Unlocking a new dimension in the speed-accuracy trade-off.

Why do we sometimes spend too much time on seemingly impossible-to-solve tasks instead of just moving on? Masís et al. provide a new perspective on the speed-accuracy trade-off (SAT), showing that, although prolonging deliberation looks suboptimal in the short run, it is a long-term investment that helps organisms reach proficient performance more rapidly.

Toward an Atlas of Canonical Cognitive Mechanisms.

A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists start focusing on simpler explananda that will enable them to chart an atlas of elementary cognitive operations. Looking forward, the next challenge for Cognitive Science will be to understand how these elementary cognitive processes are pieced together to explain complex behavior.

Human optional stopping in a heteroscedastic world.

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Clarifying the role of an unavailable distractor in human multiattribute choice.

Decisions between two economic goods can be swayed by a third unavailable 'decoy' alternative, which does not compete for choice, notoriously violating the principles of rational choice theory. Although decoy effects typically depend on the decoy's position in a multiattribute choice space, recent studies using risky prospects (i.e., varying in reward and probability) reported a novel 'positive' decoy effect operating on a single value dimension: the higher the 'expected value' (EV) of an unavailable (distractor) prospect was, the easier the discrimination between two available target prospects became, especially when their expected-value difference was small. Here, we show that this unidimensional distractor effect affords alternative interpretations: it occurred because the distractor's EV covaried positively with the subjective utility difference between the two targets. Looking beyond this covariation, we report a modest 'negative' distractor effect operating on subjective utility, as well as classic multiattribute decoy effects. A normatively meaningful model (selective integration), in which subjective utilities are shaped by intra-attribute information distortion, reproduces the multiattribute decoy effects, and as an epiphenomenon, the negative unidimensional distractor effect. These findings clarify the modulatory role of an unavailable distracting option, shedding fresh light on the mechanisms that govern multiattribute decisions.

Circuit mechanisms for the chemical modulation of cortex-wide network interactions and behavioral variability.

Influential theories postulate distinct roles of catecholamines and acetylcholine in cognition and behavior. However, previous physiological work reported similar effects of these neuromodulators on the response properties (specifically, the gain) of individual cortical neurons. Here, we show a double dissociation between the effects of catecholamines and acetylcholine at the level of large-scale interactions between cortical areas in humans. A pharmacological boost of catecholamine levels increased cortex-wide interactions during a visual task, but not rest. An acetylcholine boost decreased interactions during rest, but not task. Cortical circuit modeling explained this dissociation by differential changes in two circuit properties: the local excitation-inhibition balance (more strongly increased by catecholamines) and intracortical transmission (more strongly reduced by acetylcholine). The inferred catecholaminergic mechanism also predicted noisier decision-making, which we confirmed for both perceptual and value-based choice behavior. Our work highlights specific circuit mechanisms for shaping cortical network interactions and behavioral variability by key neuromodulatory systems.

Choices change the temporal weighting of decision evidence.

Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants' sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making.NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making.

A map of decoy influence in human multialternative choice.

Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity, and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases have been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multiattribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis reveals that the decoy influence map is highly structured even beyond the three classic biases. We identify a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps us understand why the biases are typically correlated across participants and allows us to validate a prediction about their interrelationship. This work helps to clarify the origin of three of the most widely studied biases in human decision-making.

Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains.

Decisions are often made by accumulating ambiguous evidence over time. The brain's arousal systems are activated during such decisions. In previous work in humans, we found that evoked responses of arousal systems during decisions are reported by rapid dilations of the pupil and track a suppression of biases in the accumulation of decision-relevant evidence (de Gee et al., 2017). Here, we show that this arousal-related suppression in decision bias acts on both conservative and liberal biases, and generalizes from humans to mice, and from perceptual to memory-based decisions. In challenging sound-detection tasks, the impact of spontaneous or experimentally induced choice biases was reduced under high phasic arousal. Similar bias suppression occurred when evidence was drawn from memory. All of these behavioral effects were explained by reduced evidence accumulation biases. Our results point to a general principle of interplay between phasic arousal and decision-making.

Selective Integration during Sequential Sampling in Posterior Neural Signals.

Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as "selective integration" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers' decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.

Choice history biases subsequent evidence accumulation.

