When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the (...) unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain. (shrink)

The only viable formulation of perception, thinking, and action under uncertainty is statistical inference, and the normative way of statistical inference is Bayesian. No wonder, then, that even seemingly non-Bayesian computational frameworks in cognitive science ultimately draw their justification from Bayesian considerations, as enlightened theorists know fully well.

In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...) at any given time be adequately described by a single subjective probability function. We show that monistic Bayesians cannot provide an adequate solution to the new Tweety puzzle, because this requires one to refer to a frequency-based probability function. We conclude that monistic Bayesianism cannot be a fully adequate theory of epistemic states. In the second part we describe an empirical study, which provides support for the thesis that monistic Bayesianism is also inadequate as a descriptive theory of cognitive states. In the final part of the paper we criticize Bayesian approaches in cognitive science, insofar as their monistic tendency cannot adequately address the new Tweety puzzle. We, further, argue against monistic Bayesianism in cognitive science by means of a case study. In this case study we show that Oaksford and Chater’s (2007, 2008) model of conditional inference—contrary to the authors’ theoretical position—has to refer also to a frequency-based probability function. (shrink)

Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...) posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that probability match the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely. A new account of rationality—either for inference or for decision-making—is required to successfully confirm Bayesian models in cognitive science. (shrink)

Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...) how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...) ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception's computational tractability. One of the few attempts to move toward such an explanation has been the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by (...) forgoing access to information stored in cognition. I argue that we have no compelling reason to believe that encapsulation explains (or even contributes to an explanation of) perceptual tractability, and much reason to doubt it. This is because there exist much deeper computational challenges for perception than information access, and these threaten to make the costs of access irrelevant. If this is right, it undermines a core computational motivation for encapsulation and sends us back to the drawing board for explanations of perceptual tractability. (shrink)

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with (...) tools internal to PP theory: Miller and Clark’s (2018) embodied inference; Seth’s (2013) interoceptive inference; and Joffily and Coricelli’s (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain ‘s cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception. (shrink)

Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception’s computational tractability. One of the few attempts to move toward such an explanation is the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by forgoing (...) access to information stored in cognition. I argue that we have no compelling reason to believe that encapsulation explains (or even contributes to an explanation of) perceptual tractability, and much reason to doubt it. This is because there exist much deeper computational challenges for perception than information access, and these threaten to make the costs of access irrelevant. If this is right, it undermines a core computational motivation for encapsulation and sends us back to the drawing board for explanations of perceptual tractability. (shrink)

The ultimatum game is a simple bargaining situation where the behavior of people frequently contradicts the optimal strategy according to classical game theory. Thus, according to many scholars, the commonly observed behavior should be considered irrational. We argue that this putative irrationality stems from a wrong conception of metanormativity (the study of norms about the establishment of norms). After discussing different metanormative conceptions, we defend a Quinean, naturalistic approach to the evaluation of norms. After reviewing empirical literature on the ultimatum (...) game, we argue that the common behavior in the ultimatum game is rational and justified. We therefore suggest that the norms of economic rationality should be amended. (shrink)

A deep investigation of proprioceptive processes is necessary to understand the relationship between sensory afferent inputs and motor outcomes. In this work, we investigate whether and how perception of wrist position is influenced by the direction along which the movement occurs. Most previous studies have tested Joint Position Sense through 1 degree of freedom wrist movements, such as flexion/extension or radial/ulnar deviation. However, the wrist joint has 3-DoF and many activities of daily living produce combined movements, requiring at least 2-DoF (...) wrist coordination. For this reason, in this study, target positions involved movement directions that combined wrist flexion or extension with radial or ulnar deviation. The chosen task was a robot-aided Joint Position Matching, in which blindfolded participants actively reproduced a previously passively assumed target joint configuration. The JPM performance of 20 healthy participants was quantified through measures of accuracy and precision, in terms of both perceived target direction and distance along each direction of movement. Twelve different directions of movement were selected and both hands tested. The left and right hand led to comparable results, both target extents and directions were differently perceived according to the target direction on the FE/RUD space. Moreover, during 2-DoF combined movements, subjects’ perception of directions was impaired when compared to 1-DoF target movements. In summary, our results showed that human perception of wrist position on the FE/RUD space is symmetric between hands but not isotropic among movement directions. (shrink)

