Perception and production of brief durations: beat-based versus interval-based timing

Two experiments examined whether timing of short intervals is beat- or interval-based. In Experiment 1, subjects heard a sequence of standard tones followed by 2 test tones; they compared the interval between test tones to the interval between the standards. If optimal precision required beat-based timing, performance should be best in blocks in which the interval between standard and test reliably matched the standard interval. No such effect was observed. In Experiment 2, subjects heard 2 test tones and reproduced the intertone interval by producing 2 keypress responses. Entrainment to the beat was apparent: First-response latency clustered around the standard interval and was positively correlated with the produced interval. However, responses occurring on or near the beat showed no better temporal fidelity than off-beat responses. One plausible interpretation of these findings is that the brain always times brief intervals with an interval timer; however, this timer can be used in a cyclic fashion to trigger rhythmic responses.     

Knowledge level learning and the power law: A soar model of skill acquisition in scheduling

Zusammenfassung. Das hier vorgestellte Modell lernt graduell, Planungsaufgaben aus der Klasse der Maschinenbelegungsprobleme (job-shop-scheduling problems) zu lösen. Mit Hilfe des Chunking-Mechanismus von Soar wird episodisches Wissen über die Belegungsreihenfolge von Aufträgen auf Maschinen memoriert. Bei der Entwicklung des Modells wurden zahlreiche qualitative (z. B. Transfereffekte) und quantitative Befunde (z. B. Bearbeitungszeiten) aus einer früheren empirischen Untersuchung berücksichtigt. In einer Validierungsstudie wurden dieselben Aufgaben von 14 Probanden und dem Modell bearbeitet. Die Passung von Simulationsdaten und empirischen Ergebnissen fiel insgesamt gut aus. Allerdings löst das Modell die Aufgaben schneller und zeigt auch einen etwas besseren Lernverlauf als die Probanden. Das Modell liefert eine Erklärung für das Rauschen, das typischerweise bei Bearbeitungszeiten zu beobachten ist: es handelt sich um erworbenes Wissen, das mehr oder weniger gut und auch unterschiedlich häufig auf neue Situationen übertragen wird. Der Lernverlauf der Probanden entspricht nur für aggregierte Daten einer Potenzfunktion (power law). Der vorgestellte Mechanismus zeigt, wie ein symbolisches Modell der Informationsverarbeitung graduelle Verhaltensänderungen generiert und wie der offensichtliche Erwerb allgemeiner Prozeduren ohne explizites Lernen von deklarativen Regeln erfolgen kann. Es wird nahegelegt, daß es sich hier um die Modellierung einer Form impliziten Lernens handelt. Summary. The model presented here gradually learns how to perform a job-shop scheduling task. It uses Soar's chunking mechanism to acquire episodic memories about the order to schedule jobs. The model was based on many qualitative (e.g., transfer effects) and quantitative (e.g., solution time) regularities found in previously collected data. The model was tested with new data where scheduling tasks were given to the model and to 14 subjects. The model generally fit these data with the restrictions that the model performs the task (in simulated time) faster than the subjects, and its performance improves somewhat more quickly than the subjects' performance. The model provides an explanation of the noise typically found in problem solving times - it is the result of learning actual pieces of knowledge that transfer more or less to new situations but rarely by an average amount. Only when the data are averaged (i.e., over subjects) does the smooth power law appear. This mechanism demonstrates how symbolic models can exhibit a gradual change in behavior and how the apparent acquisition of general procedures can be performed without resorting to explicit declarative rule generation. We suggest that this may represent a type of implicit learning.     

The puzzle of language evolution

Linguistics must again concentrate on the evolutionary nature of language, so that language models are more realistic with respect to human natural languages and have a greater explanatory force. Multi-agent systems are proposed as a possible route to develop such evolutionary models and an example is given of a concrete experiment in the origins and evolution of word-meaning based on a multi-agent approach.     

Calculation of signal detection theory measures

Signal detection theory (SDT) may be applied to any area of psychology in which two different types of stimuli must be discriminated. We describe several of these areas and the advantages that can be realized through the application of SDT. Three of the most popular tasks used to study discriminability are then discussed, together with the measures that SDT prescribes for quantifying performance in these tasks. Mathematical formulae for the measures are presented, as are methods for calculating the measures with lookup tables, computer software specifically developed for SDT applications, and general purpose computer software (including spreadsheets and statistical analysis software).     

Visual selection mediated by location: Feature-based selection of noncontiguous locations

Experiments using two different methods and three types of stimuli tested whether stimuli at nonadjacent locations could be selected simultaneously. In one set of experiments, subjects attended to red digits presented in multiple frames with green digits. Accuracy was no better when red digits appeared successively than when pairs of red digits occurred simultaneously, implying allocation of attention to the two locations simultaneously. Different tasks involving oriented grating stimuli produced the same result. The final experiment demonstrated split attention with an array of spatial probes. When the probe at one of two target locations was correctly reported, the probe at the other target location was more often reported correctly than were any of the probes at distractor locations, including those between the targets. Together, these experiments provide strong converging evidence that when two targets are easily discriminated from distractors by a basic property, spatial attention can be split across both locations.     

Moral principles as moral dispositions

What are moral principles? In particular, what are moral principles of the sort that (if they exist) ground moral obligations or—at the very least—particular moral truths? I argue that we can fruitfully conceive of such principles as real, irreducibly dispositional properties of individual persons (agents and patients) that are responsible for and thereby explain the moral properties of (e.g.) agents and actions. Such moral dispositions (or moral powers) are apt to be the metaphysical grounds of moral obligations and of particular truths about what is morally permissible, impermissible, etc. Moreover, they can do other things that moral principles are supposed to do: explain the phenomena “falling within their scope,” support counterfactuals, and ground moral necessities, “necessary connections” between obligating reasons and obligations. And they are apt to be the truthmakers for moral laws, or “lawlike” moral generalizations.