In search of abstraction: The varying abstraction model of categorization

A longstanding debate in the categorization literature concerns representational abstraction. Generally, when exemplar models, which assume no abstraction, have been contrasted with prototype models, which assume total abstraction, the former models have been found to be superior to the latter. Although these findings may rule out the idea that total abstraction takes place during category learning and instead suggest that no abstraction is involved, the idea of abstraction retains considerable intuitive appeal. In this article, we propose the varying abstraction model of categorization (VAM), which investigates the possibility that partial abstraction may play a role in category learning. We apply the VAM to four previously published data sets that have been used to argue that no abstraction is involved. Contrary to the previous findings, our results provide support for the idea that some form of partial abstraction can be used in people's category representations.     

Identification and categorization in visual search

Seven experiments were addressed to the general question of whether the identification of letters and numbers is a more rapid process than the categorization of such stimuli. Subjects were required to make a single response if a target stimulus specified by name (e.g., “A,” “2”) or designated by category class alone (e.g., “letter,” “number”) was presented in a trial. The principal findings were: (1) identification reaction times (RTs) were faster than categorization RTs: (2) RTs for targets shown without a context were faster than RTs for targets shown in the context of other stimuli; (3) identification RTs for targets shown in the context of stimuli from a different conceptual-taxonomic category were faster than RTs for targets shown in the context of stimuli from the same category only when target-context stimulus discriminability differencet were optimized. The results were interpreted in terms of a two-stage processing model in which context face,ors affect the duration of an initial encoding-scanning stage and search instruction (effective memory size) factors affect the duration of the memory comparison stage.     

Familiarity and categorization processes in memory search

A fundamental distinction in tasks of memory search is whether items receive varied mappings (targets and distractors switch roles across trials) or consistent mappings (targets and distractors never switch roles). The type of mapping often produces markedly different performance patterns, but formal memory-based models that account quantitatively for detailed aspects of the results have not yet been developed and evaluated. Experiments were conducted to test a modern exemplar-retrieval model on its ability to account for memory-search performance involving a wide range of memory-set sizes in both varied-mapping (VM) and consistent-mapping (CM) probe-recognition tasks. The model formalized the idea that both familiarity-based and categorization-based processes operate. The model was required to fit detailed response-time (RT) distributions of individual, highly practiced subjects. A key manipulation involved the repetition of negative probes across trials. This manipulation produced a dramatic dissociation: False-alarm rates increased and correct-rejection RTs got longer in VM, but not in CM. The qualitative pattern of results and modeling analyses provided evidence for a strong form of categorization-based processing in CM, in which observers made use of the membership of negative probes in the “new” category to make old–new recognition decisions.     

Visual search and the collapse of categorization

Categorization researchers typically present single objects to be categorized. But real-world categorization often involves object recognition within complex scenes. It is unknown how the processes of categorization stand up to visual complexity or why they fail facing it. The authors filled this research gap by blending the categorization and visual-search paradigms into a visual-search and categorization task in which participants searched for members of target categories in complex displays. Participants have enormous difficulty in this task. Despite intensive and ongoing category training, they detect targets at near-chance levels unless displays are extremely simple or target categories extremely focused. These results, discussed from the perspectives of categorization and visual search, might illuminate societally important instances of visual search (e.g., diagnostic medical screening).     

Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels

Twelve male listeners categorized 54 synthetic vowel stimuli that varied in second and third formant frequency on a Bark scale into the American English vowel categories [see text]. A neuropsychologically plausible model of categorization in the visual domain, the Striatal Pattern Classifier (SPC; Ashby & Waldron, 1999), is generalized to the auditory domain and applied separately to the data from each observer. Performance of the SPC is compared with that of the successful Normal A Posteriori Probability model (NAPP; Nearey, 1990; Nearey & Hogan, 1986) of auditory categorization. A version of the SPC that assumed piece-wise linear response region partitions provided a better account of the data than the SPC that assumed linear partitions, and was indistinguishable from a version that assumed quadratic response region partitions. A version of the NAPP model that assumed nonlinear response regions was superior to the NAPP model with linear partitions. The best fitting SPC provided a good account of each observer's data but was outperformed by the best fitting NAPP model. Implications for bridging the gap between the domains of visual and auditory categorization are discussed.     

The effect of set on categorization in visual search

Two experiments examine whether set for a categorial difference or set for a target category is necessary in order to obtain the category effect in visual search. Eliminating a set for categorial difference does not diminish the size of the effect. When subjects are prevented from developing a useful set for the target category, however, the category effect is eliminated.     

