scholarly journals Policies or Knowledge: Priors differ between perceptual and sensorimotor tasks

2017 ◽  
Author(s):  
Claire Chambers ◽  
Hugo Fernandes ◽  
Konrad Paul Kording
Keyword(s):  
Prior Distribution ◽  
Probability Distributions ◽  
Perceptual Task ◽  
Comparison Task ◽  
Estimation Task ◽  
Model And Simulation ◽  
Central Tenet ◽  
Internal States ◽  
Perceptual Comparison ◽  
The Brain

ABSTRACTIf the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g. movement vs perception, should not matter. If on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking if a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn the experimentally-imposed prior distribution in the sensorimotor estimation task, measured priors are consistently broader than expected in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors strongly resemble policies. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distribution.NEW AND NOTEWORTHYWe do not know if the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of may Bayesian models, that the brain’s representation of the world is built on generalizable knowledge.

scholarly journals Policies or knowledge: priors differ between a perceptual and sensorimotor task

2019 ◽  
Vol 121 (6) ◽  
pp. 2267-2275 ◽  
Author(s):  
Claire Chambers ◽  
Hugo Fernandes ◽  
Konrad Paul Kording
Keyword(s):  
Probability Distributions ◽  
Perceptual Task ◽  
Comparison Task ◽  
Estimation Task ◽  
Model And Simulation ◽  
Simulation Based ◽  
Central Tenet ◽  
Internal States ◽  
Perceptual Comparison ◽  
The Brain

If the brain abstractly represents probability distributions as knowledge, then the modality of a decision, e.g., movement vs. perception, should not matter. If, on the other hand, learned representations are policies, they may be specific to the task where learning takes place. Here, we test this by asking whether a learned spatial prior generalizes from a sensorimotor estimation task to a two-alternative-forced choice (2-Afc) perceptual comparison task. A model and simulation-based analysis revealed that while participants learn prior distribution in the sensorimotor estimation task, measured priors are consistently broader than sensorimotor priors in the 2-Afc task. That the prior does not fully generalize suggests that sensorimotor priors are more like policies than knowledge. In disagreement with standard Bayesian thought, the modality of the decision has a strong influence on the implied prior distributions. NEW & NOTEWORTHY We do not know whether the brain represents abstract and generalizable knowledge or task-specific policies that map internal states to actions. We find that learning in a sensorimotor task does not generalize strongly to a perceptual task, suggesting that humans learned policies and did not truly acquire knowledge. Priors differ across tasks, thus casting doubt on the central tenet of many Bayesian models, that the brain’s representation of the world is built on generalizable knowledge.

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 Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

2020 ◽  
Author(s):  
Wei Guo ◽  
Jie J. Zhang ◽  
Jonathan P. Newman ◽  
Matthew A. Wilson
Keyword(s):  
Hippocampal Neurons ◽  
Dimensional Manifold ◽  
Latent Learning ◽  
Neural Ensembles ◽  
Correlated Activity ◽  
Place Fields ◽  
Internal States ◽  
Low Dimensional ◽  
Low Dimensional Manifold ◽  
The Brain

AbstractLatent learning allows the brain the transform experiences into cognitive maps, a form of implicit memory, without reinforced training. Its mechanism is unclear. We tracked the internal states of the hippocampal neural ensembles and discovered that during latent learning of a spatial map, the state space evolved into a low-dimensional manifold that topologically resembled the physical environment. This process requires repeated experiences and sleep in-between. Further investigations revealed that a subset of hippocampal neurons, instead of rapidly forming place fields in a novel environment, remained weakly tuned but gradually developed correlated activity with other neurons. These ‘weakly spatial’ neurons bond activity of neurons with stronger spatial tuning, linking discrete place fields into a map that supports flexible navigation.

scholarly journals Conservation law for self-paced movements

2016 ◽  
Vol 113 (31) ◽  
pp. 8831-8836 ◽  
Author(s):  
Dongsung Huh ◽  
Terrence J. Sejnowski
Keyword(s):  
Optimal Control ◽  
Scaling Law ◽  
Control Variable ◽  
Level Control ◽  
Internal Motivation ◽  
Internal States ◽  
Motivation Level ◽  
High Level ◽  
The Brain ◽  
Biological Motor

