Now you see it, now you don't: The context dependent nature of category-effects in visual object recognition (original) (raw)
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Traditional models of recognition and categorization proceed from registering low-level features, perceptually organizing that input, and linking it with stored representations. Recent evidence, however, suggests that this serial model may not be accurate, with object and category knowledge affecting rather than following early visual processing. Here, we show that the degree to which an image exemplifies its category influences how easily it is detected. Participants performed a two-alternative forced-choice task in which they indicated whether a briefly presented image was an intact or phase-scrambled scene photograph. Critically, the category of the scene is irrelevant to the detection task. We nonetheless found that participants “see” good images better, more accurately discriminating them from phase-scrambled images than bad scenes, and this advantage is apparent regardless of whether participants are asked to consider category during the experiment or not. We then demonstrate that good exemplars are more similar to same-category images than bad exemplars, influencing behavior in two ways: First, prototypical images are easier to detect, and second, intact good scenes are more likely than bad to have been primed by a previous trial.
Learned Visual Categorical Perception Effects Depend on Method of Assessment and Stimulus Discriminability, 2014
Learned categorical perception (CP) effects were assessed using three different measures and two sets of stimuli differing in discriminability, both of which varied on one category-relevant and one category-irrelevant dimension. Two different kinds of analysis produced patterns of results that depended on both of these variables and show that categorical perception effects are sensitive to variations in assessment task and stimulus discriminability. Only the similarity-rating task produced evidence of between-category expansion effects, suggesting that participants used different strategies for subjective and objective tasks. Generally, there was evidence that category training caused a decrease in the salience of category-irrelevant variation, but when the assessment task cued participants to category-irrelevant differences they were equally apt at identifying category-irrelevant variation as a control group.
Category variability, exemplar similarity, and perceptual classification
Memory & Cognition, 2001
Experiments were conducted in which observers learned to classify simple perceptual stimuli into low-variability and high-variability categories. Similarities between objects were measured in independent psychological-scalingtasks. The results showed that observers classified transfer stimuli into the high-variability categories with greater probability than was predicted by a baseline version of an exemplar-similarity model. Qualitative evidence for the role of category variability on perceptual classification, which could not be explained in terms of the baseline exemplar-similaritymodel, was obtained as well. Possible accounts of the effects of category variability are considered in the General Discussion section.
Processing scene context: Fast categorization and object interference
Vision Research, 2007
The extent to which object identification is influenced by the background of the scene is still controversial. On the one hand, the global context of a scene might be considered as an ultimate representation, suggesting that object processing is performed almost systematically before scene context analysis. Alternatively, the gist of a scene could be extracted sufficiently early to be able to influence object categorization. It is thus essential to assess the processing time of scene context. In the present study, we used a go/no-go rapid visual categorization task in which subjects had to respond as fast as possible when they saw a ''man-made environment'', or a ''natural environment'', that was flashed for only 26 ms. ''Man-made'' and ''natural'' scenes were categorized with very high accuracy (both around 96%) and very short reaction times (median RT both around 390 ms). Compared with previous results from our group, these data demonstrate that global context categorization is remarkably fast: (1) it is as fast as object categorization [Fabre-Thorpe, M., Delorme, A., Marlot, C., & Thorpe, S. (2001). A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. Journal of Cognitive Neuroscience, 13(2), 171-180]; (2) it is faster than contextual categorization at more detailed levels such as sea, mountain, indoor or urban contexts [Rousselet, G. A., Joubert, O. R., & Fabre-Thorpe, M. (2005). How long to get to the ''gist'' of real-world natural scenes? Visual Cognition, 12(6), 852-877]. Further analysis showed that the efficiency of contextual categorization was impaired by the presence of a salient object in the scene especially when the object was incongruent with the context. Processing of natural scenes might thus involve in parallel the extraction of the global gist of the scene and the concurrent object processing leading to categorization. These data also suggest early interactions between scene and object representations compatible with contextual influences on object categorization in a parallel network.
The effect of internal structure of categories on perception
A novel study is presented that explores the effect that learning internally organized categories has on the ability to subsequently discriminate category members. The results demonstrate the classic categorical perception effect whereby discrimination of stimuli that belong to different categories is improved following training, while the ability to discriminate stimuli belonging to the same category is reduced. We further report a new within-category perceptual effect whereby category members that share the same category label but fall into different sub-clusters within that category are better discriminated than items that share the same category and cluster. The results show that learners are sensitive to multiple sources structure beyond simply the labels provided during supervised training. A computational model is presented to account for the results whereby multiple levels of encoding (i.e., at the item-, cluster-, and category- level) may simultaneously contribute to perception.
The Influence of Categorisation on the Perceived Shape Similarity of Everyday Objects
Psychologica Belgica, 2009
There is substantial evidence that object representations in adults are dynamically adapted by learning. Here we show that these effects are induced by active processing of objects in a particular task context, and not merely by visual exposure to objects during training. We derived behavioural sensitivity and neural selectivity for shape differences in a psychophysical and an event-related fMRI-adaptation study, respectively. We had two training conditions: "categorised objects" were categorised at a subordinate level based on fine shape differences, while "control objects" were seen equally often in a task context requiring no subordinate categorisation. After training, categorised objects were more easily discriminable than control objects and object-selective cortex was more selective for differences among categorised than control objects. These results indicate that the task context modulates the extent to which shape similarity is altered as a result of training, both at the behavioural and at the neural level.
Journal of Experimental Psychology: Human Perception and Performance, 2009
The type of visual information needed for categorizing faces and nonface objects was investigated by manipulating spatial frequency scales available in the image during a category verification task addressing basic and subordinate levels. Spatial filtering had opposite effects on faces and airplanes that were modulated by categorization level. The absence of low frequencies impaired the categorization of faces similarly at both levels, whereas the absence of high frequencies was inconsequential throughout. In contrast, basic-level categorization of airplanes was equally impaired by the absence of either low or high frequencies, whereas at the subordinate level, the absence of high frequencies had more deleterious effects. These data suggest that categorization of faces either at the basic level or by race is based primarily on their global shape but also on the configuration of details. By contrast, basic-level categorization of objects is based on their global shape, whereas category-specific diagnostic details determine the information needed for their subordinate categorization. The authors conclude that the entry point in visual recognition is flexible and determined conjointly by the stimulus category and the level of categorization, which reflects the observer's recognition goal.
Category learning can alter perception and its neural correlates
PlosONE, 2019
Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (“between-category separation”) and/or members of the same category come to look more similar (“within-category compression”) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of training trials and feedback, about half the subjects succeeded in learning the categories (“Learners”: criterion 80% accuracy) and the rest did not (“Non-Learners”). At both lower and higher levels of difficulty, successful Learners showed significant between-category separation—and, to a lesser extent, within-category compression—in pairwise dissimilarity judgments after learning, compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital N1 amplitude (usually interpreted as perceptual) decreased. LPC amplitude increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners. Non-Learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model predictive of this effect.