A Biased Bayesian Inference for Decision-Making and Cognitive Control

Matsumori, Kaosu; Koike, Yasuharu; Matsumoto, Kenji

doi:10.3389/fnins.2018.00734

Cited by 16 publications

(10 citation statements)

References 129 publications

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“…In contrast, the goal of performance monitoring for sensorimotor confidence is to accumulate error signals across time, much like the accumulation of sensory evidence for perceptual decisions with a fixed viewing time. In fact, in the accumulation-of-evidence framework, considerable effort has been made to incorporate a recency bias termed "leaky accumulation" (Busemeyer and Townsend, 1993;Usher and McClelland, 2001;Brunton et al, 2013;Matsumori et al, 2018).…”

Section: The Recency Effectmentioning

confidence: 99%

Performance monitoring for sensorimotor confidence: A visuomotor tracking study

2020

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To best interact with the external world, humans are often required to consider the quality of their actions. Sometimes the environment furnishes rewards or punishments to signal action efficacy. However, when such feedback is absent or only partial, we must rely on internally generated signals to evaluate our performance (i.e., metacognition). Yet, very little is known about how humans form such judgements of sensorimotor confidence. Do they monitor their performance? Or do they rely on cues to sensorimotor uncertainty to infer how likely it is they performed well? We investigated motor metacognition in two visuomotor tracking experiments, where participants followed an unpredictably moving dot cloud with a mouse cursor as it followed a random trajectory. Their goal was to infer the underlying target generating the dots, track it for several seconds, and then report their confidence in their tracking as better or worse than their average. In Experiment 1, we manipulated task difficulty with two methods: varying the size of the dot cloud and varying the stability of the target's velocity. In Experiment 2, the stimulus statistics were fixed and duration of the stimulus presentation was varied. We found similar levels of metacognitive sensitivity in all experiments, with the temporal analysis revealing a recency effect, where error later in the trial had a greater influence on the sensorimotor confidence. In sum, these results indicate humans predominantly monitor their tracking performance, albeit inefficiently, to judge sensorimotor confidence.

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Section: The Recency Effectmentioning

confidence: 99%

Performance monitoring for sensorimotor confidence: A visuomotor tracking study

2020

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“…inverse temperature parameters), on Bayesian inference because these were found useful in expressing bias levels. We augment the modeling approach in (Matsumori et al, 2018) to a human-AI collaborative setup. Therein we model the biased Bayesian estimation as…”

Section: Bayesian Decision-makingmentioning

confidence: 99%

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Rastogi¹,

Zhang²,

Wei³

et al. 2020

Preprint

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Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decisionmakers in AI-assisted decision-making, where humans are the consumers of AI model predictions and the ultimate decision-makers in high-stakes applications. However, people's perception and understanding is often distorted by their cognitive biases, like confirmation bias, anchoring bias, availability bias, to name a few. In this work, we use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making system and mitigate their negative effects. To this end, we mathematically model cognitive biases and provide a general framework through which researchers and practitioners can understand the interplay between cognitive biases and human-AI accuracy. We then focus on anchoring bias, a bias commonly witnessed in human-AI partnerships. We devise a cognitive science-driven, time-based approach to de-anchoring. A user experiment shows the effectiveness of this approach in human-AI collaborative decision-making. Using the results from this first experiment, we design a time allocation strategy for a resource constrained setting so as to achieve optimal human-AI collaboration under some assumptions. A second user study shows that our time allocation strategy can effectively debias the human when the AI model has low confidence and is incorrect.

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“…A Bayesian approach is used for inference on limited amounts of data. The solutions are based on Bayes' theorem that assumes prior probabilities and likelihood functions (Matsumori, K et al 2018). Tian et al (2018) use Bayesian methods with Markov chain Monte Carlo (MCMC) machine learning algorithms have been used in the integration of data set and the prediction of geological properties at the production of well production sites.…”

Section: Bayesian Methodsmentioning

confidence: 99%

AI/ML assisted shale gas production performance evaluation

Syed

Muther

Dahaghi

et al. 2021

J Petrol Explor Prod Technol

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Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.

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A Biased Bayesian Inference for Decision-Making and Cognitive Control

Cited by 16 publications

References 129 publications

Performance monitoring for sensorimotor confidence: A visuomotor tracking study

Performance monitoring for sensorimotor confidence: A visuomotor tracking study

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

AI/ML assisted shale gas production performance evaluation

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