The form of analog size information in memory

The information used to choose the larger of two objects from memory was investigated in two experiments that compared the effects of a number of variables on the performance of subjects who either were instructed to use imagery in the comparison task or were not so instructed. Subjects instructed to use imagery could perform the task more quickly if they prepared themselves with an image of one of the objects at its normal size, rather than with an image that was abnormally big or small, or no image at all. Such subjects were also subject to substantial selective interference when asked to simultaneously maintain irrelevant images of digits. In contrast, when subjects were not specifically instructed to use imagery to reach their decisions, an initial image at normal size did not produce significantly faster decisions than no image, or a large or small image congruent with the correct decision. The selective interference created by simultaneously imaging digits was reduced for subjects not told to base their size comparisons on imagery. The difficulty of the size discrimination did not interact significantly with any other variable. The results suggest that subjects, unless specifically instructed to use imagery, can compare the size of objects in memory using information more abstract than visual imagery.     

Short-term memory in deaf and hearing children in relation to stimulus characteristics

Experiment 1 examined short-term memory with a serial probe task in 20 younger (mean age: 6 years, 3 months) and 20 older (mean age: 8 years, 8 months) deaf children. Four sets of stimuli were used: familiar animals, unfamiliar nonsense forms, fingerspelled letters, and lower-case print. In contrast to earlier research with hearing children, strong primacy effects were found with all stimuli at both ages, with frequent gestural and/or oral labeling but little cumulative rehearsal evident. To determine whether results were uniquely tied to deafness, identical procedures were used in Experiment 2 with 20 younger (4 years, 9 months) and 20 older (5 years, 10 months) hearing children. Again, overt activity was common, with strong primacy effects on animals and nonsense forms in the older group and on animals in the younger group. Contrasts between present and earlier findings suggest the need to consider the degree to which stimulus labels are overlearned, the role of nonverbal mediators in rehearsal, and the effects of changing educational experiences.     

An examination of culture bias in the wonderlic personnel test

Internal evidence of cultural bias, in terms of various types of item analysis, was sought in the Wonderlic Personnel Test results in large, representative samples of Whites and Blacks totaling some 1,500 subjects. Essentially, the lack of any appreciable Race × Items interaction and the high interracial similarity in rank order of item difficulties lead to the conclusion that the Wonderlic shows very little evidence of cultural bias with respect to the present samples which, however, differ appreciably in mean scores. The items which account for the most variance within each racial group are, by and large, the same items that show the largest interracial discrimination.     

Peripheral information extraction: A developmental examination of reading processes

Three experiments examining the information extraction processes are presented. In Experiments I and II, adults and children were presented paragraphs to read and search for specified targets, respectively. Two types of typographical (spatial) manipulations were used: case of the letters within the paragraphs was as normally experienced, or in alternating upper and lower case letters. Spacing between words was either as normal, filled with a neutral symbol, or absent. The response measures were reading and search speed in words per minute. Sensitivity to case (word shape) and space (word boundary) increased as a function of experience and was different for reading compared with search. In Experiment III subjects were asked to judge whether briefly presented pairs of letters were the same or different. Retinal (spatial) location was varied from trial to trial; dependent measure was reaction time. Results showed a developmental progression in response time with complex interactions. Theoretical interpretations of these data are discussed in support of the initial stage of Hochberg's peripheral to cognitive search guidance model.     

Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts

Advances in robotics, automation, and artificial intelligence increasingly enable firms to replace human labor with technology, thereby fundamentally transforming how goods and services are produced. From both managerial and societal points of view, it is therefore important to understand demand‐side incentives for firms to employ human labor. We begin to address this question by examining for which products and services consumers are more likely to favor human (vs. robotic) labor. In six studies, we demonstrate that consumers prefer human (vs. robotic) labor more for products with higher (vs. lower) symbolic value (e.g., when expressing something about one's beliefs and personality is of greater importance). We theorize that this is because consumers have stronger uniqueness motives in more (vs. less) symbolic consumption contexts (and associate human labor more strongly with product uniqueness). In line with this account, we demonstrate that individual differences in need for uniqueness moderate the interaction between production mode and symbolic motives and that a measure of uniqueness motives mediates the effect of consumption context on preferences for human (vs. robotic) production.     

Evaluating pedestrian interaction preferences with a game theoretic autonomous vehicle in virtual reality

Localisation and navigation of autonomous vehicles (AVs) in static environments are now solved problems, but how to control their interactions with other road users in mixed traffic environments, especially with pedestrians, remains an open question. Recent work has begun to apply game theory to model and control AV-pedestrian interactions as they compete for space on the road whilst trying to avoid collisions. But this game theory model has been developed only in unrealistic lab environments. To improve their realism, this study empirically examines pedestrian behaviour during road crossing in the presence of approaching autonomous vehicles in more realistic virtual reality (VR) environments. The autonomous vehicles are controlled using game theory, and this study seeks to find the best parameters for these controls to produce comfortable interactions for the pedestrians. In a first experiment, participants’ trajectories reveal a more cautious crossing behaviour in VR than in previous laboratory experiments. In two further experiments, a gradient descent approach is used to investigate participants’ preference for AV driving style. The results show that the majority of participants were not expecting the AV to stop in some scenarios, and there was no change in their crossing behaviour in two environments and with different car models suggestive of car and last-mile style vehicles. These results provide some initial estimates for game theoretic parameters needed by future AVs in their pedestrian interactions and more generally show how such parameters can be inferred from virtual reality experiments.     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.