scholarly journals Do rats learn conditional independence?

2017 ◽  
Vol 4 (2) ◽  
pp. 160994 ◽  
Author(s):  
Robert Ian Bowers ◽  
William Timberlake
Keyword(s):  
Conditioned Stimulus ◽  
Graphical Models ◽  
Conditional Independence ◽  
Theoretical Weight ◽  
Compound Conditioning ◽  
Markov Condition ◽  
Serial Compound ◽  
Conditioning Experiment ◽  
Direct Dependence ◽  
Serial Compound Conditioning

If acquired associations are to accurately represent real relevance relations, there is motivation for the hypothesis that learning will, in some circumstances, be more appropriately modelled, not as direct dependence, but as conditional independence. In a serial compound conditioning experiment, two groups of rats were presented with a conditioned stimulus (CS1) that imperfectly (50%) predicted food, and was itself imperfectly predicted by a CS2. Groups differed in the proportion of CS2 presentations that were ultimately followed by food (25% versus 75%). Thus, the information presented regarding the relevance of CS2 to food was ambiguous between direct dependence and conditional independence (given CS1). If rats learnt that food was conditionally independent of CS2, given CS1, subjects of both groups should thereafter respond similarly to CS2 alone. Contrary to the conditionality hypothesis, subjects attended to the direct food predictability of CS2, suggesting that rats treat even distal stimuli in a CS sequence as immediately relevant to food, not conditional on an intermediate stimulus. These results urge caution in representing indirect associations as conditional associations, accentuate the theoretical weight of the Markov condition in graphical models, and challenge theories to articulate the conditions under which animals are expected to learn conditional associations, if ever.

scholarly journals Conditional independence test by generalized Kendall’s tau with generalized odds ratio

2017 ◽  
Vol 27 (11) ◽  
pp. 3224-3235 ◽  
Author(s):  
Shuang Ji ◽  
Jing Ning ◽  
Jing Qin ◽  
Dean Follmann
Keyword(s):  
Graphical Models ◽  
Odds Ratio ◽  
Conditional Independence ◽  
Model Building ◽  
Association Studies ◽  
Test Procedure ◽  
Finite Sample ◽  
Test Statistic ◽  
Kendall’S Tau ◽  
Kendall's Tau

Determining conditional dependence is a challenging but important task in both model building and in applications such as genetic association studies and graphical models. Research on this topic has focused on kernel-based methods or has used categorical conditioning variables because of the challenge of the curse of dimensionality. To overcome this challenge, we propose a class of tests for conditional independence without any restriction on the distribution of the conditioning variables. The proposed test statistic can be treated as a generalized weighted Kendall’s tau, in which the generalized odds ratio is utilized as a weight function to account for the distance between different values of the conditioning variables. The test procedure has desirable asymptotic properties and is easy to implement. We evaluate the finite sample performance of the proposed test through simulation studies and illustrate it using two real data examples.

Seasonal Variability of Relationship between Main Ionospheric Characteristics and Solar/Geomagnetic Indices via Graphical Models of Conditional Independences

2021 ◽  
Author(s):  
Kateřina Podolská ◽  
Petra Koucká Knížová ◽  
Jaroslav Chum
Keyword(s):  
Time Series ◽  
Graphical Models ◽  
Seasonal Variations ◽  
Conditional Independence ◽  
Solar Cycles ◽  
Seasonal Differences ◽  
Ionospheric Response ◽  
Geomagnetic Indices ◽  
Before And After ◽  
Independence Graphs

<p>We investigated seasonal variations of relationships between main ionospheric characteristics and solar and geomagnetic indices in longitudinal perspective. We consider statistically significant differences in connections of ionospheric response to the F10.7cm, R, and Kp indices on seasonal time-scales during years 1975 – 2010 covering 21<sup>st</sup> – 23<sup>rd</sup> Solar Cycles. The periods of 21 days before and after Winter/Summer Solstices and Vernal/Autumnal Equinoces are considered as season. The foF2 time series in our analysis represent measurements of daily observational data which were obtained using mid-latitude (41.4°N – 54°N) ionosondes (Chilton, Slough RL052/SL051, Juliusruh/Rugen JR055, Boulder BC840). We used local time noon 5-hour foF2 averages. For the investigation, we used seasonal differences method of conditional independence graphs (CIG) models. Significant seasonal variations are visible during ascending and descending phases of Solar cycles.</p>

The Influence of Partial Reinforcement on Serial Autoshaping with Pigeons

1983 ◽  
Vol 35 (4b) ◽  
pp. 275-290 ◽  
Author(s):  
Louis Collins ◽  
Douglas B. Young ◽  
Keith Davies ◽  
John M. Pearce
Keyword(s):  
Unconditioned Stimulus ◽  
Associative Strength ◽  
Reinforcement Schedule ◽  
Partial Reinforcement ◽  
Compound Conditioning ◽  
Continuous Reinforcement Schedule ◽  
Continuous Reinforcement ◽  
Serial Compound

The effect of partial reinforcement on the rate of responding during the first element of a serial compound was investigated using autoshaping in pigeons. Experiment I employed the illumination of a response key by two different colours as the elements of the compound. Responding during the first element was faster when this stimulus was intermittently paired with the second element and the unconditioned stimulus than when a continuous reinforcement schedule was employed. Experiment II demonstrated that this effect of partial reinforcement is unaffected by maniuplating the associative strength of the second element at the outset of compound conditioning. A similar effect of partial reinforcement was also found in Experiment III which used a tone as the first element of the serial compound.

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