Perceptual choices depend not only on the current sensory input but also on the behavioral context, such as the history of one's own choices. Yet, it remains unknown how such history signals shape the dynamics of later decision formation. In models of decision formation, it is commonly assumed that choice history shifts the starting point of accumulation toward the bound reflecting the previous choice. We here present results that challenge this idea. We fit bounded-accumulation decision models to human perceptual choice data, and estimated bias parameters that depended on observers' previous choices. Across multiple task protocols and sensory modalities, individual history biases in overt behavior were consistently explained by a history-dependent change in the evidence accumulation, rather than in its starting point. Choice history signals thus seem to bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation.

Attentional Selection Mediates Framing and Risk-Bias Effects.

Humans display a number of puzzling choice patterns that contradict basic principles of rationality. For example, they show preferences that change as a result of task framing or of adding irrelevant alternatives into the choice set. A recent theory has proposed that such choice and risk biases arise from an attentional mechanism that increases the relative weighting of goal-consistent information and protects the decision from noise after the sensory stage. Here, using a divided-attention method based on the dot-probe technique, we showed that attentional selection toward values congruent with the task goal takes place while participants make choices between alternatives that consist of payoff sequences. Moreover, we demonstrated that the magnitude of this attentional selection predicts risk attitudes, indicating a common underlying cognitive process. The results highlight the dynamic interplay between attention and choice mechanisms in producing framing effects and risk biases.

Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence.

People's assessments of the state of the world often deviate systematically from the information available to them [1]. Such biases can originate from people's own decisions: committing to a categorical proposition, or a course of action, biases subsequent judgment and decision-making. This phenomenon, called confirmation bias [2], has been explained as suppression of post-decisional dissonance [3, 4]. Here, we provide insights into the underlying mechanism. It is commonly held that decisions result from the accumulation of samples of evidence informing about the state of the world [5-8]. We hypothesized that choices bias the accumulation process by selectively altering the weighting (gain) of subsequent evidence, akin to selective attention. We developed a novel psychophysical task to test this idea. Participants viewed two successive random dot motion stimuli and made two motion-direction judgments: a categorical discrimination after the first stimulus and a continuous estimation of the overall direction across both stimuli after the second stimulus. Participants' sensitivity for the second stimulus was selectively enhanced when that stimulus was consistent with the initial choice (compared to both, first stimuli and choice-inconsistent second stimuli). A model entailing choice-dependent selective gain modulation explained this effect better than several alternative mechanisms. Choice-dependent gain modulation was also established in another task entailing averaging of numerical values instead of motion directions. We conclude that intermittent choices direct selective attention during the evaluation of subsequent evidence, possibly due to decision-related feedback in the brain [9]. Our results point to a recurrent interplay between decision-making and selective attention.

Competing theories of multialternative, multiattribute preferential choice.

In accounting for phenomena present in preferential choice experiments, modern models assume a wide array of different mechanisms such as lateral inhibition, leakage, loss aversion, and saliency. These mechanisms create interesting predictions for the dynamics of the deliberation process as well as the aggregate behavior of preferential choice in a variety of contexts. However, the models that embody these different mechanisms are rarely subjected to rigorous quantitative tests of suitability by way of model fitting and evaluation. Recently, complex, stochastic models have been cast aside in favor of simpler approximations, which may or may not capture the data as well. In this article, we use a recently developed method to fit the four extant models of context effects to data from two experiments: one involving consumer goods stimuli, and another involving perceptual stimuli. Our third study investigates the relative merits of the mechanisms currently assumed by the extant models of context effects by testing every possible configuration of mechanism within one overarching model. Across all tasks, our results emphasize the importance of several mechanisms such as lateral inhibition, loss aversion, and pairwise attribute differences, as these mechanisms contribute positively to model performance. Together, our results highlight the notion that mathematical tractability, while certainly a convenient feature of any model, should neither be the primary impetus for model development nor the promoting or demotion of specific model mechanisms. Instead, model fit, balanced with model complexity, should be the greatest burden to bear for any theoretical account of empirical phenomena. (PsycINFO Database Record

The standard Bayesian model is normatively invalid for biological brains.

We show that the benchmark Bayesian framework that Rahnev & Denison (R&D) used to assess optimality is actually suboptimal under realistic assumptions about how noise corrupts decision making in biological brains. This model is therefore invalid qua normative standard. We advise against generally forsaking optimality and argue that a biologically constrained definition of optimality could serve as an important driver for scientific progress.

