scholarly journals Human inferences about sequences: A minimal transition probability model

2016 ◽  
Author(s):  
Florent Meyniel ◽  
Maxime Maheu ◽  
Stanislas Dehaene
Keyword(s):  
Transition Probabilities ◽  
Transition Probability ◽  
Probability Model ◽  
Reaction Times ◽  
Time Varying ◽  
Sequential Effects ◽  
The Core ◽  
Human Sequence ◽  
Transition Probability Model ◽  
The Brain

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.

scholarly journals Revisiting the Relationship between Military Expenditure and Economic Growth in Pakistan

2017 ◽  
Vol II (I) ◽  
pp. 18-46
Author(s):  
Waqar Qureshi ◽  
Noor Pio Khan
Keyword(s):  
Economic Growth ◽  
Transition Probabilities ◽  
Empirical Test ◽  
Transition Probability ◽  
Probability Model ◽  
Growth Period ◽  
Military Expenditure ◽  
Time Varying ◽  
Indicator Variable ◽  
Military Expenditures

This study aims to examine relationship of military expenditure and economic growth in different phases of military regimes in the context of Pakistan. This study uses two-state Markov switching models with Constant Transition Probability (CTP) and Time Varying Transition Probabilities (TVTP) for the time period: 1973-2014. This investigation analyses two sorts of relations between military expenditures and economic development through fixed transition probability Markov exchanging models. To begin with, there is negative connection between GDP growth and military expenditures during a high variance state (i.e. having low economic growth). Second, there is positive relation between both variables, during low variance state (i.e. having higher economic growth) which is also supported by idea of Keynesian income multiplier. Another, empirical test of time varying transition probability model was used to capture the switch through indicator variable. Results of the study suggest that chances of switching are increased from low to high economic growth. The chances of switching increase from lower to higher economic growth period (or high variance period) if non-military expenditure increases. The study concludes that military expenditure and economic growth are state dependent. If conditions of economy are stable then increase of expenditure results in positive outcomes, otherwise, it affects negatively. Empirical findings suggest that military spending should be planned in accordance to the economic performance of the country.

scholarly journals The 3T3 cell cycle at low proliferation rates

1988 ◽  
Vol 90 (4) ◽  
pp. 601-612
Author(s):  
R.F. Brooks ◽  
P.N. Riddle
Keyword(s):  
Cell Cycle ◽  
Cycle Time ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Probability Model ◽  
Proliferation Rate ◽  
Growth Fraction ◽  
Minor Role ◽  
Regulation Of Proliferation ◽  
Transition Probability Model

When the proliferation rate of Swiss 3T3 cells is decreased by limiting the availability of growth factors, cell cycle variability increases, as predicted by the transition probability model. Nevertheless, the transition probabilities would appear to play a relatively minor role in the regulation of proliferation rate. Instead, at least 40% of the increase in the average cycle time is brought about by an elongation of the minimum cycle time (i.e. the ‘deterministic’ part of the cycle). In addition, we have found that a substantial proportion of the cells (roughly 20%, in the present experiments, for doubling times of the order of 35–40 h) drop out of cycle in each generation, leading to a growth fraction of less than 1.0. The non-dividing cells, which we have previously shown to remain capable of division, would seem to support the existence of a Go state outside the normal cell cycle, and distinct from the indeterminate states postulated by the transition probability model. Because of the generation of nondividing cells at low proliferation rates, the log alpha and beta plots (distributions of cycle times, and sibling cycle time differences, respectively) are markedly concave, with a continuously decreasing slope. The transition probabilities cannot therefore be estimated directly and it is impossible to determine the extent to which they contribute to the regulation of proliferation rate. Rather, our data suggest that the transition probabilities are not uniform throughout the population under these conditions, but vary substantially from cell to cell. In addition to the changes in cell cycle kinetics, we also report an increased failure rate of cytokinesis, at low proliferation rates, leading initially to the appearance of binucleate cells. Such failures of cytokinesis may be responsible for the well-known rise in the incidence of binucleate and polyploid cells in the liver, with age.

scholarly journals A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans

2021 ◽  
Vol 17 (1) ◽  
pp. e1008598
Author(s):  
Samuel Planton ◽  
Timo van Kerkoerle ◽  
Leïla Abbih ◽  
Maxime Maheu ◽  
Florent Meyniel ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Comparison ◽  
Transition Probability ◽  
Probability Model ◽  
Working Memory Capacity ◽  
Compression Algorithm ◽  
Binary Sequences ◽  
Fixed Sequence ◽  
Transition Probability Model ◽  
Human Adults

Working memory capacity can be improved by recoding the memorized information in a condensed form. Here, we tested the theory that human adults encode binary sequences of stimuli in memory using an abstract internal language and a recursive compression algorithm. The theory predicts that the psychological complexity of a given sequence should be proportional to the length of its shortest description in the proposed language, which can capture any nested pattern of repetitions and alternations using a limited number of instructions. Five experiments examine the capacity of the theory to predict human adults’ memory for a variety of auditory and visual sequences. We probed memory using a sequence violation paradigm in which participants attempted to detect occasional violations in an otherwise fixed sequence. Both subjective complexity ratings and objective violation detection performance were well predicted by our theoretical measure of complexity, which simply reflects a weighted sum of the number of elementary instructions and digits in the shortest formula that captures the sequence in our language. While a simpler transition probability model, when tested as a single predictor in the statistical analyses, accounted for significant variance in the data, the goodness-of-fit with the data significantly improved when the language-based complexity measure was included in the statistical model, while the variance explained by the transition probability model largely decreased. Model comparison also showed that shortest description length in a recursive language provides a better fit than six alternative previously proposed models of sequence encoding. The data support the hypothesis that, beyond the extraction of statistical knowledge, human sequence coding relies on an internal compression using language-like nested structures.

Application of FTRF in Cascading Failure Probability Model in Adverse Weather

2013 ◽  
Vol 336-338 ◽  
pp. 471-474
Author(s):  
Shi Guang ◽  
Hai Jing Yang ◽  
Qi Wei Wang ◽  
Yan Jin
Keyword(s):  
Failure Probability ◽  
Power Flow ◽  
Transition Probability ◽  
Probability Model ◽  
First Line ◽  
Line Load ◽  
State Transfer ◽  
Adverse Weather ◽  
Load Rate ◽  
Transition Probability Model

In allusion to the issues of system line state transfer that may arise in adverse weather, a new method of probability calculation is proposed. In a statistical analysis, this article firstly defines that failure probability of the first line subjects to Poisson distribution. Secondly, we figure out the power flow transferring distribution after first line fault, according to the method of Flow Transferring Relativity Factor (FTRF), and combine with the protective possibility so as to build the probability model between the load rate and protection action. Then, the method defines the severity of line load rate. Finally, the approach constructs the line state transition probability model considering direct and indirect factors in adverse weather. The effectiveness and correctness of the proposed method are verified by simulation based on IEEE 39-node system.

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