Cognitive Neuroscience of Causal Reasoning

Author(s):  
Joachim T. Operskalski ◽  
Aron K. Barbey

The era of functional neuroimaging promised to shed light on dark corners of the brain’s inner workings, breathing new life into subfields of psychology beset by controversy. Although revelations from neuroscience provide the foundation for current views on many aspects of human cognition, there continue to be areas of study in which a mismatch between the questions asked by psychologists and neuroscientists renders the implications of neuroscience research unclear. Causal reasoning is one such topic, for which decades of cognitive neuroscience findings have revealed a heterogeneity of participating brain regions and networks across different experimental paradigms. This chapter discusses (i) three cognitive and computational models of causal reasoning (mental models, causal models, and force composition theory), (ii) experimental findings on causal judgment and reasoning using cognitive neuroscience methods, and (iii) the need for a multidisciplinary approach to understanding the nature and mechanisms of causal reasoning.

2019 ◽  
Vol 28 (4) ◽  
pp. 380-386 ◽  
Author(s):  
Meghan L. Meyer

Social-neuroscience research has identified a set of medial frontoparietal brain regions that reliably engage during social cognition. At the same time, cognitive-neuroscience research has shown that these regions comprise part of the default network, so named because they reliably activate during mental breaks by default. Although the anatomical similarity between the social brain and the default brain is well documented, why this overlap exists remains a mystery. Does the tendency to engage these regions by default during rest have particular social functions, and if so, what might these be? Here, it is suggested that the default network performs two critical social functions during rest: social priming and social consolidation. These constructs will be defined, recently published empirical findings that support them will be reviewed, and directions for future research on the topic will be proposed.


2004 ◽  
Vol 359 (1451) ◽  
pp. 1755-1762 ◽  
Author(s):  
S. Zeki ◽  
O. R. Goodenough ◽  
Sean A. Spence ◽  
Mike D. Hunter ◽  
Tom F. D. Farrow ◽  
...  

An organism may use misinformation, knowingly (through deception) or unknowingly (as in the case of camouflage), to gain advantage in a competitive environment. From an evolutionary perspective, greater tactical deception occurs among primates closer to humans, with larger neocortices. In humans, the onset of deceptive behaviours in childhood exhibits a developmental trajectory, which may be regarded as ‘normal’ in the majority and deficient among a minority with certain neurodevelopmental disorders (e.g. autism). In the human adult, deception and lying exhibit features consistent with their use of ‘higher’ or ‘executive’ brain systems. Accurate detection of deception in humans may be of particular importance in forensic practice, while an understanding of its cognitive neurobiology may have implications for models of ‘theory of mind’ and social cognition, and societal notions of responsibility, guilt and mitigation. In recent years, functional neuroimaging techniques (especially functional magnetic resonance imaging) have been used to study deception. Though few in number, and using very different experimental protocols, studies published in the peer-reviewed literature exhibit certain consistencies. Attempted deception is associated with activation of executive brain regions (particularly prefrontal and anterior cingulate cortices), while truthful responding has not been shown to be associated with any areas of increased activation (relative to deception). Hence, truthful responding may comprise a relative ‘baseline’ in human cognition and communication. The subject who lies may necessarily engage ‘higher’ brain centres, consistent with a purpose or intention (to deceive). While the principle of executive control during deception remains plausible, its precise anatomy awaits elucidation.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
N. Apurva Ratan Murty ◽  
Pouya Bashivan ◽  
Alex Abate ◽  
James J. DiCarlo ◽  
Nancy Kanwisher

AbstractCortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-response-predicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.


2019 ◽  
Author(s):  
Di Fu ◽  
Cornelius Weber ◽  
Guochun Yang ◽  
Matthias Kerzel ◽  
Weizhi Nan ◽  
...  

Selective attention plays an essential role in information acquisition and utilizationfrom the environment. In the past 50 years, research on selective attention has beena central topic in cognitive science. Compared with unimodal studies, crossmodalstudies are more complex but necessary to solve real-world challenges in both humanexperiments and computational modeling. Although an increasing number of findingson crossmodal selective attention have shed light on humans’ behavioral patterns andneural underpinnings, a much better understanding is still necessary to yield the samebenefit for intelligent computational agents. This article reviews studies of selectiveattention in unimodal visual and auditory and crossmodal audiovisual setups from themultidisciplinary perspectives of psychology and cognitive neuroscience, and evaluatesdifferent ways to simulate analogous mechanisms in computational models and robotics.We discuss the gaps between these fields in this interdisciplinary review and provideinsights about how to use psychological findings and theories in artificial intelligence fromdifferent perspectives.


