Can Affect Disengagement Produce Moral Standard Violation?

Bandura has suggested that people can escape the self-regulatory power of internalized moral standards by disengaging the standards, allowing violation. We consider the possibility that disengaging the emotional warning signs of impending self-censure may be sufficient to permit violation. To test this idea, we led some research participants to misattribute to a pill (actually a placebo) any feelings of tension and anxiety evoked by the prospect of violating their standards of fairness. As predicted, participants in the misattribution condition were more likely to self-favor than those in a no-misattribution condition. This effect was mediated by the amount of tension and anxiety attributed to the pill. The role that affect disengagement may play in moral decisions outside the laboratory is discussed.     

Learning Problem‐Solving Rules as Search Through a Hypothesis Space

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: (1) the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; (2) participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design.     

Reasoning,biases and dual processes: The lasting impact of Wason (1960)

Wason (1960) published a relatively short experimental paper, in which he introduced the 2-4-6 problem as a test of inductive reasoning. This paper became one of the most highly cited to be published in the Quarterly Journal of Experimental Psychology and is significant for a number of reasons. First, the 2-4-6 task itself was ingenious and yielded evidence of error and bias in the intelligent participants who attempted it. Research on the 2-4-6 problem continues to the present day. More importantly, it was Wason's first paper on reasoning and one which made strong claims for bias and irrationality in a period dominated by rationalist writers like Piaget. It set in motion the study of cognitive biases in thinking and reasoning, well before the start of Tversky and Kahneman's famous heuristics and biases research programme. I also show here something for which Wason has received insufficient credit. It was Wason's work on this task and his later studies of his four card selection task that led to the first development of the dual process theory of reasoning which is so dominant in the current literature on the topic more than half a century later.     

The Effects of Scrambling on Spanish and Korean Agrammatic Interpretation: Why Linear Models Fail and Structural Models Survive

Several models of comprehension deficits in agrammatic aphasia rely heavily on linear considerations in the assignment of thematic roles to structural positions (e.g., the Trace-Deletion Hypothesis, the Mapping Hypothesis, and the Argument-Linking Hypothesis). These accounts predict that constructions in languages with rules that affect syntactic structure but preserve relative linear order should be unimpaired. Other models [e.g., the Double-Dependency Hypothesis, (DDH)] do not resort to linearity but are purely structural in conception and therefore should be immune to word-order effects. We tested linear and nonlinear accounts with scrambling structures in Korean and topicalization structures in Spanish. The results are very clear. The (nonlinear) DDH is entirely compatible with the evidence, but the linear accounts are not.     

When and how less is more: reply to Tharp and Pickering

In DeCaro et al. [DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). Individual differences in category learning: Sometimes less working memory capacity is better than more. Cognition, 107, 284-294] we demonstrated that sometimes less working memory (WM) has its advantages. The lower individuals’ WM, the faster they achieved success on an information-integration (II) category learning task adopted from Waldron and Ashby [Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8, 168-176]. We attributed this success to the inability of lower WM individuals to employ explicit learning strategies heavily reliant on executive control. This in turn, we hypothesized, might push lower WM individuals to readily adopt procedural-based strategies thought to lead to success on the II task. Tharp and Pickering [Tharp, I. J., & Pickering, A. D. (2009). A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information-integration category learning. Cognition] recently questioned whether the II category learning task DeCaro et al. used really reflects procedural learning. In an effort to investigate Tharp and Pickering’s assertions with respect to individual differences in WM, we replicate and extend our previous work, in part by modeling participants’ response strategies during learning. We once again reveal that lower WM individuals demonstrate earlier II learning than their higher WM counterparts. However, we also show that low WM individuals’ initial success is not because of procedural-based responding. Instead, individuals lower in WM capacity perseverate in using simple rule-based strategies that circumvent heavy demands on WM while producing above-chance accuracy.     

Hazard perception abilities of experienced and novice motorcyclists: An interactive simulator experiment

This study reports an experiment that compared the hazard perception abilities of experienced and novice motorcycle riders using an interactive, closed-loop, simulator. Participants (n = 49) were categorized into four groups: experienced motorcycle riders with full driver licence, inexperienced motorcycle riders with full driver licence, novice motorcycle riders with full driver licence, and novice motorcycle riders with probationary driver licence. The participants were tested on three scenarios, each consisting of eight hazardous events. They were instructed to ride normally, but to respond appropriately to avoid the hazards. Under certain conditions in the simulator, we found that experienced riders (relative to inexperienced or novice riders) crashed less often, received better performance evaluations, and approached hazards at more appropriate speeds. Interestingly, we also found that some novice riders were overconfident in their riding ability. We discuss how this overconfidence might be related to hazard perception.     

汉语口吃者在不出声言语中的语音编码

口吃者与非口吃者在不出声言语中语音编码的差异是口吃者语音加工异常的有力证据。通过三个实验,分别考察了口吃者与非口吃者监控汉语拼音中声母、韵母及声调的差异。结果表明,口吃者对声母的监控与非口吃者相比没有显著的差异,但在监控韵母及声调时,口吃者的反应显著慢于非口吃者。研究结果支持了关于口吃的“内在修正假说”,对研究汉语的语音编码有启发,对于口吃的诊断和治疗也有重要的启示     

The neural evidence for simulation is weaker than I think you think it is

Simulation theory accounts of mind-reading propose that the observer generates a mental state that matches the state of the target and then uses this state as the basis for an attribution of a similar state to the target. The key proposal is thus that mechanisms that are primarily used online, when a person experiences a kind of mental state, are then co-opted to run Simulations of similar states in another person. Here I consider the neuroscientific evidence for this view. I argue that there is substantial evidence for co-opted mechanisms, leading from one individual’s mental state to a matching state in an observer, but there is no evidence that the output of these co-opted mechanisms serve as the basis for mental state attributions. There is also substantial evidence for attribution mechanisms that serve as the basis for mental state attributions, but there is no evidence that these mechanisms receive their input from co-opted mechanisms.     

Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning

The objective of this study is to model the microscopic behaviour of mixed traffic (cyclist-pedestrian) interactions in non-motorized shared spaces. Video data were collected at two locations of Robson Square non-motorized shared space in downtown Vancouver, British Columbia. Trajectories of cyclists and pedestrians involved in interactions were extracted using computer vision algorithms. The extracted trajectories were used to obtain several variables that describe elements of road users’ behaviour including longitudinal and lateral distances, speed and speed differences, interaction angle, and cyclist acceleration and yaw rate. The road users behaviour was modeled as utility-based intelligent rational agents using the finite-state Markov Decision Process (MDP) framework with unknown reward functions. The study implemented Inverse Reinforcement Learning (IRL) using two algorithms: the Maximum Entropy (ME) algorithm, and the Feature Matching (FM) algorithm to recover/estimate the reward function weights of cyclists in two types of interactions with pedestrians: following and overtaking interactions. Reward function weights infer cyclist preferences during their interactions with pedestrians in non-motorized shared spaces, and can form the key component in developing agent based microsimulation model for road users. Furthermore, the estimated reward functions were used to estimate cyclists’ optimal policy for such interactions. A simulation platform was developed using the estimated reward functions and the cyclist optimal policies to simulate cyclist trajectories for the validation dataset. Results show that the Maximum Entropy (ME) IRL algorithm outperformed the Feature Matching (FM) IRL algorithm, and generally provided reasonable results for modeling such interactions in non-motorized shared spaces, considering the high degrees of freedom in movement and the more-complex road users interactions in such facilities. This research is considered an important step toward developing a full Agent-Based Model (ABM) for road users in shared space facilities to evaluate the safety and efficiency of such facilities.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.