Effects of information presentation mode and learning paradigm on the learning of probabilistic inference tasks under different levels of memory strain

The effects of information presentation mode (i.e. visual access vs. not access to an ordered array of cue combinations) and learning paradigm (i.e. selection vs. reception of cue-combinations) on learning of two-cue probabilistic inference tasks were studied in two 2 × 2 factorial experiments representing two levels of memory strain. The results showed that information presentation mode affected performance regardless of amount of memory strain, while learning paradigm affected performance only under conditions of high memory strain.     

Length of the postfeedback interval and learning of probabilistic inference tasks

Learning of negative linear and J-shaped cue-criterion relations in single-cue learning tasks was studied in an experiment which varied the length of the postfeedback interval. Contrary to the predictions from a hypothesis construction model assuming that subjects construct hypotheses from the data provided by the task, there was no effect of the postfeedback interval. The results were interpreted to mean that the subjects' constructions are based on elements stored in memory, rather than on data from the task.     

Effects of information presentation mode on subjects' hypotheses in a probabilistic inference task

The effects of two modes of information-presentation (visual access to an ordered array of cue values vs. no access) on subjects' performance in a two-cue probability learning (MPL) task were investigated.The results showed that the visual access to an ordered array of cue values led to a larger proportion of correct hypotheses and of optimal selection strategies and to a better learning performance. These results were interpreted as supporting the conjecture that the situational variables may affect the subject's hypotheses about the cue-criterion relations. The results were also discussed with respect to their implications for the theory of MPL.     

Effect of cue validity on learning of complex rules in probabilistic inference tasks

The effect of cue validity on the learning of positive and negative J-shaped relations in single-cue probability learning was studied. As would be expected from a hypothesis construction model, learning of the J-shaped relations was better when cue validity was high than when it was low. However, the results also showed that positive J-rules were learned more efficiently than negative J-rules. This shows that subjects do not construct their hypotheses on the basis of data from task only. It was concluded that subjects construct their hypotheses from elements stored in memory, and that data are used only to test their hypotheses.     

Note on hypothesis testing in probabilistic inference tasks

Subjects' ability to test hypotheses about the form of functional relations in probabilistic inference tasks was investigated in two experiments which varied the functional relation in the task, the hypotheses to be tested, and the validity of the cue. The results showed that the subjects used the same amount of information regardless of the validity of the cue, that nonlinear hypotheses were harder to test accurately than linear hypothesis, and that hypotheses were harder to test when the relation in the task was nonlinear than when it was linear.     

Learning complex rules in probabilistic inference tasks

Abstract.— A hypothesis-sampling theory for rule learning predicts that only those rules that are available for sampling can be learned. Earlier results show that subjects have a very limited set of hypotheses about rules relating scaled cue and criterion variables, consisting mainly of linear and symmetric quadratic functions, but not of complex functions, such as J-shaped rules. Tasks requiring the use of such rules should, therefore, not be learned. The results of the present experiment show, however, that subjects are able to find J-shaped relations. The results were interpreted to mean that subjects are able to construct hypotheses, and that they are not limited to sampling of hypotheses from a preestablished set of possible hypotheses.     

Learning and hypothesis testing in probabilistic inference tasks

The relation between learning of probabilistic inference tasks and the availability of correct hypotheses for the tasks in individual subjects was investigated in two experiments. The results showed that having the correct hypotheses as measured before learning the tasks was neither a sufficient nor a necessary condition for learning the task. It was concluded that the hypothesis measurements are not reliable enough to allow predictions on an individual level. The results also showed that subjects have not only the four basic functions: positive linear, negative linear, U-shaped and inversely U-shaped, in their hypothesis hierarchies but also J-shaped functions. That the subjects can leam such functions is thus no discodinnation of the hypotheses sampling conception of learning as assumed in earlier studies.     

Estimation of the hypothesis hierarchy in probabilistic inference tasks*

Is there a hierarchical order among the hypotheses about functional rules in probabilistic inference tasks, i.e. what is the construction and the procedure of the “hypothesis sampling mechanism” employed by the subjects in this kind of task? According to the hypothesis sampling model initially proposed by Brehmer (1974) there should be a hierarchical order among the hypotheses in the subject's hypothesis pools. The procedures of hypothesis sampling and testing ought to follow this strict data independent order (see e.g. Sniezek, 1986; Brehmer, 1987). Knez (1991a, b) showed, however, that this assumption may be incorrect. As a follow up to these results the question regarding the construction of the subject's hypothesis pools was reapproached in the present study. The results indicated a consistency with the hierarchical assumption (Brehmer, 1974) only regarding the relation between the linear and nonlinear rules but not within these types of rules.     

Subjects' inferential performance and the interaction of data and hypotheses in probabilistic inference tasks

In a recent paper, Knez (1991) showed an interaction of data and hypotheses in probabilistic inference tasks. The results illustrated two, earlier not obtained, significant main effects on subjects' hypothesis sampling , viz. the effect of different forms of data presentation and subjects' execution of cognitive control over their hypothesis pool throughout the series of trials. The present paper followed up these results in that the subjects' hypothesis testing , in Knez (1991) was subjected to an analysis. Hence, to see if the effects mentioned above significantly influenced the subjects' hypothesis testing, as they did for subjects' hypothesis sampling. The results showed a consistency with Knez (1991), i.e. the results emphasize the interaction of data and hypothesis in probabilistic inference tasks, as well as the subjects' execution of cognitive control over their hypothesis pool concerning both the subjects' hypothesis sampling and testing.     

