scholarly journals Amortized Hypothesis Generation

2017 ◽  
Author(s):  
Ishita Dasgupta ◽  
Eric Schulz ◽  
Noah D. Goodman ◽  
Samuel J. Gershman
Keyword(s):  
Cognitive Load ◽  
Probability Distributions ◽  
Hypothesis Generation ◽  
Natural Scenes ◽  
Summary Statistics ◽  
Summary Statistic ◽  
Computationally Efficient ◽  
Present Evidence ◽  
Bayesian Models Of Cognition ◽  
The Brain

AbstractBayesian models of cognition posit that people compute probability distributions over hypotheses, possibly by constructing a sample-based approximation. Since people encounter many closely related distributions, a computationally efficient strategy is to selectively reuse computations – either the samples themselves or some summary statistic. We refer to these reuse strategies as amortized inference. In two experiments, we present evidence consistent with amortization. When sequentially answering two related queries about natural scenes, we show that answers to the second query vary systematically depending on the structure of the first query. Using a cognitive load manipulation, we find evidence that people cache summary statistics rather than raw sample sets. These results enrich our notions of how the brain approximates probabilistic inference.

scholarly journals Remembrance of Inferences Past

2017 ◽  
Author(s):  
Ishita Dasgupta ◽  
Eric Schulz ◽  
Noah D. Goodman ◽  
Samuel J. Gershman
Keyword(s):  
Probabilistic Reasoning ◽  
Probability Distributions ◽  
Computational Cost ◽  
Limited Resources ◽  
Cognitive Resources ◽  
Natural Scenes ◽  
Computationally Efficient ◽  
Bayesian Models Of Cognition ◽  
The Brain ◽  
Entire Distribution

AbstractBayesian models of cognition assume that people compute probability distributions over hypotheses. However, the required computations are frequently intractable or prohibitively expensive. Since people often encounter many closely related distributions, selective reuse of computations (amortized inference) is a computationally efficient use of the brain’s limited resources. We present three experiments that provide evidence for amortization in human probabilistic reasoning. When sequentially answering two related queries about natural scenes, participants’ responses to the second query systematically depend on the structure of the first query. This influence is sensitive to the content of the queries, only appearing when the queries are related. Using a cognitive load manipulation, we find evidence that people amortize summary statistics of previous inferences, rather than storing the entire distribution. These findings support the view that the brain trades off accuracy and computational cost, to make efficient use of its limited cognitive resources to approximate probabilistic inference.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Neuroendocrine modulation of the “menopause”: insights into the aging brain

1999 ◽  
Vol 277 (6) ◽  
pp. E965-E970 ◽  
Author(s):  
Phyllis M. Wise
Keyword(s):  
Animal Models ◽  
Ovarian Follicles ◽  
Physiological Change ◽  
Menopausal Transition ◽  
Aging Brain ◽  
Neural Signals ◽  
Present Evidence ◽  
The Brain

The menopause marks the permanent end of fertility in women. It was once thought that this dramatic physiological change could be explained simply by the exhaustion of the reservoir of ovarian follicles. New data from studies performed in women and animal models make us reassess this assumption. An increasing body of evidence suggests that there are multiple pacemakers that contribute to the transition to irregular cycles, decreasing fertility, and the timing of the menopause. We will present evidence that lends credence to the possibility that a dampening and desynchronization of the precisely orchestrated neural signals lead to miscommunication between the brain and the pituitary-ovarian axis, and that this constellation of hypothalamic-pituitary-ovarian events leads to the deterioration of regular cyclicity and heralds menopausal transition.

scholarly journals Intrinsic spatial knowledge about terrestrial ecology favors the tall for judging distance

2016 ◽  
Vol 2 (8) ◽  
pp. e1501070 ◽  
Author(s):  
Liu Zhou ◽  
Teng Leng Ooi ◽  
Zijiang J. He
Keyword(s):  
Target Location ◽  
Spatial Relationship ◽  
Ground Surface ◽  
Spatial Knowledge ◽  
Superior Performance ◽  
Natural Scenes ◽  
Terrestrial Environment ◽  
Optical Images ◽  
Intermediate Distance ◽  
The Brain

