Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Analysis of human posterior EEG alpha frequency as a function of implicit learning of probabilistic lateralized stimuli in a visual detection task

Alpha oscillatory activity has long been associated with perceptual and cognitive processes related to attention control. The aim of this study is to explore the task-dependent role of alpha frequency in a lateralized visuo-spatial detection task. Specifically, the thesis focuses on consolidating the scientific literature's knowledge about the role of alpha frequency in perceptual accuracy, and deepening the understanding of what determines trial-by-trial fluctuations of alpha parameters and how these fluctuations influence overall task performance. The hypotheses, confirmed empirically, were that different implicit strategies are put in place based on the task context, in order to maximize performance with optimal resource distribution (namely alpha frequency, associated positively with performance): “Lateralization” of the attentive resources towards one hemifield should be associated with higher alpha frequency difference between contralateral and ipsilateral hemisphere; “Distribution” of the attentive resources across hemifields should be associated with lower alpha frequency difference between hemispheres; These strategies, used by the participants according to their brain capabilities, have proven themselves adaptive or maladaptive depending on the different tasks to which they have been set: "Distribution" of the attentive resources seemed to be the best strategy when the distribution probability between hemifields was balanced: i.e. the neutral condition task. "Lateralization" of the attentive resources seemed to be more effective when the distribution probability between hemifields was biased towards one hemifield: i.e., the biased condition task.

Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory? (Commentary on Pothos and Busemeyer)

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.

Cognitive behaviour therapy (CBT) for depression by computer vs. therapist: patient experiences and therapeutic processes

This case series compares patient experiences and therapeutic processes between two modalities of cognitive behaviour therapy (CBT) for depression: computerized CBT (cCBT) and therapist-delivered CBT (tCBT). In a mixed-methods repeated-measures case series, six participants were offered cCBT and tCBT in sequence, with the order of delivery randomized across participants. Questionnaires about patient experiences were administered after each session and a semi-structured interview was completed with each participant at the end of each therapy modality. Therapy expectations, patient experiences and session impact ratings in this study generally favoured tCBT. Participants typically experienced cCBT sessions as less meaningful, less positive and less helpful compared to tCBT sessions in terms of developing understanding, facilitating problem-solving and building a therapeutic relationship.

Conceptual spaces and consciousness: Integrating cognitive and affective processes

In the book Conceptual Spaces: the Geometry of Thought [2000] Peter Gärdenfors proposes a new framework for cognitive science. Complementary to symbolic and subsymbolic [connectionist] descriptions, conceptual spaces are semantic structures constructed from empirical data representing the universe of mental states. We argue that Gärdenfors' modeling can be used in consciousness research to describe the phenomenal conscious world, its elements and their intrinsic relations. The conceptual space approach affords the construction of a universal state space of human consciousness, where all possible kinds of human conscious states could be mapped. Starting from this approach, we discuss the inclusion of feelings and emotions in conceptual spaces, and their relation to perceptual and cognitive states. Current debate on integration of affect/emotion and perception/cognition allows three possible descriptive alternatives: emotion resulting from basic cognition; cognition resulting from basic emotion, and both as relatively independent functions integrated by brain mechanisms. Finding a solution for this issue is an important step in any attempt of successful modeling of natural or artificial consciousness. After making a brief review of proposals in this area, we summarize the essentials of a new model of consciousness based on neuro-astroglial interactions. © 2011 World Scientific Publishing Company.

Cognitive and affective processes in social actions and decisions

The question of how we make, and how we should make judgments and decisions has occupied thinkers for many centuries. This thesis has the aim to add new evidences to clarify the brain’s mechanisms for decisions. The cognitive and the emotional processes of social actions and decisions are investigated with the aim to understand which brain areas are mostly involved. Four experimental studies are presented. A specific kind of population is involved in the first study (as well as in study III) concerning patients with lesion of ventromedial prefrontal cortex (vmPFC). This region is collocated in the ventral surface of frontal lobe, and it seems have an important role in social and moral decision in forecasting the negative emotional consequences of choice. In study I, it is examined whether emotions, specifically social emotions subserved by the vmPFC, affect people’s willingness to trust others. In study II is observed how incidental emotions could encourage trusting behaviour, especially when individuals are not aware of emotive stimulation. Study III has the aim to gather a direct psychophysiological evidence, both in healthy and neurologically impaired individuals, that emotions are crucially involved in shaping moral judgment, by preventing moral violations. Study IV explores how the moral meaning of a decision and its subsequent action can modulate the basic component of action such as sense of agency.

The power of emotional valence – from cognitive to affective processes in reading

The comprehension of stories requires the reader to imagine the cognitive and affective states of the characters. The content of many stories is unpleasant, as they often deal with conflict, disturbance or crisis. Nevertheless, unpleasant stories can be liked and enjoyed. In this fMRI study, we used a parametric approach to examine (1) the capacity of increasing negative valence of story contents to activate the mentalizing network (cognitive and affective theory of mind, ToM), and (2) the neural substrate of liking negatively valenced narratives. A set of 80 short narratives was compiled, ranging from neutral to negative emotional valence. For each story mean rating values on valence and liking were obtained from a group of 32 participants in a prestudy, and later included as parametric regressors in the fMRI analysis. Another group of 24 participants passively read the narratives in a three Tesla MRI scanner. Results revealed a stronger engagement of affective ToM-related brain areas with increasingly negative story valence. Stories that were unpleasant, but simultaneously liked, engaged the medial prefrontal cortex (mPFC), which might reflect the moral exploration of the story content. Further analysis showed that the more the mPFC becomes engaged during the reading of negatively valenced stories, the more coactivation can be observed in other brain areas related to the neural processing of affective ToM and empathy.

Cognitive change processes in a group cognitive behavior therapy of depression

The present study attempted to examine the causal relationships among changes in automatic thoughts, dysfunctional attitudes, and depressive symptoms in a 12-week group cognitive behavior therapy (GCBT) program for depression. In all, 35 depressed patients attending the GCBT program were monitored with the Automatic Thoughts Questionnaire, Dysfunctional Attitudes Scale, and Beck Depression Inventory at the pre-treatment, 4th and 8th sessions, and post-treatment. The results were as follows: (1) GCBT reduces negative cognitions; (2) changes in automatic thoughts and dysfunctional attitudes lead to change in depressive symptoms; and (3) automatic thoughts play a mediating role between dysfunctional attitudes and depression. The findings taken as a whole support the Causal Cognition Model of depression. (C) 2003 Elsevier Science Ltd. All rights reserved.

Components of group processes: Have they conributed to the outcome of mood and anxiety diorder patients in a group cognitive-behaviour therapy program

The present study assessed the influence of group processes on clinical outcomes of patients with anxiety and depression following group Cognitive Behavior Therapy (CBT). Five group environment variables were measured: cohesion, leader support, expressiveness, independence, and self-discovery. One hundred and sixty two patients attended a group CBT program and were assessed at pre and post-treatment. Results provided evidence for the effectiveness of group therapy as patients reported significantly lower depression and anxiety at the conclusion of treatment. Expressiveness was the only predictor of post-treatment anxiety, whereas leader support, expressiveness, and independence were significant predictors of post-treatment depression. Overall, findings suggest that the patients benefited from high levels of expressiveness and independence within their therapy group. In contrast, they failed to benefit from high levels of leader support, whereas both group cohesion and self-discovery appeared to be unrelated to outcome