In Susanna Siegel’s compelling presentation of the case for the rationality of perception, a “significant part of the constructive defense” is played by the idea that there are “inferential routes to perceptual experience” (Siegel 2017, p. 94). Inferences, after all, are epistemically evaluable and bear on the rational standing of their conclusions. She argues that an obstacle to accepting this idea is a “Reckoning Model” of inference, and shows by example that we recognize as inferences various familiar kinds of responses (...) to information that do not fit this model. She offers a more general approach to the nature of inference that fits these examples and accommodates inferential routes to perceptual experience. I argue that Siegel needs to say more about the mental processes involved in such inferrings, and how it can be more than merely associative and yet still distinct from Reckoning. Fortunately, a psychologically‐ and conceptually‐grounded distinction between model‐free vs. model‐based learning and guidance processes can provide a characterization that plays the role Siegel needs. (shrink)

Bayesian models are often criticized for postulating computations that are computationally intractable (e.g., NP-hard) and therefore implausibly performed by our resource-bounded minds/brains. Our letter is motivated by the observation that Bayesian modelers have been claiming that they can counter this charge of “intractability” by proposing that Bayesian computations can be tractably approximated. We would like to make the cognitive science community aware of the problematic nature of such claims. We cite mathematical proofs from the computer science literature that show intractable (...) Bayesian computations, such as postulated in existing Bayesian models, cannot be tractably approximated. This does not mean that human brains do not (or cannot) implement the type of algorithms that Bayesian modelers are advancing, but it does mean that proposing that they do by itself does nothing to parry the charge of intractability, because the postulated algorithms are as intractable (i.e., require exponential time) as the computations they try to approximate. Besides our negative message for the community, our letter also makes a positive contribution by referring to a methodology that Bayesian modelers can use to try and parry the charge of intractability in a mathematically sound way. (shrink)

This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such a (...) potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development towards the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper. (shrink)

A view that has been gaining prevalence over the past decade is that the human conceptual system is malleable, dynamic, context‐dependent, and task‐dependent, that is, flexible. Within the flexible conceptual representation framework, conceptual representations are constructed ad hoc, forming a different, idiosyncratic instantiation upon each occurrence. In this review, we scrutinize the neurocognitive literature to better understand the nature of this flexibility. First, we identify some key characteristics of these representations. Next, we consider how these flexible representations are constructed by (...) addressing some of the open questions in this framework: We review the age‐old question of how to reconcile flexibility with the apparent need for shareable stable definitions to anchor meaning and come to mutual understanding, as well as some newer questions we find critical, namely, the nature of relations among flexible representations, the role of feature saliency in activation, and the viability of all‐or‐none feature activations. We suggest replacing the debate about the existence of a definitional stable core that is obligatorily activated with a question of the degree and probability of activation of the information constituting a conceptual representation. We rely on published works to suggest that (1) prior featural salience matters, (2) feature activation may be graded, and (3) Bayesian updating of prior information according to current demands offers a viable account of how flexible representations are constructed. This proposal provides a theoretical mechanism for incorporating a changing momentary context into a constructed representation, while still preserving some of the concept's constituent meaning. (shrink)

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the (...) ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future. (shrink)

Given the ubiquity and centrality of social and relational influences to the human experience, our conception of self-governance must adequately account for these external influences. The inclusion of socio-historical, externalist considerations into more traditional internalist accounts of autonomy has been an important feature of the debate over personal autonomy in recent years. But the relevant socio-temporal dynamics of autonomy are not only historical in nature. There are also important, and under-examined, future-oriented questions about how we retain autonomy while incorporating new (...) values into the existing set that guides our interaction with the world. In this paper, we examine these questions from two complementary perspectives: philosophy and neuroscience. After contextualizing the philosophical debate, we show the importance to theories of autonomous agency of the capacity to appropriately adapt our values and beliefs, in light of relevant experiences and evidence, to changing circumstances. We present a plausible philosophical account of this process, which we claim is generally applicable to theories about the nature of autonomy, both internalist and externalist alike. We then evaluate this account by providing a model for how the incorporation of values might occur in the brain; one that is inspired by recent theoretical and empirical advances in our understanding of the neural processes by which our beliefs are updated by new information. Finally, we synthesize these two perspectives and discuss how the neurobiology might inform the philosophical discussion. (shrink)

