The effect of frequency of judgement and the type of trials on covariation learning.

Abstract

This study showed that accuracy of the estimated relationship between a fictitious symptom and a disease depends on the interaction between the frequency of judgment and the last trial type. This effect appeared both in positive and zero contingencies (Experiment 1), and judgments were less accurate as frequency increased (Experiment 2). The effect can be explained neither by interference of previous judgments or memory demands (Experiment 3), nor by the perceptual characteristics of the stimuli (Experiments 4 and S), and instructions intended to alter processing strategies do not produce any reliable effect. The interaction between frequency and trial type on covariation judgment is not predicted by any model (either statistical or associative) currently used to explain performance in covariation detection. The authors propose a belief-revision model to explain this effect as an important response mode variable on covariation learning.Animals and humans behave adaptively because they are able to detect the relationship between events (Alloy & Tabachnik, 1984;Killeen, 1981;Mackintosh, 1977). Knowledge on covariation or correlation is very important to the organisms' cognitive functioning (Kareev, 1995) because it is needed in order to carry out such tasks as classical conditioning, category learning, or causal induction (Kareev, 1995;Waldmann & Holyoak, 1992). Therefore, it is important to understand the conditions and the mechanisms underlying this ability. A lot of data exists on these topics in the animal conditioning literature, and some authors have argued that human contingency judgments are useful for these purposes (Shanks, 1987). In fact, recent reviews (Allan, 1993;Shanks, 1993;Young, 1995) have stressed the similarity between human contingency judgments and the animal conditioning data. These studies usually use two dichotomous variables; a cue (S) and an outcome (O), with present or absent conditions possible for each. These conditions produce four types of trials: (a) the signal and the outcome are presented together; (b) S but not O occurs; (c) only O is present; and (d) neither S nor O is shown.Human contingency judgments are affected by conditions such as contiguity (Reed, 1992;Shanks, Pearson, & Dickinson, 1989), outcome value (Chatlosh, Neunaber, & Wasserman, 1985), cue competition (blocking [Chapman & Rob-

Judgement Frequency, Belief Revision, and Serial processing of Causal Information

“…This effect implies that making a judgement is a crucial determinant of judgement accuracy (Catena et al, 1998;Maldonado et al, 1999). We submit that various singlemechanism theories cannot account for this effect and instead propose an account that includes two mechanisms that operate in a serial manner: The first of these computes the statistical relationship, and the second one integrates this information with any other information or beliefs about the task.…”

“…These models could yield different predictions, however, if they assumed that the judgement frequency affects learning rate parameters. This change might, for example, explain the differences frequently found at the end of a given period of training (i.e., a response mode effect, Catena et al, 1998), as performance reached will depend on these parameters (Allan, 1993). After a non-exhaustive search in the learning rate parameter space to fit the data for group HH and group LL in Experiment 1, we found that Markman's (1989) model predicted significant differences between a and d versus b and c trials not only in the high-but also in the low-frequency conditions (see Table 1).…”

Contrasting cue-density effects in causal and prediction judgments

“…It does not seem reasonable to compare prediction and causation dependent variables that lie on different measuring scales and are collected at different points in time during the experiment. Indeed, it has long been shown that the moment when a judgment is collected and the frequency with which it is collected can change dramatically the participants' responses (e.g., Catena, Maldonado, & Cándido, 1998;Collins & Shanks, 2002;Matute, Vegas, & De Marez, 2002).…”

Web-based experiment control software for research and teaching on human learning

“…Other researchers have also demonstrated a wide variety of critical learning effects using different versions of this task (e.g., Allan, Siegel, & Tangen, 2005;Buehner & Cheng, 1997;Catena, Maldonado, & Cándido, 1998;Cobos, López, Caño, Almaraz, & Shanks, 2002;Dickinson & Burke, 1996;Karazinov & Boakes, 2004;Le Pelley & McLaren, 2001;Vila & Rosas, 2001;Waldmann, 2001;Wasserman, 1990).…”