The role of categorization in visual search for orientation.

Visual search for 1 target orientation is fast and virtually independent of set size if all of the distractors are of a single, different orientation. However, in the presence of distractors of several orientations, search can become inefficient and strongly dependent on set size (Exp. 1). Search can be inefficient even if only 2 distractor orientations are used and even if those orientations are quite remote from the target orientation (e.g. 20 degrees or even 40 degrees away, Exp. 2). Search for 1 orientation among heterogeneous distractor orientations becomes more efficient if the target orientation is the only item possessing a categorical attribute such as steep, shallow (Exp. 3), tilted left or tilted right (Exp. 4), or simply tilted (Exps. 5 and 6). Orientation categories appear to be 1 of several strategies used in visual search for orientation. These serve as a compromise between the limits on parallel visual processing and the demands of a complex visual world.     

A hybrid model of categorization

Category learning is often modeled as either an exemplar-based or a rule-based process. This paper shows that both strategies can be combined in a cognitive architecture that was developed to model other task domains. Variations on the exemplar-based random walk (EBRW) model of Nosofsky and Palmeri (1997b) and the rule-plus-exception (RULEX) rule-based model of Nosofsky, Palmeri, and McKinley (1994) were implemented in the ACT-R cognitive architecture. The architecture allows the two strategies to be mixed to produce classification behavior. The combined system reproduces latency, learning, and generalization data from three category-learning experiments--Nosofsky and Palmeri (1997b), Nosofsky et al., and Erickson and Kruschke (1998). It is concluded that EBRW and ACT-R have different but equivalent means of incorporating similarity and practice. In addition, ACT-R brings a theory of strategy selection that enables the exemplar and the rule-based strategies to be mixed.     

Induction and categorization in young children: a similarity-based model

The authors present a similarity-based model of induction and categorization in young children (SINC). The model suggests that (a). linguistic labels contribute to the perceived similarity of compared entities and (b). categorization and induction are a function of similarity computed over perceptual information and linguistic labels. The model also predicts young children's similarity judgment, induction, and categorization performance under different stimuli and task conditions. Predictions of the model were tested and confirmed in 6 experiments, in which 4- to 5-year-olds performed similarity judgment, induction, and categorization tasks using artificial and real labels (Experiments 1-4) and recognition memory tasks (Experiments 5A and 5B). Results corroborate the similarity-based account of young children's induction and categorization, and they support both qualitative and quantitative predictions of the model.     

Strategies in probabilistic categorization: results from a new way of analyzing performance

The "Weather Prediction" task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. Prior studies showed that subjects can improve their performance with training, and that there is considerable individual variation in the strategies subjects use to approach this task. Here, we discuss a recently introduced analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Moreover, learning is shown to be highly nonlinear in probabilistic categorization. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.     

The benefit of categorization in visual search: Target location without identification

Prior studies have shown that between-category visual search (e.g., looking for a “6” among letters) is easier that within-category search (e.g., looking for a “6” among other digits). Four experiments were performed to determine what particular benefits are conferred in between-category search. The first experiment demonstrates that this category-effect occurs even when the subject has to identify the target, this suggests that the between-category search can “tag” the categorially different item for further processing. Three further experiments provide evidence that this tag is based on the item’s spatial location within the array.     

Selective search in short-term memory under ideal conditions of test stimulus categorization

It has often been claimed that short-term memory search is not selective because it takes longer to categorize the test stimulus than to search throughout the whole memory set. Four experiments investigated whether evidence for selective search can be obtained when effectively no time is required to categorize the test stimulus. Even under this favorable condition, no evidence was found for selective search of only the relevant memory subset. Therefore, the time to categorize a test stimulus cannot be the only reason why short-term memory search is not selective.     

In search of a learning model

While the four commentaries reflect a range of different perspectives on my target paper (Vihman, 2017), all basically accept the overall approach, which has been central to my research for 30 years. Each commentary proposes ways of deepening aspects of the ideas expressed or points out limitations and potential areas in which elaboration would be useful. This response takes up each commentary in turn.     

Crossed categorization beyond the two-group model

Four studies examined processing of in-group and out-group information with stimuli that are more complex than those used in previous crossed categorization studies. A diverse set of predictions is generated by previous theoretical work to account for how participants will integrate information stemming from multiple group memberships. Heuristic, threshold-based processing of information was supported over algebraic processing. Participants appeared to divide stimuli into "in-grouplike" and "out-grouplike" metacategories. However, the threshold at which this distinction was made, and whether it was based on in-group favoritism or out-group derogation, was influenced by the nature of the situation and the task participants performed. Advantages of crossed categorization research that moves beyond the traditional two-group model are discussed.     

