Evaluating single-case research data for systematic review: A commentary for the special issue

The purpose of this commentary is to provide observation on the statistical procedures described throughout this special section from the perspective of researchers with experience in conducting systematic reviews and meta-analyses of single-case research to address issues of evidence-based practice. It is our position that both visual and statistical analyses are complimentary methods for evaluating single-case research data for these purposes. Given the recent developments regarding the use of single-case research to inform evidence-based practice and policy, the developments described in the present issue will be contextualized within the need for a widely accepted process for data evaluation to assist with extending the impact of single-case research. The commentary will, therefore, begin with providing an overview of the conceptual underpinnings of a systematic review of single-case research and will be followed by a discussion of several features that are essential to the development of a conceptually sound and widely used statistical procedure for single-case research. The commentary will conclude with recommendations and guidelines for the use of both visual and statistical analyses within primary research reports and recommendations for future research.     

Toward ecologically realistic theories in visual short-term memory research

Recent evidence from neuroimaging and psychophysics suggests common neural and representational substrates for visual perception and visual short-term memory (VSTM). Visual perception is adapted to a rich set of statistical regularities present in the natural visual environment. Common neural and representational substrates for visual perception and VSTM suggest that VSTM is adapted to these same statistical regularities too. This article discusses how the study of VSTM can be extended to stimuli that are ecologically more realistic than those commonly used in standard VSTM experiments and what the implications of such an extension could be for our current view of VSTM. We advocate for the development of unified models of visual perception and VSTM—probabilistic and hierarchical in nature—incorporating prior knowledge of natural scene statistics.     

Colour vision is aligned with natural scene statistics at 4 months of age

Visual perception in adult humans is thought to be tuned to represent the statistical regularities of natural scenes. For example, in adults, visual sensitivity to different hues shows an asymmetry which coincides with the statistical regularities of colour in the natural world. Infants are sensitive to statistical regularities in social and linguistic stimuli, but whether or not infants’ visual systems are tuned to natural scene statistics is currently unclear. We measured colour discrimination in infants to investigate whether or not the visual system can represent chromatic scene statistics in very early life. Our results reveal the earliest association between vision and natural scene statistics that has yet been found: even as young as 4 months of age, colour vision is aligned with the distributions of colours in natural scenes.

Research Highlights

1. We find infants’ colour sensitivity is aligned with the distribution of colours in the natural world, as it is in adults.
2. At just 4 months, infants’ visual systems are tailored to extract and represent the statistical regularities of the natural world.
3. This points to a drive for the human brain to represent statistical regularities even at a young age.

     

Visual and verbal codes: Spatial information survives the icon

Three experiments are reported to demonstrate partial independence of identity and spatial position information concerning visually presented symbols. Experiment I shows that performance on these forms of information improves at different rates as a function of exposure duration. Experiment II shows that performance on one can be traded against the other. Experiment III demonstrates partial statistical independence of item and position responses and shows that increases in the duration and delay of the probe facilitate performance. Some implications of these experiments for theories involving mandatory verbal encoding of visual symbol-arrays are discussed. A model is proposed comprising visual and name stores with different acquisition rates and capacities. Both are indexed by identity but the visual code can also be interrogated by spatial cues.     

Encoding multielement scenes: statistical learning of visual feature hierarchies

The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.     

High regularities in eye-movement patterns reveal the dynamics of the visual working memory allocation mechanism

With only two to five slots of visual working memory (VWM), humans are able to quickly solve complex visual problems to near optimal solutions. To explain the paradox between tightly constrained VWM and impressively complex human visual problem-solving ability, we propose several principles for dynamic VWM allocation. In particular, we propose that complex visual information is represented in a temporal manner using only a few slots of VWM that include global and local visual chunks. We built a model of human traveling salesman problem solving based on these principles of VWM allocation and tested the model with eye-movement data. Exactly as the model predicted, human eye movements during traveling salesman problem solving have precise quantitative regularities with regard to both the general statistical pattern of attentional fixations and how they vary across individuals with different VWM capacities. Even though VWM capacity is very limited, eye movements dynamically allocate VWM resources to both local and global information, enabling attention to fine details without loss of the big picture.     