Inductive reasoning requires exploiting links between evidence and hypotheses. This can be done focusing either on the posterior probability of the hypothesis when updated on the new evidence or on the impact of the new evidence on the credibility of the hypothesis. But are these two cognitive representations equally reliable? This study investigates this question by comparing probability and impact judgments on the same experimental materials. The results indicate that impact judgments are more consistent in time and more accurate than (...) probability judgments. Impact judgments also predict the direction of errors in probability judgments. These findings suggest that human inductive reasoning relies more on estimating evidential impact than on posterior probability. (shrink)

Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16–26 trajectory features during a choice-reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, (...) or emotional pictures, and they indicated their emotions with questionnaires. Our 10-fold cross-validation analysis shows that statistical models formed from “known” participants could predict nearly 10%–20% of the variance of positive affect and attentiveness ratings of “unknown” participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users. (shrink)

Experts play a crucial role in underpinning decision-making in most management situations. While recent studies have disclosed the impacts of individuals’ inherent cognition and the external environment on expert performance, these two-dimensional mechanisms remain poorly understood. In this study, we identified 14 factors that influence expert performance in a bid evaluation and applied cross-impact matrix multiplication to examine the interdependence of the factors. The results indicate that the two dimension-related factors affect each other within a person–environment system, and a poor (...) situation perception gives rise to the deviation of expert performance. Expert performance can be improved if external supervision and expertise are strengthened through deliberate practices. The study proposes a new expert performance research tool, elucidates its mechanism in bid evaluation from a cognitive psychology perspective, and provides guidelines for its improvement in workplace contexts. (shrink)

While slowness of movement is an obligatory characteristic of Parkinson’s disease (PD), there are conditions in which patients move uncharacteristically fast, attributed to deficient motor inhibition. Here we investigate deficient inhibition in an optimal sensory-motor integration framework, using a game in which subjects used a paddle to catch a virtual ball. Display of the ball was extinguished as soon as the catching movement started, segregating the task into a sensing and acting phase. We analyzed the behavior of 9 PD patients (...) (ON medication) and 10 age-matched controls (HC). The switching times (between sensing and acting phase) were compared to the predicted optimal switching time, based on the individual estimates of sensory and motor uncertainties. The comparison showed that deviation from predicted optimal switching times were similar between groups. However, PD patients showed a weaker correlation between variability in switching time and sensory-motor uncertainty, indicating a reduced propensity to generate exploratory behavior for optimizing goal-directed movements. Analysis of the movement kinematics revealed that PD patients, compared to controls, used a lower peak velocity of the paddle and intercepted the ball with greater velocity. Adjusting the trial duration to the time for the paddle to stop moving, we found that PD patients spent a smaller proportion of the trial duration for observing the ball. Altogether, the results do not show the premature movement initiation and truncated sensory processing that we predicted to ensue from deficient inhibition in PD. (shrink)

In a series of three behavioral experiments, we found a systematic distortion of probability judgments concerning elementary visual stimuli. Participants were briefly shown a set of figures that had two features (e.g., a geometric shape and a color) with two possible values each (e.g., triangle or circle and black or white). A figure was then drawn, and participants were informed about the value of one of its features (e.g., that the figure was a “circle”) and had to predict the value (...) of the other feature (e.g., whether the figure was “black” or “white”). We repeated this procedure for various sets of figures and, by varying the statistical association between features in the sets, we manipulated the probability of a feature given the evidence of another (e.g., the posterior probability of hypothesis “black” given the evidence “circle”) as well as the support provided by a feature to another (e.g., the impact, or confirmation, of evidence “circle” on the hypothesis “black”). Results indicated that participants’ judgments were deeply affected by impact, although they only should have depended on the probability distributions over the features, and that the dissociation between evidential impact and posterior probability increased the number of errors. The implications of these findings for lower and higher level cognitive models are discussed. (shrink)

Many systems neuroscientists want to understand neurons in terms of mediation; we want to understand how neurons are involved in the causal chain from stimulus to behavior. Unfortunately, most tools are inappropriate for that while our language takes mediation for granted. Here we discuss the contrast between our conceptual drive toward mediation and the difficulty of obtaining meaningful evidence.