The authors report a series of studies designed to determine whether effects similar to those observed in the innate categorical perception of color and phonemes are induced during the learning of simple unidimensional categories and more complex multidimensional ones. In Experiment 1 no evidence was found for such effects when stimuli varied on 1 dimension. Experiments 2 and 3 demonstrated a within-category compression effect but no betweencategory expansion effect for stimuli varying in 2 dimensions. Compression only was also shown in Experiment 4, which used pictures of actual objects. Multidimensional scaling analyses illustrate how within-category compression without expansion was sut~cient to produce categorical clustering of items in the similarity space. These analyses also show that learning changed the dimensional structure of similarity space. Results are compared with those from other studies exploring similar phenomena and with neural network simulations.Most contemporary models of category learning rely to some degree on relative similarities between category members and nonmembers (

A series of four studies explore how the presentation of multiple items on each trial of a categorization task affects the course of category learning. In a three-category supervised classification task involving multi-dimensionally varying artificial organism-like stimuli, learners are shown a target plus two context items on every trial, with the context items' category membership explicitly identified. These triads vary in whether one, two, or all three categories are represented. This presentation context can support within-category comparison and/or between-category contrast. The most successful learning occurs when all categories are represented in each trial. This pattern occurs across two different underlying category structures and across variations in learners' prior knowledge of the relationship between the target and context items. These results appear to contrast with some other recent findings and make clear the potential importance of context-based inter-item evaluation in human category learning, which has implications for psychological theory and for real-world learning environments.

Contemporary theories of categorization propose that concepts are coherent in virtue of being embedded in a network of theories about the world. Those theories function to pick out some of the many possible features of a set of objects as most salient for purposes of classification, a process that is complex and still poorly understood (Murphy & Medin, 1985). Part of what makes this account incomplete is a lack of information as to (1) what makes a feature salient on a given occasion and (2) how feature salience interacts with category structure to determine the course of learning. We report on the results of three studies of category learning using complex schematic drawings to show that (1) the contrast set defined by one's initial encounters with category exemplars can be a source of individual differences in feature salience assignments; (2) such effects are short-lived in the face of clear evidence about actual feature diagnosticity; and (3) more robust prior hypotheses interact with category structure to either enhance learning or impede it. The enhancement occurs when the hypothesis emphasizes category-relevant features, even if the hypothesis is in fact incorrect. A hypothesis that assigns high salience to irrelevant features impedes learning. Learning does occur as feedback concerning category structure leads to enhanced salience for relevant features. Salience of irrelevant features remains high, however, suggesting that such learning as occurs involves augmentation and not total revision of the (incorrect) prior hypothesis.

After learning to categorize a set of alien-like stimuli in the context of a story, a group of 5-year-old children and adults judged pairs of stimuli from different categories to be less similar than did groups not learning the category distinction. In a same-different task, the learning group made more errors on pairs of non-identical stimuli from the same category than did the other groups, suggesting increased within-category item similarity, or compression. These expansion and compression effects add further support to the view that concept formation involves systematic changes in the metric of similarity space within which objects are represented. They also suggest that these processes do not vary with age, which is at least consistent with the hypothesis that they are fundamental to the mechanisms underlying concept formation.

Núñez and colleagues (2019) question whether cognitive science still exists “as a coherent academic field with a well‐defined and cohesive interdisciplinary research program.” This worry may be premature on two grounds. First, we are not convinced that the Lakatosian criterion of coalescence around a core framework is the best standard for judging whether a field is well‐defined and productive. Second, although we acknowledge that cognitive science is not as visible as we would like, we doubt that this low profile accurately reflects the state of actual research and teaching programs based on the cognitive science approach.

Learned visual categorical perception (CP) effects were assessed using three different measures (similarity rating, same-different judgment, and an XAB task) and two sets of stimuli differing in discriminability and varying on one category-relevant and one category-irrelevant dimension. Participant scores were converted to a common scale to allow assessment method to serve as an independent variable. Two different analyses using the Bayes Factor approach produced patterns of results consistent with learned CP effects: compared to a control group, participants trained on the category distinction could better discriminate between-category pairs of stimuli and were more sensitive to the category-relevant dimension. In addition, performance was better in general for the more highly discriminable stimuli, but stimulus discriminability did not influence the pattern of observed CP effects. Furthermore, these results were consistent regardless of how performance was assessed. This suggests that, for these methods at least, learned CP effects are robust across substantially different performance measures. Four different kinds of learned CP effects are reported in the literature singly or in combination: greater sensitivity between categories, reduced sensitivity within categories, increased sensitivity to category-relevant dimensions, and decreased sensitivity to category-irrelevant dimensions. The results of the current study suggest that the cause of these different patterns of CP effects is not due to either stimulus discriminability or assessment task. Other possible causes of the differences in reported CP findings are discussed.

The genetic operators (GOs) of recombination, mutation, and selection are commonly included in studies of evolution and evolvability, but they are not the only operators that can affect the genotype-to-phenotype (G → P) map and thus the outcomes of evolution. In this paper, we present experiments with an epigenetic operator (EO), interactive wiring of a circuit, alongside common GOs, investigating both epigenetic and GO effects on the evolution of both simulated and physically embodied Braitenberg-inspired robots. As a platform for our experiments, we built a system that encoded the genetics for the physical circuitry of the analog robots and made explicit rules for how that circuitry would be constructed; phenotypic expression consisted of the placement of wires to form the circuitry and thus govern robot behavior. We then varied the presence of gene interactions across populations of robots, studying how the EO-and its effects on G → P maps-affected the results of evolution over several generations. Additionally, a variant of these experiments was run in simulation to provide an independent test of the evolutionary impact of this EO. Our results demonstrate that robot populations with the EO had quantitatively different and potentially less adaptive evolution than populations without it. For example, selection increased the rate at which functional circuitry was lost in the population with the EO, compared to the population without it. In addition, in simulation, EO populations were significantly less fit than populations without it. More generally, results such as these demonstrate the interaction of genetic and EOs during evolution, suggesting the broad importance of including EOs in investigations of evolvability. To our knowledge, our work represents the first physically embodied EO to be used in the evolution of physically embodied robots.

Given that selection removes genetic variance from evolving populations, thereby reducing exploration opportunities, it is important to find mechanisms that create genetic variation without the disruption of adapted genes and genomes caused by random mutation. Just such an alternative is offered by random epigenetic error, a developmental process that acts on materials and parts expressed by the genome. In this system of embodied computational evolution, simulated within a physics engine, epigenetic error was instantiated in an explicit genotype-to-phenotype map as transcription error at the initiation of gene expression. The hypothesis was that transcription error would create genetic variance by shielding genes from the direct impact of selection, creating, in the process, masquerading genomes. To test this hypothesis, populations of simulated embodied biorobots and their developmental systems were evolved under steady directional selection as equivalent rates of random mutation and random transcriptional error were covaried systematically in an 11 × 11 fully factorial experimental design. In each of the 121 different experimental conditions (unique combinations of mutation and transcription error), the same set of 10 randomly created replicate populations of 60 individuals were evolved. Selection for the improved locomotor behavior of individuals led to increased mean fitness of populations over 100 generations at nearly all levels and combinations of mutation and transcription error. When the effects of both types of error were partitioned statistically, increasing transcription error was shown to increase the final genetic variance of populations, incurring a fitness cost but acting on variance independently and differently from genetic mutation. Thus, random epigenetic errors in development feed back through selection of individuals with masquerading genomes to the population’s genetic variance over generational time. Random developmental processes offer an additional mechanism for exploration by increasing genetic variation in the face of steady, directional selection.