From Blickets to Synapses: Inferring Temporal Causal Networks by Observation

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike‐time dependent plasticity, long‐term depression, and heterosynaptic competition rules to implement Rescorla–Wagner‐like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well‐known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening‐off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.     

Multiplicatively interacting factors selectively influencing parameters in multiple response class processing and rate trees

Evidence in many experiments indicates that the processes involved in producing responses are arranged in a tree structure. Evidence often indicates further that an experimental factor, such as item similarity, changes a single parameter, leaving others invariant. In typical studies, a few tree structures are hypothesized a priori, and tested by goodness of fit. With the method of Tree Inference, a tree is constructed by examining the data to see if patterns occur that are predicted when two factors selectively influence different processes (Schweickert & Chen, 2008). The patterns can reveal, for example, whether selectively influenced processes are executed in order, and what the order is. If the patterns do not occur, one can conclude that no tree is possible in which the factors selectively influence processes. In earlier work, three restrictions were imposed on the trees considered: There were two classes of responses; parameters were probabilities, bounded above by 1; and factors were assumed to change parameters associated with children of a single vertex. More general results are derived here, removing these restrictions. Results on representation, uniqueness of parameters, uniqueness of tree structure, and mixtures of trees are presented.     

Inference making and linking both require thinking: Spontaneous trait inference and spontaneous trait transference both rely on working memory capacity

Past research suggests that spontaneous trait inference (STI) and spontaneous trait transference (STT) may reflect different cognitive processes, the former being inferential and the latter associational. The present research was designed to explore whether either or both of these processes involve thinking that occupies cognitive capacity. Four studies suggest that reductions in available cognitive capacity reduce both STI and STT effects, both on measures of savings in relearning (which reflect the strength of trait associations with a person) and on trait ratings measures (which reflect the strength of trait inferences made about a person). Similar results were obtained using an individual difference measure of cognitive capacity. Although these results suggest that STI and STT are similar, in that both exhibit interference from reductions in cognitive capacity, other results, such as halo effects in trait ratings, support previous assertions that their underlying processes are distinct.     

Rewarding performance feedback alters reported time of action

Past studies have shown that the perceived time of actions is retrospectively influenced by post-action events. The current study examined whether rewarding performance feedback (even when false) altered the reported time of action. In Experiment 1, participants performed a speeded button press task and received monetary reward for a presumed “fast,” or a monetary punishment for a presumed “slow” response. Rewarded trials resulted in the false perception that the response action occurred earlier than punished trials. In Experiments 2 and 3, the need for a speeded response and reward were independently manipulated in order to decouple the cognitive and reward components in the feedback signal. When tested independently, neither variable affected the judged time of action. We conclude that meaningful feedback (fast or slow) is only used when made salient by reward, to modulate the judged time of an action.     

Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence

Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor’s knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator’s pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children’s imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naïve demonstrator. In particular, in the pedagogical case children are more likely to “overimitate” by reproducing the entire demonstrated sequence. This behavior is consistent with our model’s predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.     

Non-Prioritized Ranked Belief Change

Traditional accounts of belief change have been criticized for placing undue emphasis on the new belief provided as input. A recent proposal to address such issues is a framework for non-prioritized belief change based on default theories (Ghose and Goebel, 1998). A novel feature of this approach is the introduction of disbeliefs alongside beliefs which allows for a view of belief contraction as independently useful, instead of just being seen as an intermediate step in the process of belief revision. This approach is, however, restrictive in assuming a linear ordering of reliability on the received inputs. In this paper, we replace the linear ordering with a preference ranking on inputs from which a total preorder on inputs can be induced. This extension brings along with it the problem of dealing with inputs of equal rank. We provide a semantic solution to this problem which contains, as a special case, AGM belief change on closed theories.     

文本阅读过程中目标焦点的预期推理

探讨文本阅读过程中目标焦点是否引起即时的预期推理。采用移动窗口技术,让被试阅读16篇含有目标信息的记叙文。实验1和实验2探讨已进入长时记忆中的尚未实现的目标是否作为阅读的焦点对阅读目标行为进行预期推理。实验3探讨已经实现的目标是否作为阅读的焦点对阅读目标行为产生预期作用。实验结果表明,对目标的预期推理是可以即时发生的,在目标未实现的情况下,读者会对目标信息进行预期推理;在目标已经实现的情况下,读者对有关目标的信息不产生预期推理。     

文本阅读中预期推理生成的容量制约性

本研究通过操纵读者信息加工的时间量考察了工作记忆容量对预期推理生成的影响性质。结果表明,如果没有信息加工时间的限制,则工作记忆容量低的读者也能表现出在阅读中生成预期推理的证据,而如果读者的阅读加工时间受一定限制,则工作记忆容量低者建构预期推理的证据随着被允许的加工时间的缩短而逐渐消失。结果支持“工作记忆容量只影响阅读中预期推理建构速度”的观点。