On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization

Biased category payoff matrices engender separate reward- and accuracy-maximizing decision criteria Although instructed to maximize reward, observers use suboptimal decision criteria that place greater emphasis on accuracy than is optimal. In this study, objective classifier feedback (the objectively correct response) was compared with optimal classifier feedback (the optimal classifier's response) at two levels of category discriminability when zero or negative costs accompanied incorrect responses for two payoff matrix multiplication factors. Performance was superior for optimal classifier feedback relative to objective classifier feedback for both zero- and negative-cost conditions, especially when category discriminability was low, but the magnitude of the optimal classifier advantage was approximately equal for zero- and negative-cost conditions. The optimal classifier feedback performance advantage did not interact with the payoff matrix multiplication factor. Model-based analyses suggested that the weight placed on accuracy was reduced for optimal classifier feedback relative to objective classifier feedback and for high category discriminability relative to low category discriminability. In addition, the weight placed on accuracy declined with training when feedback was based on the optimal classifier and remained relatively stable when feedback was based on the objective classifier. These results suggest that feedback based on the optimal classifier leads to superior decision criterion learning across a wide range of experimental conditions.     

A theoretical framework for understanding the effects of simultaneous base-rate and payoff manipulations on decision criterion learning in perceptual categorization

Observers completed perceptual categorization tasks in which base rates and payoffs were manipulated separately or simultaneously across a range of category discriminabilities. Decision criterion estimates from the simultaneous base-rate/payoff conditions were closer to optimal than those predicted from the independence assumption, in line with predictions from the flat-maxima hypothesis. A hybrid model that instantiated the flat-maxima and competition between reward and accuracy maximization hypotheses was applied to the data as well as used in a reanalysis of C. J. Bohil and W.J. Maddox's (2001) study. The hybrid model was superior to a model that incorporated the independence assumption, suggesting that violations of the independence assumption are to be expected and are well captured by the flat-maxima hypothesis, without requiring any additional assumptions.     

Toward a unified theory of decision criterion learning in perceptual categorization

Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are unequal, suboptimal decision criterion placement is observed, even when the optimal decision criterion is identical in both cases. A series of studies are reviewed that examine the generality of this finding, and a unified theory of decision criterion learning is described (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms operate in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also places some importance on accuracy maximization. The second mechanism involves a flat-maxima hypothesis that assumes that the observer's estimate of the reward-maximizing decision criterion is determined from the steepness of the objective reward function that relates expected reward to decision criterion placement. Experiments used to develop and test the theory require each observer to complete a large number of trials and to participate in all conditions of the experiment. This provides maximal control over the reinforcement history of the observer and allows a focus on individual behavioral profiles. The theory is applied to decision criterion learning problems that examine category discriminability, payoff matrix multiplication and addition effects, the optimal classifier's independence assumption, and different types of trial-by-trial feedback. In every case the theory provides a good account of the data, and, most important, provides useful insights into the psychological processes involved in decision criterion learning.     

Linear transformations of the payoff matrix and decision criterion learning in perceptual categorization

The effects of payoff-matrix multiplication, payoff-matrix addition, the presence of long-run gains versus long-run losses, category discriminability, and base rate on decision criterion learning were examined in 2 perceptual categorization experiments. Observers were found to be sensitive to the effects of payoff-matrix multiplication (and category discriminability) on the steepness of the objective reward function in line with predictions from the flat-maxima hypothesis and contrary to the predictions from the payoff-variance hypothesis. Decision criterion learning was best in base-rate conditions, was worst when losses were associated with incorrect responding, and was intermediate when no losses were associated with incorrect responding. This performance profile was well captured by the competition between reward and accuracy (COBRA) hypothesis. A hybrid model framework that instantiates both the flat-maxima and COBRA hypotheses was necessary to account for the data from both experiments.     

A feedback model of perceptual learning and categorization

Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This paper employs a computational model of the cortical region inter-actions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorized and could also facilitate the learning of subordinate level representations suitable for object identification and perceptual expertise.     

Category discriminability, base-rate, and payoff effects in perceptual categorization

The optimality of perceptual categorization performance under manipulations of category discriminability (i.e., d' level), base rates, and payoffs was examined. Base-rate and payoff manipulations across two category discriminabilities allowed a test of the hypothesis that the steepness of the objective reward function affects performance (i.e., the flat-maxima hypothesis), as well as the hypothesis that observers combine base-rate and payoff information independently. Performance was (1) closer to optimal for the steeper objective reward function, in line with the flat-maxima hypothesis, (2) closer to optimal in base-rate conditions than in payoff conditions, and (3) in partial support of the hypothesis that base-rate and payoff knowledge is combined independently. Implications for current theories of base-rate and payoff learning are discussed.     

