An Analysis of U-Value as a Measure of Variability

Kong, Xiuyuan; McEwan, James; Bizo, Lewis A.; Foster, Timothy

doi:10.1007/s40732-017-0219-2

Cited by 14 publications

(19 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The field of research concerned with behavioral variability is limited by the current analytic techniques (Kong et al, 2017). U-value is the measure most commonly used in behavioral variability studies (for reviews, see Neuringer, 2002Neuringer, , 2009Neuringer, , 2012Neuringer, , 2016.…”

Section: Discussionmentioning

confidence: 99%

“…Higher U-values indicate higher levels of behavioral variability. Although U-value can be a useful molar measure of behavioral variability, it has limitations (Kong, McEwan, Bizo, & Foster, 2017;Neuringer et al, 2001). Namely, U-value is dependent on the number of sequences included in the calculation, that is, the number of trials completed in a session.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Persistence and relapse of reinforced behavioral variability

Galizio

Frye

Haynes

et al. 2018

J Exper Analysis Behavior

View full text Add to dashboard Cite

The present study examined persistence and relapse of reinforced behavioral variability in pigeons. Pigeons emitted four‐response sequences across two keys. Sequences produced food according to a lag schedule, in which a response sequence was followed by food if it differed from a certain number of previous sequences. In Experiment 1, food was delivered for sequences that satisfied a lag schedule in both components of a multiple schedule. When reinforcement was removed for one component (i.e., extinction), levels of behavioral variability decreased for only that component. In Experiment 2, food was delivered for sequences satisfying a lag schedule in one component of a multiple schedule. In the other component, food was delivered at the same rate, but without the lag variability requirement (i.e., yoked). Following extinction, levels of behavioral variability returned to baseline for both components after response‐independent food delivery (i.e., reinstatement). In Experiment 3, one group of pigeons responded on a lag variability schedule, and the other group responded on a lag repetition schedule. For both groups, levels of behavioral variability increased when alternative reinforcement was suspended (i.e., resurgence). In each experiment, we observed some evidence for extinction‐induced response variability and for variability as an operant dimension of behavior.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Persistence and relapse of reinforced behavioral variability

Galizio

Frye

Haynes

et al. 2018

J Exper Analysis Behavior

View full text Add to dashboard Cite

show abstract

“…d , Rat paradigm: subjects choose between three noseports with different probabilities of sucrose pellet reward. e , Reward contingency schedule for rat experiment 39 .…”

Section: Resultsmentioning

confidence: 99%

“…3b; F (1)=9.207, P =0.003, η p 2 =0.030, MD=0.059, CI=[0.021, 0.097]; version trend: F (3)=2.263 P =0.081, η p 2 =0.022; low paranoia: m= 0.06 [0.01], high paranoia: m= 0.12 [0.02]). To elucidate whether this behaviour was stochastic or predictable (e.g., switching back to a previously rewarding option), we calculated U-values 39 , a metric of behavioural variability employed by behavioural ecologists (increasingly an inspiration for human behavioural analysis 40 ), particularly with regards to predator-prey relationships 41 . When a predator is approaching a prey animal, the prey’s best course of action is to behave randomly, or in a protean fashion, in order to evade capture 41 .…”

