imitational learning and can create new domains. Levels of support reduce task difficulty, increasing performance. Task-performance may be generalized to other domains. Stages of developing tools and empathy are presented to demonstrate domains' roles in the evolution of human intelligence.

The paper reports our research aiming at utilization of human interactive communication modeling principles in application to a novel interaction paradigm designed for brain–computer/machine-interfacing (BCI/BMI) technologies as well as for socially aware intelligent environments or communication support systems. Automatic procedures for human affective responses or emotional states estimation are still a hot topic of contemporary research. We propose to utilize human brain and bodily physiological responses for affective/emotional as well as communicative interactivity estimation, which potentially could be used in the future for human–machine/environment interaction design. As a test platform for such an intelligent human–machine communication application, an emotional stimuli paradigm was chosen to evaluate brain responses to various affective stimuli in an emotional empathy mode. Videos with moving faces expressing various emotional displays as well as speech stimuli with similarly emotionally articulated sentences are presented to the subjects in order to further analyze different affective responses. From information processing point of view, several challenges with multimodal signal conditioning and stimuli dynamic response extraction in time frequency domain are addressed. Emotions play an important role in human daily life and human-to-human communication. This is why involvement of affective stimuli principles to human–machine communication or machine-mediated communication with utilization of multichannel neurophysiological and periphery physiological signals monitoring techniques, allowing real-time subjective brain responses evaluation, is discussed. We present our preliminary results and discuss potential applications of brain/body affective responses estimation for future interactive/smart environments.