The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

"Cognitive, social, and physiological determinants of emotional state": Erratum

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide vari- ety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and eval- uate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Per- ception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

/pdf/the-emulation-theory-of-representation-motor-control-imagery-2cq16sb7pi.pdf

The emulation theory of representation: motor control, imagery, and perception.

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of air emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

/pdf/the-emulation-theory-of-representation-motor-control-imagery-1vydkap3vi.pdf

The emulation theory of representation: Motor control, imagery, and perception - eScholarship

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links bet...

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Understanding Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . 54Distinguishing Emotion from Related Constructs . . . . 55Mood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Sentiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Effects of Affect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Causes of Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Needs and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Appraisal Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Contagion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Moods and Sentiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Previous Emotional State . . . . . . . . . . . . . . . . . . . . . . . . . . 59Causes of Mood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Contagion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Other Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Measuring Affect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Neurological Responses . . . . . . . . . . . . . . . . . . . . . . . . . . 61Autonomic Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Facial Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Voice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Self-Report Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Affect Recognition by Users . . . . . . . . . . . . . . . . . . . . . . . 63Open Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641. With which emotion should HCI designersbe most concerned? . . . . . . . . . . . . . . . . . . . . . . . . . 642. When and how should interfaces attempt todirectly address users’ emotions and basicneeds (vs. application-specific goals)? . . . . . . . . . . . . 643. How accurate must emotion recognition beto be useful as an interface technique? . . . . . . . . . . . 644. When and how should users be informedthat their affective states are being monitoredand adapted to? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645. How does emotion play out in computer-mediated communication (CMC)? . . . . . . . . . . . . . . 64Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Emotion is a fundamental component of being human. Joy,
hate, anger, and pride, among the plethora of other emotions,
motivate action and add meaning and richness to virtually all
human experience. Traditionally, human-computer interaction
(HCI) has been viewed as the “ultimate” exception; users must
discard their emotional selves to work efficiently and rationality with computers, the quintessentially unemotional artifact.
Emotion seemed at best marginally relevant to HCI and at worst
oxymoronic.

Emotion in human-computer interaction

This paper describes a work on transfer learning in neural networks carried out in 1970s and early 1980s, which produced its first publication in 1976. In the contemporary research on transfer learning there is a belief that pioneering work on transfer learning took place in early 1990s, and this paper updates that knowledge, pointing out that the transfer learning research started more than a decade earlier. This paper reviews that 1970s research and addresses important issues relevant for the current transfer learning research. It gives a mathematical model and geometric interpretation of transfer learning, and  a measure of transfer learning indicating positive, negative, and no transfer learning. It presents experimental investigation in the mentioned types of transfer learning. And it gives an application of transfer learning in pattern recognition using datasets of images.

/pdf/reminder-of-the-first-paper-on-transfer-learning-in-neural-o11al7rara.pdf

Reminder of the First Paper on Transfer Learning in Neural Networks, 1976

A report is presented of experiments in start/stop control of microrobot movement using EEG alpha waves. The robot is a trajectory-follower and uses an optical sensor. Experimental design allows the subject to start robot movement by closing his or her eyes and to stop the robot by opening them. EEG alpha wave recognition software controls the movement of the robot. >

Using EEG alpha rhythm to control a mobile robot

Mobile Robot Trajectory Control: From Fixed Rails to Direct Bioelectric Control

This paper addresses the issue of personality of an animat in terms of anticipation, motivation and emotion. It also discusses some relevant models and theories of personality, and their relation to the consequence driven systems theory. The main result of this work is a fundamental mathematical equation between the emotion, motivation, and behavior. In essence the result can be stated that what motivates an animat behavior is the value of the anticipated emotional consequence of that behavior. Experimental research with an artificial personality architecture is provided, supporting the obtained result.

Anticipation Driven Artificial Personality: Building on Lewin and Loehlin

An extension of the classical CNV paradigm is defined using a closed-loop design. A new electrocognitive phenomenon from the human brain is observed, denoted as electroexpectogram (EXG). The experimental design and the algorithms are described. >

Stevo Bozinovski

Papers

Reminder of the First Paper on Transfer Learning in Neural Networks, 1976

Using EEG alpha rhythm to control a mobile robot

Mobile Robot Trajectory Control: From Fixed Rails to Direct Bioelectric Control

Anticipation Driven Artificial Personality: Building on Lewin and Loehlin

Electroexpectogram: Experimental Design and Agorithms