Learning across senses: cross-modal effects in multisensory statistical learning

It is currently unknown whether statistical learning is supported by modality-general or modality-specific mechanisms. One issue within this debate concerns the independence of learning in one modality from learning in other modalities. In the present study, the authors examined the extent to which statistical learning across modalities is independent by simultaneously presenting learners with auditory and visual streams. After establishing baseline rates of learning for each stream independently, they systematically varied the amount of audiovisual correspondence across 3 experiments. They found that learners were able to segment both streams successfully only when the boundaries of the audio and visual triplets were in alignment. This pattern of results suggests that learners are able to extract multiple statistical regularities across modalities provided that there is some degree of cross-modal coherence. They discuss the implications of their results in light of recent claims that multisensory statistical learning is guided by modality-independent mechanisms.     

The Analysis of Single-Case Research Data: Current Instructional Practices

Visual analysis is the predominant method of analysis in single-case research (SCR). However, most research suggests that agreement between visual analysts is poor, which may be due to a lack of clear guidelines and criteria for visual analysis, as well as variability in how individuals are trained. We developed a survey containing questions about the content and methods used to teach visual and statistical analysis of SCR data in verified course sequences (VCS) and distributed it via the VCS Coordinator Listserv. Thirty-seven instructors completed the survey. Results suggest that there is variability across instructors in some fundamental aspects of data analysis (e.g., number of effects required for a functional relation) but a great deal of consistency in others (e.g., emphasizing visual over statistical analysis). We discuss our results along with their implications both for teaching students to analyze SCR data and for conducting additional research on behavior-analytic training programs.

     

Eye movements reveal the on-line computation of lexical probabilities during reading

Skilled readers are able to derive meaning from a stream of visual input with remarkable efficiency. In this article, we present the first evidence that statistical information latent in the linguistic environment can contribute to an account of reading behavior. In two eye-tracking studies, we demonstrate that the transitional probabilities between words have a measurable influence on fixation durations, and using a simple Bayesian statistical model, we show that lexical probabilities derived by combining transitional probability with the prior probability of a word's occurrence provide the most parsimonious account of the eye movement data. We suggest that the brain is able to draw upon statistical information in order to rapidly estimate the lexical probabilities of upcoming words: a computationally inexpensive mechanism that may underlie proficient reading.     

Prior knowledge on the illumination position

Visual perception is fundamentally ambiguous because an infinite number of three-dimensional scenes are consistent with our retinal images. To circumvent these ambiguities, the visual system uses prior knowledge such as the assumption that light is coming from above our head. The use of such assumptions is rational when these assumptions are related to statistical regularities of our environment. In confirmation of previous visual search experiments, we demonstrate here that the assumption on the illumination position is in fact biased to the above-left rather than directly above. This bias to the left reaches 26 degrees on average in a more direct shape discrimination task. Both right-handed and left-handed observers have a similar leftward bias. We discuss the possible origins of this singular bias on the illumination position.     

The effect of iconicity of visual displays on statistical reasoning: evidence in favor of the null hypothesis

Knowing which properties of visual displays facilitate statistical reasoning bears practical and theoretical implications. Therefore, we studied the effect of one property of visual diplays?– iconicity (i.e., the resemblance of a visual sign to its referent)?– on Bayesian reasoning. Two main accounts of statistical reasoning predict different effect of iconicity on Bayesian reasoning. The ecological-rationality account predicts a positive iconicity effect, because more highly iconic signs resemble more individuated objects, which tap better into an evolutionary-designed frequency-coding mechanism that, in turn, facilitates Bayesian reasoning. The nested-sets account predicts a null iconicity effect, because iconicity does not affect the salience of a nested-sets structure—the factor facilitating Bayesian reasoning processed by a general reasoning mechanism. In two well-powered experiments (N = 577), we found no support for a positive iconicity effect across different iconicity levels that were manipulated in different visual displays (meta-analytical overall effect: log OR = ?0.13, 95 % CI [?0.53, 0.28]). A Bayes factor analysis provided strong evidence in favor of the null hypothesis—the null iconicity effect. Thus, these findings corroborate the nested-sets rather than the ecological-rationality account of statistical reasoning.     

