scholarly journals Human noise blindness drives suboptimal cognitive inference

2018 ◽  
Author(s):  
Santiago Herce Castañón ◽  
Dan Bang ◽  
Rani Moran ◽  
Jacqueline Ding ◽  
Tobias Egner ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Bayesian Model ◽  
Stimulus Contrast ◽  
Cognitive Integration ◽  
Opting Out ◽  
Base Rates ◽  
Systematic Biases ◽  
Average Orientation ◽  
Optimality Gap ◽  
Cognitive Inference

AbstractHumans typically make near-optimal sensorimotor judgments but show systematic biases when making more cognitive judgments. Here we test the hypothesis that, while humans are sensitive to the noise present during early sensory processing, the “optimality gap” arises because they are blind to noise introduced by later cognitive integration of variable or discordant pieces of information. In six psychophysical experiments, human observers judged the average orientation of an array of contrast gratings. We varied the stimulus contrast (encoding noise) and orientation variability (integration noise) of the array. Participants adapted near-optimally to changes in encoding noise, but, under increased integration noise, displayed a range of suboptimal behaviours: they ignored stimulus base rates, reported excessive confidence in their choices, and refrained from opting out of objectively difficult trials. These overconfident behaviours were captured by a Bayesian model which is blind to integration noise. Our study provides a computationally grounded explanation of suboptimal cognitive inferences.

Disambiguating the Stream/Bounce Illusion With Inference

2016 ◽  
Vol 29 (4-5) ◽  
pp. 453-464 ◽  
Author(s):  
Philip M. Grove ◽  
Caitlin Robertson ◽  
Laurence R. Harris
Keyword(s):  
Sensory Processing ◽  
Critical Factor ◽  
Visual Display ◽  
The Other ◽  
Collision Point ◽  
Parsimonious Explanation ◽  
History Of ◽  
Cognitive Inference

The ‘stream/bounce’ illusion refers to the perception of an ambiguous visual display in which two discs approach each other on a collision course. The display can be seen as two discs streaming through each other or bouncing off each other. Which perception dominates, may be influenced by a brief transient, usually a sound, presented around the time of simulated contact. Several theories have been proposed to account for the switching in dominance based on sensory processing, attention and cognitive inference, but a universally applicable, parsimonious explanation has not emerged. We hypothesized that only cognitive inference would be influenced by the perceptual history of the display. We rendered the display technically unambiguous by vertically offsetting the targets’ trajectories and manipulated their history by allowing the objects to switch from one trajectory to the other up to four times before the potential collision point. As the number of switches increased, the number of ‘bounce’ responses also increased. These observations show that expectancy is a critical factor in determining whether a bounce or streaming is perceived and may form the basis for a universal explanation of instances of the stream/bounce illusion.

Pseudocontingencies

2017 ◽  
Author(s):  
Klaus Fiedler ◽  
Florian Kutzner
Keyword(s):  
Causal Inference ◽  
Contingency Table ◽  
Inference Process ◽  
Cause And Effect ◽  
Base Rates ◽  
Contingency Model ◽  
Negative Contingency ◽  
Cognitive Illusion ◽  
Cognitive Inference

In research on causal inference and in related paradigms (conditioning, cue learning, attribution), it has been traditionally taken for granted that the statistical contingency between cause and effect drives the cognitive inference process. However, while a contingency model implies a cognitive algorithm based on joint frequencies (i.e., the cell frequencies of a 2 x 2 contingency table), recent research on pseudocontingencies (PCs) suggests a different mental algorithm that is driven by base rates (i.e., the marginal frequencies of a 2 x 2 table). When the base rates of two variables are skewed in the same way, a positive contingency is inferred. In contrast, a negative contingency is inferred when base rates are skewed in opposite directions. The chapter describes PCs as a resilient cognitive illusion, as a proxy for inferring contingencies in the absence of solid information, and as a smart heuristic that affords valid inferences most of the time.

scholarly journals Sampling Frames and Inductive Inference with Censored Evidence

2018 ◽  
Author(s):  
Brett Hayes ◽  
Stephanie Banner ◽  
Suzy Forrester ◽  
Danielle Navarro
Keyword(s):  
Model Prediction ◽  
Bayesian Model ◽  
Common Property ◽  
Inductive Inference ◽  
Negative Evidence ◽  
Sampling Frame ◽  
Base Rates ◽  
Sampling Process ◽  
Model Predictions ◽  
Category Base

We propose and test a Bayesian model of property induction with censored evidence. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.

scholarly journals Sampling frames and inductive inference with censored evidence

2018 ◽  
Author(s):  
Brett Hayes ◽  
Stephanie Banner ◽  
Suzy Forrester ◽  
Danielle Navarro
Keyword(s):  
Model Prediction ◽  
Bayesian Model ◽  
Common Property ◽  
Inductive Inference ◽  
Negative Evidence ◽  
Sampling Frame ◽  
Base Rates ◽  
Sampling Process ◽  
Model Predictions ◽  
Category Base

We propose and test a Bayesian model of property induction with censored evidence. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.

Magnitude Estimation and Cross-Modal Matching of Lingual Vibrotactile and Auditory Sensation

1989 ◽  
Vol 32 (3) ◽  
pp. 698-702 ◽  
Author(s):  
Daniel Harris ◽  
Donald Fucci ◽  
Linda Petrosino
Keyword(s):  
Auditory Stimulus ◽  
Magnitude Estimation ◽  
Sensory Processing ◽  
Test Site ◽  
Systematic Investigation ◽  
Thenar Eminence ◽  
Stimulus Range ◽  
Psychophysical Scaling ◽  
Scaling Methods ◽  
Response Biases

The present experiment was a preliminary attempt to use the psychophysical scaling methods of magnitude estimation and cross-modal matching to investigate suprathreshold judgments of lingual vibrotactile and auditory sensation magnitudes for 20 normal young adult subjects. A 250-Hz lingual vibrotactile stimulus and a 1000-Hz binaural auditory stimulus were employed. To obtain judgments for nonoral vibrotactile sensory magnitudes, the thenar eminence of the hand was also employed as a test site for 5 additional subjects. Eight stimulus intensities were presented during all experimental tasks. The results showed that the slopes of the log-log vibrotactile magnitude estimation functions decreased at higher stimulus intensity levels for both test sites. Auditory magnitude estimation functions were relatively constant throughout the stimulus range. Cross-modal matching functions for the two stimuli generally agreed with functions predicted from the magnitude estimation data, except when subjects adjusted vibration on the tongue to match auditory stimulus intensities. The results suggested that the methods of magnitude estimation and cross-modal matching may be useful for studying sensory processing in the speech production system. However, systematic investigation of response biases associated with vibrotactile-auditory psychophysical scaling tasks appears to be a prerequisite.

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