1
|
Abstract
Most research has studied self-regulation by presenting experimenter-controlled test stimuli and measuring change between baseline and stimulus. In the real world, however, stressors do not flash on and off in a predetermined sequence, and there is no experimenter controlling things. Rather, the real world is continuous and stressful events can occur through self-sustaining interactive chain reactions. Self-regulation is an active process through which we adaptively select which aspects of the social environment we attend to from one moment to the next. Here, we describe this dynamic interactive process by contrasting two mechanisms that underpin it: the "yin" and "yang" of self-regulation. The first mechanism is allostasis, the dynamical principle underlying self-regulation, through which we compensate for change to maintain homeostasis. This involves upregulating in some situations and downregulating in others. The second mechanism is metastasis, the dynamical principle underling dysregulation. Through metastasis, small initial perturbations can become progressively amplified over time. We contrast these processes at the individual level (i.e., examining moment-to-moment change in one child, considered independently) and also at the inter-personal level (i.e., examining change across a dyad, such as a parent-child dyad). Finally, we discuss practical implications of this approach in improving the self-regulation of emotion and cognition, in typical development and psychopathology.
Collapse
|
2
|
Wass SV, Goupil L. Studying the Developing Brain in Real-World Contexts: Moving From Castles in the Air to Castles on the Ground. Front Integr Neurosci 2022; 16:896919. [PMID: 35910339 PMCID: PMC9326302 DOI: 10.3389/fnint.2022.896919] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Most current research in cognitive neuroscience uses standardized non-ecological experiments to study the developing brain. But these approaches do a poor job of mimicking the real-world, and thus can only provide a distorted picture of how cognitive operations and brain development unfold outside of the lab. Here we consider future research avenues which may lead to a better appreciation of how developing brains dynamically interact with a complex real-world environment, and how cognition develops over time. We raise several problems faced by current mainstream methods in the field, before briefly reviewing novel promising approaches that alleviate some of these issues. First, we consider research that examines perception by measuring entrainment between brain activity and temporal patterns in naturalistic stimuli. Second, we consider research that examines our ability to parse our continuous experience into discrete events, and how this ability develops over time. Third, we consider the role of children as active agents in selecting what they sample from the environment from one moment to the next. Fourth, we consider new approaches that measure how mutual influences between children and others are instantiated in suprapersonal brain networks. Finally, we discuss how we may reduce adult biases when designing developmental studies. Together, these approaches have great potential to further our understanding of how the developing brain learns to process information, and to control complex real-world behaviors.
Collapse
Affiliation(s)
- Sam V. Wass
- Department of Psychology, University of East London, London, United Kingdom
| | - Louise Goupil
- LPNC, Université Grenoble Alpes/CNRS, Grenoble, France
| |
Collapse
|
3
|
Wass SV. The origins of effortful control: How early development within arousal/regulatory systems influences attentional and affective control. DEVELOPMENTAL REVIEW 2021. [DOI: 10.1016/j.dr.2021.100978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
4
|
Budaev S, Kristiansen TS, Giske J, Eliassen S. Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing. ROYAL SOCIETY OPEN SCIENCE 2020; 7:201886. [PMID: 33489298 PMCID: PMC7813262 DOI: 10.1098/rsos.201886] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/04/2020] [Indexed: 05/08/2023]
Abstract
To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.
Collapse
Affiliation(s)
- Sergey Budaev
- Department of Biological Sciences, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Tore S. Kristiansen
- Research Group Animal Welfare, Institute of Marine Research, PO Box 1870, 5817 Bergen, Norway
| | - Jarl Giske
- Department of Biological Sciences, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Sigrunn Eliassen
- Department of Biological Sciences, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| |
Collapse
|
5
|
Deep learning and cognitive science. Cognition 2020; 203:104365. [PMID: 32563082 DOI: 10.1016/j.cognition.2020.104365] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 05/31/2020] [Accepted: 06/03/2020] [Indexed: 11/22/2022]
Abstract
In recent years, the family of algorithms collected under the term "deep learning" has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system.