2021 ◽  
Author(s):  
Katharina Voigt ◽  
Emma Liang ◽  
Bratislav Misic ◽  
Phillip Ward ◽  
Gary Egan ◽  
...  

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18 F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterisation of the human brain. How these two modalities differ in their contribution to cognition and behaviour is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that while for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, while for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.


2021 ◽  
Author(s):  
Dima Ayyash ◽  
Saima Malik-Moraleda ◽  
Jeanne Gallee ◽  
Josef Affourtit ◽  
Malte Hoffman ◽  
...  

To understand the architecture of human language, it is critical to examine diverse languages; yet most cognitive neuroscience research has focused on a handful of primarily Indo-European languages. Here, we report a large-scale investigation of the fronto-temporal language network across 45 languages and establish the cross-linguistic generality of its key functional properties, including general topography, left-lateralization, strong functional integration among its brain regions, and functional selectivity for language processing. 


2017 ◽  
Vol 23 (9-10) ◽  
pp. 755-767 ◽  
Author(s):  
Paul W. Burgess ◽  
Donald T. Stuss

AbstractOur knowledge of the functions of the prefrontal cortex, often called executive, supervisory, or control, has been transformed over the past 50 years. After operationally defining terms for clarification, we review the impact of advances in functional, structural, and theoretical levels of understanding upon neuropsychological assessment practice as a means of identifying 11 principles/challenges relating to assessment of executive function. Three of these were already known 50 years ago, and 8 have been confirmed or emerged since. Key themes over this period have been the emergence of the use of naturalistic tests to address issues of “ecological validity”; discovery of the complexity of the frontal lobe control system; invention of new tests for clinical use; development of key theoretical frameworks that address the issue of the role of prefrontal cortex systems in the organization of human cognition; the move toward considering brain systems rather than brain regions; the advent of functional neuroimaging, and its emerging integration into clinical practice. Despite these huge advances, however, practicing neuropsychologists are still desperately in need of new ways of measuring executive function. We discuss pathways by which this might happen, including decoupling the two levels of explanation (information processing; brain structure) and integrating very recent technological advances into the neuropsychologist’s toolbox. (JINS, 2017,23, 755–767)


2020 ◽  
Author(s):  
Emily S. Finn ◽  
Laurentius Huber ◽  
Peter A Bandettini

Recent advances in fMRI have enabled non-invasive measurements of brain function in awake, behaving humans at unprecedented spatial resolutions, allowing us to separate activity in distinct cortical layers. While most layer fMRI studies to date have focused on primary cortices, we argue that the next big steps forward in our understanding of cognition will come from expanding this technology into higher-order association cortices, to characterize depth-dependent activity during increasingly sophisticated mental processes. We outline phenomena and theories ripe for investigation with layer fMRI, including perception and imagery, selective attention, and predictive coding. We discuss practical and theoretical challenges to cognitive applications of layer fMRI, including localizing regions of interest in the face of substantial anatomical heterogeneity across individuals, designing appropriate task paradigms within the confines of acquisition parameters, and generating hypotheses for higher-order brain regions where the laminar circuitry is less well understood. We consider how applying layer fMRI in association cortex may help inform computational models of brain function as well as shed light on consciousness and mental illness, and issue a call to arms to our fellow methodologists and neuroscientists to bring layer fMRI to this next frontier.


Author(s):  
Kim Uittenhove ◽  
Patrick Lemaire

In two experiments, we tested the hypothesis that strategy performance on a given trial is influenced by the difficulty of the strategy executed on the immediately preceding trial, an effect that we call strategy sequential difficulty effect. Participants’ task was to provide approximate sums to two-digit addition problems by using cued rounding strategies. Results showed that performance was poorer after a difficult strategy than after an easy strategy. Our results have important theoretical and empirical implications for computational models of strategy choices and for furthering our understanding of strategic variations in arithmetic as well as in human cognition in general.


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