Optimal control models of biological movements introduce external task factors to specify the pace of movements. Here, we present the dual to the principle of optimality based on a conserved quantity, called “drive,” that represents the influence of internal motivation level on movement pace. Optimal control and drive conservation provide equivalent descriptions for the regularities observed within individual movements. For regularities across movements, drive conservation predicts a previously unidentified scaling law between the overall size and speed of various self-paced hand movements in the absence of any external tasks, which we confirmed with psychophysical experiments. Drive can be interpreted as a high-level control variable that sets the overall pace of movements and may be represented in the brain as the tonic levels of neuromodulators that control the level of internal motivation, thus providing insights into how internal states affect biological motor control.

Integration of Stochastic Models by Minimizing α-Divergence

2007 ◽  
Vol 19 (10) ◽  
pp. 2780-2796 ◽  
Author(s):  
Shun-ichi Amari
Keyword(s):  
Stochastic Models ◽  
Probability Distributions ◽  
Predictive Distribution ◽  
Natural Generalization ◽  
Bayesian Framework ◽  
Hellinger Distance ◽  
Mixture Of Experts ◽  
Mixtures Of Distributions ◽  
Leibler Divergence ◽  
The Brain

When there are a number of stochastic models in the form of probability distributions, one needs to integrate them. Mixtures of distributions are frequently used, but exponential mixtures also provide a good means of integration. This letter proposes a one-parameter family of integration, called α-integration, which includes all of these well-known integrations. These are generalizations of various averages of numbers such as arithmetic, geometric, and harmonic averages. There are psychophysical experiments that suggest that α-integrations are used in the brain. The α-divergence between two distributions is defined, which is a natural generalization of Kullback-Leibler divergence and Hellinger distance, and it is proved that α-integration is optimal in the sense of minimizing α-divergence. The theory is applied to generalize the mixture of experts and the product of experts to the α-mixture of experts. The α-predictive distribution is also stated in the Bayesian framework.

scholarly journals Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 516 ◽  
Author(s):  
Karl J. Friston ◽  
Wanja Wiese ◽  
J. Allan Hobson
Keyword(s):  
Probability Distributions ◽  
Information Geometry ◽  
Mental States ◽  
Extrinsic Information ◽  
Physical Sciences ◽  
Markov Blanket ◽  
Internal States ◽  
Mental Properties ◽  
Intentional Systems ◽  
The Mind

This essay addresses Cartesian duality and how its implicit dialectic might be repaired using physics and information theory. Our agenda is to describe a key distinction in the physical sciences that may provide a foundation for the distinction between mind and matter, and between sentient and intentional systems. From this perspective, it becomes tenable to talk about the physics of sentience and ‘forces’ that underwrite our beliefs (in the sense of probability distributions represented by our internal states), which may ground our mental states and consciousness. We will refer to this view as Markovian monism, which entails two claims: (1) fundamentally, there is only one type of thing and only one type of irreducible property (hence monism). (2) All systems possessing a Markov blanket have properties that are relevant for understanding the mind and consciousness: if such systems have mental properties, then they have them partly by virtue of possessing a Markov blanket (hence Markovian). Markovian monism rests upon the information geometry of random dynamic systems. In brief, the information geometry induced in any system—whose internal states can be distinguished from external states—must acquire a dual aspect. This dual aspect concerns the (intrinsic) information geometry of the probabilistic evolution of internal states and a separate (extrinsic) information geometry of probabilistic beliefs about external states that are parameterised by internal states. We call these intrinsic (i.e., mechanical, or state-based) and extrinsic (i.e., Markovian, or belief-based) information geometries, respectively. Although these mathematical notions may sound complicated, they are fairly straightforward to handle, and may offer a means through which to frame the origins of consciousness.

scholarly journals Peak-at-end rule: adaptive mechanism predicts time-dependent decision weighting