The influence of attention on value integration.

People often have to make decisions based on many pieces of information. Previous work has found that people are able to integrate values presented in a rapid serial visual presentation (RSVP) stream to make informed judgements on the overall stream value (Tsetsos et al. Proceedings of the National Academy of Sciences of the United States of America, 109(24), 9659-9664, 2012). It is also well known that attentional mechanisms influence how people process information. However, it is unknown how attentional factors impact value judgements of integrated material. The current study is the first of its kind to investigate whether value judgements are influenced by attentional processes when assimilating information. Experiments 1-3 examined whether the attentional salience of an item within an RSVP stream affected judgements of overall stream value. The results showed that the presence of an irrelevant high or low value salient item biased people to judge the stream as having a higher or lower overall mean value, respectively. Experiments 4-7 directly tested Tsetsos et al.'s (Proceedings of the National Academy of Sciences of the United States of America, 109(24), 9659-9664, 2012) theory examining whether extreme values in an RSVP stream become over-weighted, thereby capturing attention more than other values in the stream. The results showed that the presence of both a high (Experiments 4, 6 and 7) and a low (Experiment 5) value outlier captures attention leading to less accurate report of subsequent items in the stream. Taken together, the results showed that valuations can be influenced by attentional processes, and can lead to less accurate subjective judgements.

Economic irrationality is optimal during noisy decision making.

According to normative theories, reward-maximizing agents should have consistent preferences. Thus, when faced with alternatives A, B, and C, an individual preferring A to B and B to C should prefer A to C. However, it has been widely argued that humans can incur losses by violating this axiom of transitivity, despite strong evolutionary pressure for reward-maximizing choices. Here, adopting a biologically plausible computational framework, we show that intransitive (and thus economically irrational) choices paradoxically improve accuracy (and subsequent economic rewards) when decision formation is corrupted by internal neural noise. Over three experiments, we show that humans accumulate evidence over time using a "selective integration" policy that discards information about alternatives with momentarily lower value. This policy predicts violations of the axiom of transitivity when three equally valued alternatives differ circularly in their number of winning samples. We confirm this prediction in a fourth experiment reporting significant violations of weak stochastic transitivity in human observers. Crucially, we show that relying on selective integration protects choices against "late" noise that otherwise corrupts decision formation beyond the sensory stage. Indeed, we report that individuals with higher late noise relied more strongly on selective integration. These findings suggest that violations of rational choice theory reflect adaptive computations that have evolved in response to irreducible noise during neural information processing.

Near-optimal Integration of Magnitude in the Human Parietal Cortex.

Humans are often observed to make optimal sensorimotor decisions but to be poor judges of situations involving explicit estimation of magnitudes or numerical quantities. For example, when drawing conclusions from data, humans tend to neglect the size of the sample from which it was collected. Here, we asked whether this sample size neglect is a general property of human decisions and investigated its neural implementation. Participants viewed eight discrete visual arrays (samples) depicting variable numbers of blue and pink balls. They then judged whether the samples were being drawn from an urn in which blue or pink predominated. A participant who neglects the sample size will integrate the ratio of balls on each array, giving equal weight to each sample. However, we found that human behavior resembled that of an optimal observer, giving more credence to larger sample sizes. Recording scalp EEG signals while participants performed the task allowed us to assess the decision information that was computed during integration. We found that neural signals over the posterior cortex after each sample correlated first with the sample size and then with the difference in the number of balls in either category. Moreover, lateralized beta-band activity over motor cortex was predicted by the cumulative difference in number of balls in each category. Together, these findings suggest that humans achieve statistically near-optimal decisions by adding up the difference in evidence on each sample, and imply that sample size neglect may not be a general feature of human decision-making.

Examining the mechanisms underlying contextual preference reversal: Comment on Trueblood, Brown, and Heathcote (2014).

Trueblood, Brown, and Heathcote (2014) provide a new model of multiattribute choice, which accounts for 3 contextual reversal effects (similarity, attraction and compromise). We review the details of the model and highlight some novel predictions. First, we show that the model works by setting a "fine balance" between 2 opposing factors that influence choice. As a result, small changes in the attributes of choice alternatives can disturb this balance. Second, we show that the model gives a partial account of the compromise effect. We describe a number of experiments that could distinguish the MLBA from other models of multiattribute choice.