The automaticity of visual statistical learning

The visual environment contains massive amounts of information involving the relations between objects in space and time, and recent studies of visual statistical learning (VSL) have suggested that this information can be automatically extracted by the visual system. The experiments reported in this article explore the automaticity of VSL in several ways, using both explicit familiarity and implicit response-time measures. The results demonstrate that (a) the input to VSL is gated by selective attention, (b) VSL is nevertheless an implicit process because it operates during a cover task and without awareness of the underlying statistical patterns, and (c) VSL constructs abstracted representations that are then invariant to changes in extraneous surface features. These results fuel the conclusion that VSL both is and is not automatic: It requires attention to select the relevant population of stimuli, but the resulting learning then occurs without intent or awareness.     

基于名人面孔视觉特征和语义信息的视觉统计学习

视觉统计学习是指个体依据视觉刺激之间的转接概率来掌握统计规律的过程。本研究通过5个实验探讨了个体基于名人面孔视觉特征和语义信息进行视觉统计学习的加工机制。每个实验均包括熟悉(学习)和测试两个阶段：在熟悉阶段, 让被试观看名人面孔并完成重复图片探测的无关任务; 在测试阶段, 让被试进行二选一迫选任务。其中, 实验1和2分别考察基于名人面孔视觉特征和语义信息的视觉统计学习效果; 实验3分别考察基于名人面孔视觉特征和语义信息视觉进行统计学习的精确性; 实验4进一步考察基于名人面孔视觉特征和语义信息进行视觉统计学习的时间特征; 实验5验证基于名人面孔视觉特征的视觉统计学习具有面孔特异性。结果表明：个体能同时基于名人面孔视觉特征和语义信息进行精确的视觉统计学习; 基于正立名人面孔的视觉统计学习效果显著高于基于倒置名人面孔的视觉统计学习效果; 虽然基于视觉特征和语义信息的统计加工都具有一致的精确性, 但后者需要更多的加工时间。这提示：基于名人面孔视觉特征的视觉统计学习具有面孔特异性, 个体基于名人面孔视觉特征和语义信息的视觉统计学习过程是分离的, 统计运算发生于面孔特征加工完成之后。     

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event‐related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non‐verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non‐verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non‐verbal cognition and adaptive social function.     

Statistical inference in behavior analysis: Experimental control is better

Statistical inference promises automatic, objective, reliable assessments of data, independent of the skills or biases of the investigator, whereas the single-subject methods favored by behavior analysts often are said to rely too much on the investigator's subjective impressions, particularly in the visual analysis of data. In fact, conventional statistical methods are difficult to apply correctly, even by experts, and the underlying logic of null-hypothesis testing has drawn criticism since its inception. By comparison, single-subject methods foster direct, continuous interaction between investigator and subject and development of strong forms of experimental control that obviate the need for statistical inference. Treatment effects are demonstrated in experimental designs that incorporate replication within and between subjects, and the visual analysis of data is adequate when integrated into such designs. Thus, single-subject methods are ideal for shaping-and maintaining-the kind of experimental practices that will ensure the continued success of behavior analysis.     

How implicit is visual statistical learning?

