Choice-induced preference: A challenge for contrast

In his target article, Zentall asks: “to experience cognitive dissonance is it necessary for one to have conflicting beliefs or even beliefs at all?” He then argues that a simple behavioral process, the Within Trial Contrast Effect, may be sufficient to explain observed cognitive dissonance effects in nonhuman animals and possibly humans as well. We agree with Zentall that this effect is sufficient to explain many reported cognitive dissonance effects in nonhuman animals, but question its sufficiency for primate behavior (both monkeys and humans)

Ambiguity Aversion in Rhesus Macaques

People generally prefer risky options, which have fully specified outcome probabilities, to ambiguous options, which have unspecified probabilities. This preference, formalized in economics, is strong enough that people will reliably prefer a risky option to an ambiguous option with a greater expected value. Explanations for ambiguity aversion often invoke uniquely human faculties like language, self-justification, or a desire to avoid public embarrassment. Challenging these ideas, here we demonstrate that a preference for unambiguous options is shared with rhesus macaques. We trained four monkeys to choose between pairs of options that both offered explicitly cued probabilities of large and small juice outcomes. We then introduced occasional trials where one of the options was obscured and examined their resulting preferences; we ran humans in a parallel experiment on a nearly identical task. We found that monkeys reliably preferred risky options to ambiguous ones, even when this bias was costly, closely matching the behavior of humans in the analogous task. Notably, ambiguity aversion varied parametrically with the extent of ambiguity. As expected, ambiguity aversion gradually declined as monkeys learned the underlying probability distribution of rewards. These data indicate that ambiguity aversion reflects fundamental cognitive biases shared with other animals rather than uniquely human factors guiding decisions

Cognitive Control Signals in Posterior Cingulate Cortex

Efficiently shifting between tasks is a central function of cognitive control. The role of the default network – a constellation of areas with high baseline activity that declines during task performance – in cognitive control remains poorly understood. We hypothesized that task switching demands cognitive control to shift the balance of processing toward the external world, and therefore predicted that switching between the two tasks would require suppression of activity of neurons within the posterior cingulate cortex (CGp). To test this idea, we recorded the activity of single neurons in CGp, a central node in the default network, in monkeys performing two interleaved tasks. As predicted, we found that basal levels of neuronal activity were reduced following a switch from one task to another and gradually returned to pre-switch baseline on subsequent trials. We failed to observe these effects in lateral intraparietal cortex, part of the dorsal fronto-parietal cortical attention network directly connected to CGp. These findings indicate that suppression of neuronal activity in CGp facilitates cognitive control, and suggest that activity in the default network reflects processes that directly compete with control processes elsewhere in the brain

Decision Salience Signals in Posterior Cingulate Cortex

Despite its phylogenetic antiquity and clinical importance, the posterior cingulate cortex (CGp) remains an enigmatic nexus of attention, memory, motivation, and decision making. Here we show that CGp neurons track decision salience – the degree to which an option differs from a standard – but not the subjective value of a decision. To do this, we recorded the spiking activity of CGp neurons in monkeys choosing between options varying in reward-related risk, delay to reward, and social outcomes, each of which varied in level of decision salience. Firing rates were higher when monkeys chose the risky option, consistent with their risk-seeking preferences, but were also higher when monkeys chose the delayed and social options, contradicting their preferences. Thus, across decision contexts, neuronal activity was uncorrelated with how much monkeys valued a given option, as inferred from choice. Instead, neuronal activity signaled the deviation of the chosen option from the standard, independently of how it differed. The observed decision salience signals suggest a role for CGp in the flexible allocation of neural resources to motivationally significant information, akin to the role of attention in selective processing of sensory inputs

Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization

When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0676

Using Model Test Data to Assess VIV Factor of Safety for SCR and TTR in GOM

This paper presents results obtained as part of the DeepStar Phase 10 program on VIV Factors of Safety. The objective was to develop a general methodology to calibrate Factors of Safety for VIV-induced fatigue and to apply it to partially straked risers. This was achieved using reliability methods, accepted industry VIV prediction software and state-of-the-art model test experiments. Most oil companies use a Factor of Safety of 20 when predicting VIV damage using VIV software tools. There are numerous software tools currently in use in industry to predict VIV damage to straked risers and each of them will have different accuracy, and therefore an intrinsic level of conservatism. Understanding the level of conservatism in different VIV prediction software is therefore critical to determining what Factor of Safety to use. This study benchmarks the latest generation of industry accepted VIV design tools at the time of the study (2011): SHEAR7v4.6, VIVAv6.5 and VIVANAv3.7.24 against high quality VIV data from three separate straked riser experiments. A bias distribution (predicted to measured VIV damage results) is obtained for each software tool as a function of the strake coverage. A novel reliability framework approach is then developed to incorporate all uncertainties associated with VIV fatigue prediction into a limit state function, including variability in met-ocean conditions and variability in the fatigue resistance of the material characterized by a design S-N curve. The limit state function is analyzed using First Order Reliability Methods to develop Factors of Safety for target probabilities of failure. The general method is then applied on two case studies involving an SCR and TTR in Gulf of Mexico loop currents, but it can be easily extended to different locations and riser configurations. The resulting FoS range from about 1 to 15 for most software, and are lower than industry standards for VIV prediction. The FoS do not vary markedly for different riser configurations, indicating the possibility of reducing excess conservatism when predicting VIV damage on straked risers.DeepStar (Consortium)SHEAR7 JI

Testing for Fictive Learning in Decision-Making Under Uncertainty

We conduct two experiments where subjects make a sequence of binary choices between risky and ambiguous binary lotteries. Risky lotteries are defined as lotteries where the relative frequencies of outcomes are known. Ambiguous lotteries are lotteries where the relative frequencies of outcomes are not known or may not exist. The trials in each experiment are divided into three phases: pre-treatment, treatment and post-treatment. The trials in the pre-treatment and post-treatment phases are the same. As such, the trials before and after the treatment phase are dependent, clustered matched-pairs, that we analyze with the alternating logistic regression (ALR) package in SAS. In both experiments, we reveal to each subject the outcomes of her actual and counterfactual choices in the treatment phase. The treatments differ in the complexity of the random process used to generate the relative frequencies of the payoffs of the ambiguous lotteries. In the first experiment, the probabilities can be inferred from the converging sample averages of the observed actual and counterfactual outcomes of the ambiguous lotteries. In the second experiment the sample averages do not converge. If we define fictive learning in an experiment as statistically significant changes in the responses of subjects before and after the treatment phase of an experiment, then we expect fictive learning in the first experiment, but no fictive learning in the second experiment. The surprising finding in this paper is the presence of fictive learning in the second experiment. We attribute this counterintuitive result to apophenia: “seeing meaningful patterns in meaningless or random data.” A refinement of this result is the inference from a subsequent Chi-squared test, that the effects of fictive learning in the first experiment are significantly different from the effects of fictive learning in the second experiment

Extraneous color affects female macaques’ gaze preference for photographs of male conspecifics

Humans find members of the opposite sex more attractive when their image is spatially associated with the color red. This effect even occurs when the red color is not on the skin or clothing (i.e. is extraneous). We hypothesize that this extraneous color effect could be at least partially explained by a low-level and biologically innate generalization process, and so similar extraneous color effects should be observed in non-humans. To test this possibility, we examined the influence of extraneous color in rhesus macaques (Macaca mulatta). Across two experiments, we determined the influence of extraneous red on viewing preferences (assessed by looking time) in free-ranging rhesus monkeys. We presented male and female monkeys with black and white photographs of the hindquarters of same and opposite sex conspecifics on either a red (experimental condition) or blue (control condition) background. As a secondary control, we also presented neutral stimuli (photographs of seashells) on red and blue backgrounds. We found that female monkeys looked longer at a picture of a male scrotum, but not a seashell, on a red background (Experiment 1), while males showed no bias. Neither male nor female monkeys showed an effect of color on looking time for female hindquarters or seashells (Experiment 2). The finding for females viewing males suggests that extraneous color affects preferences among rhesus macaques. Further, it raises the possibility that evolutionary processes gave rise to extraneous color effects during human evolution