Human and machine perception of biological motion

More than 30 years ago, Johansson was the first to show that humans are capable of recovering information about the identity and activity of animate creatures rapidly and reliably from very sparse visual inputs – the phenomenon of biological motion. He filmed human actors in a dark setting with just a few strategic points on the body marked by lights – so-called moving light displays (MLDs). Subjects viewing the MLDs reported a vivid impression of moving human forms, and were even able to tell the activity in which the perceived humans were engaged. Subsequently, the phenomenon has been widely studied and many attempts have been made to model and to understand it. Typical questions that arise are: How precisely is the sparse low-level information integrated over space and time to produce the global percept, and how important is world knowledge (e.g., about animal form, locomotion, gravity, etc.)? In an attempt to answer such questions, we have implemented a machine-perception model of biological motion. If the computational model can replicate human data then it might offer clues as to how humans achieve the task. In particular, if it can do so with no or minimal world knowledge then this knowledge cannot be essential to the perception of biological motion. To provide human data for training and against which to assess the model, an extensive psychophysical experiment was undertaken in which 93 subjects were shown 12 categories of MLDs (e.g., normal, walking backwards, inverted, random dots, etc.) and were asked to indicate the presence or absence of natural human motion. Machine perception models were then trained on normal sequences as positive data and random sequences as negative data. Two models were used: a k-nearest neighbour (k-NN) classifier as an exemplar of ‘lazy’ learning and a back-propagation neural network as an exemplar of ‘eager’ learning. We find that the k-NN classifier is better able to model the human data but it still fails to represent aspects of knowledge about body shape (especially how relative joint positions change under rotation) that appear to be important to human judgements.     

Modeling the Developmental Patterning of Finiteness Marking in English, Dutch, German, and Spanish Using MOSAIC

In this study, we apply MOSAIC (model of syntax acquisition in children) to the simulation of the developmental patterning of children's optional infinitive (OI) errors in 4 languages: English, Dutch, German, and Spanish. MOSAIC, which has already simulated this phenomenon in Dutch and English, now implements a learning mechanism that better reflects the theoretical assumptions underlying it, as well as a chunking mechanism that results in frequent phrases being treated as 1 unit. Using 1, identical model that learns from child-directed speech, we obtain a close quantitative fit to the data from all 4 languages despite there being considerable cross-linguistic and developmental variation in the OI phenomenon. MOSAIC successfully simulates the difference between Spanish (a pro-drop language in which OI errors are virtually absent) and obligatory subject languages that do display the OI phenomenon. It also highlights differences in the OI phenomenon across German and Dutch, 2 closely related languages whose grammar is virtually identical with respect to the relation between finiteness and verb placement. Taken together, these results suggest that (a) cross-linguistic differences in the rates at which children produce OIs are graded, quantitative differences that closely reflect the statistical properties of the input they are exposed to and (b) theories of syntax acquisition need to consider more closely the role of input characteristics as determinants of quantitative differences in the cross-linguistic patterning of phenomena in language acquisition.     

Comparing self-esteem and perceived control as predictors of first-year college students’ academic achievement

Due to its widespread popularity, self-esteem is continually being promoted to students despite limited empirical support for its effectiveness in improving their academic achievement. As a result, constructs that are potentially more salient to academic performance, such as perceived control, have gone relatively unnoticed. Although past research has examined the link between students’ academic achievement and either their self-esteem or perceived control, few studies have compared both constructs simultaneously to elucidate which one is more important to academic success. This longitudinal study directly contrasted the effects of self-esteem and perceived control on the academic performance of 802 first-year college students. After accounting for incoming ability (high school grades), age, and gender, a structural equation model showed perceived control positively predicted students’ GPA. In contrast, the predictive effect of self-esteem on GPA was non-existent. Findings indicate that compared to self-esteem, perceived control is a more powerful predictor of first-year college students’ GPA. Implications for utilizing educational interventions to boost perceived control among college students are discussed.     

Distortion of probability and outcome information in risky decisions

Substantial evidence indicates that information is distorted during decision making, but very few studies have assessed the distortion of probability and outcome information in risky decisions. In two studies involving six binary decisions (e.g., banning blood donations from people who have visited England, because of “mad cow disease”), student and nonstudent participants distorted their evaluations of probability and outcome information in the direction of their preferred decision alternative and used these biased evaluations to update their preferences. Participants also evaluated the utilities of possible outcomes more positively when the outcomes could follow only from the preferred alternative and more negatively when they could follow only from the competing alternative. Such circular reasoning is antithetical to the normative consequentialist principles underlying decision analysis. Presenting numerical information as precise values or as ranges of values did not significantly affect information distortion, apparently because the manipulation did not affect perceived ambiguity as intended.     

