Training can modify the visual system to produce a substantial improvement on perceptual tasks and therefore has applications for treating visual deficits. Visual perceptual learning (VPL) is often specific to the trained feature, which gives insight into processes underlying brain plasticity, but limits VPL’s effectiveness in rehabilitation. Under what circumstances VPL transfers to untrained stimuli is poorly understood. Here we report a qualitatively new phenomenon: intrinsic variation in the representation of features determines the transfer of VPL. Orientations around cardinal are represented more reliably than orientations around oblique in V1, which has been linked to behavioral consequences such as visual search asymmetries. We studied VPL for visual search of near-cardinal or oblique targets among distractors of the other orientation while controlling for other display and task attributes, including task precision, task difficulty, and stimulus exposure. Learning was the same in all training conditions; however, transfer depended on the orientation of the target, with full transfer of learning from near-cardinal to oblique targets but not the reverse. To evaluate the idea that representational reliability was the key difference between the orientations in determining VPL transfer, we created a model that combined orientation-dependent reliability, improvement of reliability with learning, and an optimal search strategy. Modeling suggested that not only search asymmetries but also the asymmetric transfer of VPL depended on preexisting differences between the reliability of near-cardinal and oblique representations. Transfer asymmetries in model behavior also depended on having different learning rates for targets and distractors, such that greater learning for low-reliability distractors facilitated transfer. These findings suggest that training on sensory features with intrinsically low reliability may maximize the generalizability of learning in complex visual environments.
Training in fundamental visual perceptual tasks can lead to substantial improvement, a phenomenon known as Visual Perceptual Learning (VPL), which is associated with adult brain plasticity. VPL has powerful real-word applications [1–3] including improving the vision of adults with cortical blindness [4], amblyopia [5–7] and presbyopia [8]. VPL is often specific to the trained feature and location (reviewed by [9]). From a theoretical point of view, specificity can provide important insight into the neuronal mechanisms that underlie VPL. For example, specificity has been taken to imply plasticity in early-stage visual processing (e. g. , [10,11]). However, from a practical or clinical viewpoint, specificity can be a major obstacle in the development of effective training protocols, and it is therefore critical to understand the factors that determine VPL specificity and the conditions that lead to transfer. For complete transfer to occur, the visual system needs to apply learning for one stimulus to another stimulus. The ability to generalize improvements across stimuli may be most likely when the representation of the stimuli is intrinsically similar. However, the visual system has intrinsic variations in its representation of different feature values. In particular, the reliability with which different feature values are represented can vary considerably within a feature dimension. For example, the reliability of orientation representation in V1 strongly depends on the orientation value. Cells responding to orientations around cardinal are larger in number and have smaller response variability compared to cells responding to orientations around oblique [12,13]. In human V1-V3, sensory uncertainty estimated from the fMRI BOLD signal is higher near oblique orientations than near cardinal orientations, which correlates with orientation estimation behavior [14]. These studies show a gradual variation in representational reliability as a function of orientation, with higher reliability for orientations closer to cardinal (especially horizontal) and lower reliability for more oblique orientations. These intrinsic differences have been linked to substantial behavioral effects unrelated to learning. They explain the advantage that observers have in discriminating orientations around cardinal compared to around oblique [13–16]: the oblique effect [17]; and in detecting oblique targets among cardinal [18–20] or near-cardinal [21] distractors over the reverse: orientation search asymmetry [22]. Explanations of search asymmetry propose that oblique distractors have less reliable representations than cardinal distractors and thus hinder target detection more [19,20]. Intrinsic variations in representational reliability are not limited to orientation; for example, stimulus processing also varies across spatial frequency [23]. Thus far, however, no study has directly investigated the effect of these preexisting variations in representational reliability on VPL transfer and specificity. Investigation of VPL has focused instead on the manipulation of task properties. By varying task difficulty [10,24] and task precision (e. g. , orientation difference in a discrimination task; [25]), researchers varied the representational precision required to successfully perform the task, and studied its effect on VPL specificity. However, variability in task demands is distinct from initial variability in the underlying representation and may invoke different learning mechanisms. For example, increased specificity in difficult or high-precision tasks has been attributed to changes in the modulated level of representation in the visual processing hierarchy [10,24], whereas intrinsic differences in representational reliability are present within the same hierarchical level. Here, we asked whether variations in representational reliability alone can explain VPL and its specificity and transfer, when task properties such as difficulty and precision are the same. Our results show that near-cardinal and oblique orientations not only yield an orientation search asymmetry [18–22] but also show asymmetric transfer of VPL in visual search. Conversely, task difficulty, which was independently manipulated by varying the stimulus onset asynchrony (SOA) between a mask and the search display, did not affect the pattern of transfer. To test the sufficiency of a reliability-based account, we fit a computational model that combines learning-related increases in the reliability of stimulus representations with a Bayesian search strategy based on Ma et al. [26]. This Bayesian search model was well-suited to test our hypothesis, because it explicitly represents orientation reliability. Using an unchanging optimal decision rule, the model accounts for both search and transfer asymmetry via initial differences in near-cardinal and oblique orientation reliability.
