Thinking fast and slow.

By N. McGlynn. 2014

Subfields: Cognitive Science & Psychology

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In Cognitive Science & Psychology

Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “These include favoring instant gratiﬁcation (Ainslie & George, 2001), and resorting to frugal heuristics to make faster decisions (Kahneman, 2011).”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “instant gratification (Ainslie, 2001), and resorting to frugal heuristics to make faster decisions (Kahneman, 2011).”
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction (2022, background): “MWP solving is typically regarded as one of the system 2 tasks (Kahneman, 2011; Bengio et al., 2021), and our current approach to this problem is related to neural symbolic reasoning (Besold et al.”
ViperGPT: Visual Inference via Python Execution for Reasoning (2023, background): “Analogous to dual-system models [28] in cognitive science, we argue that generated programs (System 2 - analytic) should be utilized to break down tasks that require multiple steps of reasoning into simpler components, where endto-end perception modules (System 1 - pattern recognition) are the most ”
Consistent Accelerated Inference via Confident Adaptive Transformers (2021, background): “In fact, being capable of quantifying the uncertainty in a prediction and deciding when additional computation is needed (or not) is a key challenge for any intelligent system (e.g., see the System 1 vs. System 2 dichotomy explored in Kahneman (2011)).”
Towards Causal Representation Learning (2021, background): “Causal relations can also be viewed as the components of reasoning chains [151] that provide predictions for situations that are very far from the observed distribution and may even remain purely hypothetical [163, 183] or require conscious deliberation [128].”
Generalization in anti-causal learning (2018, neutral): “Finally, we draw connections to the two modes of reasoning—“fast System 1” and “slow System 2”— as presented in (Kahneman, 2011).”
The Consciousness Prior (2017, methodology): “We closely associate conscious processing to Kahneman’s system 2 cognitive abilities [Kahneman, 2011].”
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, background): “…of the trends—some increases in complexity perhaps increasing accuracy, others decreasing it—are consistent with ﬁndings that sometimes more complex tasks force humans into slower but more careful thinking, while in other cases increased complexity can lead to higher errors [Kahneman, 2011].”
Language Models are General-Purpose Interfaces (2022, background): “Cognition is usually categorized into two levels (Kahneman, 2011; Bengio, 2019): System 1 (i.”
Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division (2017, methodology): “To translate their subjective preferences into numbers, all participants used various heuristics to anchor and adjust their input [25].”
Understanding Environmental Cognition (2012, methodology): “To use a contrast common in the cognitive psychology literature, the public may arrive at decisions about environmental issues primarily via System 1 or “fast” processes—quick and susceptible to the influence of heuristics and biases (Kahneman, 2011).”
Twenty years of ecosystem services: How far have we come and how far do we still need to go? (2017, background): “, 1998), and individuals do not possess perfect information or appropriate processing abilities about the relationship between ES and their wellbeing, as is also assumed (Kahneman, 2011).”
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts (2023, background): “Mathematical reasoning, a unique facet of system-2 reasoning, stands as a testament to the intricacies of human intelligence (Kahneman, 2011).”
Logic, Fast and Slow: Advances in Dual-Process Theorizing (2019, background): “For example, it has long been assumed that experts in various fields are characterized by the automatization of previously deliberate procedures (Kahneman, 2011).”
Psychological Momentum: Why Success Breeds Success (2014, background): “The financial markets frequently overshoot (positive momentum) and undershoot (negative momentum) because investors’ and traders’ decisions are largely emotionally based (Kahneman, 2011; Lerner, Small, & Loewenstein, 2004).”