A Bayesian analysis of human decision-making on bandit problems

The bandit problem is a dynamic decision-making task that is simply described, well-suited to controlled laboratory study, and representative of a broad class of real-world problems. In bandit problems, people must choose between a set of alternatives, each with different unknown reward rates, to maximize the total reward they receive over a fixed number of trials. A key feature of the task is that it challenges people to balance the exploration of unfamiliar choices with the exploitation of familiar ones. We use a Bayesian model of optimal decision-making on the task, in which how people balance exploration with exploitation depends on their assumptions about the distribution of reward rates. We also use Bayesian model selection measures that assess how well people adhere to an optimal decision process, compared to simpler heuristic decision strategies. Using these models, we make inferences about the decision-making of 451 participants who completed a set of bandit problems, and relate various measures of their performance to other psychological variables, including psychometric assessments of cognitive abilities and personality traits. We find clear evidence of individual differences in the way the participants made decisions on the bandit problems, and some interesting correlations with measures of general intelligence.     

Achievement strategies during university studies predict early career burnout and engagement

To examine whether individuals’ achievement strategies measured during university studies would have an impact on work burnout and work engagement measured 10, 14 and 17 years later, 292 university students completed the SAQ strategy questionnaire three times while at university, and the work burnout inventory three times and work engagement inventory twice during their early career. The results showed that optimism increased during university, while task-avoidance did not change. Moreover, high and increasing optimism during university predicted a high level of work engagement and low level of burnout 10, 14 and 17 years later. By contrast, a high level of task-avoidance during university predicted a low level of work engagement and high level of burnout during the early career.     

Testing the latent factor structure and construct validity of the Ten-Item Personality Inventory

Recent efforts have aimed to develop relatively short measures of the Five-Factor Model (FFM) of personality, particularly for when time and/or space is limited. We evaluate the Ten-Item Personality Inventory (TIPI), a non-proprietary FFM measure with two items per dimension. We use a latent variable methodology to examine the TIPI’s factor structure and convergent validity with the 50-item International Personality Item Pool (IPIP) FFM measure. We provide correlations between the scale scores and latent factors, and compare each measure’s pattern of correlations with measures of other individual difference constructs. Results were favorable in terms of the factor structure and convergent validity of the TIPI, particularly regarding the correlations between the respective latent factors of the TIPI and the IPIP–FFM measures.     

Labels as features (not names) for infant categorization: a neurocomputational approach

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name-based categorization and unsupervised feature-based categorization. We describe a neurocomputational model of infant visual categorization, based on self-organizing maps, that implements the unsupervised feature-based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in Plunkett, Hu, and Cohen (2008) . It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories.     

Two routes to face perception: evidence from psychophysics and computational modeling

The aim of this study was to separately analyze the role of featural and configural face representations. Stimuli containing only featural information were created by cutting the faces into their parts and scrambling them. Stimuli only containing configural information were created by blurring the faces. Employing an old-new recognition task, the aim of Experiments 1 and 2 was to investigate whether unfamiliar faces (Exp. 1) or familiar faces (Exp. 2) can be recognized if only featural or configural information is provided. Both scrambled and blurred faces could be recognized above chance level. A further aim of Experiments 1 and 2 was to investigate whether our method of creating configural and featural stimuli is valid. Pre-activation of one form of representation did not facilitate recognition of the other, neither for unfamiliar faces (Exp. 1) nor for familiar faces (Exp. 2). This indicates a high internal validity of our method for creating configural and featural face stimuli. Experiment 3 examined whether features placed in their correct categorical relational position but with distorted metrical distances facilitated recognition of unfamiliar faces. These faces were recognized no better than the scrambled faces in Experiment 1, providing further evidence that facial features are stored independently of configural information. From these results we conclude that both featural and configural information are important to recognize a face and argue for a dual-mode hypothesis of face processing. Using the psychophysical results as motivation, we propose a computational framework that implements featural and configural processing routes using an appearance-based representation based on local features and their spatial relations. In three computational experiments (Experiments 4–6) using the same sets of stimuli, we show how this framework is able to model the psychophysical data.