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This chapter reviews the role of prior knowledge for the integration of sensory information and discusses how priors can be modified by experience. It shows that prior knowledge affects perception at different levels. First, it often serves as an additional cue at the level of cue integration. Second, prior knowledge of statistical regularities in the world is also important for interpreting cues, because it can provide information needed to disambiguate sensory information and thus determines the shape of the likelihood function. Third, prior knowledge is also effective at a higher cognitive level, where it determines whether and how cues are integrated. The chapter concludes by discussing where prior knowledge comes from and how flexible it is.

The brain receives information about the environment from all the sensory modalities, including vision, touch, and audition. To interact efficiently with the environment, this information must eventually converge to form a reliable and accurate multimodal percept. This process is often complicated by the existence of noise at every level of signal processing, which makes the sensory information derived from the world unreliable and inaccurate. There are several ways in which the nervous system may minimize the negative consequences of noise in terms of reliability and accuracy. Two key strategies are to combine redundant sensory estimates and to use prior knowledge. This chapter elaborates further on how these strategies may be used by the nervous system to obtain the best possible estimates from noisy signals.

This chapter examines two approaches to resolving the question of how prior knowledge and current knowledge interact in category learning. The first relies on mathematical models of statistical inference. The second is an implemented connectionist computational model. To illustrate the usefulness of these latter models, the chapter develops a possible connectionist model of how prior knowledge and on-line learning integrate during early concept learning.

People are normally encouraged to engage in premeditation—to think about the potential consequences of their behavior before acting. Indeed, planning, considering, and studying can be important precursors to decision-making, and often seem essential for effective action. This view of premeditation is shared by most humans, a kind of universal ideal, and it carries an additional interesting implication: Even the hint that premeditation occurred can serve as a potent cue indicating voluntary action, both to actors and observers. In legal and moral contexts, for example, actors are seen as especially culpable for the consequences of their actions if those consequences were premeditated, whether or not the premeditation influenced the decision. In this chapter, we review evidence indicating that even irrelevant premeditation can lead people to believe that an action's consequences were under personal control. We present research exploring how various forms of premeditation—including foresight, effortful forethought, wishful thinking, and the consideration of multiple possible outcomes of action—may lead actors to prefer and to feel responsible for action outcomes even when this premeditation has no causal relation to the outcomes.

Engaging in tree thinking (using phylogenetic diagrams to interpret and infer historical processes) is a prerequisite for understanding macroevolution. Tree thinking has become increasingly important in biology, with important ramifications for applied fields such as genomics, conservation, epidemiology, and pharmacology. Focusing on what is currently known about cognitive and perceptual constraints on students' tree-thinking skills the chapter reports on the effectiveness of business-as-usual instructional units on tree-thinking concepts in two upper-level classes for Biology majors and discussing how this knowledge can be used to inform curriculum development. The chapter argues for a paradigm shift in the way evolution is taught — from a strong focus on natural selection to a model that visualizes evolution as a broad hierarchical continuum which integrates both micro and macro processes with critical scientific reasoning skills.

This chapter examines Plato’s presentation of Meno’s Paradox in the Meno and Aristotle’s reference to it in Posterior Analytics 1.1. It also discusses a crucial claim closely connected to the puzzle: the Prior Knowledge Requirement for intellectual learning. Meno 80d4–e5 is examined, and the differences and similarities between Meno’s puzzle and Socrates’s dilemma are discussed. Some key terms and concepts in Aristotle’s theory of knowledge and learning are then defined, including ‘learning’ (mathēsis) and ‘knowledge’ (gnōsis). The connection between the general concept of knowledge (gnōsis) and its different species (including scientific knowledge (epistēmē)) is also examined. The chapter ends with an account of the instance of Meno’s Paradox in Posterior Analytics 1.1.

Community psychology can potentially illustrate the dynamic interplay between the qualities of people and the attributes of smaller social settings and larger social environments. This requires research methods that attempt to illustrate environmental complexity. Thinking psychologically and ecologically are interdependent approaches. The process of theory confirmation is adapted to informed insights from the community as well as current knowledge from the literature. An educated view of a particular place creates a dynamic interplay between prior knowledge and informed insights. The views of the place can be related to the concepts in the literature, and the concepts from the literature can be assessed based on their salience in this particular community. The use of ecological concepts affirms that there is need for a real interdependence between the wishes and aspirations of the community participants and the project staff.

Royce Sadler takes a fresh look at an enduring assessment issue – why is it that students often fail to undertake an assessment task in the way that it has been specified? Sadler suggests that students need to be supported to develop ‘goal knowledge’ of what a final assessment should look like in order to be able to engage with the specified task. Sadler adds to the notion of goal knowledge that of response genre, that needs to be chosen to enable students and assessors to understand the type of assessment required within a certain context. In developing these ideas, Sadler draws particularly on the work of Boulding to explore the relationship between the knowledge a person brings to a situation (such as an assessment task) and the ways in which that knowledge changes through multiple and layered processes of interaction while carrying out that task.

This chapter proposes a reconciliation of logical and probabilistic processes using theories with a longer psychological pedigree. In a detailed review of the recent empirical literature on conditional reasoning, it observes that studies on the interpretation of the conditional seem to support the probabilistic account, whereas studies on conditional inference seem to support a mental models view. It presents a revised mental model theory that shows that the basic meaning of a conditional is a procedure for constructing a particular mental model and that this procedure can be revised by prior knowledge in a similar way to semantic/pragmatic modulation. Prior knowledge can also provide probabilities as annotations to mental models that can be processed as part of the reasoning process.