Representation of Numerical and Sequential Patterns in Macaque and Human Brains

Proc. Natl. Acad. Sci. U.S.A.

2017

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107

146

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an idealobserver analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidenceweighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.T he sensory data that we receive from our environment are often captured by temporal regularities-for instance the colors of traffic lights change according to a predictable greenyellow-red pattern; thunder is often followed by rain, etc. Knowledge of those hidden regularities is often acquired through learning, by aggregating successive observations into summary estimates (e.g., the probability of the light turning red when it is currently yellow). When sensory data are received sequentially, learning can be described as an iterative process that updates the internal estimates each time a new observation is received. Learners must therefore constantly balance two sources of information: their current estimates and the new incoming observations. Any learning algorithm must find a solution to this balancing act. Finding the correct balance is especially critical in a world that is both stochastic and changing (1), i.e., where observations are governed by probabilities that can change over time (a situation called volatility). An excessive reliance on incoming observations will make the learned estimates dominated by fluctuations instead of converging to the true underlying probabilities. Conversely, an excessive reliance o...

Section: Discussionsupporting

confidence: 65%

Brain networks for confidence weighting and hierarchical inference during probabilistic learning

Meyniel

Proc. Natl. Acad. Sci. U.S.A.

2017

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107

146

“…A very small area of overlap could be seen in the left dorsal Brodmann area 44 (SI Appendix, Fig. S8B), an area also singled out in previous reports (34) and which should certainly be further investigated in future research. Note, however, that this small overlap was present only in smoothed group images and failed to reach significance in higher resolution single-subject results (SI Appendix, Table S5).…”

Section: Resultsmentioning

confidence: 51%

Origins of the brain networks for advanced mathematics in expert mathematicians

Amalric

Proc. Natl. Acad. Sci. U.S.A.

2016

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353

298

The origins of human abilities for mathematics are debated: Some theories suggest that they are founded upon evolutionarily ancient brain circuits for number and space and others that they are grounded in language competence. To evaluate what brain systems underlie higher mathematics, we scanned professional mathematicians and mathematically naive subjects of equal academic standing as they evaluated the truth of advanced mathematical and nonmathematical statements. In professional mathematicians only, mathematical statements, whether in algebra, analysis, topology or geometry, activated a reproducible set of bilateral frontal, Intraparietal, and ventrolateral temporal regions. Crucially, these activations spared areas related to language and to general-knowledge semantics. Rather, mathematical judgments were related to an amplification of brain activity at sites that are activated by numbers and formulas in nonmathematicians, with a corresponding reduction in nearby face responses. The evidence suggests that high-level mathematical expertise and basic number sense share common roots in a nonlinguistic brain circuit.

“…The prefrontal cortex appears to act as a central information sharing device and serial bottleneck in both human and nonhuman primates (43). The considerable expansion of the prefrontal cortex in the human lineage may have resulted in a greater capacity for multimodal convergence and integration (44)(45)(46). Furthermore, humans possess additional circuits in the inferior prefrontal cortex for verbally formulating and reporting information to others.…”

Section: C1 Consciousness In Human and Nonhuman Animalsmentioning

confidence: 99%

What is consciousness, and could machines have it?

Lau

Kouider

2017

Science

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512

318

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.