Using an AI creativity system to explore how aesthetic experiences are processed along the brain’s perceptual neural pathways

With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be used to explore the nature of the dual-pathway neuro-architecture of the human visual system and how this relates to higher cognitive judgments such as aesthetic experiences that rely on these divergent information streams. We propose a theoretical framework for how the movement within percepts such as video clips, causes the engagement of reflexive attention and a subsequent focus on visual information that are primarily processed via the dorsal stream, thereby modulating aesthetic experiences that rely on information relayed via the ventral stream. We outline our recent study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences.     

Jury simulation studies: To exclude or not to exclude participants based on a lack of comprehension of the case?

Jury simulation research has been the subject of longstanding criticism in regards to ecological validity. One additional factor that has received little attention that may also impact the generalizability of this research relates to excluding participants based on their memory of, or their attention paid to, the case. In order to determine how common this exclusion is, the authors conducted a scoping review of jury simulation studies within the last 10 years across three major legal psychology journals. The review revealed variability in if and how studies excluded on the basis of poor attention or for failing manipulation or general memory checks. In addition, studies that did exclude on this basis varied greatly in the proportion of the sample that was excluded. Recommendations are provided in order to assist jury researchers in future with how best to manage exclusions on the basis of juror memory and attention.     

动作理解因境而异:动作加工中情境信息的自动整合

针对动作理解的机制,模拟论主张大脑自发模拟他人的动作,就相同的动作其理解也相同,而理论论则认为人们基于合理性原则对他人动作进行推理,相同的动作发生在不同的情境时会有不同的理解。但以往研究所采用动作材料的运动学特性和发生情境存在共变,其难以区分动作理解是支持模拟论还是理论论。通过两项实验,采用动画制作技术来产生有无约束情境下的追逐动作,以指示动作加工过程的脑电μ抑制为指标,对前述两种观点进行了检验。其中,在约束情境中存在障碍物,追逐者需改变运动方向以绕过障碍物,从后方逐渐趋近目标;而无约束情境中不存在障碍物,但追逐者依然保持与存在约束情景下相同的运动模式。结果发现,当追逐动作发生在存在约束的情境时,其基于合理性原则推测可获得清晰的动作目标,该条件下的μ抑制程度高于不存在约束情境的条件(实验1);而当仅追逐者运动,即趋近的目标不确定时,虽然有约束和无约束情境间的物理差异与实验1相同,但条件间μ抑制的差异消失(实验2);且上述μ抑制并非与注意相关的枕叶α活动的泛化。该结果提示,动作发生的情境信息影响人们对动作的理解,即基于推理过程理解动作,支持理论论观点。     

Mental Rotation in False Belief Understanding

This study examines the spontaneous use of embodied egocentric transformation (EET) in understanding false beliefs in the minds of others. EET involves the participants mentally transforming or rotating themselves into the orientation of an agent when trying to adopt his or her visuospatial perspective. We argue that psychological perspective taking such as false belief reasoning may also involve EET because of what has been widely reported in the embodied cognition literature, showing that our processing of abstract, propositional information is often grounded in concrete bodily sensations which are not apparently linked to higher cognition. In Experiment 1, an agent placed a ball into one of two boxes and left. The ball then rolled out and moved either into the other box (new box) or back into the original one (old box). The participants were to decide from which box they themselves or the agent would try to recover the ball. Results showed that false belief performance was affected by increased orientation disparity between the participants and the agent, suggesting involvement of embodied transformation. In Experiment 2, false belief was similarly induced and the participants were to decide if the agent would try to recover the ball in one specific box. Orientation disparity was again found to affect false belief performance. The present results extend previous findings on EET in visuospatial perspective taking and suggest that false belief reasoning, which is a kind of psychological perspective taking, can also involve embodied rotation, consistent with the embodied cognition view.     

Comparing Computational Power

It is common practice to compare the computational power ofdifferent models of computation. For example, the recursivefunctions are strictly more powerful than the primitive recursivefunctions, because the latter are a proper subset of the former(which includes Ackermann's function). Side-by-side with this"containment" method of measuring power, it is also standardto base comparisons on "simulation". For example, one says thatthe (untyped) lambda calculus is as powerful—computationallyspeaking—as the partial recursive functions, because thelambda calculus can simulate all partial recursive functionsby encoding the natural numbers as Church numerals. The problem is that unbridled use of these two distinct waysof comparing power allows one to show that some computationalmodels (sets of partial functions) are strictly stronger thanthemselves! We argue that a better definition is that modelA is strictly stronger than B if A can simulate B via some encoding,whereas B cannot simulate A under any encoding. We show thatwith this definition, too, the recursive functions are strictlystronger than the primitive recursive. We also prove that therecursive functions, partial recursive functions, and Turingmachines are "complete", in the sense that no injective encodingcan make them equivalent to any "hypercomputational" model.¹     

A model of plausibility

Plausibility has been implicated as playing a critical role in many cognitive phenomena from comprehension to problem solving. Yet, across cognitive science, plausibility is usually treated as an operationalized variable or metric rather than being explained or studied in itself. This article describes a new cognitive model of plausibility, the Plausibility Analysis Model (PAM), which is aimed at modeling human plausibility judgment. This model uses commonsense knowledge of concept-coherence to determine the degree of plausibility of a target scenario. In essence, a highly plausible scenario is one that fits prior knowledge well: with many different sources of corroboration, without complexity of explanation, and with minimal conjecture. A detailed simulation of empirical plausibility findings is reported, which shows a close correspondence between the model and human judgments. In addition, a sensitivity analysis demonstrates that PAM is robust in its operations.     

Uncertainty about the rest of the sentence

A word-by-word human sentence processing complexity metric is presented. This metric formalizes the intuition that comprehenders have more trouble on words contributing larger amounts of information about the syntactic structure of the sentence as a whole. The formalization is in terms of the conditional entropy of grammatical continuations, given the words that have been heard so far. To calculate the predictions of this metric, Wilson and Carroll's (1954) original entropy reduction idea is extended to infinite languages. This is demonstrated with a mildly context-sensitive language that includes relative clauses formed on a variety of grammatical relations across the Accessibility Hierarchy of Keenan and Comrie (1977). Predictions are derived that correlate significantly with repetition accuracy results obtained in a sentence-memory experiment (Keenan & Hawkins, 1987).