This paper shows that the positive effects on decision making of financial incentives, performance feedback, and the requirement to justify one's decisions to others can be undermined or even reversed by the availability of a decision aid. More specifically, in the absence of a decision aid, subjects achieved greater classification accuracy in a repetitive decision task when a monetary incentive was offered, or when feedback about past performance was provided, or when they were required to justify their choices, relative to the absence of these three variables. In contrast, when a statistically valid decision aid was available, the same incentive, feedback, and justification requirements resulted in lower classification accuracy, again relative to the absence of these three variables. These results are interpreted within a framework having two basic tenets. First, financial incentives, performance feedback, and a justifi-

Information evaluation model, Decision making, Lens model, Feedback

Robert H. Ashton, Cognitive Changes Induced by Accounting Changes: Experimental Evidence on the Functional Fixation Hypothesis, Journal of Accounting Research, Vol. 14, Studies on Human Information Processing in Accounting (1976), pp. 1-17

The work of independent auditors involves two basic activities evidence collection and evidence evaluation. Substantial agreement exists concerning the types of evidence that should be collected in particular situations and the appropriate collection techniques. The evidence-evaluation activity, however, is less susceptible to codification. As a result, the auditing profession relies heavily upon professional in evaluating audit evidence. For example, the AICPA has stated that of the independent auditor's work in formulating his opinion on financial statements consists of obtaining and examining evidential matter. The measure of the validity of such evidence for audit purposes lies in the judgment of the auditor. 1 The importance of judgment in auditing is accepted almost without question; yet auditors' judgments are seldom subjected to systematic research. A typical rationale for this situation is the AICPA's statement that 'judgment is the most important factor in the making of any audit, but in many situations it is practically impossible to write out in specific language how the auditor applies judgment. 2 The study reported in this paper was concerned with the evidence-evaluation function of independent auditing. One type of audit evidence was * Assistant Professor, University of Texas at Austin. This paper is based upon the author's doctoral dissertation; for the full report, see R. H. Ashton, Judgment Formation in the Evaluation of Internal Control: An Application of Brunswik's Lens Model (Minneapolis: University of Minnesota, 1973).

This article presents a series of short cases, taken from the human information processing research literature in auditing, which can be used as supplementary teaching material in undergraduate and graduate auditing courses. The purpose of the cases is to illustrate to future auditors some of the subtle judgmental biases that can adversely affect their judgments and decisions. Use of such materials should constitute one step toward a more effective integration of teaching and research in auditing.

This article examines the issue of cue weighting in judgment models used to represent human information processors. Illustrative results are presented which show that predictions produced by linear models are very robust with respect to alternative cue-weighting schemes unless the number of predictors included in the models is small, the mean correlation among the predictors is low, and the dispersion of the weights is large relative to their mean. Implications of these results for judgment modeling research in accounting are discussed. Generally, these implications concern the potential improvement in human decision making that might obtain from the search for alternative predictors and/or measurement methods rather than the search for improved methods of weighting predictors in linear models.

This article presents the consensus concept of objectivity and two problems of the concept which never have been addressed adequately: (1) observed objectivity (agreement among different measurers applying the same measurement system) may be spuriously inflated by the consensus inherent on various other combinations of rules and measurers; (2) suggestions for improving objectivity typically focus entirely upon measurement systems and virtually ignore the impact of measurers on objectivity. A framework which enables the investigation of these problems is presented. The suggested framework, a multirule-multimeasurer matrix, is an adaptation of the multitrait-multimethod matrix often used to assess the validity of psychological tests and enables investigation of these problems.

The article presents a response by the author on comments made by scholar Cornelius J. Casey on his article "The Predictive Ability Criterion and User Prediction Models," published in the October 1974 issue of the periodical "The Accounting Review." Cassey addresses three areas contained in my previous article. My response, which is intended to be constructive and elaborative, considers these areas in the order adopted by Casey. Casey's major contribution lies in his discussion of the severity of criterion problems in accounting contexts. For example. in commenting on my discussion of the imprecision of criterion values, he correctly observes that decision makers act on the basis of criterion values whether they are precise or not. Further, he recognizes that consistent measurement of criterion values is a possible means of minimizing imprecision. These points are well-taken. In fact, research and study subsequent to my original article have led me to question whether criterion problems in accounting contexts are more severe than similar problems in other contexts, for example, the criterion of hospital staff diagnosis, in psychology, or grade point average in education, as I previously had believed. On the other hand, much of Casey's concern apparently arises from misinterpretation of my position.

The article is concerned with improving the utilization of accounting information in prediction situations. In this context improvement is defined in terms of an increase in prediction accuracy. If adequate criterion information is available, increase in prediction accuracy may be "directly" assessed by measuring the correspondence between criterion values and predictions of those values. The article recognizes that there are at least two approaches to improving prediction accuracy. The first approach is to improve the data which are supplied to decision-makers and used as a basis for predictions. The second approach is to improve the way that predictions are made on the basis of whatever data are used. The author presents evidence from the psychology literature that individuals may usefully be replaced by their models in prediction tasks, and he also discusses the reasons for this situation. He presents evidence from the management literature which leads to the same conclusion and discusses implications for accounting of these findings.