The relationship between visual search and categorization of own- and other-age faces

Young adult participants are faster to detect young adult faces in crowds of infant and child faces than vice versa. These findings have been interpreted as evidence for more efficient attentional capture by own-age than other-age faces, but could alternatively reflect faster rejection of other-age than own-age distractors, consistent with the previously reported other-age categorization advantage: faster categorization of other-age than own-age faces. Participants searched for own-age faces in other-age backgrounds or vice versa. Extending the finding to different other-age groups, young adult participants were faster to detect young adult faces in both early adolescent (Experiment 1) and older adult backgrounds (Experiment 2). To investigate whether the own-age detection advantage could be explained by faster categorization and rejection of other-age background faces, participants in experiments 3 and 4 also completed an age categorization task. Relatively faster categorization of other-age faces was related to relatively faster search through other-age backgrounds on target absent trials but not target present trials. These results confirm that other-age faces are more quickly categorized and searched through and that categorization and search processes are related; however, this correlational approach could not confirm or reject the contribution of background face processing to the own-age detection advantage.     

Face detection differs from categorization: Evidence from visual search in natural scenes

In this study, we examined whether the detection of frontal, ¾, and profile face views differs from their categorization as faces. In Experiment 1, we compared three tasks that required observers to determine the presence or absence of a face, but varied in the extents to which participants had to search for the faces in simple displays and in small or large scenes to make this decision. Performance was equivalent for all of the face views in simple displays and small scenes, but it was notably slower for profile views when this required the search for faces in extended scene displays. This search effect was confirmed in Experiment 2, in which we compared observers’ eye movements with their response times to faces in visual scenes. These results demonstrate that the categorization of faces at fixation is dissociable from the detection of faces in space. Consequently, we suggest that face detection should be studied with extended visual displays, such as natural scenes.     

A diffusion model of early visual search: Theoretical analysis and experimental results

Summary Meinecke (1989, Exp. 1, cond. HO) showed that the detectability of a visual target embedded in a linear noise array decreases with increasing retinal eccentricity, while the reaction time (RT) of the hits increases. One of the most interesting features of her results was that the RT of the correct rejections is consistently larger than the RT for signals presented near the fovea. This finding suggests that initially visual attention is concentrated near the fixation point and then diffuses across the stimulus array to perform a serial, exhaustive search. We present a diffusion model of early visual-search processes that quantitatively describes this evolution of attention in time and space; in contrast to most previous conceptions, it is based on a genuine relation between the spatial and temporal dimensions of the search processes performed. The model predicts quantitatively both detection performance and RT. We conducted an experiment similar to that of Meinecke (1989), but with an additional variation of the presentation time. All the main features of the 189 predictions could be explained by the model. The interpretation of the four model's parameters is discussed in some detail and compared with previous estimates of the microscopic search speed derived from alternative models. Finally, we consider some possible modifications related to results of Kehrer (1987, 1989), and some generalizations to multi target detection and two-dimensional stimulus arrays.     

The cost of categorization in visual search: Incomplete processing of targets and field items

A partial processing hypothesis is proposed to account for performance under a visual search condition where target and field items belong to the different conceptual categories, letter and digit (between-category search), as compared to a condition in which they belong to the same category (within-category search). This hypothesized mechanism implies that less information is registered and/or retained in between- than in within-category search. This prediction was tested and confirmed in three experiments. The results indicate that both targets and field items are processed less deeply in between- than in within-category search.     

Searching through the hierarchy: How level of target categorization affects visual search

Does the same basic-level advantage commonly observed in the categorization literature also hold for targets in a search task? We answered this question by first conducting a category verification task to define a set of categories showing a standard basic-level advantage, which we then used as stimuli in a search experiment. Participants were cued with a picture preview of the target or its category name at either superordinate, basic, or subordinate levels, then shown a target-present/absent search display. Although search guidance and target verification was best using pictorial cues, the effectiveness of the categorical cues depended on the hierarchical level. Search guidance was best for the specific subordinate-level cues, whereas target verification showed a standard basic-level advantage. These findings demonstrate different hierarchical advantages for guidance and verification in categorical search. We interpret these results as evidence for a common target representation underlying categorical search guidance and verification.     

A feedback model of perceptual learning and categorization

Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This paper employs a computational model of the cortical region inter-actions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorized and could also facilitate the learning of subordinate level representations suitable for object identification and perceptual expertise.