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.     

Perception: The interface between cognition and the external world

In contrast to researchers who emphasize the distinction between perception and cognition it is claimed that the perceptual system is closely intertwined with cognition and with our comprehension of the external world. This claim is supported by evidence for two characteristic features of human information processing. One of these features is the use of mental models. If mental models are assumed to be anchored in the perceptual system it will not be necessary to assume a "little man in the head" who reads off the model. Moreover, perceptually based mental models will provide a referential semantics by anchoring mentally represented concepts in the external world. The other feature is the use of perceptually based perspectives in cognition, such as actor or observer perspectives in social cognition. It is concluded that human beings consistently interact within the real world--also when they are just thinking--and that this becomes possible via perceptual processes.     

Unity of perception

Perceptual scientists have recently enjoyed success in constructing mathematical theories for specific perceptual capacities, capacities such as stereovision, auditory localization, and color perception. Analysis of these theories suggests that they all share a common mathematical structure. If this is true, the elucidation of this structure, the study of its properties, the derivation of its consequences, and the empirical testing of its predictions are promising directions for perceptual research. We consider a candidate for the common structure, a candidate called an "observer". Observers, in essence, perform inferences; each observer has a characteristic class of perceptual premises, a characteristic class of perceptual conclusions, and its own functional relationship between these premises and conclusions. If observers indeed capture the structure common to perceptual capacities, then each capacity, regardless of its modality or manner of instantiation, can be described as some observer. In this paper we develop the definition of an observer. We first consider two examples of perceptual capacities: the measurement of visual motion, and the perception of depth from visual motion. In each case, we review a formal theory of the capacity and abstract its structural essence. From this essence we construct the definition of observer. We then exercise the definition in discussions of transduction, perceptual illusions, perceptual uncertainty, regularization theory, the cognitive penetrability of perception, and the theory neutrality of observation.     

The effect of external representations on numeric tasks

This article explores the effect of external representations on numeric tasks. Through several minor modifications on the previously reported two-digit number comparison task, we obtained different results. Rather than holistic comparison, we found parallel comparison. We argue that this difference was a reflection of different representational forms: The comparison was based on internal representations in previous studies but on external representations in our present study. This representational effect is discussed under a framework of distributed number representations. We propose that in numerical tasks involving external representations, numbers should be considered as distributed representations, and the behaviour in these tasks should be considered as the interactive processing of internal and external information through the interplay of perceptual and cognitive processes. We suggest that theories of number representations and process models of numerical cognition should consider external representations as an essential component.     

Signal detection theory analyses of semantic priming in word recognition

Word recognition, semantic priming, and cognitive impenetrability research have used signal detection theory (SDT) measures to separate perceptual and postperceptual processes. In the D. Norris (1986) checking model and model simulation (D. Norris, 1995), priming alters only postperceptual word decision criteria: Stimulus-related priming reduces uncertainty, increasing sensitivity; stimulus-unrelated priming increases false alarms more than hits, reducing sensitivity. This work is cited as strong evidence that criterion changes can alter perceptual sensitivity and that SDT is inappropriate for investigating complex cognitive processes. The authors' current SDT ideal observer analysis of the model demonstrates that related priming does not directly alter sensitivity and that unrelated priming increases only false-alarm rate, reducing sensitivity. This analysis provides new perspectives on SDT concepts of complex decision processing.     

The planning and control model (PCM) of motorvisual priming: reconciling motorvisual impairment and facilitation effects

Previous research on dual-tasks has shown that, under some circumstances, actions impair the perception of action-consistent stimuli, whereas, under other conditions, actions facilitate the perception of action-consistent stimuli. We propose a new model to reconcile these contrasting findings. The planning and control model (PCM) of motorvisual priming proposes that action planning binds categorical representations of action features so that their availability for perceptual processing is inhibited. Thus, the perception of categorically action-consistent stimuli is impaired during action planning. Movement control processes, on the other hand, integrate multi-sensory spatial information about the movement and, therefore, facilitate perceptual processing of spatially movement-consistent stimuli. We show that the PCM is consistent with a wider range of empirical data than previous models on motorvisual priming. Furthermore, the model yields previously untested empirical predictions. We also discuss how the PCM relates to motorvisual research paradigms other than dual-tasks.     

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.     