Procedural learning in perceptual categorization

In two experiments, observers learned two types of category structures: those in which perfect accuracy could be achieved via some explicit rule-based strategy and those in which perfect accuracy required integrating information from separate perceptual dimensions at some predecisional stage. At the end of training, some observers were required to switch their hands on the response keys, whereas the assignment of categories to response keys was switched for other observers. With the rule-based category structures, neither change in response instructions interfered with categorization accuracy. However, with the information-integration structures, switching response key assignments interfered with categorization performance, but switching hands did not. These results are consistent with the hypothesis that abstract category labels are learned in rule-based categorization, whereas response positions are learned in information-integration categorization. The association to response positions also supports the hypothesis of a procedural-learning-based component to information integration categorization.     

Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning

W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule out 2 single-system interpretations. The authors conducted an experiment that remedied these problems and replicated W. T. Maddox et al.'s findings. The experiment revealed a strong performance decrement for information-integration but not rule-based category learning under delayed feedback that was due to an increase in the number of observers using hypothesis-testing strategies to solve the information-integration task, and lower accuracy rates for the few observers using information-integration strategies.     

Adaptive criterion setting in perceptual decision making

Pigeons responded in a perceptual categorization task with six different stimuli (shades of gray), three of which were to be classified as "light" or "dark", respectively. Reinforcement probability for correct responses was varied from 0.2 to 0.6 across blocks of sessions and was unequal for correct light and dark responses. Introduction of a new reinforcement contingency resulted in a biphasic process of adjustment: First, choices were strongly biased towards the favored alternative, which was followed by a shift of preference back towards unbiased choice allocation. The data are well described by a signal detection model in which adjustment to a change in reinforcement contingency is modeled as the change of a criterion along a decision axis with fixed stimulus distributions. Moreover, the model shows that pigeons, after an initial overadjustment, distribute their responses almost optimally, although the overall benefit from doing so is extremely small. The strong and swift effect of minute changes in overall reinforcement probability precludes a choice strategy directly maximizing expected value, contrary to the assumption of signal detection theory. Instead, the rapid adjustments observed can be explained by a model in which reinforcement probabilities for each action, contingent on perceived stimulus intensity, determine choice allocation.     

On the relation between base-rate and cost-benefit learning in simulated medical diagnosis.

Observers completed a series of simulated medical diagnosis tasks that differed in category discriminability and base-rate/cost-benefit ratio. Point, accuracy, and decision criterion estimates were closer to optimal (a) for category d' = 2.2 than for category d' = 1.0 or 3.2, (b) when base-rates as opposed to cost-benefits were manipulated, and (c) when the cost of an incorrect response resulted in no point loss (nonnegative cost) as opposed to a point loss (negative cost). These results support the "flat-maxima" and competition between reward and accuracy (COBRA) hypotheses. A hybrid model that instantiated simultaneously both hypotheses was applied to the data. The model parameters indicated that (a) the reward-maximizing decision criterion quickly approached the optimal criterion, (b) the importance placed on accuracy maximization early in learning was larger when the cost of an incorrect response was negative as opposed to nonnegative, and (c) by the end of training the importance placed on accuracy was equal for negative and nonnegative costs.     

On the relation between decision rules and perceptual representation in multidimensional perceptual categorization

This article examines the relation between changing categorization decision rules and the nature of the underlying perceptual representation. Observers completed a matching task that required them to adjust the length and orientation of a single line stimulus until they perceived it to "match" a second line stimulus (Alfonso-Reese, 1996, 1997). The same observers then completed four categorization tasks with the same stimuli. Data from the matching task were used to estimate a perceptual representation for each stimulus and observer. Three hypotheses regarding potential interactions between categorization decision rules and perceptual representation were examined. One assumed that there was no interaction between decision rules and perceptual representation. The second assumed that linear categorization rules affect the perceptual representation differently from nonlinear categorization rules. The third assumed that dimensional integration rules affected the perceptual representation differently from decision rules that require the observer to set a criterion along one stimulus dimension while ignoring the other; this is referred to as decisional selective attention. The results suggested that (1) the matching task perceptual representation provided a good account of the categorization data, (2) decisional selective attention affected the perceptual representation differently from decisional integration, and (3) decisional selective attention generally decreased the perceptual variability along the attended dimension.     

Disrupting feedback processing interferes with rule-based but not information-integration category learning

The effect of a sequentially presented memory scanning task on rule-based and information-integration category learning was investigated. On each trial in the short feedback-processing time condition, memory scanning immediately followed categorization. On each trial in the long feedback-processing time condition, categorization was followed by a 2.5-sec delay and then memory scanning. In the control condition, no memory scanning was required. Rule-based category learning was significantly worse in the short feedback-processing time condition than in the long feedback-processing time condition or control condition, whereas information-integration category learning was equivalent across conditions. In the rule-based condition, a smaller proportion of observers learned the task in the short feedback-processing time condition, and those who learned took longer to reach the performance criterion than did those in the long feedback-processing time or control condition. No differences were observed in the information integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.     

Confidence and decision type under matched stimulus conditions: overconfidence in perceptual but not conceptual decisions

Within the domain of metacognition, there is disagreement whether different processes underlie evaluations of confidence in perceptual versus conceptual decisions. The relationship between confidence and accuracy for perceptual and conceptual decisions was compared using newly created stimuli that could be used to elicit either decision type. Based on theories of Brunswikian and Thurstonian uncertainties, significant underconfidence for perceptual decisions and overconfidence for conceptual decisions were predicted. Three within‐subjects experiments did not support this hypothesis. Participants showed significant overconfidence for perceptual decisions and no overconfidence for conceptual decisions. In addition, significant hard‐easy effects were consistently found for both decision types. Incorporating our findings with past results reveals that both over‐ and underconfidence are attainable on perceptual tasks. This conclusion, in addition to the common presence of hard‐easy effects and significant across‐task correlations in over/underconfidence, suggests that confidence judgments for the two decision types may depend on largely shared processes. Possible contributions to confidence and over/underconfidence are explored, focusing on response time factors and participants' knowledge bases. Copyright © 2007 John Wiley & Sons, Ltd.     

How to tell a wife from a hat: Affective feedback in perceptual categorization

How do people understand that their perception is correct? In line with the recurring idea of perception as prediction, the affective feedback account of hypotheses testing suggests that correct perceptual predictions are reinforced with positive affect. In four experiments, we tested whether correct categorization of a degraded image will lead to more positive liking ratings. The obtained findings supported the proposed approach: subjects liked the images they were able to perceive correctly more than others. Importantly, these findings were independent of the initial affective valence of stimuli. A further investigation demonstrated that this effect exists only when answers are at least moderately confident. The obtained findings add to the growing amount of literature on the role of affect in basic cognitive processing.     

Evidence for criterion shifts in visual perceptual learning: data and implications

Work on visual perceptual learning for contrast detection has shown that reliable decreases in detection thresholds are accompanied by reliable increases in false alarm rates (Wenger & Rasche, 2006). The present study assesses the robustness and replicability of these changes, demonstrating that they are independent of a variety of task demands (i.e., the specific method used for perceptual practice and threshold estimation) and the presence or absence of trial-by-trial feedback and that the source of the increases can be found in shifts in changes in sensitivity and in bias for detection, identification, or both. Although the increase in false alarm rates suggests a strategic shift in response criteria for detection, we demonstrate that there are multiple potential explanations, including explanations that do not require strategic shifts on the part of the observer. The empirical evidence and analysis of alternative explanations reinforce the inference that visual perceptual learning may involve more than changes in perceptual sensitivity and that cortical circuits beyond the primary visual areas may be involved.     

When more is less: feedback effects in perceptual category learning

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed.     

The interaction of criterion and feedback information in learning a drawing task

Experiments were designed to examine the influence of criterion and feedback information in the learning of a two-dimensional drawing task. Experiment 1 showed that when the task criterion is well known to the subject, the combined presentation of criterion information and information feedback facilitates the rate of acquisition of the skill but not its overall performance level of achievement. Experiment 2 showed that when the task criterion information is not well known to the subject, presentation of criterion information facilitates both the rate of acquisition and the overall performance level and, furthermore, is essential if configuration information feedback is to be utilized effectively. Experiment 3 showed that it is the combined presentation of criterion and configuration information feedback rather than the isolate presentation of either type of information alone, that facilitates learning and performance. Collectively, the findings from the three experiments suggest an interactive effect of prior knowledge by the learner and type of augmented information in facilitating the acquisition of skill, according to the constraints imposed in the task. The data are consistent with the proposal that the degrees of freedom in the information available to support motor skill learning must match the degrees of freedom to be constraint in the perceptual-motor workspace.     

Identification of gender in domestic-cat faces with and without training: perceptual learning of a natural categorization task

Three experiments were conducted to determine whether human observers could identify the gender of 40 domestic cats (20 female, 20 male) depicted in individual color photographs. In experiment 1a, observers performed at chance for photographs depicting whole cats, cat heads (bodies occluded), and cat bodies (heads occluded). Experiment 1b showed that chance performance was also obtained when the photographs were full-face close-ups of the cats. Experiment 2a revealed that even with gender-identification training on 30 (15 female, 15 male) of the 40 face close-ups, observers were unable to generalize their training to reliably identify the gender of the 10 remaining test faces (5 female, 5 male). However, experiment 2b showed that gender-identification training with the 14 most accurately identified faces from experiment 1b (7 female, 7 male) was successful in raising gender identification of the 10 test faces above chance. Experiments 3a and 3b extended this facilitative effect of gender-identification training to a population of animal-care workers. The findings indicate that, with appropriate training, human observers can identify the gender of cat faces at an above-chance level. A perceptual category learning account emphasizing the on-line formation of differentiated male versus female prototypes during training is offered as an explanation of the findings.