Collapse
|
6
|
Dynamical Emergence Theory (DET): A Computational Account of Phenomenal Consciousness. Minds Mach (Dordr) 2020. [DOI: 10.1007/s11023-020-09516-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
7
|
Parde CJ, Hu Y, Castillo C, Sankaranarayanan S, O'Toole AJ. Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification. Cogn Sci 2019; 43:e12729. [DOI: 10.1111/cogs.12729] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 01/08/2019] [Accepted: 03/19/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Connor J. Parde
- School of Behavioral and Brain Sciences The University of Texas at Dallas
| | - Ying Hu
- School of Behavioral and Brain Sciences The University of Texas at Dallas
| | - Carlos Castillo
- University of Maryland Institute for Advanced Computer Studies University of Maryland
| | | | - Alice J. O'Toole
- School of Behavioral and Brain Sciences The University of Texas at Dallas
| |
Collapse
|
8
|
Budaev S, Jørgensen C, Mangel M, Eliassen S, Giske J. Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00164] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
9
|
Del Giudice M, Crespi BJ. Basic functional trade-offs in cognition: An integrative framework. Cognition 2018; 179:56-70. [DOI: 10.1016/j.cognition.2018.06.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 06/05/2018] [Accepted: 06/11/2018] [Indexed: 01/23/2023]
|
10
|
van Gerven M. Computational Foundations of Natural Intelligence. Front Comput Neurosci 2017; 11:112. [PMID: 29375355 PMCID: PMC5770642 DOI: 10.3389/fncom.2017.00112] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 11/22/2017] [Indexed: 01/14/2023] Open
Abstract
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.
Collapse
Affiliation(s)
- Marcel van Gerven
- Computational Cognitive Neuroscience Lab, Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| |
Collapse
|
11
|
The evolution of cognitive mechanisms in response to cultural innovations. Proc Natl Acad Sci U S A 2017; 114:7915-7922. [PMID: 28739938 DOI: 10.1073/pnas.1620742114] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
When humans and other animals make cultural innovations, they also change their environment, thereby imposing new selective pressures that can modify their biological traits. For example, there is evidence that dairy farming by humans favored alleles for adult lactose tolerance. Similarly, the invention of cooking possibly affected the evolution of jaw and tooth morphology. However, when it comes to cognitive traits and learning mechanisms, it is much more difficult to determine whether and how their evolution was affected by culture or by their use in cultural transmission. Here we argue that, excluding very recent cultural innovations, the assumption that culture shaped the evolution of cognition is both more parsimonious and more productive than assuming the opposite. In considering how culture shapes cognition, we suggest that a process-level model of cognitive evolution is necessary and offer such a model. The model employs relatively simple coevolving mechanisms of learning and data acquisition that jointly construct a complex network of a type previously shown to be capable of supporting a range of cognitive abilities. The evolution of cognition, and thus the effect of culture on cognitive evolution, is captured through small modifications of these coevolving learning and data-acquisition mechanisms, whose coordinated action is critical for building an effective network. We use the model to show how these mechanisms are likely to evolve in response to cultural phenomena, such as language and tool-making, which are associated with major changes in data patterns and with new computational and statistical challenges.
Collapse
|
12
|
Jilk DJ, Herd SJ, Read SJ, O’Reilly RC. Anthropomorphic reasoning about neuromorphic AGI safety. J EXP THEOR ARTIF IN 2017. [DOI: 10.1080/0952813x.2017.1354081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | | | - Stephen J. Read
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Randall C. O’Reilly
- eCortex, Inc., Westminster, CO USA
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO USA
| |
Collapse
|
13
|
Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness. PLoS One 2016; 11:e0153193. [PMID: 27144982 PMCID: PMC4856412 DOI: 10.1371/journal.pone.0153193] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 03/24/2016] [Indexed: 11/19/2022] Open
Abstract
We offer and test a simple operationalization of hedonic and eudaimonic well-being ("happiness") as mediating variables that link outcomes to motivation. In six evolutionary agent-based simulation experiments, we compared the relative performance of agents endowed with different combinations of happiness-related traits (parameter values), under four types of environmental conditions. We found (i) that the effects of attaching more weight to longer-term than to momentary happiness and of extending the memory for past happiness are both stronger in an environment where food is scarce; (ii) that in such an environment "relative consumption," in which the agent's well-being is negatively affected by that of its neighbors, is more detrimental to survival when food is scarce; and (iii) that having a positive outlook, under which agents' longer-term happiness is increased by positive events more than it is decreased by negative ones, is generally advantageous.
Collapse
|