The effects of feedback in psychophysical tasks

Previous results of the reinforcement effects of feedback in psychophysical tasks have been interpreted as changes in the S's response bias in the uncertain sensory state. The present study varied the percentage of feedback in a two-alternative loud-soft recognition task. The values of the probability of correct feedback were 1,.8, .6, .4, .2, and O. The results indicated that Ss learned to respond to agree with the experimental feedback rather than with the actual cue presented on a trial. The probability of a response on Trial n+ 1 was highly dependent upon the response and feedback of Trial n only when the two trials were the same. It was concluded that feedback must influence the response probability vector associated with a given detection state. In a two-alternative psychophysical task, Ss identify which of two stimuli was presented on a given trial. Two responses, A, and A2 , identifying Stimuli T I and T 2, respectively, are available to the S. Two feedback events, E J and E2, indicate to the Ss whether T I or T2 was presented. The effects of information feedback in this paradigm have been investigated (Atkinson & Kinchla, 1965; Friedman, Carterette, Nakatoni, & Ahumada, 1968). The results have indicated that the probability of an A, response, P(AI), is positively related to peEd. The analysis of this bias in the learning models of Atkinson and Kinchla (1965) and those discussed by Friedman et al (1968) has assumed that feedback can change the response probability vector associated with the uncertain sensory state, So. Furthermore, letting States S, and S2 be the detection of T I and T2 , respectively, it was sufficient to assume that PtA, ISt)= I and P(AI I S2) = O. That is, given Detection States SI or S2, the S's response is determined with Probability I. As suggested by Atkinson and Kinchla (1965), it may be that feedback also influences the response vectors associated with the Detection States SI and S2. For example, the results of a discriminative probability learning study (Massaro & Moore, 1%8) indicated that the response probabilities to identified cues were highly dependent on the response and feedback of the previous trial. The S's task in that study was to indicate whether a loud or soft tone was presented and then predict which of two events, EI or E2 would appear on that trial. Under reinforcement schedules of 71"1 = p(EI IT 1) =.8 and 71"2 = p(E I IT 2) = .2, Ss learned to respond appropriately to the identified cues. When the Ss were reinforced for responding appropriately (i.e., predicting the most frequent event given the cue), they would be more likely to predict the most frequent event given the identified cue on the following trial. More specifically, when Trials n + I and n were identified as the same, P(AI I AIEd > p(A I IAIE2) > p(A t I A2Ed > P(AI I A2E 2). When Trials n + I and n were identified as different, the rank-ordering of the conditional response probabilities was exactly the opposite of that given when the trials were identified as the same. Hence, it seems likely that Ss could learn also to behave appropriately to identified cues in a psychophysical detection task. In the framework of the learning models, feedback should also affect the response probability vectors associated with the detection states, St and S2'