Tag Archives: Prediction In Cognition

Normal blindness to visible objects seems to be the result of limited-capacity prediction mechanisms in the brain

Jeremy M. Wolfe, Anna Kosovicheva, Benjamin Wolfe, Normal blindness: when we Look But Fail To See, Trends in Cognitive Sciences, Volume 26, Issue 9, 2022, Pages 809-819 DOI: 10.1016/j.tics.2022.06.006.

Humans routinely miss important information that is \u2018right in front of our eyes\u2019, from overlooking typos in a paper to failing to see a cyclist in an intersection. Recent studies on these \u2018Looked But Failed To See\u2019 (LBFTS) errors point to a common mechanism underlying these failures, whether the missed item was an unexpected gorilla, the clearly defined target of a visual search, or that simple typo. We argue that normal blindness is the by-product of the limited-capacity prediction engine that is our visual system. The processes that evolved to allow us to move through the world with ease are virtually guaranteed to cause us to miss some significant stimuli, especially in important tasks like driving and medical image perception.

It seems that our brain predicts semantic features of sensory stimuli to come

Friedemann Pulvermüller, Luigi Grisoni, Semantic Prediction in Brain and Mind. Trends in Cognitive Sciences, Volume 24, Issue 10, 2020, Pages 781-784 DOI: 10.1016/j.tics.2020.07.002.

We highlight a novel brain correlate of prediction, the prediction potential (or PP), a slow negative-going potential shift preceding visual, acoustic, and spoken or written verbal stimuli that can be predicted from their context. The cortical sources underlying the prediction potential reflect perceptual and semantic features of anticipated stimuli before these appear.

Predicting optimistically seems to lead to better response of the agent to achieve the best goals

Zekun Sun, Chaz Firestone, Optimism and Pessimism in the Predictive Brain, . Trends in Cognitive Sciences, Volume 24, Issue 9, 2020 DOI: 10.1016/j.tics.2020.06.001.

(no abstract).

Do we prefer that our predictions fit observations -to validate our expectations- or that they surprise us -to acquire new knowledge-?

Clare Press, Peter Kok, Daniel Yon, The Perceptual Prediction Paradox, Trends in Cognitive Sciences, Volume 24, Issue 1, January 2020, Pages 4-6, DOI: 10.1016/j.tics.2019.11.003.

From the noisy information bombarding our senses, our brains must construct percepts that are veridical – reflecting the true state of the world – and informative – conveying what we did not already know. Influential theories suggest that both challenges are met through mechanisms that use expectations about the likely state of the world to shape perception. However, current models explaining how expectations render perception either veridical or informative are mutually incompatible. While the former propose that perceptual experiences are dominated by events we expect, the latter propose that perception of expected events is suppressed. To solve this paradox we propose a two-process model in which probabilistic knowledge initially biases perception towards what is likely and subsequently upweights events that are particularly surprising.

On the role and limitations of motor internal simulation as a way of predicting the effects of a future action in the brain

Myrthel Dogge, Ruud Custers, Henk Aarts, Moving Forward: On the Limits of Motor-Based Forward Models. Trends in Cognitive Sciences, Volume 23, Issue 9, 2019, Pages 743-753, DOI: 10.1016/j.tics.2019.06.008.

The human ability to anticipate the consequences that result from action is an essential building block for cognitive, emotional, and social functioning. A dominant view is that this faculty is based on motor predictions, in which a forward model uses a copy of the motor command to predict imminent sensory action-consequences. Although this account was originally conceived to explain the processing of action-outcomes that are tightly coupled to bodily movements, it has been increasingly extrapolated to effects beyond the body. Here, we critically evaluate this generalization and argue that, although there is ample evidence for the role of predictions in the processing of environment-related action-outcomes, there is hitherto little reason to assume that these predictions result from motor-based forward models.

A developmental architecture for sensory-motor skills based on predictors, and a nice state-of-the-art in cognitive architectures for sensory-motor skill learning

E. Wieser and G. Cheng, A Self-Verifying Cognitive Architecture for Robust Bootstrapping of Sensory-Motor Skills via Multipurpose Predictors, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1081-1095, DOI: 10.1109/TCDS.2018.2871857.

The autonomous acquisition of sensory-motor skills along multiple developmental stages is one of the current challenges in robotics. To this end, we propose a new developmental cognitive architecture that combines multipurpose predictors and principles of self-verification for the robust bootstrapping of sensory-motor skills. Our architecture operates with loops formed by both mental simulation of sensory-motor sequences and their subsequent physical trial on a robot. During these loops, verification algorithms monitor the predicted and the physically observed sensory-motor data. Multiple types of predictors are acquired through several developmental stages. As a result, the architecture can select and plan actions, adapt to various robot platforms by adjusting proprioceptive feedback, predict the risk of self-collision, learn from a previous interaction stage by validating and extracting sensory-motor data for training the predictor of a subsequent stage, and finally acquire an internal representation for evaluating the performance of its predictors. These cognitive capabilities in turn realize the bootstrapping of early hand-eye coordination and its improvement. We validate the cognitive capabilities experimentally and, in particular, show an improvement of reaching as an example skill.