Temporal uncertainty during overshadowing: A temporal difference account

Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study

Kunci Identifikasi Zooplankton

viii,98 hal,;ill,;23 c

An exploratory study to understand the phenomena of eye-tracking technology: a case of the education environment

Technology has played a pivotal role in revolutionizing the formative aspects of learning and teaching in the current digital age. Due to technology, there is an expectation of having customized medicine, customized interaction, and customized formative communication instead of traditional mass reporting approaches. Formative assessment within higher education teaching and learning environments are no exception to such an approach in the 21st century digital environment. Eye-tracking technology in recent years has provided an insight to understand the human eye movements and concentration patterns, which has application in education. Eye-tracking can be used to examine the processes of individuals in their learning to establish how learning contents are delivered and perceived by all involved (e.g., teaching staff, students, and markers). This chapter proposes that critical and specific information from eye-tracking software can lead to tailored educational content to accommodate, customize, and optimize the unique learning methods for an individual student as per their learning habits. This chapter describes the available eye-tracking technologies and their application in educational processes