Comparison of implicit and explicit learning processes in a probabilistic task

This experiment compared the performance with explicit (rule-application and rule-discovery) and implicit (nonrule-instructed) learning approaches on the performance of a probabilistic video game task requiring fine motor control. The task required visual tracking of a small ball of light and "catching" it by means of joystick manipulation. A general pattern of improvement with practice occurred for all conditions. All conditions showed use of predictive relations among stimulus events. However, task performance of the rule-application and rule-discovery conditions were inferior to the nonrule-instructed implicit condition, particularly during the early phases of rule acquisition and application. This pattern strongly suggests substantial performance costs associated with attempting to discover or apply probabilistic rules. Decrements are likely due to increased cognitive demands associated with attempting to remember and strategically apply provided probability rules or attempting to discover and apply potentially important and useful probability information from a complex visual display.     

The influence of task variation and maintenance tasks on the learning and affect of autistic children

Analyses of autistic children's learning and affect have begun to consider the role of antecedent variables, including the manner with which tasks are sequenced within instructional sessions. Within the context of a simultaneous-treatments design, this experiment evaluated autistic children's affect and rate of task acquisition under three experimental conditions. The conditions were (1) a constant task condition in which only one acquisition task was presented per session; (2) a varied-acquisition-task condition, in which 10 acquisition tasks were randomly interspersed throughout each session; and (3) a varied-with-maintenance-task condition, which randomly interspersed 5 acquisition tasks and 5 which had been previously acquired. The results showed significantly more efficient learning under the varied-maintenance condition, with no consistent differences separating the other two conditions. In addition, observers' ratings of the children's affect showed that the most positive judgments were produced by the varied-maintenance condition. The varied-acquisition condition was next while the constant task condition always produced the least favorable ratings. These findings are discussed in relation to the literature on massed versus distributed practice, stimulus variation, and success-induced motivation. The implications for upgrading the treatment and education of handicapped children are also reviewed.     

Effects of grammar complexity on artificial grammar learning

The present study identified two aspects of complexity that have been manipulated in the implicit learning literature and investigated how they affect implicit and explicit learning of artificial grammars. Ten finite state grammars were used to vary complexity. The results indicated that dependency length is more relevant to the complexity of a structure than is the number of associations that have to be learned. Although implicit learning led to better performance on a grammaticality judgment test than did explicit learning, it was negatively affected by increasing complexity: Performance decreased as there was an increase in the number of previous letters that had to be taken into account to determine whether or not the next letter was a grammatical continuation. In particular, the results suggested that implicit learning of higher order dependencies is hampered by the presence of longer dependencies. Knowledge of first-order dependencies was acquired regardless of complexity and learning mode.     

Quiet eye duration,expertise, and task complexity in near and far aiming tasks

. Skilled (n = 12) and less skilled (n = 12) billiards players participated in 2 experiments in which the relationship between quiet eye duration, expertise, and task complexity was examined in a near and a far aiming task. Quiet eye was defined as the final fixation on the target prior to the initiation of movement. In Experiment 1, skilled performers exhibited longer fixations on the target (quiet eye) during the preparation phase of the action than their less skilled counterparts did. Quiet eye duration increased as a function of shot difficulty and was proportionally longer on successful than on unsuccessful shots for both groups of participants. In Experiment 2, participants executed shots under 3 different time-constrained conditions in which quiet eye periods were experimentally manipulated. Shorter quiet eye periods resulted in poorer performance, irrespective of participant skill level. The authors argue that quiet eye duration represents a critical period for movement programming in the aiming response.     

Order effects in contingency learning: The role of task complexity

Dennis and Ahn (2001) found that during contingency learning, initial evidence influences causal judgments more than does later evidence (a primacy effect), whereas López, Shanks, Almaraz, and Fernández (1998) found the opposite (a recency effect). We propose that in contingency learning, people use initial evidence to develop an anchoring hypothesis that tends to be underadjusted by later evidence, resulting in a primacy effect. Thus, factors interfering with initial hypothesis development, such as simultaneously learning too many contingencies, as in López et al., would reduce the primacy effect. Experiment 1 showed a primacy effect with learning contingencies involving only one outcome but no primacy effect with two outcomes. Experiment 2 demonstrated that the magnitude of the primacy effect correlated with participants' verbal working memory capacity. It is concluded that a critical moderator for exhibition of the primacy effect is task complexity, presumably because it interferes with initial hypothesis development.     

The influence of degree of expertise and objective task complexity on perceived task complexity and performance

Research on expertise has shown that nonexperts may sometimes outperform experts. Some researchers have suggested that superior performance by experts depends on the match between the experts' cognition and the demands of the task. The authors explored this issue using a quasi-experiment set in an organization. They examined how 3 sets of similar tasks that differ in their type of complexity can lead to differences in task perceptions and performance among experts, intermediates, and novices. The results suggest that experts and novices pay attention to different aspects of a task and that this affects both their perceptions of task complexity (i.e., task analyzability and variability) and their performance on the task.