A test of Bayesian observer models of processing in the Eriksen flanker task

Two Bayesian observer models were recently proposed to account for data from the Eriksen flanker task, in which flanking items interfere with processing of a central target. One model assumes that interference stems from a perceptual bias to process nearby items as if they are compatible, and the other assumes that the interference is due to spatial uncertainty in the visual system (Yu, Dayan, & Cohen, 2009). Both models were shown to produce one aspect of the empirical data, the below-chance dip in accuracy for fast responses to incongruent trials. However, the models had not been fit to the full set of behavioral data from the flanker task, nor had they been contrasted with other models. The present study demonstrates that neither model can account for the behavioral data as well as a comparison spotlight-diffusion model. Both observer models missed key aspects of the data, challenging the validity of their underlying mechanisms. Analysis of a new hybrid model showed that the shortcomings of the observer models stem from their assumptions about visual processing, not the use of a Bayesian decision process.     

Decision noise: An explanation for observed violations of signal detection theory

In signal detection theory (SDT), responses are governed by perceptual noise and a flexible decision criterion. Recent criticisms of SDT (see, e.g., Balakrishnan, 1999) have identified violations of its assumptions, and researchers have suggested that SDT fundamentally misrepresents perceptual and decision processes. We hypothesize that, instead, these violations of SDT stem from decision noise: the inability to use deterministic response criteria. In order to investigate this hypothesis, we present a simple extension of SDT—the decision noise model—with which we demonstrate that shifts in a decision criterion can be masked by decision noise. In addition, we propose a new statistic that can help identify whether the violations of SDT stem from perceptual or from decision processes. The results of a stimulus classification experiment—together with model fits to past experiments—show that decision noise substantially affects performance. These findings suggest that decision noise is important across a wide range of tasks and needs to be better understood in order to accurately measure perceptual processes.     

Modeling external events in the three-level analysis of multiple-baseline across-participants designs: A simulation study

In this study, we focus on a three-level meta-analysis for combining data from studies using multiple-baseline across-participants designs. A complicating factor in such designs is that results might be biased if the dependent variable is affected by not explicitly modeled external events, such as the illness of a teacher, an exciting class activity, or the presence of a foreign observer. In multiple-baseline designs, external effects can become apparent if they simultaneously have an effect on the outcome score(s) of the participants within a study. This study presents a method for adjusting the three-level model to external events and evaluates the appropriateness of the modified model. Therefore, we use a simulation study, and we illustrate the new approach with real data sets. The results indicate that ignoring an external event effect results in biased estimates of the treatment effects, especially when there is only a small number of studies and measurement occasions involved. The mean squared error, as well as the standard error and coverage proportion of the effect estimates, is improved with the modified model. Moreover, the adjusted model results in less biased variance estimates. If there is no external event effect, we find no differences in results between the modified and unmodified models.     

Relations among categorization, induction, recognition, and similarity: comment on Sloutsky and Fisher (2004)

V. M. Sloutsky and A. V. Fisher reported 5 experiments documenting relations among categorization, induction, recognition, and similarity in children as well as adults and proposed a new model of induction, SINC (similarity, induction, categorization). Those authors concluded that induction depends on perceptual similarity rather than conceptual knowledge. Despite the useful contributions of this work, there are some important limitations. The experimental designs examined a limited range of phenomena that are not the most revealing about the use of nonperceptual information. The main results involved a simple triad task, for which the SINC model's predictions are equivalent to the predictions of previous models of inductive reasoning. It is also unclear whether the SINC model can account for the observed relations between similarity and recognition. Implications for future work on induction and related cognitive activities are discussed.     

Mental action and emotion – What happens in the mind when the stimulus changes but not the perceptual intention

While mainstream psychology and cognitive science still rely on the measurement of external behavioural data, systematic recording and analysis of qualitative and process-related first-person data is largely missing. However, it is possible to overcome these limitations leading to an incomplete picture by adapting conventional methodological criteria to first-person research. This study gives an example of this, referring to perceptual reversals and exploring the phenomenal and processual qualities of mental action and emotion as they occur during the task of holding a particular percept while the stimulus changes. The results are in support of a four-phase dynamics of voluntary mental action and indicate a processual correlation of mental action and concomitant emotional experience. This is discussed in the context of common psychological constructs such as self-control and self-efficacy, other introspective research results, the bottom-up/top-down debate and in view of neurophysiological studies in this field.     

