scholarly journals Neural Correlates of Causal Confounding

2020 ◽  
Vol 32 (2) ◽  
pp. 301-314
Author(s):  
Mimi Liljeholm
Keyword(s):  
Cognitive Modeling ◽  
Causal Model ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Learning Context ◽  
Causal Knowledge ◽  
Causal Induction ◽  
General Constraints ◽  
Computational Cognitive Modeling ◽  
Adults And Children

As scientists, we are keenly aware that if putative causes perfectly covary, the independent influence of neither can be discerned—a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and computational cognitive modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or nonconfounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial pFC was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain-general constraints on causal induction, are discussed.

scholarly journals Neural Correlates of Causal Confounding

2018 ◽  
Author(s):  
Mimi Liljeholm
Keyword(s):  
Prefrontal Cortex ◽  
Computational Modeling ◽  
Causal Model ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Learning Context ◽  
Causal Knowledge ◽  
Dorsomedial Prefrontal Cortex ◽  
General Constraints ◽  
Adults And Children

AbstractAs scientists, we are keenly aware that if putative causes perfectly co-vary, the independent influence of neither can be discerned – a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and cognitive computational modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or non-confounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial prefrontal cortex (DMPFC) was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain general constraints on causal induction, are discussed.

scholarly journals Reducing the Complexity of Casual Representation in Bayesian Belief Network

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1243
Author(s):  
Yit Yin Wee ◽  
Shing Chiang Tan ◽  
KuokKwee Wee
Keyword(s):  
Causal Model ◽  
Knowledge Engineering ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Joint Probability Distribution ◽  
Causal Knowledge ◽  
Belief Network ◽  
Track Record ◽  
Probability Table ◽  
Causal Framework

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool.   Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model.  Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN.   Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

Inferring Causal Relations by Analogy

2017 ◽  
Author(s):  
Keith J. Holyoak ◽  
Hee Seung Lee
Keyword(s):  
Analogical Reasoning ◽  
Causal Model ◽  
Causal Knowledge ◽  
Common Pattern ◽  
Causal Relations ◽  
Analogical Inference ◽  
Psychological Evidence ◽  
Inductive Inferences ◽  
Novel Target ◽  
Constituent Elements

When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.

scholarly journals Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective

2019 ◽  
Vol 2 (3-4) ◽  
pp. 242-246 ◽  
Author(s):  
Chris Emmery ◽  
Ákos Kádár ◽  
Travis J. Wiltshire ◽  
Andrew T. Hendrickson
Keyword(s):  
Machine Learning ◽  
Cognitive Modeling ◽  
Computational Cognitive Modeling

Neuroimaging of Direction-Selective Mechanisms for Second-Order Motion

2003 ◽  
Vol 90 (5) ◽  
pp. 3242-3254 ◽  
Author(s):  
Shin'ya Nishida ◽  
Yuka Sasaki ◽  
Ikuya Murakami ◽  
Takeo Watanabe ◽  
Roger B. H. Tootell
Keyword(s):  
Neural Substrates ◽  
Neural Correlates ◽  
Selective Adaptation ◽  
Motion Processing ◽  
Motion Adaptation ◽  
Motion Stimulus ◽  
Fmri Study ◽  
Wide Range ◽  
Contingent Response ◽  
Visual Cortical

Psychophysical findings have revealed a functional segregation of processing for 1st-order motion (movement of luminance modulation) and 2nd-order motion (e.g., movement of contrast modulation). However neural correlates of this psychophysical distinction remain controversial. To test for a corresponding anatomical segregation, we conducted a new functional magnetic resonance imaging (fMRI) study to localize direction-selective cortical mechanisms for 1st- and 2nd-order motion stimuli, by measuring direction-contingent response changes induced by motion adaptation, with deliberate control of attention. The 2nd-order motion stimulus generated direction-selective adaptation in a wide range of visual cortical areas, including areas V1, V2, V3, VP, V3A, V4v, and MT+. Moreover, the pattern of activity was similar to that obtained with 1st-order motion stimuli. Contrary to expectations from psychophysics, these results suggest that in the human visual cortex, the direction of 2nd-order motion is represented as early as V1. In addition, we found no obvious anatomical segregation in the neural substrates for 1st- and 2nd-order motion processing that can be resolved using standard fMRI.

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