To test for learning, we compared the first and the last training days. For all three dependent measurements (sensitivity, bias and RT) we conducted a (2X2X2) three-way mixed design analysis of variance (ANOVA) with training effect (training day 1 vs. 6) and SOA (35,59,94 and 129 ms) as within-observers factors and group (near-cardinal vs. oblique training) as a between-observers factor. To test for the transfer of learning for each of the three dependent measurements, we conducted a (2X2X2) three-way mixed design ANOVA with tests (color test vs. orientation test) and SOA (35,59,94 and 129 ms) as within-observers factors and group (near-cardinal vs. oblique group) as a between-observers factor. As Fig 1E reveals, whereas color test performance was very similar to the last day of learning in both groups, orientation test performance was dependent on the group. Because baseline performance (training day 1) for the near-cardinal condition was lower than for the oblique condition, it may be that during the orientation test (when target and distractor orientations swapped) specificity was inflated by the baseline difference. In order to control for this possibility, we additionally assessed transfer and specificity by comparing performance (d′) in the orientation transfer test from one group with the baseline performance (training day 1) and trained performance (training day 6) of the other group, such that the orientation condition was the same within each comparison (Fig 2B). First we tested whether transfer performance is higher than baseline, which would indicate that at least some learning partially transferred to the untrained orientation. Two independent sample one-tailed t-tests revealed significant transfer both to near-cardinal and to oblique orientations, t (8) = 3. 08, p = 0. 007, Cohen’s d = 1. 94, t (8) = 2. 01, p = 0. 039, Cohen’s d = 1. 28, respectively. Next we tested whether transfer performance is different than trained performance; a difference would indicate specificity, while no difference would indicate full transfer of learning to the untrained orientation. Two independent sample t-tests revealed significant partial specificity following oblique orientation training, t (8) = 2. 96, p = 0. 018, Cohen’s d = 2. 05, but not following near-cardinal orientation training, t<1. The same results were obtained when a nonparametric test was used (S1 Table). Thus, learning only partly transferred to the near-cardinal orientation but fully transferred to the oblique orientation. Because we found that VPL specificity and transfer depended on the trained orientation–despite equated task difficulty and task precision–we hypothesized that differences in the representational reliability of near-cardinal and oblique orientations may lead to both search and VPL transfer asymmetries. To investigate this possibility, we used computational modeling. We developed a model that consists of two parts: optimal orientation search [26,28] and reliability improvement over the course of learning. The goal was to determine whether orientation reliability and its improvement with learning could explain the behavioral data. We compared four models to test different hypotheses about the role of orientation reliability in learning and transfer in the orientation search task. We tested whether initial reliability differences between near-cardinal and oblique orientations alone (Reliability model), different learning rates for targets and distractors alone (Learning model), both of these factors together (Reliability-and-Learning model), or these factors with independent learning rates for the two groups (Reliability-Learning-Group model) best accounted for the data. Detailed descriptions of the models can be found in the Methods, and all model fits are shown in S1 Fig. Model comparison using the AICc metric indicated that initial reliability differences between near-cardinal and oblique orientations were critical to explain the data. The Reliability model (three parameters, AICc = 10. 61) and the Reliability-and-Learning model (four parameters, AICc = 13. 00) outperformed the Learning model (three parameters, AICc = 21. 05) and the Reliability-Learning-Group model (six parameters, AICc = 24. 56). When we compared cross-validated r2, the Reliability-and-Learning model fit the data better than the Reliability model. For the Reliability-and-Learning model, cross-validated r2 was 0. 81 (SD 0. 09), falling within the noise ceiling (lower and upper bounds, [0. 75 0. 84]), Model performance was therefore as good as possible given the noise in the data. For the Reliability model, cross-validated r2 was 0. 70 (SD 0. 24), falling below the noise ceiling. To determine whether transfer and specificity in the two best models could be predicted based only on the learning phase, we fit the models to the training days only and predicted the transfer test performance for each group. For the Reliability-and-Learning model, the predicted orientation test performance was similar to the observed performance, namely, transfer in the near-cardinal group and specificity in the oblique group (Fig 3A, stars). The Reliability model predicted more transfer in the oblique group than was observed in the data (Fig 3A, plus signs), similar to its fit to all data points (S1 Fig). The pattern of transfer and specificity therefore did not depend on including the test session data when fitting the model, and the Reliability-and-Learning model better explained transfer behavior. The Reliability-and-Learning model, then, captured the three key features of the data: 1) the search asymmetry at baseline, 2) the performance improvement with learning, and 3) the orientation dependence of VPL specificity and transfer (Fig 3A). Learning in the oblique group maintained the difference in reliability between the near-cardinal and oblique orientations, thereby maintaining the search asymmetry present at baseline and preventing full transfer. Conversely, learning in the near-cardinal group decreased the reliability difference between orientations, effectively overcoming the search asymmetry and allowing similar near-cardinal and oblique performance by the end of training. Fig 3B shows Reliability-and-Learning model estimates of near-cardinal and oblique reliability as a function of training session for each group. The model estimated greater sensory uncertainty (lower reliability) for the oblique than for the near-cardinal orientation, consistent with physiological and behavioral findings [12,13]. For the best-fitting parameter estimates, the distractor learning rate was 0. 65 and the target learning rate was 0. 24.