A Bayesian model for implicit effects in perceptual identification

Retrieving effectively from memory (REM; R. M. Shiffrin & M. Steyvers, 1997), an episodic model of memory, is extended to implicit memory phenomena, namely the perceptual identification studies reported in R. Ratcliff and G. McKoon (1997). In those studies, the influence of prior study was greatest when words were presented most briefly and when forced-choice targets and foils were most similar. R. Ratcliff and G. McKoon use these data to argue against models in which prior study changes a word's representation. A model in which prior study changes a word's representation by adding context information is fit to their data; at test, the model uses a Bayesian decision process to compare the perceptual and context features associated with the test flash to stored traces. The effects of prior study are due to matching extra context information and are larger when alternatives share many features, thereby reducing noise that attenuates these effects.     

Exemplar accounts of blending and distinctiveness effects in perceptual old-new recognition

Recent findings from the perceptual old-new recognition literature indicate that observers have extremely high false-alarm rates to new items that are "blends" of old ones. In addition, evidence suggests that "distinctive" old items--that is, those located in isolated regions of the similarity space--are recognized with higher probability than are typical old items. Both types of phenomena challenge the predictions of global-familiarity exemplar models of perceptual old-new recognition, which posit that the probability that an observer judges an item as old is based on its summed similarity to previously presented exemplars. In the present research the authors pursued these blending and distinctiveness effects by testing paradigms in which similarity relations among objects are highly controlled and in which the variables of blending and distinctiveness are not confounded with other properties associated with the individual objects themselves. In contrast to previous results, the authors found effects of blending and distinctiveness that are compatible with the predictions of a pure summed-similarity exemplar model.     

Neurological disorders and the structure of human consciousness

Recent studies that identify distinct neural correlates of perceptual awareness offer a promising step towards improved understanding of the neurological underpinnings of conscious experience. Such studies indicate that perceptual awareness is modular in nature, with neural correlates of awareness consisting of the specialized structures involved in perceptual processing. However, the integrative, multimodal nature of conscious experience appears to require a functional architecture that overcomes this modular segregation of function. We propose a model in which experience emerges from the dynamic interactions of specialized component processes via a distributed neural network. Such a model offers a mechanism to explain several empirical observations of the neural correlates of perceptual awareness, cognitive function, and symptoms of neurological damage.     

The Multigroup Multilevel Categorical Latent Growth Curve Models

Longitudinal data describe developmental patterns and enable predictions of individual changes beyond sampled time points. Major methodological issues in longitudinal data include modeling random effects, subject effects, growth curve parameters, and autoregressive residuals. This study embedded the longitudinal model within a multigroup multilevel framework and allowed for autoregressive residuals. The parameter in the new model can be estimated using the computer program WinBUGS, which adopts Markov Chain Monte Carlo algorithms. Two simulation studies were conducted. An empirical example was raised and established based on models generated by the results of empirical data, which have been fitted and compared.     

The applicability of deadline models: Comment on Glickman,Gray, and Morales (2005)

Glickman, Gray, and Morales (this issue) propose a statistical model for measuring the unobserved latency of stimulus-controlled processes. The model accounts for both speed and accuracy and does so by assuming participants set an internal deadline. If a stimulus-controlled response is not produced by the deadline, the participant then guesses. The applicability the model is discussed in this comment. The deadline model yields specific predictions for the case in which stimulus difficulty is manipulated in a within-block manner. In this case, it is reasonable to assume that stimulus difficulty does not affect the deadline. It is shown that in common perceptual and cognitive domains, extant data do not fully meet these predictions. Hence, practitioners need be aware of the possibility and consequences of model misspecification. This research is supported by NSF grant SES - 0095919 to J. Rouder, D. Sun, and P. Speckman.     

Turning the attentional blink on and off: Opposing effects of spatial and temporal noise

Brain oscillations in various frequency bands have been shown to be an important means of enabling interarea communication for high-level cognitive performance. Interestingly, perturbation to such oscillations in the form of weak noise has been shown to benefit perception in tasks such as the attentional blink (AB). Here, we investigated perturbation intrinsic to the AB task in two conditions in which noise arose from either temporal or spatial discontinuity. Consistent with theoretical predictions, temporal discontinuity resulted in a reduced AB, whereas spatial discontinuity resulted in an increased AB. The results are discussed in the framework of both stochastic resonance theory and pretarget alpha oscillations, two neurally based accounts of perceptual processing.