Our sense of vision reliably directs and guides our everyday actions, such as reaching and walking. This ability is especially fascinating because the optical images of natural scenes that project into our eyes are insufficient to adequately form a perceptual space. It has been proposed that the brain makes up for this inadequacy by using its intrinsic spatial knowledge. However, it is unclear what constitutes intrinsic spatial knowledge and how it is acquired. We investigated this question and showed evidence of an ecological basis, which uses the statistical spatial relationship between the observer and the terrestrial environment, namely, the ground surface. We found that in dark and reduced-cue environments where intrinsic knowledge has a greater contribution, perceived target location is more accurate when referenced to the ground than to the ceiling. Furthermore, taller observers more accurately localized the target. Superior performance was also observed in the full-cue environment, even when we compensated for the observers’ heights by having the taller observer sit on a chair and the shorter observers stand on a box. Although fascinating, this finding dovetails with the prediction of the ecological hypothesis for intrinsic spatial knowledge. It suggests that an individual’s accumulated lifetime experiences of being tall and his or her constant interactions with ground-based objects not only determine intrinsic spatial knowledge but also endow him or her with an advantage in spatial ability in the intermediate distance range.

scholarly journals Effects of ozone on sickness and depressive-like behavioral and biochemical phenotypes and their relations to serum amyloid A and kynurenine in blood

2021 ◽  
Author(s):  
Kristen K Baumann ◽  
Wei-Shan Sandy Liang ◽  
Daniel V Quaranta ◽  
Miranda L Wilson ◽  
Helina S Asrat ◽  
...  
Keyword(s):  
Serum Amyloid A ◽  
Pulmonary Inflammation ◽  
Kynurenine Pathway ◽  
Air Pollutant ◽  
Behavioral Changes ◽  
Serum Amyloid ◽  
Present Evidence ◽  
Dose Time ◽  
Amyloid A ◽  
The Brain

Ozone (O3) is an air pollutant which primarily damages the lungs, but growing evidence supports that O3 exposure can also affect the brain. Serum amyloid A (SAA) and kynurenine have been identified as circulating factors that are upregulated by O3, and both can contribute to depressive-like behaviors in mice. However, little is known about the relations of O3 exposure to sickness and depressive-like behaviors in experimental settings. In this study, we evaluated O3 dose-, time- and sex- dependent changes in circulating SAA in context of pulmonary inflammation and damage, sickness and depressive-like behavioral changes, and systemic changes in kynurenine and indoleamine 2,3-dioxygenase (IDO), an enzyme that regulates kynurenine production and contributes to inflammation-induced depressive-like behaviors. Our results in Balb/c and CD-1 mice showed that 3ppm O3, but not 2 or 1ppm O3, caused elevations in serum SAA and pulmonary neutrophils, and these responses resolved by 48 hours. Sickness and depressive-like behaviors were observed at all O3 doses (1-3ppm), although the detection of certain behavioral changes varied by dose. We also found that Ido1 mRNA expression was increased in the brain and spleen 24 hours after 3ppm O3, and that kynurenine was increased in blood. Together, these findings indicate that acute O3 exposure induces transient symptoms of sickness and depressive-like behaviors which may occur in the presence or absence of overt pulmonary neutrophilia and systemic increases of SAA. We also present evidence that the IDO/kynurenine pathway is upregulated systemically following an acute exposure to O3 in mice.

scholarly journals Task-Dependent Warping of Semantic Representations During Search for Visual Action Categories

2021 ◽  
Author(s):  
Mo Shahdloo ◽  
Emin Çelik ◽  
Burcu A Urgen ◽  
Jack L. Gallant ◽  
Tolga Çukur
Keyword(s):  
Visual Search ◽  
Social Interactions ◽  
Brain Activity ◽  
Semantic Representation ◽  
Natural Scenes ◽  
Action Perception ◽  
Semantic Representations ◽  
Object Categories ◽  
Search For Objects ◽  
The Brain