Existing models of VPL predict the same level of specificity across the same levels of task-difficulty [24], task precision [25,29] and feature exposure during training [30]. The demonstration that a mere difference in the trained feature value, near-cardinal vs. oblique orientation, determined VPL specificity challenges these views. Supported by computational modeling, we suggest that intrinsic differences in the representational reliabilities of near-cardinal and oblique orientations governed VPL specificity and transfer in orientation search. Our design enabled us to control for the involvement of task-related factors and to assess the effect of representational reliability per se. In both groups the equal orientation difference between targets and distractors (30°), equated performance controlled by SOA, and identical exposure to the transfer feature insured independence from task precision [25], difficulty [24], and feature exposure [30], respectively. Our analyses confirmed that both learning rate and magnitude were equal for the two groups. In addition, our results cannot be explained in terms of differences in number of difficult trials during training. A larger number of difficult trials during training has been found to increase specificity [31]. This relationship would predict a result opposite to ours: specificity in the near-cardinal group, which was more difficult on average (across SOAs). Thus, stimulus-related properties, rather than task, determined specificity here. The dependence of transfer on the specific orientation value has implications for the investigation and interpretation of VPL transfer and specificity using oriented stimuli. Indeed such stimuli have been commonly used to investigate VPL, including in orientation discrimination tasks (e. g. , [25,30,32–34]), visual search (e. g. , [10,30,35,36]) and texture discrimination tasks (e. g. , [37–41]). Some VPL studies have varied orientation values to manipulate task properties, such as task difficulty, and then linked those task properties to the resulting feature specificity (e. g. , [10,30,37]). Our study suggests that orientation differences alone can affect the pattern of feature specificity and transfer and therefore should be controlled, particularly in displays with more than one orientation. Researchers have inferred the site of the underlying plasticity in VPL based on specificity and transfer results (reviewed by [42]). Specificity and transfer have been taken to indicate learning in early and late visual areas, respectively [10,11,24,30]. Here we show that preexisting variation in representational reliability, which can occur within the same level of processing, can determine VPL transfer. Our findings, therefore, suggest that specificity and transfer are not always appropriate diagnostic tools for the level of VPL plasticity. Our model combined orientation-dependent reliability, improvement of reliability with learning, and an optimal search strategy. We based the search strategy on the optimal visual search model by Ma et al. [26], because that model provides a parsimonious explanation of orientation search with minimal parameters. We found that a single change to the model–letting reliability depend on orientation–captured orientation search asymmetry prior to learning. According to the model, the lower reliability of oblique compared to near-cardinal stimuli leads to more uncertainty during the local decision regarding the identity of an item. The disrupting effect of this uncertainty on visual search performance is larger with oblique distractors (near-cardinal target) than with near-cardinal distractors (oblique target), simply because there are many distractors but only one possible target in any given display. The improvement of reliability across training days captured the behavioral pattern of both learning and transfer. Importantly, the model uses the same optimal decision rule throughout training and during the transfer tests. Search asymmetry and learning, therefore, could be attributed to variation in sensory reliability only, rather than changes in decision strategy and rule based learning [30]. Comparing alternative versions of the model allowed us to determine which factors were critical to explain the behavioral data. Preexisting differences in reliability were essential–a model without this component failed to fit the data–but independent learning for targets and distractors also improved model performance, particularly in capturing transfer behavior. This result is consistent with a previous study that found independent target and distractor learning in an orientation search task [43,44]. Our learning rate estimates correspond well to that study’s finding of about twice as much learning for distractors as for targets [43]. It is therefore the combination of preexisting reliability differences and greater learning for distractors than targets that best explained behavior, in this family of models. Specifically, greater learning for the initially low-reliability oblique distractors eliminated the search asymmetry and enabled full transfer for the near-cardinal group. Our model follows the account that differences between the reliabilities of the cardinal (or near-cardinal) and oblique representations cause orientation search asymmetry [18–20]. A key component of these accounts is the ratio of target signal to background noise, which depends on the target and distractor identities [18,19,45]. Alternative accounts have also been proposed. One influential theory explains visual search asymmetries by considering a map of feature dimensions and their interactions [46]. This theory suggests that targets with larger feature values (e. g. more oriented, i. e. oblique) are inherently more detectable than targets with smaller values (e. g. less oriented, i. e. cardinal) (e. g. , [46,47]). Based on this