In visual statistical learning, participants learn the statistical regularities present in a sequence of visual shapes. A recent study (Kim, Seitz, Feenstra, & Shams, 2009) suggests that visual statistical learning occurs implicitly, as it is not accompanied by conscious awareness of these regularities. However, that interpretation of the data depends on 2 unwarranted assumptions concerning the nature and sensitivity of the tasks used to measure learning. In a replication of this study, we used a 4-choice completion task as a direct measure of learning, in addition to an indirect measure consisting of a rapid serial visual presentation task. Moreover, binary confidence judgments were recorded after each completion trial. This way, we measured systematically the extent to which sequence knowledge was available to consciousness. Supporting the notion that the role of unconscious knowledge was overestimated in Kim et al.'s study, our results reveal that participants' performance cannot be exclusively accounted for by implicit knowledge. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Role of Attention and Perceptual Grouping in Visual Statistical Learning

Statistical learning has been widely proposed as a mechanism by which observers learn to decompose complex sensory scenes. To determine how robust statistical learning is, we investigated the impact of attention and perceptual grouping on statistical learning of visual shapes. Observers were presented with stimuli containing two shapes that were either connected by a bar or unconnected. When observers were required to attend to both locations at which shapes were presented, the degree of statistical learning was unaffected by whether the shapes were connected or not. However, when observers were required to attend to just one of the shapes' locations, statistical learning was observed only when the shapes were connected. These results demonstrate that visual statistical learning is not just a passive process. It can be modulated by both attention and connectedness, and in natural scenes these factors may constrain the role of stimulus statistics in learning.     

Multidimensional processing of dynamic sounds: more than meets the ear

Strong cross-modal interactions exist between visual and auditory processing. The relative contributions of perceptual versus decision-related processes to such interactions are only beginning to be understood. We used methodological and statistical approaches to control for potential decision-related contributions such as response interference, decisional criterion shift, and strategy selection. Participants were presented with rising-, falling-, and constant-amplitude sounds and were asked to detect change (increase or decrease) in sound amplitude while ignoring an irrelevant visual cue of a disk that grew, shrank, or stayed constant in size. Across two experiments, testing context was manipulated by varying the grouping of visual cues during testing, and cross-modal congruency showed independent perceptual and decision-related effects. Whereas a change in testing context greatly affected criterion shifts, cross-modal effects on perceptual sensitivity remained relatively consistent. In general, participants were more sensitive to increases in sound amplitude and less sensitive to sounds paired with dynamic visual cues. As compared with incongruent visual cues, congruent cues enhanced detection of amplitude decreases, but not increases. These findings suggest that the relative contributions of perceptual and decisional processing and the impacts of these processes on cross-modal interactions can vary significantly depending on asymmetries in within-modal processing, as well as consistencies in cross-modal dynamics.     

Mutual interference between statistical summary perception and statistical learning

The visual system is an efficient statistician, extracting statistical summaries over sets of objects (statistical summary perception) and statistical regularities among individual objects (statistical learning). Although these two kinds of statistical processing have been studied extensively in isolation, their relationship is not yet understood. We first examined how statistical summary perception influences statistical learning by manipulating the task that participants performed over sets of objects containing statistical regularities (Experiment 1). Participants who performed a summary task showed no statistical learning of the regularities, whereas those who performed control tasks showed robust learning. We then examined how statistical learning influences statistical summary perception by manipulating whether the sets being summarized contained regularities (Experiment 2) and whether such regularities had already been learned (Experiment 3). The accuracy of summary judgments improved when regularities were removed and when learning had occurred in advance. In sum, calculating summary statistics impeded statistical learning, and extracting statistical regularities impeded statistical summary perception. This mutual interference suggests that statistical summary perception and statistical learning are fundamentally related.     

The representation of simple ensemble visual features outside the focus of attention

The representation of visual information inside the focus of attention is more precise than the representation of information outside the focus of attention. We found that the visual system can compensate for the cost of withdrawing attention by pooling noisy local features and computing summary statistics. The location of an individual object is a local feature, whereas the center of mass of several objects (centroid) is a summary feature representing the mean object location. Three experiments showed that withdrawing attention degraded the representation of individual positions more than the representation of the centroid. It appears that information outside the focus of attention can be represented at an abstract level that lacks local detail, but nevertheless carries a precise statistical summary of the scene. The term ensemble features refers to a broad class of statistical summary features that we propose collectively make up the representation of information outside the focus of attention.