Category Archives: Psycho-physiological Bases Of Engineering

On how children learn with progressive environment changes aimed at improving their learning statistically

March 21, 2018 08:49 ,

Linda B. Smith, Swapnaa Jayaraman, Elizabeth Clerkin, Chen Yu, The Developing Infant Creates a Curriculum for Statistical Learning, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 325-336, DOI: 10.1016/j.tics.2018.02.004.

New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning.

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A theory that integrates motivation and control

March 21, 2018 08:46 ,

Giovanni Pezzulo, Francesco Rigoli, Karl J. Friston, Hierarchical Active Inference: A Theory of Motivated Control, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 294-306, DOI: 10.1016/j.tics.2018.01.009.

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects − goal propagation and prioritization, respectively − of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.

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A critic of the “two types of thinking” myth (deliberative, slow, rational, optimal vs. reactive, quick, emotional, suboptimal)

March 21, 2018 08:43 ,

David E. Melnikoff, John A. Bargh, The Mythical Number Two, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 280-293, DOI: 10.1016/j.tics.2018.02.001.

It is often said that there are two types of psychological processes: one that is intentional, controllable, conscious, and inefficient, and another that is unintentional, uncontrollable, unconscious, and efficient. Yet, there have been persistent and increasing objections to this widely influential dual-process typology. Critics point out that the ‘two types’ framework lacks empirical support, contradicts well-established findings, and is internally incoherent. Moreover, the untested and untenable assumption that psychological phenomena can be partitioned into two types, we argue, has the consequence of systematically thwarting scientific progress. It is time that we as a field come to terms with these issues. In short, the dual-process typology is a convenient and seductive myth, and we think cognitive science can do better.

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Survey on the concept of affordance and its use in robotics (the rest of this issue of the journal also deals with affordances in robotics)

March 15, 2018 09:54 ,

L. Jamone et al, Affordances in Psychology, Neuroscience, and Robotics: A Survey,, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 1, pp. 4-25, March 2018, DOI: 10.1109/TCDS.2016.2594134.

The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics.

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A model of others’ emotions that predicts very well experimental results

February 24, 2018 08:29 ,

Rebecca Saxe, Seeing Other Minds in 3D, Trends in Cognitive Sciences, Volume 22, Issue 3, 2018, Pages 193-195, DOI: 10.1016/j.tics.2018.01.003.

Tamir and Thornton [1] have identified three key dimensions that organize our understanding of other minds. These dimensions (glossed as valence, social impact, and rationality) can capture the similarities and differences between concepts of internal experiences (anger, loneliness, gratitude), and also between concepts of personalities (aggressive, introverted, agreeable). Most impressively, the three dimensions explain the patterns of hemodynamic activity in our brains as we consider these experiences [2] (Box 1). States such as anger and gratitude are invisible, but the patterns evoked in our brain as we think about them are as predictable by the model of Tamir and Thornton as the patterns evoked in our visual cortex when we look at chairs, bicycles, or pineapples are predictable by models of high-level vision [3]. Human social prediction follows the same dimensions: observers predict that transitions are more likely between states that are ‘nearby’ in this abstract 3D space [4]. Thus, we expect that a friend now feeling ‘anxious’ will be more likely to feel ‘sluggish’ than ‘energetic’ later.

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A model of the interdependence of previous sensorimotor experiences in the following decision making

January 31, 2018 08:41 ,

Evelina Dineva & Gregor Schöner, How infants’ reaches reveal principles of sensorimotor decision making, Connection Science vol. 30 iss. 1, p. 53-80, DOI: 10.1080/09540091.2017.1405382.

In Piaget’s classical A-not-B-task, infants repeatedly make a sensorimotor decision to reach to one of two cued targets. Perseverative errors are induced by switching the cue from A to B, while spontaneous errors are unsolicited reaches to B when only A is cued. We argue that theoretical accounts of sensorimotor decision-making fail to address how motor decisions leave a memory trace that may impact future sensorimotor decisions. Instead, in extant neural models, perseveration is caused solely by the history of stimulation. We present a neural dynamic model of sensorimotor decision-making within the framework of Dynamic Field Theory, in which a dynamic instability amplifies fluctuations in neural activation into macroscopic, stable neural activation states that leave memory traces. The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. A conditional probabilities analysis accounts quantitatively how motor decisions depend on the history of reaching. The results provide evidence for the interdependence among subsequent reaching decisions that is explained by the model, showing that by amplifying small differences in activation and affecting learning, decisions have consequences beyond the individual behavioural act.

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A survey in interactive perception in robots: interacting with the environment to improve perception and using internal models and prediction too

December 6, 2017 08:54 ,

J. Bohg et al, Interactive Perception: Leveraging Action in Perception and Perception in Action, IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1273-1291, DOI: 10.1109/TRO.2017.2721939.

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

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On how psychologists realize that the brain, after all, may be creating symbols (concepts), like deep neural networks show

November 15, 2017 09:24 ,

Jeffrey S. Bowers, Parallel Distributed Processing Theory in the Age of Deep Networks, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 950-961, DOI: 10.1016/j.tics.2017.09.013.

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory.

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Towards taking into account the complexity of finding the best option in decision-making systems

November 15, 2017 09:21 ,

Peter Bossaerts, Carsten Murawski, Computational Complexity and Human Decision-Making, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 917-929, DOI: 10.1016/j.tics.2017.09.005.

The rationality principle postulates that decision-makers always choose the best action available to them. It underlies most modern theories of decision-making. The principle does not take into account the difficulty of finding the best option. Here, we propose that computational complexity theory (CCT) provides a framework for defining and quantifying the difficulty of decisions. We review evidence showing that human decision-making is affected by computational complexity. Building on this evidence, we argue that most models of decision-making, and metacognition, are intractable from a computational perspective. To be plausible, future theories of decision-making will need to take into account both the resources required for implementing the computations implied by the theory, and the resource constraints imposed on the decision-maker by biology.

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On numerical cognition and the inexistence of an innate concept of number but the existence of an innate concept of quantity

November 15, 2017 09:17 ,

Tom Verguts, Qi Chen, Numerical Cognition: Learning Binds Biology to Culture, Trends in Cognitive Sciences, Volume 21, Issue 12, 2017, Pages 913-914, DOI: 10.1016/j.tics.2017.09.004.

First, we address the issue of which quantity representations are innate. Second, we consider the role of the number list, whose characteristics are no doubt highly culturally dependent.

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