Arthur M. Jacobs

Bio: Arthur M. Jacobs is an academic researcher from Free University of Berlin. The author has contributed to research in topics: Word recognition & Lexical decision task. The author has an hindex of 67, co-authored 260 publications receiving 14636 citations. Previous affiliations of Arthur M. Jacobs include Ruhr University Bochum & School for Advanced Studies in the Social Sciences.

Orthographic Processing in Visual Word Recognition: A Multiple Read-Out Model

Abstract: A model of orthographic processing is described that postulates read-out from different information dimensions, determined by variable response criteria set on these dimensions. Performance in a perceptual identification task is simulated as the percentage of trials on which a noisy criterion set on the dimension of single word detector activity is reached. Two additional criteria set on the dimensions of total lexical activity and time from stimulus onset are hypothesized to be operational in the lexical decision task. These additional criteria flexibly adjust to changes in stimulus material and task demands, thus accounting for strategic influences on performance in this task. The model unifies results obtained in response-limited and data-limited paradigms and helps resolve a number of inconsistencies in the experimental literature that cannot be accommodated by other current models of visual word recognition.

Coregistration of eye movements and EEG in natural reading: analyses and review.

Abstract: Brain-electric correlates of reading have traditionally been studied with word-by-word presentation, a condition that eliminates important aspects of the normal reading process and precludes direct comparisons between neural activity and oculomotor behavior. In the present study, we investigated effects of word predictability on eye movements (EM) and fixation-related brain potentials (FRPs) during natural sentence reading. Electroencephalogram (EEG) and EM (via video-based eye tracking) were recorded simultaneously while subjects read heterogeneous German sentences, moving their eyes freely over the text. FRPs were time-locked to first-pass reading fixations and analyzed according to the cloze probability of the currently fixated word. We replicated robust effects of word predictability on EMs and the N400 component in FRPs. The data were then used to model the relation among fixation duration, gaze duration, and N400 amplitude, and to trace the time course of EEG effects relative to effects in EM behavior. In an extended Methodological Discussion section, we review 4 technical and data-analytical problems that need to be addressed when FRPs are recorded in free-viewing situations (such as reading, visual search, or scene perception) and propose solutions. Results suggest that EEG recordings during normal vision are feasible and useful to consolidate findings from EEG and eye-tracking studies.

The Berlin Affective Word List Reloaded (BAWL-R).

Abstract: The study presented here provides researchers with a revised list of affective German words, the Berlin Affective Word List Reloaded (BAWL-R). This work is an extension of the previously published BAWL (Vo, Jacobs, & Conrad, 2006), which has enabled researchers to investigate affective word processing with highly controlled stimulus material. The lack of arousal ratings, however, necessitated a revised version of the BAWL. We therefore present the BAWL-R, which is the first list that not only contains a large set of psycholinguistic indexes known to influence word processing, but also features ratings regarding emotional arousal, in addition to emotional valence and imageability. The BAWL-R is intended to help researchers create stimulus material for a wide range of experiments dealing with the affective processing of German verbal material.

The Word Frequency Effect A Review of Recent Developments and Implications for the Choice of Frequency Estimates in German

Abstract: We review recent evidence indicating that researchers in experimental psychology may have used suboptimal estimates of word frequency. Word frequency measures should be based on a corpus of at least 20 million words that contains language participants in psychology experiments are likely to have been exposed to. In addition, the quality of word frequency measures should be ascertained by correlating them with behavioral word processing data. When we apply these criteria to the word frequency measures available for the German language, we find that the commonly used Celex frequencies are the least powerful to predict lexical decision times. Better results are obtained with the Leipzig frequencies, the dlexDB frequencies, and the Google Books 2000-2009 frequencies. However, as in other languages the best performance is observed with subtitle-based word frequencies. The SUBTLEX-DE word frequencies collected for the present ms are made available in easy-to-use files and are free for educational purposes.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Eye movements in reading and information processing: 20 years of research.

Abstract: Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

A theory of lexical access in speech production.

Abstract: Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly naming a clear picture of an object can easily be initiated within 600 msec after picture onset. The underlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feed-forward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, phonetic encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access), and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging.