The binary exclusion task involves “subtle priming effects” and a measure of awareness that is reliable, relevant, immediate, and sensitive. This task, which meets the criteria outlined in the target article, has been used to demonstrate subconscious processing.

Self-insight assessment compares outcomes from two model-recovery exercises: a statistical exercise to infer a judge's (implicit) policy and an elicitation exercise whereby the judge describes his or her (explicit) policy. When these policies are mismatched, limited self-insight is not necessarily implied: Shortcomings in either exercise could be implicated, whereby Newell & Shanks' (N&S's) relevance or sensitivity criteria for assessing awareness may not be met. Appropriate self-insight assessment requires that both exercises allow the original processes to be captured.

The hypothesis of unconscious influences on complex behavior is observationally equivalent to the dissociability of cognition and metacognition (reportability). The target article convincingly argues that evidence for unconscious influence is limited by the quality of the metacognitive measure used. However, it understates the empirical evidence for unconscious influences and overlooks considerations of cognitive architecture that make cognitive/metacognitive dissociations likely.

While showing unconscious influences on complex decisions is indeed difficult, relevant awareness in relatively simpler subliminal paradigms is more easily assessed. Utilizing objective detection (vs. more typical identification or classification) tasks to assess awareness overcomes longstanding residual methodological problems, and prior work using such methods (e.g., Snodgrass & Shevrin 2006) clearly shows unconscious influences on simple decisions.

In this commentary, we focus on the role of attentional mechanisms in unconscious thought. We argue that even distracted or unconscious thought is capacity limited and differences in scope of attention influence processing during unconscious thought. Attention also would influence processes at different stages in the proposed lens model. We conclude that there is a clear need to understand the role of attention to better understand conscious or unconscious thought.

Newell & Shanks (N&S) conclude that healthy participants learn to differentiate between the good and bad decks of the Iowa Gambling Task, and that healthy participants even have conscious knowledge about the task's payoff structure. Improved methods of analysis and new behavioral findings suggest that this conclusion is premature.

Newell & Shanks (N&S) argue that when awareness measures are more reliable and valid, greater evidence of awareness of supposedly unconscious influences is revealed. A related issue is that unconsciousness is typically the null hypothesis that evidence of awareness will not emerge. As it is difficult to conclude the null, it is also difficult to conclude a lack of conscious awareness.

Newell & Shanks' (N&S's) critical review considers only a very limited sense in which mental processes can be thought of as either conscious or unconscious and consequently gives a misleading analysis of the role of consciousness in human information processing. This commentary provides an expanded analysis of conscious processing that also reveals the various ways in which mental processes are unconscious.

The unconscious-thought effect occurs when distraction improves complex decision making. Recent studies suggest that this effect is more likely to occur with low- than high-demanding distraction tasks. We discuss implications of these findings for Newell & Shanks' (N&S's) claim that evidence is lacking for the intervention of unconscious processes in complex decision making.

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive.

The behavioral sciences have flourished by studying how traditional and/or rational behavior has been governed throughout most of human history by relatively well-informed individual and social learning. In the online age, however, social phenomena can occur with unprecedented scale and unpredictability, and individuals have access to social connections never before possible. Similarly, behavioral scientists now have access to “big data” sets – those from Twitter and Facebook, for example – that did not exist a few years ago. Studies of human dynamics based on these data sets are novel and exciting but, if not placed in context, can foster the misconception that mass-scale online behavior is all we need to understand, for example, how humans make decisions. To overcome that misconception, we draw on the field of discrete-choice theory to create a multiscale comparative “map” that, like a principal-components representation, captures the essence of decision making along two axes: (1) an east–west dimension that represents the degree to which an agent makes a decision independently versus one that is socially influenced, and (2) a north–south dimension that represents the degree to which there is transparency in the payoffs and risks associated with the decisions agents make. We divide the map into quadrants, each of which features a signature behavioral pattern. When taken together, the map and its signatures provide an easily understood empirical framework for evaluating how modern collective behavior may be changing in the digital age, including whether behavior is becoming more individualistic, as people seek out exactly what they want, or more social, as people become more inextricably linked, even “herdlike,” in their decision making. We believe the map will lead to many new testable hypotheses concerning human behavior as well as to similar applications throughout the social sciences.

We demonstrate by means of a simulation that the conceptual map presented by Bentley et al. is incomplete without taking into account people's decision processes. Within the same environment, two decision processes can generate strikingly different collective behavior; in two environments that fundamentally differ in transparency, a single process gives rise to virtually identical behavior.

Bentley et al.’s claim that their “map … captures the essence of decision making” (target article, Abstract) is deconstructed and shown to originate in a serious misunderstanding of the role of principal components and statistical graphics in the generation of pattern claims and hypotheses from profile data. Three alternative maps are offered, each with its radiation of further investigations.

Bentley et al.’s analysis of how human decision-making has changed in the online age does not mention emotion. Although the suggested dimensions involving social influence and transparency are interesting and suggestive, the engines behind the changes wrought by online media are arguably largely emotional, and implications of the communication of specific emotions via online media need to be better understood.

We discuss ambiguities of the two main dimensions of the map proposed by Bentley and colleagues that relate to the degree of self-reflection the observed agents have upon their behavior. This self-reflection is a variant of the “looping effect” which denotes that, in social research, the product of investigation influences the object of investigation. We outline how this can be understood as a dimension of “height” in the map of Bentley et al.

This commentary argues that Bentley et al.’s mapping of shifts in collective human behavior provides a novel vision of how social science theory can inform large data set analysis.

Bentley et al.’s framework assigns phenomena of personal and collective decision-making to regions of a dual-axis map. Here, we propose that understanding the collective dynamics of decision-making requires consideration of factors that guide movement across the map. One such factor is self-awareness, which can lead a group to seek out new knowledge and re-position itself on the map.

We propose an extension to the map of Bentley et al. by incorporating an aspect of underlying network structure that is likely relevant for many modes of collective behavior. This dimension, which captures a feature of network community structure, is known both from theory and from experiments to be relevant for decision-making processes.

Bentley et al. bypass the relevance of emotions in decision-making, resulting in a possible over-simplification of behavioral types. We propose integrating emotions, both in the north–south axis (in relation to cognition) as well as in the west–east axis (in relation to social influence), by suggesting a Z-axis, in charge of registering emotional depth and involvement.

Bentley et al. propose a thought-provoking approach to the question of causal factors underlying human choice behavior. Their map model is interesting, but too simplified to capture the essence of decision making. They disregard, among other matters, qualitative differences between various subcategories of social influences, and the role of neurobiological factors engaged in interdependent individual and social decision-making processes.