Temporal Stimulus Generalization in Humans

Two experiments investigated temporal generalization in humans using a computer based task which presented red dots with a range of lines at different angles and durations. After training with a standard S+ stimulus duration, generalization testing commenced with an asymmetrical series of presentations of lines of varying angles and durations. Experiment 1 had four conditions, with a standard S+ duration being the presentation of a red dot for a fixed duration. Two of the conditions had the addition of the line tilt. In Experiment 1, 11 participants produced a peak shift effect in all four conditions. Experiment 2 was the same as Experiment 1 except that there were two conditions. Condition 2 was the same as Condition 1 except that the participants were given a verbal instruction to think of the line tilt as if hands on a clock. All 9 participants produced a peak shift effect in both conditions. In Experiment 2, the effect of categorising the stimuli and in turn changing the stimuli from a continuous dimension to discrete stimuli (one in which could be labelled) and the verbal instruction of to think of the line tilt as if hands on a clock did not have an effect on the peak shift as predicted. The results for both experiments were in accordance with predictions of adaptation level theory

Probabilistic Motion Estimation Based on Temporal Coherence

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques used by engineers to study motion sequences. Our temporal-grouping theory is expressed in terms of the Bayesian generalization of standard Kalman filtering. To implement the theory we derive a parallel network which shares some properties of cortical networks. Computer simulations of this network demonstrate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers.Comment: 40 pages, 7 figure

Temporal evolution of generalization during learning in linear networks

We study generalization in a simple framework of feedforward linear networks with n inputs and n outputs, trained from examples by gradient descent on the usual quadratic error function. We derive analytical results on the behavior of the validation function corresponding to the LMS error function calculated on a set of validation patterns. We show that the behavior of the validation function depends critically on the initial conditions and on the characteristics of the noise. Under certain simple assumptions, if the initial weights are sufficiently small, the validation function has a unique minimum corresponding to an optimal stopping time for training for which simple bounds can be calculated. There exists also situations where the validation function can have more complicated and somewhat unexpected behavior such as multiple local minima (at most n) of variable depth and long but finite plateau effects. Additional results and possible extensions are briefly discussed

Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video

We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples--only specific-to-general learning method based on these algorithms. We also present a polynomial-time--computable ``syntactic'' subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. Finally, we apply this algorithm to the task of learning relational event definitions from video and show that it yields definitions that are competitive with hand-coded ones

Generalization Strategies for the Verification of Infinite State Systems

We present a method for the automated verification of temporal properties of infinite state systems. Our verification method is based on the specialization of constraint logic programs (CLP) and works in two phases: (1) in the first phase, a CLP specification of an infinite state system is specialized with respect to the initial state of the system and the temporal property to be verified, and (2) in the second phase, the specialized program is evaluated by using a bottom-up strategy. The effectiveness of the method strongly depends on the generalization strategy which is applied during the program specialization phase. We consider several generalization strategies obtained by combining techniques already known in the field of program analysis and program transformation, and we also introduce some new strategies. Then, through many verification experiments, we evaluate the effectiveness of the generalization strategies we have considered. Finally, we compare the implementation of our specialization-based verification method to other constraint-based model checking tools. The experimental results show that our method is competitive with the methods used by those other tools. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 24 pages, 2 figures, 5 table

Temporal stability of network partitions

We present a method to find the best temporal partition at any time-scale and rank the relevance of partitions found at different time-scales. This method is based on random walkers coevolving with the network and as such constitutes a generalization of partition stability to the case of temporal networks. We show that, when applied to a toy model and real datasets, temporal stability uncovers structures that are persistent over meaningful time-scales as well as important isolated events, making it an effective tool to study both abrupt changes and gradual evolution of a network mesoscopic structures.Comment: 15 pages, 12 figure

Learning functional object categories from a relational spatio-temporal representation

Abstract. We propose a framework that learns functional objectcategories from spatio-temporal data sets such as those abstracted from video. The data is represented as one activity graph that encodes qualitative spatio-temporal patterns of interaction between objects. Event classes are induced by statistical generalization, the instances of which encode similar patterns of spatio-temporal relationships between objects. Equivalence classes of objects are discovered on the basis of their similar role in multiple event instantiations. Objects are represented in a multidimensional space that captures their role in all the events. Unsupervised learning in this space results in functional object-categories. Experiments in the domain of food preparation suggest that our techniques represent a significant step in unsupervised learning of functional object categories from spatio-temporal patterns of object interaction.