ACT-PRO action protocol analyzer: A tool for analyzing discrete action protocols

This article presents a top-down approach for analyzing sequential events in behavioral data. Analysis of behavioral sequential data often entails identifying patterns specified by the researchers. Algorithms were developed and applied to analyze a kind of behavioral data, calleddiscrete action protocol data. Discrete action protocols consist of discrete user actions, such as mouse clicks and keypresses. Unfortunately, the process of analyzing the huge volume of actions (typically, > 10⁵) is very labor intensive. To facilitate this process, we developed an action protocol analyzer (ACT-PRO) that provides two levels of pattern matching. Level one uses formal grammars to identify sequential patterns. Level two matches these patterns to a hierarchical structure. ACT-PRO can be used to determine how well data fit the patterns specified by an experimenter. Complementarily, it can be used to focus an experimenter’s attention on data that do not fit the prespecified patterns.     

Identification of sequential dependencies in conversations: A Pascal program

A Pascal program that provides an analysis of the sequential dependencies between conversational events (e.g., speech acts) generated by two sources is described It produces z scores, chirsquare values, and confidence limits for the difference between estimated unconditional probabilities of the occurrence of selected events (valid events) and estimated conditional probabilities of these events following specified target events immediately (at Lag 1) and, if required, more distantly (at Lags 2, 3, etc.). Valid events and target events may be independently selected from the set of events generated by the first source, the second source, or both. Events not selected are ignored. The user may also elect to exclude the target event from the set of valid events (an adjustment for autocorrelation). The program is especially appropriate for conversational events because it does not assume that two events from the same category can never follow one another.     

A conceptual framework for establishing trust in real world intelligent systems

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user’s set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks.     

基于概念外延的用户认知表达-——以智能冰箱的用户认知研究为例

--用户认知具有主观性、模糊性、进化性及多维性等不确定性特点,难以编码与度量。提出以概念外延表达的集合论方法与模糊数学把研究用户认知转换成研究代表它的因素集合;提出用数据驱动的互联网文本挖掘获取因素集,采用TF-IDF与模糊统计求解因素的隶属度,完成用户认知的数学表达,为进一步研究提供数学基础;最后以海信智能冰箱项目进行论证。由于用户认知属于典型的不确定性复杂对象,论文所用方法适用于所有复杂对象的建模与求解。     

The acquisiton of time properties associated with a sequential motor skill

Three experiments are reported that examined the relative importance of phasing and duration training in the motor learning of a sequential task. In all three experiments, the task involved knocking down three barriers in a specified order. The Phasing task required the subject to contact each of the barriers in a particular goal time interval, that is, each segment had a particular movement-time goal. The Duration task required the subject to contact the final barrier in a total elapsed-time goal defined by the experimenter. Following training, half of the subjects in each training condition transferred to either a novel Duration or a novel Phasing task. Phasing-trained subjects, compared to Duration-trained subjects, produced equivalent transfer performance on the Duration transfer task but superior performance on the Phasing transfer task. These results suggest that phasing serves as a higher-order source of information for the performer in a sequential motor task. in addition, these experiments complement and extend previous work by Shapiro (1977) and Summers (1975) which demonstrated that learned phasing patterns were not modified despite changes in the overall rate of performing a motor sequence. Our experiments indicate that phasing training increases the performer's sensitivity to phasing patterns such that novel temporal patterns can be produced when they are well-defined.     

The Acquisition of Time Properties Associated with a Sequential Motor Skill

Three experiments are reported that examined the relative importance of phasing and duration training in the motor learning of a sequential task. In all three experiments, the task involved knocking down three barriers in a specified order. The Phasing task required the subject to contact each of the barriers in a particular goal time interval, that is, each segment had a particular movement-time goal. The Duration task required the subject to contact the final barrier in a total elapsed-time goal defined by the experimenter. Following training, half of the subjects in each training condition transferred to either a novel Duration or a novel Phasing task. Phasing-trained subjects, compared to Duration-trained subjects, produced equivalent transfer performance on the Duration transfer task but superior performance on the Phasing transfer task. These results suggest that phasing serves as a higher-order source of information for the performer in a sequential motor task. In addition, these experiments complement and extend previous work by Shapiro (1977) and Summers (1975) which demonstrated that learned phasing patterns were not modified despite changes in the overall rate of performing a motor sequence. Our experiments indicate that phasing training increases the performer’s sensitivity to phasing patterns such that novel temporal patterns can be produced when they are well-defined.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