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryuto Yashiro ◽  
Isamu Motoyoshi
Keyword(s):  
Decision Process ◽  
Temporal Dynamics ◽  
Temporal Integration ◽  
Time Dependent ◽  
Adaptive Mechanism ◽  
Perceptual Decision Making ◽  
Estimation Task ◽  
Leaky Integrator ◽  
Sensory Evidence ◽  
The Brain

Abstract Humans make decisions under various natural circumstances, integrating multiple pieces of information that are distributed over space and time. Although psychophysical and physiological studies have investigated temporal dynamics underlying perceptual decision making, weighting profiles for inliers and outliers during temporal integration have yet to be fully investigated in most studies. Here, we examined the temporal weighting profile of a computational model characterized by a leaky integrator of sensory evidence. As a corollary of its leaky nature, the model predicts the recency effect and overweights outlying elements around the end of the stream. Moreover, we found that the model underweights outlying values occurring earlier in the stream (i.e., robust averaging). We also show that human observers exhibit exactly the same weighting profile in an average estimation task. These findings suggest that the adaptive decision process in the brain results in the time-dependent decision weighting, the “peak-at-end” rule, rather than the peak-end rule in behavioral economics.

The Sources of Sequential Modulations of Control Processes in Arithmetic Strategies: A Magnetoencephalography Study

2017 ◽  
Vol 29 (6) ◽  
pp. 1033-1043 ◽  
Author(s):  
Thomas Hinault ◽  
Jean-Michel Badier ◽  
Sylvain Baillet ◽  
Patrick Lemaire
Keyword(s):  
Executive Control ◽  
Time Course ◽  
Cognitive Strategies ◽  
Brain Regions ◽  
Strategy Execution ◽  
Estimation Task ◽  
Cognitive Domains ◽  
Computational Estimation ◽  
Control Processes ◽  
The Brain

In a wide variety of cognitive domains, performance is determined by the selection and execution of cognitive strategies to solve problems. We used magnetoencephalography to identify the brain regions involved and specify the time course of dynamic modulations of executive control processes during strategy execution. Participants performed a computational estimation task in which they were instructed to execute a poorer or better strategy to estimate results of two-digit multiplication problems. When participants were asked to execute the poorer strategy, two distinct sets of brain activations were identified, depending on whether the poorer strategy (engaging the left inferior frontal junction) or the better strategy (engaging ACC) had been executed on the immediately preceding items. Our findings also revealed the time course of activations in regions involved in sequential modulations of cognitive control processes during arithmetic strategy execution. These findings point at processes of proactive preparation on items after poorer strategy items and dynamics of reactive adjustments after better strategy items.

scholarly journals Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

1999 ◽  
Vol 11 (2) ◽  
pp. 417-441 ◽  
Author(s):  
Te-Won Lee ◽  
Mark Girolami ◽  
Terrence J. Sejnowski
Keyword(s):  
Stability Analysis ◽  
Probability Distributions ◽  
Projection Pursuit ◽  
Learning Rule ◽  
High Dimensional ◽  
Simple Type ◽  
Natural Gradient ◽  
Eye Blinks ◽  
The Stability ◽  
The Brain

An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.

scholarly journals Individual differences in numerical comparison is independent of numerical precision

2019 ◽  
Vol 5 (2) ◽  
pp. 220-240
Author(s):  
Richard Prather
Keyword(s):  
Mathematical Model ◽  
Individual Variation ◽  
Numerical Comparison ◽  
Behavioral Experiments ◽  
Comparison Task ◽  
Free Response ◽  
Estimation Task ◽  
Decision Making Factors ◽  
Numerical Precision ◽  
Value Representation

Numeracy, as measured by performance on the non-symbolic numerical comparison task, is a key construct in numerical and mathematical cognition. The current study examines individual variation in performance on the numerical comparison task. We contrast the hypothesis that performance on the numerical comparison task is primarily due to more accurate representations of numbers with the hypothesis that performance dependent on decision-making factors. We present data from two behavioral experiments and a mathematical model. In both behavioral experiments we measure the precision of participant’s numerical value representation using a free response estimation task. Taken together, results suggest that individual variation in numerical comparison performance is not predicted by variation in the precision of participants’ numerical value representation.

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