Object and action perception in cluttered dynamic natural scenes relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. It has been suggested that during visual search for objects, distributed semantic representation of hundreds of object categories is warped to expand the representation of targets. Yet, little is known about whether and where in the brain visual search for action categories modulates semantic representations. To address this fundamental question, we studied human brain activity recorded via functional magnetic resonance imaging while subjects viewed natural movies and searched for either communication or locomotion actions. We find that attention directed to action categories elicits tuning shifts that warp semantic representations broadly across neocortex, and that these shifts interact with intrinsic selectivity of cortical voxels for target actions. These results suggest that attention serves to facilitate task performance during social interactions by dynamically shifting semantic selectivity towards target actions, and that tuning shifts are a general feature of conceptual representations in the brain.

scholarly journals A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

2018 ◽  
Author(s):  
Seth W. Egger ◽  
Mehrdad Jazayeri
Keyword(s):  
Probability Distributions ◽  
Nonlinear Function ◽  
Alternative View ◽  
Time Interval ◽  
Prior Beliefs ◽  
Optimal Behavior ◽  
Multiple Measurements ◽  
The Brain ◽  
Full Probability ◽  
Sequential Measurements

AbstractBayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability distributions. An alternative view is that brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property makes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that inference strategies humans deploy may deviate from Bayes-optimal integration when the computational demands are high.

scholarly journals Taxing the brain to uncover lying? Meta-analyzing the effect of imposing cognitive load on the reaction-time costs of lying

2018 ◽  
Author(s):  
Bruno Verschuere ◽  
Nils Köbis ◽  
yoella meyer ◽  
David Gertler Rand ◽  
Shaul Shalvi
Keyword(s):  
Reaction Time ◽  
Response Time ◽  
Cognitive Load ◽  
Lie Detection ◽  
Mental Effort ◽  
Cognitive Resources ◽  
Truth Telling ◽  
Total N ◽  
The Difference ◽  
The Brain

Lying typically requires greater mental effort than telling the truth. Imposing cognitive load may improve lie detection by limiting the cognitive resources needed to lie effectively, thereby increasing the difference in speed between truths and lies. We test this hypothesis meta-analytically. Across 21 studies using response-time (RT) paradigms (11 unpublished; total N = 792), we consistently found that truth telling was faster than lying, but found no evidence that imposing cognitive load increased that difference (Control, d = 1.45; Load, d = 1.28). Instead, load significantly decreased the lie-truth RT difference by increasing the RT of truths, g = -.18, p = .027. Our findings therefore suggest that imposing cognitive load does not necessarily improve RT-based lie detection, and may actually worsen it by taxing the mental system and thus impeding people’s ability to easily—and thus quickly—tell the truth

Role of spinal neurons in the maintenance of elevated sympathetic activity: a novel therapeutic target?

2020 ◽  
Vol 319 (3) ◽  
pp. R282-R287
Author(s):  
Maycon I. O. Milanez ◽  
Erika E. Nishi ◽  
Cássia T. Bergamaschi ◽  
Ruy R. Campos
Keyword(s):  
Cardiovascular Diseases ◽  
Therapeutic Target ◽  
Sympathetic Activity ◽  
Spinal Neurons ◽  
Potential Therapeutic Target ◽  
Present Evidence ◽  
Extensive Range ◽  
Vasomotor Activity ◽  
The Brain

The control of sympathetic vasomotor activity involves a complex network within the brain and spinal circuits. An extensive range of studies has indicated that sympathoexcitation is a common feature in several cardiovascular diseases and that strategies to reduce sympathetic vasomotor overactivity in such conditions can be beneficial. In the present mini-review, we present evidence supporting the spinal cord as a potential therapeutic target to mitigate sympathetic vasomotor overactivity in cardiovascular diseases, focusing mainly on the actions of spinal angiotensin II on the control of sympathetic preganglionic neuronal activity.

Sign in / Sign up

Export Citation Format

Share Document