Qiufang Fu

Implicit sequence learning and conscious awareness.

This paper uses the Process Dissociation Procedure to explore whether people can acquire unconscious knowledge in the serial reaction time task [Destrebecqz, A., & Cleeremans, A. (2001). Can sequence learning be implicit? New evidence with the Process Dissociation Procedure. Psychonomic Bulletin &Review, 8, 343-350; Wilkinson, L., & Shanks, D. R. (2004). Intentional control and implicit sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 354-369]. Experiment 1 showed that people generated legal sequences above baseline levels under exclusion instructions. Reward moved exclusion performance towards baseline, indicating that the extent of motivation in the test phase influenced the expression of unconscious knowledge. Experiments 2 and 3 revealed that even with reward, adding noise to the sequences or shortening training led to above-baseline exclusion performance, suggesting that task difficulty and the amount of training also affected the expression of unconscious knowledge. The results help resolve some current debates about the role of conscious awareness in sequence learning.

Can unconscious knowledge allow control in sequence learning

This paper investigates the conscious status of both the knowledge that an item is legal (judgment knowledge) and the knowledge of why it is legal (structural knowledge) in sequence learning. We compared ability to control use of knowledge (Process Dissociation Procedure) with stated awareness of the knowledge (subjective measures) as measures of the conscious status of knowledge. Experiment 1 showed that when people could control use of judgment knowledge they were indeed conscious of having that knowledge according to their own statements. Yet Experiment 2 showed that people could exert such control over the use of judgment knowledge when claiming they had no structural knowledge: i.e. conscious judgment knowledge could be based on unconscious structural knowledge. Further implicit learning research should be clear over whether judgment or structural knowledge is claimed to be unconscious as the two dissociate in sequence learning.

Implicit Learning of Recursive Context-Free Grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning.

Learning without consciously knowing: Evidence from event-related potentials in sequence learning

This paper investigated how implicit and explicit knowledge is reflected in event-related potentials (ERPs) in sequence learning. ERPs were recorded during a serial reaction time task. The results showed that there were greater RT benefits for standard compared with deviant stimuli later than early on, indicating sequence learning. After training, more standard triplets were generated under inclusion than exclusion tests and more standard triplets under exclusion than chance level, indicating that participants acquired both explicit and implicit knowledge. However, deviant targets elicited enhanced N2 and P3 components for targets with explicit knowledge but a larger N2 effect for targets with implicit knowledge, revealing that implicit knowledge expresses itself in relatively early components (N2) and explicit knowledge in additional P3 components. The results help resolve current debate about the neural substrates supporting implicit and explicit learning.

Negative Affect Reduces Performance in Implicit Sequence Learning

Background It is well documented that positive rather than negative moods encourage integrative processing of conscious information. However, the extent to which implicit or unconscious learning can be influenced by affective states remains unclear. Methodology/Principal Findings A Serial Reaction Time (SRT) task with sequence structures requiring integration over past trials was adopted to examine the effect of affective states on implicit learning. Music was used to induce and maintain positive and negative affective states. The present study showed that participants in negative rather than positive states learned less of the regularity. Moreover, the knowledge was shown by a Bayesian analysis to be largely unconscious as participants were poor at recognizing the regularity. Conclusions/Significance The results demonstrated that negative rather than positive affect inhibited implicit learning of complex structures. Our findings help to understand the effects of affective states on unconscious or implicit processing.

The distinction between intuition and guessing in the SRT task generation: A reply to Norman and Price

We (Fu, Dienes, & Fu, 2010) investigated the extent to which people could generate sequences of responses based on knowledge acquired from the Serial Reaction Time task, depending on whether it felt subjectively like the response was based on pure guessing, intuition, conscious rules or memories. Norman and Price (2010) argued that in the context of our task, intuition responses were the same as guessing responses. In reply, we argue that not only do subjects apparently claim to be experiencing different phenomenologies when saying intuition versus guess, but also intuition and guess responses are associated with different behaviors. We found that people could control the knowledge when generating responses felt to be based on intuition but not those felt to be pure guessing. We present further evidence here that triplets associated with intuition but not guessing were also processed fluently.

