The amount of mental resources needed to understand and interact with an interface.
Cognitive Offloading
If you have ever tilted your head to perceive a rotated image, or programmed a smartphone to remind you of an upcoming appointment, you have engaged in cognitive offloading: the use of physical action to alter the information processing requirements of a task so as to reduce cognitive demand. Despite the ubiquity of this type of behavior, it has only recently become the target of systematic investigation in and of itself. We review research from several domains that focuses on two main questions: (i) what mechanisms trigger cognitive offloading, and (ii) what are the cognitive consequences of this behavior? We offer a novel metacognitive framework that integrates results from diverse domains and suggests avenues for future research.
Generative artificial intelligence reliance and executive function attenuation: Behavioral evidence of cognitive offload in high-use adults.
Cognitive Load and Social Media Advertising
Social media engagement requires cognitive resources, which subsequently impact the advertisements consumers see while browsing. For the most part, however, advertising practitioners and scholars s...
Excess Capacity Learning
We introduce a new framework for understanding how cognitive systems (e.g., humans) learn from experience, based on the concept of representational capacity—the relative amount of representational resources devoted to encoding past experiences. Most paradigms in cognitive science have operated under the assumption that these resources are constrained, forcing cognitive systems to compress rich and noisy experiences to effectively generalize to new situations. We leverage recent advances in computer science to outline the implications of learning with excess capacity, or applying even more representational resources than needed to perfectly memorize all the details of one’s past experiences. In particular, we review evidence suggesting that excess capacity systems can exhibit many of the characteristics of human learning, such as the simultaneous ability to memorize individual experiences and generalize knowledge to new situations. We define and differentiate between constrained (not enough), sufficient (just enough), and excess (more than enough to perfectly capture all the details of one’s past experiences) capacity. We derive empirical properties of learning in each of these capacity regimes, and compare these predictions to effects documented for human learning. We highlight the broad implications of this framework for advancing theoretical and empirical work across cognitive, clinical, and developmental psychology.
Working Minds: A Practitioner's Guide to Cognitive Task Analysis
How to collect data about cognitive processes and events, how to analyze CTA findings, and how to communicate them effectively: a handbook for managers, tr
Cognitive engineering
Cognitive engineering is an interdisciplinary field that applies principles from cognitive psychology, cognitive neuroscience, and human factors to design and develop engineering systems that effectively support or enhance human cognitive processes.[1][2] The field emerged in the 1980s when Donald Norman and others recognized the need to better understand how humans interact with complex technological systems.[3]
AI as a Tool for the Mental Load | Brittany Ellich | Offprint
AI didn't make me faster at tasks. It took over the tracking, the invisible remembering that runs a household, and gave me back creative energy I forgot I had
Design Frictions for Mindful Interactions: The Case for Microboundaries
Design frictions, a term found in popular media articles about user experience design, refer to points of difficulty occurring during interaction with technology. Such articles often argue that these frictions should be removed from interaction flows in order to reduce the risk of user frustration and disengagement. In this paper we argue that, in many scenarios, designing friction into interactions through the introduction of microboundaries, can, in fact, have positive effects. Design frictions can disrupt "mindless" automatic interactions, prompting moments of reflection and more "mindful" interaction. The potential advantages of intentionally introduced frictions are numerous: from reducing the likelihood of errors in data-entry tasks, to supporting health-behaviour change.
Mental Models and User Experience Design
What users believe they know about a user interface impacts how they use it. Mismatched mental models are common, especially with designs that try something new.
(PDF) The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis
PDF | On Jan 1, 2005, P. Pirolli and others published The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis | Find, read and cite all the research you need on ResearchGate
Cognitive Surrender
Cognitive offloading is delegating to the AI and still owning the answer. Cognitive surrender is when the AI's output quietly becomes your output and there i...
Demand characteristics in human–computer experiments
Demand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance. In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.
Verbalizable Representations Form a Global Workspace in Language Models
If the mind is an ocean, we spend our lives floating at the surface. Beneath us, an enormous amount of processing takes place without our knowledge: our visual systems parsing the contours of a face, our motor circuits maintaining our posture. At any given moment, only a small fraction of this neural activity is accessible to us. Yet it is this privileged sliver of activity that we rely on to reason deliberately: to plan what ingredients to buy for a recipe, or to puzzle out why an engine won’t start. Such thoughts can be articulated out loud, deliberately held in mind, and brought to bear on whatever task the moment demands. This distinction, between our accessible thoughts and our unconscious processing, is perhaps the most striking feature of human cognition.
Verbalizable Representations Form a Global Workspace in Language Models
If the mind is an ocean, we spend our lives floating at the surface. Beneath us, an enormous amount of processing takes place without our knowledge: our visual systems parsing the contours of a face, our motor circuits maintaining our posture. At any given moment, only a small fraction of this neural activity is accessible to us. Yet it is this privileged sliver of activity that we rely on to reason deliberately: to plan what ingredients to buy for a recipe, or to puzzle out why an engine won’t start. Such thoughts can be articulated out loud, deliberately held in mind, and brought to bear on whatever task the moment demands. This distinction, between our accessible thoughts and our unconscious processing, is perhaps the most striking feature of human cognition.
The Personalized Learning Revolution
The allure of AI lies predominantly in its unmatched potential for efficiency, convenience, and accuracy. However, this unprecedented convenience brings with it a hidden yet profound threat: the subtle erosion of human capacity for critical thinking through cognitive offloading.
The Cognitive Debt of Digging Through Preprints
Your Brain on MIT Media Lab
