Modeling response signal and response time data

The diffusion model (Ratcliff, 1978) and the leaky competing accumulator model (LCA, Usher & McClelland, 2001) were tested against two-choice data collected from the same subjects with the standard response time procedure and the response signal procedure. In the response signal procedure, a stimulus is presented and then, at one of a number of experimenter-determined times, a signal to respond is presented. The models were fit to the data from the two procedures simultaneously under the assumption that responses in the response signal procedure were based on a mixture of decision processes that had already terminated at response boundaries before the signal and decision processes that had not yet terminated. In the latter case, decisions were based on partial information in one variant of each model or on guessing in a second variant. Both variants of the diffusion model fit the data well and both fit better than either variant of the LCA model, although the differences in numerical goodness-of-fit measures were not large enough to allow decisive selection between the models.     

Automatic sequential response priming and intentional response preparation in choice reaction tasks: Evidence from response repetition and response cuing

This study examined the interaction of response repetition and response cuing in a finger cuing task with a short and a long cue-stimulus interval (CSI). We observed shorter reaction times (RTs) with increasing CSI and a substantial response repetition benefit. However, this benefit was abolished at the long CSI, suggesting that response cuing neutralized the repetition effect. According to additive-factors logic, the observed interaction suggests that both repetition and cuing exert their influence on a common processing stage, which we identify as the response selection stage. We argue that cuing and repetition effects are expressions of distinct mental operations: cuing is based on intentional response code activation, whereas repetition is based on sequential, automatic response code priming. Cue-based intentional code activation starts slowly and increases with CSI, but sequential response priming is independent of CSI, explaining why cuing abolishes the response repetition benefit at the long CSI.     

Author's response

In Search of Philosophic Understanding E. A. Burtt Mentor Book. New York: New American Library, 1967 Pp.xvii+350,$1.25     

Author's response

The year 1996 was regarded by a considerable part of contemporary literature on global political economy as a definite turning point in modern history. The majority of experts tended to see the starting point of take‐off that year, but others—not a negligible minority—saw omens of disastrous recession and lasting depression. It appears the time has not yet come. The question is now that of the incalculable resultant of runaway (deregulated) forces of the international financial “whirlpool”, of a random process of global, regional and local accumulation of capital. The year 1997 will set in with a foreseeable and calculable agrarian and related cycle and it may find an outlet for accumulated tensions of capital accumulation at the high tide of unemployment (demographic or migratory) wave with unforeseeable and incalculable effects and side‐effects. This will be just the beginning of the end and a Black Weekday still lies ahead of us. There can be no doubt that we live in a transition period but we do not know where this transition leads to. Loose talk about “post‐industrial society”, “post‐modern age” and “postsomething anything” has limited interpretative power. Very few students of social sciences venture to see and verify secular trends in world history.     

The orienting response: Stimulus factors and response measures

Integrative Psychological and Behavioral Science - This paper outlines some of the basic ideas of the orienting response (OR) that have developed from the classical writings of E.N. Sokolov, in...     

A mixture hierarchical model for response times and response accuracy

In real testing, examinees may manifest different types of test‐taking behaviours. In this paper we focus on two types that appear to be among the more frequently occurring behaviours – solution behaviour and rapid guessing behaviour. Rapid guessing usually happens in high‐stakes tests when there is insufficient time, and in low‐stakes tests when there is lack of effort. These two qualitatively different test‐taking behaviours, if ignored, will lead to violation of the local independence assumption and, as a result, yield biased item/person parameter estimation. We propose a mixture hierarchical model to account for differences among item responses and response time patterns arising from these two behaviours. The model is also able to identify the specific behaviour an examinee engages in when answering an item. A Monte Carlo expectation maximization algorithm is proposed for model calibration. A simulation study shows that the new model yields more accurate item and person parameter estimates than a non‐mixture model when the data indeed come from two types of behaviour. The model also fits real, high‐stakes test data better than a non‐mixture model, and therefore the new model can better identify the underlying test‐taking behaviour an examinee engages in on a certain item.     

Inspection time and response strategies in a choice response task

The effects of varying stimulus duration on two response measures, percentage correct (p_c) and mean reaction time (RT) were investigated in a lights-keys RT task similar to an inspection time (IT) task. One of either N = 2, 4 or 8 lights came on as the stimulus, followed after a range of stimulus exposure durations (D) by all 8, as a backward mask. It was found that p_c was an ogival function of D for all three levels of choice, and that the standard deviations of the best-fitting normal ogives were equal. It is argued that the standard deviation can be taken as a measure of the neural noise, or error-inducing variability introduced by processing the stimulus, and so this result indicated that noise in the choice reaction task is independent of N. The effect of D on RT was as predicted by an optional accumulator model for 7 Ss with RT decreasing as D increased, while another 2 Ss appeared to have adopted a temporal deadline criterion giving RTs independent of D and equal error and correct RTs. The same two response styles were found in a re-analysis of RT data from an IT study. The p_c vs D curves were comparable for both response styles. The accumulator model commonly assumed to underly IT tasks thus does not describe the behaviour of all Ss. Such individual differences in strategic approach to the IT task should be investigated further, since assuming that all Ss use one approach may hide or confound relationships between IT and other measures of individual difference, such as intelligence. Recommendations for better IT measurement techniques are given.