A distributed computational cognitive model for object recognition

Based on cognitive functionalities in human vision processing, we propose a computational cognitive model for object recognition with detailed algorithmic descriptions. The contribution of this paper is of two folds. Firstly, we present a systematic review on psychological and neurophysiological studies, which provide collective evidence for a distributed representation of 3D objects in the human brain. Secondly, we present a computational model which simulates the distributed mechanism of object vision pathway. Experimental results show that the presented computational cognitive model outperforms five representative 3D object recognition algorithms in computer science research.

Implicit Learning and Recursion

Implicit learning research has focused on learning simple structures, such as chunks, even though such structures do not capture the richness of real-world human accomplishments. In particular, music and language exhibit certain recursive features that cannot be captured by regular grammars, let alone mechanisms that learn only chunks. We show in the domains of music, language, poetry and movement that people can implicitly learn recursive grammars in ways that go beyond learning chunks or mere repetition patterns. This is supported by the fact that participants are found to generalise from training materials to novel sequences following the underlying rules. In this context we further propose a parsimony argument that states that although performance on new test items can always be explained by a catch-all finite-state or chunking mechanism, such explanations can be more complex than postulating learning a supra-finite-state mechanism in that they may postulate considerably more rules or states than necessary to explain learning. This is especially true when the finite-state rather than supra-finite-state mechanism, in order to perform on the test material, needs to acquire states or chunks not required for learning the training material. We highlight both the strength and weakness of our current evidence in this regard.

Emotional Context Influences Micro-Expression Recognition

Micro-expressions are often embedded in a flow of expressions including both neutral and other facial expressions. However, it remains unclear whether the types of facial expressions appearing before and after the micro-expression, i.e., the emotional context, influence micro-expression recognition. To address this question, the present study used a modified METT (Micro-Expression Training Tool) paradigm that required participants to recognize the target micro-expressions presented briefly between two identical emotional faces. The results of Experiments 1 and 2 showed that negative context impaired the recognition of micro-expressions regardless of the duration of the target micro-expression. Stimulus-difference between the context and target micro-expression was accounted for in Experiment 3. Results showed that a context effect on micro-expression recognition persists even when the stimulus similarity between the context and target micro-expressions was controlled. Therefore, our results not only provided evidence for the context effect on micro-expression recognition but also suggested that the context effect might result from both the stimulus and valence differences.

A Sketch-Based Approach for Interactive Organization of Video Clips

With the rapid growth of video resources, techniques for efficient organization of video clips are becoming appealing in the multimedia domain. In this article, a sketch-based approach is proposed to intuitively organize video clips by: (1) enhancing their narrations using sketch annotations and (2) structurizing the organization process by gesture-based free-form sketching on touch devices. There are two main contributions of this work. The first is a sketch graph, a novel representation for the narrative structure of video clips to facilitate content organization. The second is a method to perform context-aware sketch recommendation scalable to large video collections, enabling common users to easily organize sketch annotations. A prototype system integrating the proposed approach was evaluated on the basis of five different aspects concerning its performance and usability. Two sketch searching experiments showed that the proposed context-aware sketch recommendation outperforms, in terms of accuracy and scalability, two state-of-the-art sketch searching methods. Moreover, a user study showed that the sketch graph is consistently preferred over traditional representations such as keywords and keyframes. The second user study showed that the proposed approach is applicable in those scenarios where the video annotator and organizer were the same person. The third user study showed that, for video content organization, using sketch graph users took on average 1/3 less time than using a mass-market tool Movie Maker and took on average 1/4 less time than using a state-of-the-art sketch alternative. These results demonstrated that the proposed sketch graph approach is a promising video organization tool.