The role of decision confidence in advice-taking and trust formation

In a world where ideas flow freely between people across multiple platforms, we often find ourselves relying on others' information without an objective standard to judge whether those opinions are accurate. The present study tests an agreement-in-confidence hypothesis of advice perception, which holds that internal metacognitive evaluations of decision confidence play an important functional role in the perception and use of social information, such as peers' advice. We propose that confidence can be used, computationally, to estimate advisors' trustworthiness and advice reliability. Specifically, these processes are hypothesized to be particularly important in situations where objective feedback is absent or difficult to acquire. Here, we use a judge-advisor system paradigm to precisely manipulate the profiles of virtual advisors whose opinions are provided to participants performing a perceptual decision making task. We find that when advisors' and participants' judgments are independent, people are able to discriminate subtle advice features, like confidence calibration, whether or not objective feedback is available. However, when observers' judgments (and judgment errors) are correlated - as is the case in many social contexts - predictable distortions can be observed between feedback and feedback-free scenarios. A simple model of advice reliability estimation, endowed with metacognitive insight, is able to explain key patterns of results observed in the human data. We use agent-based modeling to explore implications of these individual-level decision strategies for network-level patterns of trust and belief formation

Measuring the World: Olfaction as a Process Model of Perception

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes

Towards an alternative to Benner’s theory of expert intuition in nursing: A discussion paper

Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner (1984). We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise

An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal