The reverse hierarchy theory of visual perceptual learning

Perceptual learning can be defined as practice-induced improvement in the ability to perform specific perceptual tasks. We previously proposed the Reverse Hierarchy Theory as a unifying concept that links behavioral findings of visual learning with physiological and anatomical data. Essentially, it asserts that learning is a top-down guided process, which begins at high-level areas of the visual system, and when these do not suffice, progresses backwards to the input levels, which have a better signal-to-noise ratio. This simple concept has proved powerful in explaining a broad range of findings, including seemingly contradicting data. We now extend this concept to describe the dynamics of skill acquisition and interpret recent behavioral and electrophysiological findings.     

Imprinting and perceptual learning

Filial responses are first considered by reference to the initial stimulus situations. Findings on variability in responsiveness of chicks are reported and discussed. Facilitated responsiveness subsequent to visual stimulation is reported. The concept of critical period is examined in the light of other workers' and our own findings; it is concluded that imprinting ends as a result of its own action rather than through the effects of fear. Following responses are further considered by reference to the influences of early experiences and the act of following upon the occurrence and strength of subsequent responses. The degree of attachment to a moving object tends to be proportional to the amount of experience. Individual chicks were allowed to spend several hours following a box in a runway and their ability to discriminate between familiar and unfamiliar moving objects and static environments was studied. Strong evidence for imprintability to environment has been found. Imprinting phenomena are discussed in terms of perceptual learning.     

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.     

Visual perceptual learning

Perceptual learning refers to the phenomenon that practice or training in perceptual tasks often substantially improves perceptual performance. Often exhibiting stimulus or task specificities, perceptual learning differs from learning in the cognitive or motor domains. Research on perceptual learning reveals important plasticity in adult perceptual systems, and as well as the limitations in the information processing of the human observer. In this article, we review the behavioral results, mechanisms, physiological basis, computational models, and applications of visual perceptual learning.     

Acetylcholine and olfactory perceptual learning

Olfactory perceptual learning is a relatively long-term, learned increase in perceptual acuity, and has been described in both humans and animals. Data from recent electrophysiological studies have indicated that olfactory perceptual learning may be correlated with changes in odorant receptive fields of neurons in the olfactory bulb and piriform cortex. These changes include enhanced representation of the molecular features of familiar odors by mitral cells in the olfactory bulb, and synthetic coding of multiple coincident odorant features into odor objects by cortical neurons. In this paper, data are reviewed that show the critical role of acetylcholine (Ach) in olfactory system function and plasticity, and cholinergic modulation of olfactory perceptual learning at both the behavioral and cortical level.     

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.     

Unconsciously learning task-irrelevant perceptual sequences

We demonstrated unconscious learning of task-irrelevant perceptual regularities in a Serial Reaction Time (SRT) task in both visual and auditory domains. Participants were required to respond to different letters (‘F’ or ‘J’, experiment 1) or syllables (‘can’ or ‘you’, experiment 2) which occurred in random order. Unbeknownst to participants, the color (red, green, blue or yellow) of the two letters or the tone (1–4) of the syllables varied according to certain rules. Reaction times indicated that people indeed learnt both the color and tonal regularities indicating that task-irrelevant sequence structure can be learned perceptually. In a subsequent prediction test of knowledge of the color or tonal cues using subjective measures, we showed that people could acquire task irrelevant knowledge unconsciously.     

Visuospatial perceptual sequence learning and eye movements

We examined perceptual sequence learning by means of an adapted serial reaction time task in which eye movements were unnecessary for performing the sequence learning task. Participants had to respond to the identity of a target letter pair ("OX" or "XO") appearing in one of four locations. On the other locations, similar distractor letter pairs ("QY" or "YQ") were shown. While target identity changed randomly, target location was structured according to a deterministic sequence. To render eye movements superfluous, (1) stimulus letter pairs appeared around a fixation cross with a visual angle of 0.63°, which means that they appeared within the foveal visual area and (2) the letter pairs were presented for only 100 ms, a period too short to allow proper eye movements. Reliable sequence knowledge was acquired under these conditions, as responses were both slower and less accurate when the trained sequence was replaced by an untrained sequence. These results support the notion that perceptual sequence learning can be based on shifts of attention without overt oculomotor movements.     

Attention and the measurement of perceptual learning

Novel and familiar letters were presented to Ss under conditions which controlled momentary attention states. The latencies of letter matching for the novel and familiar letters did not differ when Ss were expecting the particular letters which were presented. However, latencies to the two types of letters differed significantly when Ss were not expecting the particular letters which were presented. Additional exposures significantly reduced this difference, thereby generating a perceptual learning curve in terms of response latency. The main findings were interpreted in terms of a model of perceptual processing which involves mechanisms for hierarchical coding, selective attention, and automatic processing.     

Characterizing perceptual learning with external noise

Performance in perceptual tasks often improves with practice. This effect is known as ‘perceptual learning,’ and it has been the source of a great deal of interest and debate over the course of the last century. Here, we consider the effects of perceptual learning within the context of signal detection theory. According to signal detection theory, the improvements that take place with perceptual learning can be due to increases in internal signal strength or decreases in internal noise. We used a combination of psychophysical techniques (external noise masking and double-pass response consistency) that involve corrupting stimuli with externally added noise to discriminate between the effects of changes in signal and noise as observers learned to identify sets of unfamiliar visual patterns. Although practice reduced thresholds by as much as a factor of 14, internal noise remained virtually fixed throughout training, indicating learning served to predominantly increase the strength of the internal signal. We further examined the specific nature of the changes that took place in signal strength by correlating the externally added noise with observer’s decisions across trials (response classification). This technique allowed us to visualize some of the changes that took place in the linear templates used by the observers as learning occurred, as well as test the predictions of a linear template-matching model. Taken together, the results of our experiments offer important new theoretical constraints on models of perceptual learning.     

Late maturation of auditory perceptual learning

Adults can improve their performance on many perceptual tasks with training, but when does the response to training become mature? To investigate this question, we trained 11‐year‐olds, 14‐year‐olds and adults on a basic auditory task (temporal‐interval discrimination) using a multiple‐session training regimen known to be effective for adults. The adolescents all began with performance in the adult range. However, while all of the adults improved across sessions, none of the 11‐year‐olds and only half of the 14‐year‐olds did. The adolescents who failed to learn did so even though the 10‐session training regimen provided twice the number of sessions required by adults to reach asymptotic performance. Further, over the course of each session, the performance of the adults was stable but that of the adolescents, including those who learned, deteriorated. These results demonstrate that the processes that underlie perceptual learning can continue to develop well into adolescence.