Section: Resultsmentioning

confidence: 99%

Expecting the unexpected: the paranoid style of belief updating across species

Reed

Uddenberg

Mathys

et al. 2020

Preprint

View full text Add to dashboard Cite

55Paranoia is the belief that harm is intended by others. It may arise from selective pressures to infer and avoid 56 social threats, particularly in ambiguous or changing circumstances. We propose that uncertainty may be 57 sufficient to elicit learning differences in paranoid individuals, without social threat. We used reversal learning 58 behaviour and computational modelling to estimate belief updating across individuals with and without mental 59 illness, online participants, and rats exposed to chronic methamphetamine, an elicitor of paranoia in humans. 60Paranoia is associated with a strong but immutable prior on volatility, accompanied by elevated sensitivity to 61 perceived changes in the task environment. Methamphetamine exposure in rats recapitulates this impaired 62 uncertainty-driven belief updating and rigid anticipation of a volatile environment. Our work provides evidence 63 of fundamental, domain-general learning differences in paranoid individuals. This paradigm enables further 64 assessment of the interplay between uncertainty and belief-updating across individuals and species. 66 67Paranoia & Belief Updating 3 Paranoia is excessive concern that harm will occur due to deliberate actions of others 1 . It manifests along a 68 continuum of increasing severity 2-5 . Fleeting paranoid thoughts prevail in the general population 6 . A survey of 69 over 7,000 individuals found that nearly 20% believed people were against them at times in the past year; 70 approximately 8% felt people had intentionally acted to harm them 4 . At a national level, paranoia may fuel 71 divisive ideological intolerance. Historian Richard Hofstadter famously described catastrophizing, context 72 insensitive political discourse as the 'paranoid style': 73 "The paranoid spokesman sees the fate of conspiracy in apocalyptic terms-he traffics in the birth 74 and death of whole worlds, whole political orders, whole systems of human values. He is always 75 manning the barricades of civilization. He constantly lives at a turning point." 7 76 77 At its most severe, paranoia manifests as rigid beliefs known as delusions of persecution. These delusions 78 occur frequently in psychotic illness, including nearly 90% of first episode patients 8 . However, paranoid beliefs 79 are common across psychiatric and neurologic disorders, such as anxiety 9 , depression 10 , epilepsy 11 , and 80 Alzheimer's disease 12 . Psychostimulants elicit severe paranoid states. Methamphetamine evoked new 81paranoid ideation in nearly half of 274 respondents, particularly after repeated exposure (86%) or escalating 82 dose (68%) 13 . Of those who became paranoid, the majority engaged in evasive defence strategies (hiding or 83 fleeing), but 37% obtained weapons, and 15% attacked others. There is a clear need to better manage 84 paranoia, and to understand and address its broader societal impact. 86Paranoia has thus far defied explanation in mechanistic terms, either at the levels of behaviour or brain 87 function. Obvious links with fear processing and social ...

show abstract

“…The simulations for each experiment were conducted in sequence and the random seed value was not changed across the analyses. Galizio et al (2018) reported results with data summarized across blocks of five sessions to account for the potential difficulty of calculating U-value as a measure of variability during extinction when only a few response sequences are emitted (Kong, McEwan, Bizo, & Foster, 2017). To make the results of this Monte Carlo simulation comparable to the results reported in Galizio et al (2018), we used the same data from five-session blocks.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

An alternative approach to relapse analysis: Using Monte Carlo methods and proportional rates of response

Friedel

Galizio

Berry

et al. 2018

J Exper Analysis Behavior

View full text Add to dashboard Cite

Relapse is the recovery of a previously suppressed response. Animal models have been useful in examining the mechanisms underlying relapse (e.g., reinstatement, renewal, reacquisition, resurgence). However, there are several challenges to analyzing relapse data using traditional approaches. For example, null hypothesis significance testing is commonly used to determine whether relapse has occurred. However, this method requires several a priori assumptions about the data, as well as a large sample size for between‐subjects comparisons or repeated testing for within‐subjects comparisons. Monte Carlo methods may represent an improved analytic technique, because these methods require no prior assumptions, permit smaller sample sizes, and can be tailored to account for all of the data from an experiment instead of some limited set. In the present study, we conducted reanalyses of three studies of relapse (Berry, Sweeney, & Odum, ; Galizio et al., ; Odum & Shahan, ) using Monte Carlo techniques to determine if relapse occurred and if there were differences in rate of response based on relevant independent variables (such as group membership or schedule of reinforcement). These reanalyses supported the previous findings. Finally, we provide general recommendations for using Monte Carlo methods in studies of relapse.

show abstract

An Analysis of U-Value as a Measure of Variability

Cited by 14 publications

References 19 publications

Persistence and relapse of reinforced behavioral variability

Persistence and relapse of reinforced behavioral variability

Expecting the unexpected: the paranoid style of belief updating across species

An alternative approach to relapse analysis: Using Monte Carlo methods and proportional rates of response

Contact Info

Product

Resources

About