Graze: A program to analyze recordings of the jaw movements of ruminants

Graze is a user friendly, Microsoft Windows 95/NT program for analyzing jaw movement recordings taken with the IGER Behavior Recorder. The program displays a plot of the jaw movement amplitude (vertical axis) against time (horizontal axis). Individual jaw movements can be automatically identified, using amplitude and frequency criteria specified by the user. Bouts of jaw movements can then be analyzed and are automatically identified as either grazing or ruminating. Behaviors other than grazing and ruminating (such as drinking or eating supplements) can also be identified and marked by the user. Identified jaw movements and bouts of behavior are superimposed on the jaw data display. Finally, the user can perform a bout analysis, which discriminates between bites and chews during eating and generates a summary file.     

Statistical learning within and between modalities: pitting abstract against stimulus-specific representations

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.     

Computing lag sequential statistics: The ELAG program

ELAG, a FORTRAN program that computes lag sequential statistics from event sequence data, is described, and its use is demonstrated. ELAG lets the user specify positive or negative lags of any length, whether codes may follow themselves or not, and whether z scores are to be computed as suggested by Allison and Liker (1982) or by Sackett (1980). Data sequences for individual subjects need not be continuous but can be broken into separate observations. Summary statistics for different groups of subjects may be printed. In addition, the initial data may be modified. The user may specify that an instance of one of a group of codes or of a particular two-code sequence is to be replaced with a new code before ELAG proceeds with its computations.     

A simple encoding and recording system for small group observation

A simple and inexpensive system for coding and recording interaction patterns in small groups is described. It consists of a keyboard and a standard tape recorder, and it is capable of storing sequential data using up to 36 codes. Its main application is in the study of speaker-target patterns, but it can also be used in encoding up to 12 behavioral codes, or six speakers and 6 codes. The keyboard costs less than $200 and is used with conventional tape recorders and minicomputers available at most research sites.     

Relational Semantics and Domain Semantics for Epistemic Modals

The standard account of modal expressions in natural language analyzes them as quantifiers over a set of possible worlds determined by the evaluation world and an accessibility relation. A number of authors have recently argued for an alternative account according to which modals are analyzed as quantifying over a domain of possible worlds that is specified directly in the points of evaluation. But the new approach only handles the data motivating it if it is supplemented with a non-standard account of attitude verbs and conditionals. It can be shown the the relational account handles the same data equally well if it too is supplemented with a non-standard account of such expressions.     

The CLASSI-N Method for the Study of Sequential Processes

In many psychological research domains stimulus-response profiles are explained by conjecturing a sequential process in which some variables mediate between stimuli and responses. Charting sequential processes is often a complex task because (1) many possible mediating variables may exist, and (2) interindividual differences may occur in the relationship between these mediating variables and the response. Recently, Ceulemans and Van Mechelen (Psychometrika 73(1):107–124, 2008) addressed these challenges by developing the CLASSI model. A major drawback of CLASSI is that it requires information about the same set of stimuli for all participants (i.e., crossed data), whereas recently a number of data gathering techniques have been proposed in which the set of stimuli differs across participants, yielding nested data. Therefore we present the CLASSI-N model, which extends the CLASSI model to nested data. A simulated annealing algorithm is proposed. The results of a simulation study are discussed as well as an application to data concerning depression.     