theory, a neural computational model was developed that explains search asymmetry in terms of a salience map in V1 [48]. However, it is unclear how the elimination of search asymmetry following near-cardinal training could be predicted if search asymmetry arises from inherent feature properties like “more tilted” [46,48,49]. Moreover, no previous model addresses VPL in orientation search. Analogous to the reliability differences between orientation values represented by our model, neurons responding to oblique orientations have larger tuning curve widths than those responding to near-horizontal orientations in macaque V1 [13], and there is more cortical area tuned to near-cardinal orientations than to oblique orientations in ferret cortex [12]. Higher sensory uncertainty has also been estimated for oblique compared to near-cardinal orientations in human V1-V3 [14]. The Ma et al. [26] model on which the orientation search component of our model is based has been implemented as a biologically plausible neural network model, strengthening the connection between the physiological literature and our current computational results. Learning was modeled as an increase in the representational reliability of the stimulus orientations. This increase could be implemented either as a reduction of the tuning curve width of V1 or V4 neurons with training [50–53] or as an improvement in readout from the early sensory response [29]. Both mechanisms have been proposed previously for an orientation discrimination task. Our model, therefore, applies VPL principles derived from orientation discrimination tasks to explain VPL for more complex visual search tasks. Our findings are limited to VPL in orientation search, and more study is required to determine whether they generalize to other stimuli and tasks. Our study also does not rule out alternative models for orientation search asymmetry and VPL in visual search, but it shows that a parsimonious optimal decision rule, preexisting differences in orientation reliability, and reliability learning suffice to explain both search and transfer asymmetry. For simplicity our model assumes the same representational reliability for all stimulus locations. However, stimulus reliability can vary as function of eccentricity (e. g. , [18,23]) and polar angle [54,55]. It will be interesting to test the relation between location-dependent feature reliability and VPL transfer and specificity. Researchers have sought to understand the perceptual and neuronal processes that underlie VPL by studying how task demands affect VPL specificity. In the present study we control for task while testing the effect of the intrinsic reliability of feature representations on VPL specificity in visual search. We found a striking difference in VPL transfer depending on the orientation of the trained target, which we interpret as an effect of representational reliability. This interpretation is supported by both previous neurophysiological findings and computational modeling of the present data. We conclude that preexisting variation in the reliability of feature representations within a single level of processing may have a critical effect on VPL transfer and specificity, calling into question the logic that the degree of feature specificity can be used to infer the neural level at which VPL occurs, especially for complex visual displays. A growing body of research demonstrates the potential benefits of VPL in clinical (e. g. , [4–8,56,57]) and professional (e. g. , [58]) applications. Our study suggests a testable hypothesis: to increase the generalizability of perceptual learning in real-world applications, efficient training protocols should focus training on low-reliability features–oblique orientations and motion directions [59], peripheral spatial locations [60], and so forth–which may limit performance in a variety of natural tasks.
We developed a model that consisted of two parts: optimal search (based on Ma et al. [26]) and reliability improvement over the course of learning. We compared alternative versions of the model to determine which parameters were required to explain, in a single fit, the data from both observer groups, including the initial search asymmetry, performance improvement over the course of training, and the transfer asymmetry.
Training can modify the visual system to produce improvements on perceptual tasks (visual perceptual learning), which is associated with adult brain plasticity. Visual perceptual learning has important clinical applications: it improves the vision of adults with visual deficits, e. g. amblyopia and cortical blindness, and even presbyopia (aging eye). A critical issue in visual perceptual learning is its specificity to the trained stimulus. Specificity gives insight into the processes underling experience-dependent plasticity but can be an obstacle in the development of efficient rehabilitation protocols. Under what circumstances visual perceptual learning transfers to untrained stimuli is poorly understood. Here we report a qualitatively new phenomenon: specificity in visual search depends on intrinsic variations in the reliability of feature representations; e. g. , vertically oriented lines are represented in V1 with greater reliability than tilted lines. Our data and computational model suggest that training on sensory features with intrinsically low reliability can maximize the generalizability of learning, particularly in complex natural environments in which task performance is limited by low-reliability features. Our study has possible implications for the development of efficient clinical applications of perceptual learning.
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computer vision
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Feature reliability determines specificity and transfer of perceptual learning in orientation search