Humanizing autonomous vehicle driving: Understanding,modeling and impact assessment

The advent of autonomous vehicles brings major changes in the transportation systems influencing the infrastructure design, the network performance, as well as driving functions and habits. The penetration rate of this new technology highly depends on the acceptance of the automated driving services and functions, as well as on their impacts on various traffic, user oriented and environmental aspects. This research aims to present a methodological framework aiming to facilitate the modelling of the behaviour of new AV driving systems and their impacts on traffic, safety and environment. This framework introduces a stepwise approach, which will be leveraged by stakeholders in order to evaluate the new technology and its components at the design or implementation phase in order to increase acceptance and favor the adoption of the new technology. The proposed framework consists of four sequential steps: i. conceptual design, ii. data collection, processing and mining, iii. modelling and iv. autonomous vehicles impact assessment. The connection between these steps is illustrated and various Key Performance Indicators are specified for each impact area. The paper ends with highlighting some conceptual and modeling challenges that may critically affect the study of acceptance of autonomous vehicles in future mobility scenarios.     

Are multi‐layer backpropagation networks catastrophically amnesic?

Connectionist models with a backpropagation learning rule are known to have a serious problem. Such models exhibit catastrophic interference (or forgetting) with sequential training. Having learned a set of patterns, if the model is trained on another set of patterns, its performance on the first set can dramatically deteriorate very rapidly. The present study reconsiders this issue with four simulations. The model learned arithmetic facts sequentially, but the interference was only modest with random (hence approximately orthogonal) inputs. Essentially the same result was obtained when the inputs are made less orthogonal by adding irrelevant elements. Reducing the number of hidden units did not have major effects. This study suggests that the interference problem has been somewhat overstated.     

Implementation of experimental procedures in the PACER system of on-line control

PACER comprises an Executive and associated routines that together provide the run-time facilities commonly needed by experiment control programs. Although the implementation is specific to a particular PDP-11 installation, the nature of the support provided is of more general interest. Simple experimental procedures are straightforwardly handled by direct program control of timing and of external apparatus, and by direct inspection of the states of input lines connected to such apparatus. For more complicated situations, where events may not occur in a strict sequence, it becomes more convenient to make use of priority interrupts to inititiate response to events. Tasks to be performed under interrupt control are coded by the user as special subroutines known as “Actions.” It is a feature of PACER that the user can arrange for the Executive to run Actions automatically at specified times. The main program is then able to carry out processing that it would not be able to undertake if it had to make repeated checks on the time.     

Detection of visual patterns disturbed by noise: An exploratory study

An introductory study of the perception of stochastically specified events is reported. The initial problem was to determine whether the perceiver can split visual input data of this kind into random and determined components. The inability of subjects to do so with the stimulus material used (a filmlike sequence of dot patterns), led to the more general question of how subjects code this kind of visual material. To meet the difficulty of defining the subjects' responses, two experiments were designed. In both, patterns were presented as a rapid sequence of dots on a screen. The patterns were more or less disturbed by “noise,” i.e. the dots did not appear exactly at their proper places. In the first experiment the response was a rating on a semantic scale, in the second an identification from among a set of alternative patterns. The results of these experiments give some insight in the coding systems adopted by the subjects. First, noise appears to be detrimental to pattern recognition, especially to patterns with little spread. Second, this shows connections with the factors obtained from analysis of the semantic ratings, e.g. easily disturbed patterns show a large drop in the semantic regularity factor, when only a little noise is added.     

When you fail to see what you were told to look for: Inattentional blindness and task instructions

Inattentional blindness studies have shown that an unexpected object may go unnoticed if it does not share the property specified in the task instructions. Our aim was to demonstrate that observers develop an attentional set for a property not specified in the task instructions if it allows easier performance of the primary task. Three experiments were conducted using a dynamic selective-looking paradigm. Stimuli comprised four black squares and four white diamonds, so that shape and colour varied together. Task instructions specified shape but observers developed an attentional set for colour, because we made the black–white discrimination easier than the square–diamond discrimination. None of the observers instructed to count bounces by squares reported an unexpected white square, whereas two-thirds of observers instructed to count bounces by diamonds did report the white square. When attentional set departs from task instructions, you may fail to see what you were told to look for.